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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/04/2024 has been considered by the examiner.
Priority
Acknowledgment is made of applicant’s claim for foreign priority based on European Patent Application No EP23218764.1, filed on December 20, 2023.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: processing unit as in SPEC (paras 40-41) in claim 1.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3 and 5-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
In particular, claims are directed to a judicial exception (abstract idea) without significantly more.
Re Claim 1:
Claim 1 recites:
The computer implemented method for determining control parameters for driving a host vehicle, the method comprising:
determining static and dynamic characteristics detected in an external environment of the host vehicle via a perception system of the host vehicle, via a processing unit of the host vehicle:
determining a plurality of driving hypotheses by applying the static and dynamic characteristics, each driving hypothesis including a drivable path and a distribution of trajectories which matches to the drivable path for the host vehicle,
selecting a most suitable driving hypothesis from the plurality of driving hypotheses,
determining a most suitable trajectory for the distribution of trajectories associated with the most suitable driving hypothesis,
determining the control parameters for driving the host vehicle in accordance with the most suitable trajectory, wherein: for each driving hypothesis, a respective set of cost functions is determined with respect to each of the distribution of trajectories,
the most suitable driving hypothesis is selected from the plurality of driving hypotheses by utilizing the respective set of cost functions and by relating the drivable path of each driving hypothesis to navigation information for the host vehicle,
the cost functions include a physical cost function which is related to physical conditions for the driving path and the distribution of trajectories of the respective driving hypothesis,
the cost functions include a safety cost function which is related to safety conditions for the driving path and the distribution of trajectories of the respective driving hypothesis,
and the safety cost function is associated with a lower priority than the physical cost function.
Under Step 1 Claim 1 is a method claim same as claims 3 and 5-15.
Under Step 2A -Prong 1:
The identified claim limitations that recite an abstract idea fall within the enumerated groupings of abstract ideas in Section 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance published in the Federal Register (84 FR 50) on January 7, 2019. These fall under mental process.
Claim 1 recites “trajectories, the most suitable driving hypothesis is selected from the plurality of driving hypotheses by utilizing the respective set of cost functions and by relating the drivable path of each driving hypothesis to navigation information for the host vehicle, the cost functions include a physical cost function which is related to physical conditions for the driving path and the distribution of trajectories of the respective driving hypothesis, the cost functions include a safety cost function which is related to safety conditions for the driving path and the distribution of trajectories of the respective driving hypothesis, and the safety cost function is associated with a lower priority than the physical cost function”. These limitations, under their broadest reasonable interpretation, cover performance of the limitation as mental processes. As a person/driver, could evaluate multiple alternatives using cost functions and selecting an optimal option based on weighted criteria. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a concept performed in the human mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Claims 1, 3 and 5-15 are also abstract for similar reasons.
Under Step 2A - Prong 2; the claims recite the additional elements of “The computer implemented” and “via a processing unit” steps is not more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea without a practical application because they do not impose any meaningful limits on practicing the abstract idea and are at a high level of generality. Therefore, claim 1 is directed to an abstract idea without a practical application.
Under Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer hardware amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Accordingly, these additional elements, do not change the outcome of the analysis, when considered separately and as an ordered combination. Thus, claims 1, 3 and 5-15 are not patent eligible.
Dependent claims 3 and 5-15 Dependent claims further define the abstract idea that is present in their respective independent claim 1 and thus correspond to Mental Processes and hence are abstract for the reasons presented above. The dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, the dependent claims are directed to an abstract idea. Thus, the claims 1, 3 and 5-15 are not patent-eligible.
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 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.
Claims 1, 3 and 5-15 are rejected under 35 U.S.C. 103 as being unpatentable in view of Begnell et al (US 11,787,439 B1) in view of Mirkovic et al (US 2024/0190452 A1).
Regarding claim 1, Begnell teaches the computer implemented method for determining control parameters for driving a host vehicle, the method comprising: (see Begnell figure 2 and col 7, lines 50-61 “In some implementations, the autonomy system 200 can be implemented by a computing system of the autonomous platform (e.g., the onboard computing system(s) 180 of the autonomous platform 110).”),
determining static and dynamic characteristics detected in an external environment of the host vehicle via a perception system of the host vehicle, via a processing unit of the host vehicle “e.g., planning systems 250 and 400” (see Begnell fig. 7 steps “702” and “704” and col 11, lines 45-53 “The autonomy system 200 can include the perception system 240, which can allow an autonomous platform to detect, classify, and track objects and actors in its environment. Environmental features or objects perceived within an environment can be those within the field of view of the sensor(s) 202 or predicted to be occluded from the sensor(s) 202”),
determining a plurality of driving hypotheses by applying the static and dynamic characteristics, (see Begnell fig. 4 steps “706” and col 16, lines 50-52 “the example planning system 400 can include a strategy generator 402 for generating collapsed candidate strategies 406 (e.g., as a first stage).”), each driving hypothesis including a drivable path and a distribution of trajectories which matches to the drivable path for the host vehicle “110” (see Begnell figs. 6-7, step 708 “candidate trajectory” and col 12, lines 21-25 “The planning system 250 can determine one or more motion plans for an autonomous platform. A motion plan can include one or more trajectories (e.g., motion trajectories) that indicate a path for an autonomous platform to follow”),
selecting a most suitable driving hypothesis from the plurality of driving hypotheses (see Begnell fig. 4 and col 16, lines 59-63 “A plan arbiter 412 can decide which of the optimized candidate trajectories 410 to execute. For instance, the plan arbiter 412 can select an optimal {strategy, trajectory} pair. The plan arbiter 412 can output selected behavior 414”),
determining a most suitable trajectory for the distribution of trajectories associated with the most suitable driving hypothesis (see Begnell fig. 4 and col 16, lines 55-63 “For instance, given a candidate strategy, the trajectory optimizer 408 can generate a trajectory that optimally executes the discrete decisions associated with that candidate strategy. A plan arbiter 412 can decide which of the optimized candidate trajectories 410 to execute. For instance, the plan arbiter 412 can select an optimal {strategy, trajectory} pair. The plan arbiter 412 can output selected behavior 414”),
determining the control parameters for driving the host vehicle in accordance with the most suitable trajectory (see Begnell fig. 4 and col 7, lines 59-61 and col 16, lines 55-63 “The autonomy system 200 can generate control outputs for controlling the autonomous platform (e.g., through platform control devices 212, etc.)” and “For instance, given a candidate strategy, the trajectory optimizer 408 can generate a trajectory that optimally executes the discrete decisions associated with that candidate strategy. A plan arbiter 412 can decide which of the optimized candidate trajectories 410 to execute. For instance, the plan arbiter 412 can select an optimal {strategy, trajectory} pair. The plan arbiter 412 can output selected behavior 414”),
wherein: for each driving hypothesis, a respective set of cost functions is determined with respect to each of the distribution of trajectories (see Begnell fig. 4 and col 12, lines 33-41 “The motion planning system 250 can determine a strategy for the autonomous platform. A strategy may be a set of discrete decisions (e.g., yield to actor, reverse yield to actor, merge, lane change) that the autonomous platform makes. The strategy may be selected from a plurality of potential strategies. The selected strategy may be a lowest cost strategy as determined by one or more cost functions. The cost functions may, for example, evaluate the probability of a collision with another actor or object.”),
the most suitable driving hypothesis is selected from the plurality of driving hypotheses by utilizing the respective set of cost functions and by relating the drivable path of each driving hypothesis to navigation information for the host vehicle (see Begnell col 28, lines 13-34 “With reference again to FIG. 7, at 708, method 700 can include determining candidate trajectories respectively for the plurality of candidate strategies. For instance, a trajectory optimizer 408 can determine trajectories for a plurality of candidate strategies (e.g., received from the strategy generator 402). For example, in the hypothetical scenario from above, the trajectory optimizer 408 can obtain an optimal strategy for each of the candidate strategies (represented by equivalence classes E1 to E3)”),
but Begnell fails to explicitly teach the cost functions include a physical cost function which is related to physical conditions for the driving path and the distribution of trajectories of the respective driving hypothesis, the cost functions include a safety cost function which is related to safety conditions for the driving path and the distribution of trajectories of the respective driving hypothesis, and the safety cost function is associated with a lower priority than the physical cost function.
However, Mirkovic teaches the cost functions include a physical cost function which is related to physical conditions for the driving path and the distribution of trajectories of the respective driving hypothesis (see Mirkovic para “0065” “For example, the valid particles may be used for generating a trajectory of the AV through the environment (e.g., for prediction, motion planning, etc.). Specifically, since every valid particle represents a hypothesis corresponding to a potentially occluded actor, such hypotheses can be directly used or modeled for prediction or motion planning purposes to, for example, generate a set of constraints and/or cost function features for trajectory generation, as potential actor inputs for trajectory generation, for forecasting trajectories during prediction, or the like. Furthermore, every valid particle represents hypothesis corresponding to a feasible unroll of the state of the environment (i.e., history) from the initial state onward allowing for modeling of both high-resolution state and long-term temporal dependencies (i.e., spatiotemporal reasoning of the occluded regions of an environment). For example, as shown in FIGS. 8A and 8B, the valid sets of particles 810, 812, and 814 represent the state of the occluded regions of the environment 300 at a first time while valid sets of particles 816, 818, and 819 represent the state of the occluded regions of the environment 300 at a second time, and are indicative of both high-resolution state and long-term temporal dependencies”),
the cost functions include a safety cost function which is related to safety conditions for the driving path and the distribution of trajectories of the respective driving hypothesis, and the safety cost function is associated with a lower priority than the physical cost function (see Mirkovic paras “0048” and “0065-0069” “The assigned initial probability for a particle is typically close to 1 upon particle initialization, but may take other values depending on availability of prior probability information based on the state of the environment (e.g., when there is available information that leads to assumption of a lower probability of an object being occluded at a particular position depending on various conditions such as, without limitation, time of day, type of the environment (e.g., intersection type, or the like)” and “For example, the valid particles may be used for generating a trajectory of the AV through the environment (e.g., for prediction, motion planning, etc.). Specifically, since every valid particle represents a hypothesis corresponding to a potentially occluded actor, such hypotheses can be directly used or modeled for prediction or motion planning purposes to, for example, generate a set of constraints and/or cost function features for trajectory generation, as potential actor inputs for trajectory generation, for forecasting trajectories during prediction, or the like. Furthermore, every valid particle represents hypothesis corresponding to a feasible unroll of the state of the environment (i.e., history) from the initial state onward allowing for modeling of both high-resolution state and long-term temporal dependencies (i.e., spatiotemporal reasoning of the occluded regions of an environment). For example, as shown in FIGS. 8A and 8B, the valid sets of particles 810, 812, and 814 represent the state of the occluded regions of the environment 300 at a first time while valid sets of particles 816, 818, and 819 represent the state of the occluded regions of the environment 300 at a second time, and are indicative of both high-resolution state and long-term temporal dependencies”),
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Begnell for multistage autonomous vehicle motion planning to “generate a navigation route that minimizes Euclidean distance traveled or other cost function during the route, and may further access the traffic information” as taught by Mirkovic (paras. [0048]- [0065-0069]) in order to affect an amount of time it will take to travel on a particular route to reach faster and safer.
Regarding claim 3, Begnell teaches wherein: by using the static and dynamic characteristics detected in the external environment of the host vehicle, a prediction is determined for a traffic scene within the environment of the host vehicle, and the predicted traffic scene is utilized for evaluating the set of cost functions (see Begnell col 24, lines 3-15 “With reference again to FIG. 4 , the planning system 400 can include one or more machine-learned components. For instance, any one or all of the strategy generator 402, trajectory optimizer 408, and plan arbiter 412 can include one or more machine-learned components. For instance, the strategy generator 402 can leverage a learned prioritization component to prune the search space of strategies. For instance, a learned prioritization component can predict a value associated with a discrete decision and, based on the value, deprioritize a portion of the search space differentiated by that discrete decision (e.g., terminate growth of a branch of a graph structure from that decision edge)”).
Regarding claim 5, Begnell fails to explicitly teach wherein: the cost functions further include a rule cost function which is related to rules for the driving path and the distribution of trajectories of the respective driving hypothesis, and the rule cost function is associated with a lower priority than the safety cost function.
However, Mirkovic wherein: the cost functions further include a rule cost function which is related to rules for the driving path and the distribution of trajectories of the respective driving hypothesis, and the rule cost function is associated with a lower priority than the safety cost function (see Mirkovic paras “0048” and “0065-0069” “The assigned initial probability for a particle is typically close to 1 upon particle initialization, but may take other values depending on availability of prior probability information based on the state of the environment (e.g., when there is available information that leads to assumption of a lower probability of an object being occluded at a particular position depending on various conditions such as, without limitation, time of day, type of the environment (e.g., intersection type, or the like)” and “For example, the valid particles may be used for generating a trajectory of the AV through the environment (e.g., for prediction, motion planning, etc.). Specifically, since every valid particle represents a hypothesis corresponding to a potentially occluded actor, such hypotheses can be directly used or modeled for prediction or motion planning purposes to, for example, generate a set of constraints and/or cost function features for trajectory generation, as potential actor inputs for trajectory generation, for forecasting trajectories during prediction, or the like. Furthermore, every valid particle represents hypothesis corresponding to a feasible unroll of the state of the environment (i.e., history) from the initial state onward allowing for modeling of both high-resolution state and long-term temporal dependencies (i.e., spatiotemporal reasoning of the occluded regions of an environment). For example, as shown in FIGS. 8A and 8B, the valid sets of particles 810, 812, and 814 represent the state of the occluded regions of the environment 300 at a first time while valid sets of particles 816, 818, and 819 represent the state of the occluded regions of the environment 300 at a second time, and are indicative of both high-resolution state and long-term temporal dependencies”),
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Begnell for multistage autonomous vehicle motion planning to “generate a navigation route that minimizes Euclidean distance traveled or other cost function during the route, and may further access the traffic information” as taught by Mirkovic (paras. [0048]- [0065-0069]) in order to affect an amount of time it will take to travel on a particular route to reach faster and safer.
Regarding claim 5, Begnell fails to explicitly teach wherein: the cost functions further include a comfort cost function which is related to comfort for a passenger of the host vehicle with respect to the driving path and the distribution of trajectories of the respective driving hypothesis, and the comfort cost function is associated with a lower priority than the rule cost function.
However, Mirkovic wherein: the cost functions further include a comfort cost function which is related to comfort for a passenger of the host vehicle with respect to the driving path and the distribution of trajectories of the respective driving hypothesis, and the comfort cost function is associated with a lower priority than the rule cost function (see Mirkovic paras “0048” and “0065-0069” “The assigned initial probability for a particle is typically close to 1 upon particle initialization, but may take other values depending on availability of prior probability information based on the state of the environment (e.g., when there is available information that leads to assumption of a lower probability of an object being occluded at a particular position depending on various conditions such as, without limitation, time of day, type of the environment (e.g., intersection type, or the like)” and “For example, the valid particles may be used for generating a trajectory of the AV through the environment (e.g., for prediction, motion planning, etc.). Specifically, since every valid particle represents a hypothesis corresponding to a potentially occluded actor, such hypotheses can be directly used or modeled for prediction or motion planning purposes to, for example, generate a set of constraints and/or cost function features for trajectory generation, as potential actor inputs for trajectory generation, for forecasting trajectories during prediction, or the like. Furthermore, every valid particle represents hypothesis corresponding to a feasible unroll of the state of the environment (i.e., history) from the initial state onward allowing for modeling of both high-resolution state and long-term temporal dependencies (i.e., spatiotemporal reasoning of the occluded regions of an environment). For example, as shown in FIGS. 8A and 8B, the valid sets of particles 810, 812, and 814 represent the state of the occluded regions of the environment 300 at a first time while valid sets of particles 816, 818, and 819 represent the state of the occluded regions of the environment 300 at a second time, and are indicative of both high-resolution state and long-term temporal dependencies”),
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Begnell for multistage autonomous vehicle motion planning to “generate a navigation route that minimizes Euclidean distance traveled or other cost function during the route, and may further access the traffic information” as taught by Mirkovic (paras. [0048]- [0065-0069]) in order to affect an amount of time it will take to travel on a particular route to reach faster and safer.
Regarding claim 7, Begnell teaches wherein: a plurality of trajectories is generated for the distribution of trajectories associated with the selected driving hypothesis, the set of cost functions associated with the most suitable driving hypothesis is applied to the plurality of trajectories, and the most suitable trajectory is selected from the plurality of trajectories by evaluating the cost functions (see Begnell fig. 4 and col 12, lines 45-65 “The planning system 250 can evaluate trajectories or strategies (e.g., with scores, costs, rewards, constraints, etc.) and rank them. For instance, the planning system 250 can use forecasting output(s) that indicate interactions (e.g., proximity, intersections, etc.) between trajectories for the autonomous platform and one or more objects to inform the evaluation of candidate trajectories or strategies for the autonomous platform. In some implementations, the planning system 250 can utilize static cost(s) to evaluate trajectories for the autonomous platform (e.g., “avoid lane boundaries,” “minimize jerk,” etc.)”).
Regarding claim 8, Begnell teaches wherein: a planning time horizon is determined for the plurality of trajectories generated for the distribution of trajectories associated with the most suitable driving hypothesis (see Begnell col 12, lines 25-27 “A trajectory can be of a certain length or time range. The length or time range can be defined by the computational planning horizon of the planning system 250.”).
Regarding claim 9, Begnell teaches wherein: at least one control point is determined for each of the plurality of trajectories in accordance with the planning time horizon, and the drivable path and respective control points are used by an algorithm which determines the control parameters associated with the most suitable trajectory (see Begnell col 12, lines 28-32 “A motion trajectory can be defined by one or more waypoints (with associated coordinates). The waypoint(s) can be future location(s) for the autonomous platform. The motion plans can be continuously generated, updated, and considered by the planning system 250.”).
Regarding claim 10, Begnell teaches wherein: the control parameters are refined with respect to the static characteristics determined for the external environment of the host vehicle, with respect to previously planned trajectories and/or with respect to predefined rules (see Begnell col 13, lines 64 thru col 14 line 14 “To implement selected motion plan(s), the autonomy system 200 can include a control system 260 (e.g., a vehicle control system). Generally, the control system 260 can provide an interface between the autonomy system 200 and the platform control devices 212 for implementing the strategies and motion plan(s) generated by the planning system 250. For instance, the control system 260 can implement the selected motion plan/trajectory to control the autonomous platform's motion through its environment by following the selected trajectory (e.g., the waypoints included therein). The control system 260 can, for example, translate a motion plan into instructions for the appropriate platform control devices 212 (e.g., acceleration control, brake control, steering control, etc.). By way of example, the control system 260 can translate a selected motion plan into instructions to adjust a steering component (e.g., a steering angle) by a certain number of degrees, apply a certain magnitude of braking force, increase/decrease speed, etc”).
Regarding claim 11, Begnell teaches wherein: by using the static and dynamic characteristics detected in the external environment of the host vehicle, a prediction is determined for a traffic scene within the environment of the host vehicle and for target trajectories with respect to the most suitable trajectory, and the control parameters are checked regarding safety by applying the predictions for the traffic scene and for the target trajectories (see Begnell col 24, lines 3-15 “With reference again to FIG. 4 , the planning system 400 can include one or more machine-learned components. For instance, any one or all of the strategy generator 402, trajectory optimizer 408, and plan arbiter 412 can include one or more machine-learned components. For instance, the strategy generator 402 can leverage a learned prioritization component to prune the search space of strategies. For instance, a learned prioritization component can predict a value associated with a discrete decision and, based on the value, deprioritize a portion of the search space differentiated by that discrete decision (e.g., terminate growth of a branch of a graph structure from that decision edge)”).
Regarding claim 12, Begnell teaches receive static and dynamic characteristics detected in an external environment of a host vehicle via a perception system of the host vehicle, and carry out the computer implemented method (see Begnell fig. 4, steps “706” and col 2, lines 58 thru col 3, lines 1-15 and col 16, lines 50-52 “the example planning system 400 can include a strategy generator 402 for generating collapsed candidate strategies 406 (e.g., as a first stage).”).
Regarding claim 13, Begnell teaches a perception system, a computer system, and a control system configured to receive control parameters determined by the computer system (see Begnell “110” and “200”).
Regarding claim 14, Begnell teaches wherein: the control system is further configured to validate the received control parameters and to apply the received control parameters during operation of the vehicle (see Begnell “260” and “212”).
Regarding claim 15, Begnell teaches wherein: the control system is further configured to validate the received control parameters and to apply the received control parameters during operation of the vehicle (see Begnell col 2, lines 58 thru col 3, lines 1-15).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HOSSAM M ABD EL LATIF whose telephone number is (571)272-5869. The examiner can normally be reached M-F 8 am-5 pm EST.
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/HOSSAM M ABD EL LATIF/Examiner, Art Unit 3664