DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12 June 2026 has been entered.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Claims 1-20 are pending in this application.
Claims 1 and 6 are amended.
Claims 1-20 are presented for examination.
RESPONSE TO AMENDMENTS
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-16 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ramamoorthy et al. (US Publication 2021/0370980 A1) in view of Olson et al. (US Publication 2017/0031361 A1).
Regarding claim 1, Ramamoorthy teaches a method for controlling a vehicle, comprising: during a first time period: sampling a first set of measurements of a set of environmental objects in an environment of the vehicle (Ramamoorthy: Para. 60; object tracking may be applied to the sensor inputs, in order track at least one external actor in the encountered driving scenario, and thereby determine an observed trace of the external actor over a time interval); and based on the first set of measurements, determining a set of risks associated with the set of environmental objects (Ramamoorthy: Para. 47, 62; observed trace may be used to predict a current maneuver and/or a future maneuver of the external actor); and during a second time period subsequent to the first time period: based on the set of risks, determining a plurality of actions (Ramamoorthy: Para. 34, 48; a sequence of multiple maneuvers may be determined for at least one goal); determining a first combination of actions from the plurality of actions (Ramamoorthy: Para. 185; the AV planner selects one of the expanded paths determined to be most promising, and generates control signals for controlling the AV to execute the corresponding sequence of manoeuvres in the real-world driving scenario is has encountered); determining a second combination of actions from the plurality of actions, the first combination of actions and the second combination of actions differing by at least one action (Ramamoorthy: Para. 161; if the vehicle is at a particular location relative to a T-junction (corresponding to the parent state), there may be three possible manoeuvres, to stop, turn left, and turn right, but continuing straight would not be an option); determining a first set of vehicle control policies according to the first combination of actions (Ramamoorthy: Para. 185; the AV planner selects one of the expanded paths determined to be most promising, and generates control signals for controlling the AV to execute the corresponding sequence of manoeuvres in the real-world driving scenario is has encountered); determining a second set of vehicle control policies according to the second combination of actions (Ramamoorthy: Para. 185; the AV planner selects one of the expanded paths determined to be most promising, and generates control signals for controlling the AV to execute the corresponding sequence of manoeuvres in the real-world driving scenario is has encountered); sampling a second set of measurements of the set of environmental objects in an environment of the vehicle (Ramamoorthy: Para. 119, 187; sensor data from which it is possible to extract detailed information about the surrounding environment and the state of the AV and other actors; when a new actor is detected during an ongoing MCTS procedure); ………. ; and controlling the vehicle according to the selected set of vehicle control policies (Ramamoorthy: Para. 186; generates control signals (E12) for controlling the AV to execute the corresponding sequence of manoeuvres in the real-world driving scenario is has encountered).
Ramamoorthy doesn’t explicitly teach based on the second set of measurements sampled during the second time period: performing a set of first forward simulations of the set of environmental objects according to the first set of vehicle control policies; and performing a set of second forward simulations of the set of environmental objects according to the second set of vehicle control policies; based on a comparison of the set of first forward simulations and the set of second forward simulations, selecting one of the first set of vehicle control policies and the second set of vehicle control policies to be executed to control the vehicle.
However Olson, in the same field of endeavor, teaches based on the second set of measurements sampled during the second time period: performing a set of first forward simulations of the set of environmental objects according to the first set of vehicle control policies (Olson: Para. 21, 25, 100; timestep may be defined as a period of time between consecutive updates of a policy to govern the host vehicle; timestep for the multipolicy decision-making system may be periodic; iterative driving decisions based on coupled interactions with the non-host vehicles); and performing a set of second forward simulations of the set of environmental objects according to the second set of vehicle control policies (Olson: Para. 101; multiple simulations are run based on different sets of selected policies for the non-host vehicles and host vehicles); based on a comparison of the set of first forward simulations and the set of second forward simulations, selecting one of the first set of vehicle control policies and the second set of vehicle control policies to be executed to control the vehicle (Olson: Para. 25; results of the simulations are compared to a reward function; based on the results of the one or more simulations, the host vehicle is further programmed to decide upon a policy to govern driving behavior at a current timestep).
It would have been obvious to one having ordinary skill in the art to modify the forward simulating the potential trajectories (Ramamoorthy: Para. 398) by simulated forward in time the interactions of both the vehicle and the non-host vehicles under their assigned policies (Olson: Para. 21) with a reasonable expectation of success because multiple simulations are run based on different sets of selected policies for the non-host vehicles and host vehicles and compared to a reward function to decide the best policy to govern driving behavior at a current timestep (Olson: Para. 25, 101).
Regarding claim 2, Ramamoorthy teaches the method of Claim 1, wherein determining the set of risks comprises aggregating metrics from multiple simulations of the set of environmental objects, wherein the multiple simulations are performed during a same policy election cycle (Ramamoorthy: Para. 131; determining a globally optimal sequence of manoeuvres to be taken in the encountered driving scenario to execute a defined goal (i.e. achieve a desired outcome, such as reaching a particular location on the map)).
Regarding claim 3, Ramamoorthy teaches the method of Claim 1, wherein the set of risks includes a first risk identified at a first timestep and a second risk identified at a second timestep distinct from the first timestep (Ramamoorthy: Para. 155, 239; certain nodes are terminating nodes; the vehicle crashing or otherwise failing for safety reasons; the trace of the other vehicle as actually observed over the time period is matched to the distribution of paths associated with the goal in question for that time period).
Regarding claim 4, Ramamoorthy teaches the method of Claim 1, wherein the plurality of actions are determined based on a predetermined mapping between each risk and a respective set of actions (Ramamoorthy: Para. 234; probabilistic risk of collision along a given trajectory is calculated, and used to rank order the candidate trajectories by safety).
Regarding claim 5, Ramamoorthy teaches the method of Claim 1, wherein determining the first combination of actions comprises amending the first combination of actions such that the actions of the first combination are compatible (Ramamoorthy: Para. 349; step 1008, for each goal G.sub.1, G.sub.2, the goal likelihood L(O|G) is computed in terms of the cost penalty, i.e. difference between cost of optimal plan computed at step 1004 and cost of best available plan computed at step 1006 for that goal).
Regarding claim 6, Ramamoorthy teaches a method for controlling a vehicle, comprising: determining a set of risks based on a first set of measurements obtained during a first time period, each risk associated with at least one object of a set of objects in an environment of the vehicle (Ramamoorthy: Para. 47, 62; observed trace may be used to predict a current maneuver and/or a future maneuver of the external actor; determining a set of possible maneuvers for the external actor in the encountered driving scenario); based on the set of risks, selecting a plurality of actions from a predetermined set of actions (Ramamoorthy: Para. 34, 48; a sequence of multiple maneuvers may be determined for at least one goal); from the plurality of actions, selecting a first combination of actions and a second combination of actions, wherein the first combination of actions and the second combination of actions are different (Ramamoorthy: Para. 161; if the vehicle is at a particular location relative to a T-junction (corresponding to the parent state), there may be three possible manoeuvres, to stop, turn left, and turn right, but continuing straight would not be an option); based on the first combination of actions, constructing a first set of policies for controlling the vehicle (Ramamoorthy: Para. 185; the AV planner selects one of the expanded paths determined to be most promising, and generates control signals for controlling the AV to execute the corresponding sequence of manoeuvres in the real-world driving scenario is has encountered); simulating, based on a second set of measurements obtained during a second time period subsequent to the first time period, the first set of policies applied to the set of objects in the environment of the vehicle (Ramamoorthy: Para. 398; choosing a maneuver for the ego vehicle; collision checker is applied to check whether the ego vehicle collides with any of the other vehicles during the forward simulation); determining a first set of metrics associated with the first set of policies (Ramamoorthy: Para. 158; score assigned to each possible path through the game tree is simply the score assigned to its terminating node; indicates a desirability of the outcome it represents); ……….. ; and controlling the vehicle according to the selected set of policies (Ramamoorthy: Para. 186; generates control signals (E12) for controlling the AV to execute the corresponding sequence of manoeuvres in the real-world driving scenario is has encountered).
Ramamoorthy doesn’t explicitly teach based on the second combination of actions, constructing a second set of policies for controlling the vehicle; simulating, based on the second set of measurements obtained during the second time period, the second set of policies applied to the set of objects in the environment of the vehicle; determining a second set of metrics associated with the second set of policies; based on a comparison of the first set of metrics and second set of metrics, selecting one of the first set of policies and the second set of policies to be executed to control the vehicle.
However Olson, in the same field of endeavor, teaches based on the second combination of actions, constructing a second set of policies for controlling the vehicle; simulating, based on the second set of measurements obtained during the second time period, the second set of policies applied to the set of objects in the environment of the vehicle (Olson: Para. 21, 25, 100; timestep may be defined as a period of time between consecutive updates of a policy to govern the host vehicle; timestep for the multipolicy decision-making system may be periodic; iterative driving decisions based on coupled interactions with the non-host vehicles); determining a second set of metrics associated with the second set of policies (Olson: Para. 101; multiple simulations are run based on different sets of selected policies for the non-host vehicles and host vehicles); based on a comparison of the first set of metrics and second set of metrics, selecting one of the first set of policies and the second set of policies to be executed to control the vehicle (Olson: Para. 25; results of the simulations are compared to a reward function; based on the results of the one or more simulations, the host vehicle is further programmed to decide upon a policy to govern driving behavior at a current timestep).
It would have been obvious to one having ordinary skill in the art to modify the forward simulating the potential trajectories (Ramamoorthy: Para. 398) by simulated forward in time the interactions of both the vehicle and the non-host vehicles under their assigned policies (Olson: Para. 21) with a reasonable expectation of success because multiple simulations are run based on different sets of selected policies for the non-host vehicles and host vehicles and compared to a reward function to decide the best policy to govern driving behavior at a current timestep (Olson: Para. 25, 101).
Regarding claim 7, Ramamoorthy teaches the method of Claim 6, wherein the set of risks are determined using a forward simulation of the vehicle implementing a set of policies determined at a prior timestep (Ramamoorthy: Para. 398; after choosing a maneuver for the ego vehicle, the environment is simulated forward until the end of the maneuver; includes forward-simulating the trajectories of other cars to the same point in time as the ego vehicle; check whether the ego vehicle collides with any of the other vehicles during the forward simulation).
Regarding claim 8, Ramamoorthy teaches the method of Claim 7, wherein the set of risks are determined by aggregating metrics from a plurality of distinct forward simulations of sets of policies (Ramamoorthy: Para. 209; driving area is divided into grid cells, and occupation probabilities for grid cells and/or transition probabilities between grid cells are determined through long-term observation).
Regarding claim 9, Ramamoorthy teaches the method of Claim 8, wherein the first set of policies and second set of policies are determined at the prior timestep (Ramamoorthy: Para. 54, 398, 411; collision checker is applied to check whether the ego vehicle collides with any of the other vehicles during the forward simulation; cost factors can be computed iteratively by stepping forward in time through the trajectory).
Regarding claim 10, Ramamoorthy teaches the method of Claim 7, wherein the set of risks include risks identified at multiple different timesteps (Ramamoorthy: Para. 155, 239; certain nodes are terminating nodes; the vehicle crashing or otherwise failing for safety reasons; the trace of the other vehicle as actually observed over the time period is matched to the distribution of paths associated with the goal in question for that time period).
Regarding claim 11, Ramamoorthy teaches the method of Claim 6, wherein the first combination of actions comprises a set of semantic action identifiers with a predetermined mapping to types of risks included in the set of risks, wherein selecting the first combination of actions comprises using the predetermined mapping (Ramamoorthy: Para. 234; probabilistic risk of collision along a given trajectory is calculated, and used to rank order the candidate trajectories by safety).
Regarding claim 12, Ramamoorthy teaches the method of Claim 6, wherein the first combination of actions is determined independently of a subset of risks within the set of risks (Ramamoorthy: Para. 398; if there is a collision, that branch in the search tree is immediately “cut” i.e. no longer explored).
Regarding claim 13, Ramamoorthy teaches the method of Claim 6, wherein the first simulation comprises multiple iterations of simulation using the same first set of policies, wherein behavior of another agent in the environment differs between simulations of the multiple iterations of simulation (Ramamoorthy: Para. 47, 62, 82; observed trace may be used to predict a current maneuver and/or a future maneuver of the external actor; determining a set of possible maneuvers for the external actor in the encountered driving scenario; anticipated behaviour may be simulated by sampling at least one maneuver from the possible maneuvers based on a maneuver distribution determined for the external agent).
Regarding claim 14, Ramamoorthy teaches the method of Claim 6, wherein selecting the first combination of actions comprises determining a compatibility of actions within the first combination of actions (Ramamoorthy: Para. 349; step 1008, for each goal G.sub.1, G.sub.2, the goal likelihood L(O|G) is computed in terms of the cost penalty, i.e. difference between cost of optimal plan computed at step 1004 and cost of best available plan computed at step 1006 for that goal).
Regarding claim 15, Ramamoorthy teaches the method of Claim 6, wherein the first set of policies is determined independently of a subset of risks within the set of risks (Ramamoorthy: Para. 398; if there is a collision, that branch in the search tree is immediately “cut” i.e. no longer explored).
Regarding claim 16, Ramamoorthy teaches the method of Claim 6, wherein determining the set of risks comprises estimating a kinetic energy associated with avoiding a collision (Ramamoorthy: Para. 155, 398; a sequence of manoeuvres may be required to reach a terminating node; a point at which the defined goal is determined to have failed; collision checker is applied to check whether the ego vehicle collides with any of the other vehicles during the forward simulation).
Regarding claim 19, Ramamoorthy teaches the method of Claim 6, wherein the first set of policies comprises a first set of constraints distinct from a second set of constraints of the second set of policies (Ramamoorthy: Para. 398, 411; collision checker is applied to check whether the ego vehicle collides with any of the other vehicles during the forward simulation; cost factors can be computed iteratively by stepping forward in time through the trajectory).
Regarding claim 20, Ramamoorthy teaches the method of Claim 6, wherein the first set of policies comprises a vehicle controller (Ramamoorthy: Para. 186; generates control signals (E12) for controlling the AV to execute the corresponding sequence of manoeuvres in the real-world driving scenario is has encountered).
Claims 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Ramamoorthy et al. (US Publication 2021/0370980 A1) in view of Olson et al. (US Publication 2017/0031361 A1) and in further view of Crossman et al. (US Publication 2024/0194076 A1).
Regarding claim 17, Ramamoorthy and Olson don’t explicitly teach wherein a first risk of the set of risks is a risk associated with a conflict zone in the environment, wherein the first risk is associated with one or more of: a location of the conflict zone; a future time of reaching the conflict zone; an estimated probability of the risk; and an estimated severity of the risk.
However Crossman, in the same field of endeavor, teaches wherein a first risk of the set of risks is a risk associated with a conflict zone in the environment, wherein the first risk is associated with one or more of: a location of the conflict zone; a future time of reaching the conflict zone; an estimated probability of the risk; and an estimated severity of the risk (Crossman: Para. 58, 60; checking for conflict zones includes checking to see if the ego vehicle is approaching and/or within an intersection (e.g., as shown in FIG. 3A, within the forward simulation time, at a time in which another object is predicted to be in the conflict zone, etc.); severity can be determined based on the distance of the ego vehicle from the conflict zone, the speed of the objects, the headings of the objects).
It would have been obvious to one having ordinary skill in the art to modify the forward simulating the potential trajectories (Ramamoorthy: Para. 398) by simulated forward in time the interactions of both the vehicle and the non-host vehicles under their assigned policies (Olson: Para. 21) and conflict zones (Crossman: Para. 58, 60) with a reasonable expectation of success because risk severity in conflict zones found through forward simulation helps select a safe vehicle trajectory (Crossman: Para. 58, 60).
Regarding claim 18, Ramamoorthy and Olson don’t explicitly teach wherein selecting the first combination of actions comprises using information selected from a set consisting of: the location of the conflict zone; the future time of reaching the conflict zone; the estimated probability of the risk; and the estimated severity of the risk.
However Crossman, in the same field of endeavor, teaches wherein selecting the first combination of actions comprises using information selected from a set consisting of: the location of the conflict zone; the future time of reaching the conflict zone; the estimated probability of the risk; and the estimated severity of the risk (Crossman: Para. 58, 60; checking for conflict zones includes checking to see if the ego vehicle is approaching and/or within an intersection (e.g., as shown in FIG. 3A, within the forward simulation time, at a time in which another object is predicted to be in the conflict zone, etc.); severity can be determined based on the distance of the ego vehicle from the conflict zone, the speed of the objects, the headings of the objects).
It would have been obvious to one having ordinary skill in the art to modify the forward simulating the potential trajectories (Ramamoorthy: Para. 398) by simulated forward in time the interactions of both the vehicle and the non-host vehicles under their assigned policies (Olson: Para. 21) and conflict zones (Crossman: Para. 58, 60) with a reasonable expectation of success because risk severity in conflict zones found through forward simulation helps select a safe vehicle trajectory (Crossman: Para. 58, 60).
Response to Arguments
Applicant’s arguments, filed 12 May 2026, with respect to the rejection of claims 1-20 under 35 U.S.C. 103 have been fully considered, but they are not persuasive.
The applicant’s attorney argues that “executing a tree search algorithm to determine a sequence of AV maneuvers corresponding to a path through a constructed game tree” is not taught by the prior arts.
In response to the argument above, Ramamoorthy teaches an AV planner that makes various high-level decisions and then increasingly lower-level decisions that are needed to implement the higher-level decisions (Ramamoorthy: Para. 129).The prior art teaches a fully-constructed game tree data structure captures every possible outcome (Ramamoorthy: Para. 134). Ramamoorthy teaches a Monte Carlo Tree Search where not all possibilities are fully explored. This method allows for a convergence towards a sufficiently optimized solution given a reasonable amount of time and computational resources (Ramamoorthy: Para. 135). Ramamoorthy teaches possible manoeuvres for the ego vehicle are hypothesized given its current state and the defined goal to be executed (Ramamoorthy: Para. 144) and performance of the possible manoeuvres are simulated using an action policy (Ramamoorthy: Para. 149) to determine which possibilities have successfully executed the defined goal or the possibility will fail, such as forced to abort, insufficient progress, crashing, failing for safety reasons. Each possibility will be assigned a score based on success, and the failing scores are terminated possibilities (Ramamoorthy: Para. 155, 158). All of this is using a forward simulation calculations (Ramamoorthy: Para. 398).
The applicant’s specification includes “the decision-making in S400 is performed in a hierarchical (e.g., decision tree) fashion, wherein a reward metric is only calculated in an event that no risks are detected in S300 and/or any risks detected in S300 can be avoided (Specification: Para. 122). Ramamoorthy’s leveled decision described above is an example of a decision tree.
The applicant next argues that “based on the first set of measurements, determining a set of risks associated with the set of environmental objects” is not taught by the prior arts.
In response to the argument above, the applicant’s specification includes “wherein each risk is associated with at least one object of a set of objects in an environment surrounding the vehicle” (Specification: Para. 16). The applicant’s specification includes “determining and assessing a set of risks encounterable (e.g., potentially encountered in the future) by the ego vehicle” (Specification: Para. 14). A risk is associated with an object in the vehicle’s surrounding environment. The set of risks encounterable or potentially encountered in the future by the ego vehicle. These risks are inputted into the element selector to determine the policy elements (Figure 2). The geo vehicle’s options are: follow agents, change lanes, pass agents, stopping point selector, or pullover maneuver (Figure 2). The applicant’s specification supports the examiner’s interpretation of the current and future maneuver of an external actor (agent) to the ego vehicle is a risk. This risk is needed to select one of the elements: follow agents, change lanes, pass agents, stopping point selector, or pullover maneuver to prevent potential encounter in the future by the ego vehicle.
Ramamoorthy teaches object tracking based on sensor inputs tracks at least one external actor in the encountered driving scenario over a time interval (Ramamoorthy: Para. 60). This is an example of collecting a first set of measurements over a first period of time. A risk associated with an environmental object is the predicted path of a vehicle or pedestrian around the vehicle. A set of risks are predicted paths for more than one vehicle or pedestrian in the environmental surroundings. Ramamoorthy teaches object tracking determining an observed trace of at least one external actor (Ramamoorthy: Para. 60) that is used to predict a future maneuver of the external actor (Ramamoorthy: Para. 47). The predicted path is a second further period of time that is estimated from the data obtained in the first period of time. External actors of vehicles or pedestrians (Ramamoorthy: Para. 119) are a set of environmental objects. The predicted paths of at least one external vehicle or pedestrian is a set of risks.
The applicant next argues that object tracking determining is the same as the claimed “determining a set of risks associated with the set of environmental objects” is not taught by the prior arts.
In response to the argument above, the applicant’s specification includes “wherein each risk is associated with at least one object of a set of objects in an environment surrounding the vehicle” (Specification: Para. 16). The applicant’s specification includes “determining and assessing a set of risks encounterable (e.g., potentially encountered in the future) by the ego vehicle” (Specification: Para. 14). A risk is associated with an object in the vehicle’s surrounding environment. The set of risks encounterable or potentially encountered in the future by the ego vehicle. These risks are inputted into the element selector to determine the policy elements (Figure 2). The ego vehicle’s options are: follow agents, change lanes, pass agents, stopping point selector, or pullover maneuver (Figure 2).
The applicant’s specification supports the examiner’s interpretation of the current and future maneuver of an external actor (agent) to the ego vehicle is a risk. This risk is needed to select one of the elements: follow agents, change lanes, pass agents, stopping point selector, or pullover maneuver to prevent potential encounter in the future by the ego vehicle.
The applicant argues that “applicant respectfully notes that ‘object tracking’ disclosed in Ramamoorthy is not defined therein as being the same or analogous to one or more ‘risk’ or ‘set of risks’.” The system tracking surrounding objects helps the system determine whether to follow the agent, change lanes, pass agent, stopping, or pullover. It would be obvious to one of ordinary skill in the art to identify and track the vehicle in front of the ego vehicle in order to decide whether to follow the agent or pass the agent. The applicant’s invention takes in information to create a set of risk, predicted trajectories of moving objects in the surrounding environment, in order to consider the next action for the ego vehicle.
The applicant next argues that “during a second time period subsequent to the first time period” is not taught by the prior arts.
The argument above is moot because it is directed towards a prior art not used in this rejection.
The applicant next argues that “based on a comparison of the set of first forward simulations and the set of second forward simulations, selecting one of the first set of vehicle control policies and the second set of vehicles control policies to be executed to control the vehicle” is not taught by the prior arts.
The argument above is moot because it is directed towards a prior art not used in this rejection.
The applicant next argues that “set of simulations preferably includes forward simulations, which examine future scenarios (based on forward stepping in time) for the ego vehicle and objects (e.g., other vehicles, pedestrians, etc.) in its” (i.e., the ego vehicle’s) “environment, such as in an event” (i.e., such as in the event or instance) “that the ego vehicle (and/or other vehicles) performs a certain policy (e.g., behavior, action, etc.)” are not taught by the prior arts.
The argument above is moot because it is directed towards a prior art not used in this rejection.
The applicant’s arguments have failed to point out the distinguishing characteristics of the amended claim language over the prior art. For the above reasons, Ramamoorthy’s forward simulated Monte Carlo Tree Search and Olson’s forward simulations with Crossman’s conflict zones reads on applicant’s system and method for risk-aware behavior poly selection by an autonomous agent. The rejection is maintained.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LAURA E LINHARDT whose telephone number is (571) 272-8325. The examiner can normally be reached on M-TR, M-F: 8am-4pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Angela Ortiz can be reached on (571) 272-1206. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/L.E.L./Examiner, Art Unit 3663
/ANGELA Y ORTIZ/ Supervisory Patent Examiner, Art Unit 3663
