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
1. This Office Action is in response to the amendment filed on 02/13/2026. Claims 1-20 are pending in this application. Claims 1, 9 and 16 are independent claims.
Claim Rejections - 35 USC § 103
2. 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.
3. 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.
4. Claims 1, 8, 9, 15 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Park (US PGPub 20170267252), in view of Nakamura (US PGPub 20200331458).
As per Claim 1. Park teaches of a vehicle driving policy recommendation method, wherein the method comprises: receiving, by a terminal device of a vehicle, at least one piece of environment information from at least one sensor of the vehicle, wherein the at least one piece of environment information comprises at least one of external environment data or internal environment data; (Fig. 6 and Par 10, The vehicle may further include a communication unit configured to receive traffic information [external environment information] from an external traffic information collecting device. The vehicle may further include a communication unit configured to receive traffic information from an external traffic information collecting device. The traffic information collecting device may include at least one selected from the group consisting of an image sensing device, a vehicle detection system (VDS), a fire detector, and smoke removing facilities. Par 59, For example, when an input button to execute a specific ADAS function is selected by the user, the controller 190 may be configured to execute the selected function [one driving scenario]. In particular, the controller 190 may be configured to analyze information regarding a current driving situation or an expected driving situation of the vehicle 100 based on information received from the communication unit 175 and recommend an ADAS mode [a to-be-recommended driving policy] suitable for the situation based on the analyzed information. For example, upon determination that a vehicle ahead suddenly stops based on the information received from the communication unit 175, the controller 190 may be configured to recommend the AEB mode. Meanwhile, the AEB mode may be executed at the same time as recommended for driver's safety. Claims 3, 7-8 Fig. 11 and par 210-240. Par 70, determining a recommendable ADAS mode based on the traffic information (250), and outputting the determined ADAS mode (260). Par 49, The collection unit 180 may be configured to collect behavior information of the driver [internal environment information]. The collected information may be transmitted to the controller 190 to be provided to the ADAS mode recommendation process.)
in response to receiving the at least one piece of environment information from the at least one sensor of the vehicle, displaying, by the terminal device, at least one to-be-recommended driving policy in a to-be-recommended policy library on a display of the terminal device, (Par 67, Further, the determining of an ADAS mode recommendable to the driver based on the behavior information of the driver may be performed (220). The determining of the ADAS mode recommendable to the driver may include determining an ADAS mode suitable for execution in a current situation among various ADAS modes [to-be-recommended policy library] stored in the memory 170. In other words, an ADAS mode suitable for the current situation may be determined based on an ADAS database stored in the memory 170. Par 70, determining a recommendable ADAS mode based on the traffic information (250), Claims 1 and 9, Fig. 5, a vehicle, comprising: a collection unit configured to collect behavior information of a driver; a controller configured to determine an advanced driver assistance system (ADAS) mode recommendable to the driver based on the collected behavior information; and an output unit configured to output the determined ADAS mode. Par 40, For example, the memory 170 may be configured to store ADAS function providing programs or applications provided by the vehicle 100 and store ADAS function recommending programs [library] or applications to recommend an ADAS function suitable for the driver.)
wherein the at least one to-be-recommended driving policy is displayed based on the to-be-recommended policy library; (Figs. 6-7 and Par 67, The outputting of the determined ADAS mode may include outputting an ADAS mode recommendation screen to the display unit 165 [terminal device of a vehicle]. Par 68, Referring to FIG. 6, the mode recommendation screen may output a pop-up window P that displays the recommendable ADAS mode. Par 69, Referring to FIG. 7, the pop-up window P of the mode recommendation screen may output an explanation regarding the recommendable ADAS mode together with the ADAS mode.)
receiving, by the terminal device, a user instruction selecting one or more displayed to-be- recommended driving policies; (Par 38. For example, the input unit 160 may be configured to receive a command to select a function on an ADAS mode recommendation screen from the user and transmit the input control signal to the controller 190. The input unit 160 may be implemented using a touch panel together with the display unit 165 of the AVN device 134. However, the input unit 160 is not limited thereto. The display unit 165 may be configured to display a screen related to functions being performed in the vehicle 100. For example, the display unit 165 may be configured to display an ADAS mode recommendation screen. The ADAS mode recommendation screen may provide one or a plurality of recommended functions, guide messages regarding the recommended functions may also be provided therewith according to an exemplary embodiment.)
executing, by the terminal device, the selected one or more to-be-recommended driving policies for driving the vehicle. and (Par 59, For example, when an input button to execute a specific ADAS function is selected by the user, the controller 190 may be configured to execute the selected function. In particular, the controller 190 may be configured to analyze information regarding a current driving situation or an expected driving situation of the vehicle 100 based on information received from the communication unit 175 and recommend an ADAS mode suitable for the situation based on the analyzed information. For example, upon determination that a vehicle ahead suddenly stops based on the information received from the communication unit 175, the controller 190 may be configured to recommend the AEB mode. Meanwhile, the AEB mode may be executed at the same time as recommended for driver's safety.)
Park does not specifically teach, however Nakamura teaches wherein the to-be-recommended policy library comprises a one-to-one correspondence between a plurality of driving scenarios and a plurality of to-be- recommended driving policies (Par 11-12, (6) In the vehicle control system according to any one of (3), the vehicle controller may switch between the first and second driving modes [driving policy] at the predetermined timing in accordance with a change in a traveling scenario [driving scenario] of the vehicle, and limit the switching to the second driving mode when a predetermined condition is not satisfied after the switching to the first driving mode due to lowering of the vigilance of the driver estimated by the vigilance estimator in the second driving mode. Par 54, The action plan generator 140 determines a driving mode appropriate for a traveling scenario based on detection results of the camera 10, the radar device 12, the finder 14, the object recognition device 16, the vehicle sensor 40, the MPU 60, the operation sensors (the accelerator opening degree sensor 83, the brake sensor 85, the steering sensor 87, and the grasping sensor 88), or the like and controls the own vehicle M in accordance with the determined driving mode. For example, the action plan generator 140 determines a traveling situation based on the above-described detection results and switches the driving mode to the driving mode in accordance with the determination results. The action plan generator 140 may switch the driving mode in accordance with a change in a traveling speed of the own vehicle M. The driving mode includes, for example, a manual driving mode, a first automated driving mode, a second automated driving mode, and a third automated driving mode. In the embodiment, modes for the automated driving are defined as the first to third driving modes for convenience.)
the at least one to-be-recommended driving policy is displayed based on … at least one driving scenario associated with the at least one piece of environment information; (Par 54, The action plan generator 140 determines a driving mode [driving policy] appropriate for a traveling scenario [driving scenario] based on detection results [environment information (internal data (driver’s or car information from ECU) and external data (road condition, map and other objects outside the car)) from the sensors] of the camera 10, the radar device 12, the finder 14, the object recognition device 16, the vehicle sensor 40, the MPU 60, the operation sensors (the accelerator opening degree sensor 83, the brake sensor 85, the steering sensor 87, and the grasping sensor 88), or the like and controls the own vehicle M in accordance with the determined driving mode. For example, the action plan generator 140 determines a traveling situation based on the above-described detection results and switches the driving mode to the driving mode in accordance with the determination results. Par 66, The vigilance estimator 184 estimates vigilance of the driver based on detection results by detectors such as various sensors (including, for example, the camera 10, the radar device 12, the finder 14, the object recognition device 16, the vehicle sensor 40, the MPU 60, the accelerator opening degree sensor 83, the brake sensor 85, the steering sensor 87, the grasping sensor 88, and the occupant recognizer 182) provided in the own vehicle M. Par 70, The predetermined traveling scenario includes, for example, a scenario in which the own vehicle M is traveling for a period T1 or more (long-time travel), a scenario in which strength (average amplitude) of vibration received from a road surface by the own vehicle M which is traveling is equal to or greater than a standard value X1, a scenario in which the number of curves [external information] in traveling within a period T2 is equal to or greater than a standard value X2, a scenario in which the own vehicle M is traveling on a straight road for a period T3 or more, and a scenario in which the own vehicle M is traveling at a speed V1 or less on a congested road for a period T4 or more. In this way, the vigilance estimator 184 can estimate the vigilance in accordance with the degree of fatigue of the driver [internal information].)
Therefore, it would have been obvious for one of the ordinary skill in the art before the effective filing date of the claimed invention to add wherein the to-be-recommended policy library comprises a one-to-one correspondence between a plurality of driving scenarios and a plurality of to-be- recommended driving policies; the at least one to-be-recommended driving policy is displayed based on … at least one driving scenario associated with the at least one piece of environment information, as conceptually seen from the teaching of Nakamura, into that of Park because this modification can help select a particular driving policy or driving mode based on a corresponding driving scenario among many other candidates to match with a corresponding recommendable driving mode using the external information such as traffic information, road condition, objects on the road and map as well as internal information such as driver’s or vehicle’s condition to facilitate the autonomous driving for the user.
As per Claim 8. Park further teaches of the method according to claim 1, wherein the method further comprises: collecting the at least one piece of environment information. (Par 8, A vehicle according to an aspect may include a collection unit configured to collect behavior information of a driver, a controller configured to determine an advanced driver assistance system (ADAS) mode recommendable to the driver based on the collected behavior information, and an output unit configured to output the determined ADAS mode. Par 10, The vehicle may further include a communication unit configured to receive traffic information from an external traffic information collecting device.)
Re Claim 9, it is the system claim, having similar limitations of claim 1. Thus, claim 9 is also rejected under the similar rationale as cited in the rejection of claim 1.
Re Claim 15, it is the system claim, having similar limitations of claim 8. Thus, claim 15 is also rejected under the similar rationale as cited in the rejection of claim 8.
Re Claim 16, it is the system claim, having similar limitations of claim 1. Thus, claim 16 is also rejected under the similar rationale as cited in the rejection of claim 1.
5. Claims 2, 6, 7, 10, 14 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Park (US PGPub 20170267252), in view of Nakamura (US PGPub 20200331458), and further in view of Van Hoecke (US PGPub 20190322290).
As per Claim 2, neither Park nor Nakamura specifically teaches, however Van Hoecke teaches of the method according to claim 1, wherein before the at least one piece of environment information is collected, the method further comprises: obtaining an update package, wherein the update package comprises the to-be-recommended driving policy and the driving scenario corresponding to the to-be-recommended driving policy; and adding the to-be-recommended driving policy and the driving scenario corresponding to the to-be-recommended driving policy to the to-be-recommended policy library. (Par 42-47, In this illustrative example, the process onboard the vehicle may have a list of trackable features [as part of software update package] or may receive instructions or requests from the server to track certain features. That is, the server may dynamically determine underused features and may update which features are tracked. Or, in other examples, the process may track a predefined set of features. When a user engages a feature, the process detects 401 usage of the feature. The illustrative embodiments allow for real-time feature recommendation based on observing other users who engage features under certain conditions, improving feature usage and recommendation over typical models. Thus, it’s obvious to add a particular scenario or driving policy to improve feature usage. Par 38, The process may add 303 the feature to a queue on the mobile device, so when the user next launches the corresponding mobile application, the process may display 305 the current queue of features that have been recommended or newly used. This list can also include any configurable features, whether or not they are frequently used, if they have not yet been customized by a user.)
Therefore, it would have been obvious for one of the ordinary skill in the art before the effective filing date of the claimed invention to add obtaining an update package, wherein the update package comprises the to-be-recommended driving policy and the driving scenario corresponding to the to-be-recommended driving policy; and adding the to-be-recommended driving policy and the driving scenario corresponding to the to-be-recommended driving policy to the to-be-recommended policy library, as conceptually seen from the teaching of Van Hoecke, into that of Park and Nakamura because this modification can help select and update a particular driving features among many other candidates for a corresponding recommendable driving policy using the external and internal environment information to optimize the autonomous driving experience for the user.
As per Claim 6, neither Park nor Nakamura specifically teaches, however Van Hoecke teaches of the method according to claim 1, wherein the method further comprises: removing at least one to-be-recommended driving policy that has been executed from the to-be-recommended policy library. (Par 42, In this illustrative example, the process onboard the vehicle may have a list of trackable features or may receive instructions or requests from the server to track certain features. That is, the server may dynamically determine underused features and may update which features are tracked. Or, in other examples, the process may track a predefined set of features. When a user engages a feature, the process detects 401 usage of the feature. Par 47, The illustrative embodiments allow for real-time feature recommendation based on observing other users who engage features under certain conditions, improving feature usage and recommendation over typical models. Thus, it’s obvious to remove the feature if not used more frequently than the threshold.)
Therefore, it would have been obvious for one of the ordinary skill in the art before the effective filing date of the claimed invention to add removing at least one to-be-recommended driving policy that has been executed from the to-be-recommended policy library, as conceptually seen from the teaching of Van Hoecke, into that of Park and Nakamura because this modification can help select and update a particular driving features among many other candidates for a corresponding recommendable driving policy using the external and internal environment information to optimize the autonomous driving experience for the user.
As per Claim 7, neither Park nor Nakamura specifically teaches, however Van Hoecke teaches of the method according to claim 1, wherein the method further comprises: increasing a quantity of execution times of at least one to-be-recommended driving policy that has been executed by one, wherein an initial value of the quantity of execution times of each to-be-recommended driving policy is zero; and removing a to-be-recommended driving policy with a quantity of execution times greater than or equal to a quantity threshold of recommendations from the to-be-recommended policy library. (Par 41, FIG. 4 shows an illustrative process between a vehicle and a server, for gathering data about feature usage and dynamically adjusting parameters related to feature usage for distribution to other user vehicles. In this example, the cloud is in communication with a plurality of traveling vehicles, and whenever a tracked feature is engaged, the process can track the feature usage as well as the states and conditionals associated with the feature usage. Par 42, In this illustrative example, the process onboard the vehicle may have a list of trackable features or may receive instructions or requests from the server to track certain features. That is, the server may dynamically determine underused features and may update which features are tracked. Or, in other examples, the process may track a predefined set of features. When a user engages a feature, the process detects 401 usage of the feature.)
Therefore, it would have been obvious for one of the ordinary skill in the art before the effective filing date of the claimed invention to add increasing a quantity of execution times of at least one to-be-recommended driving policy that has been executed by one, wherein an initial value of the quantity of execution times of each to-be-recommended driving policy is zero; and removing a to-be-recommended driving policy with a quantity of execution times greater than or equal to a quantity threshold of recommendations from the to-be-recommended policy library, as conceptually seen from the teaching of Van Hoecke, into that of Park and Nakamura because this modification can help select and update a particular driving features among many other candidates for a corresponding recommendable driving policy using the external and internal environment information to optimize the autonomous driving experience for the user.
Re Claim 10, it is the system claim, having similar limitations of claim 2. Thus, claim 10 is also rejected under the similar rationale as cited in the rejection of claim 2.
Re Claim 14, it is the system claim, having similar limitations of claims 6 or 7. Thus, claim 14 is also rejected under the similar rationale as cited in the rejection of claims 6 or 7.
Re Claim 17, it is the system claim, having similar limitations of claim 2. Thus, claim 17 is also rejected under the similar rationale as cited in the rejection of claim 2.
6. Claims 3, 11, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Park (US PGPub 20170267252), in view of Nakamura (US PGPub 20200331458), and further in view of Burisch (US PGPub 20220188667).
As per Claim 3, neither Park nor Nakamura specifically teaches, however Burisch teaches of the method according to claim 1, wherein each to-be-recommended driving policy of the at least one to-be-recommended driving policy comprises priority information; and wherein displaying the at least one to-be-recommended driving policy comprises: displaying a to-be-recommended driving policy with a highest priority based on the priority information. (Par 40, In certain embodiments, the planning module 208 may generate, based on a given predicted contextual representation, several different planned trajectories 214 (e.g., paths, goals or navigation instructions) for the vehicle 102A. For each plan, the planning module 208 may compute a score that represents the desirability of that plan. For example, if the plan would likely result in the vehicle 102A performing a hard stop or otherwise performing an uncomfortable or jarring movement the score for the plan may be penalized accordingly. Another plan that would cause the vehicle 102A to violate traffic rules or take a lengthy detour to avoid possible collisions may also have a score that is penalized, but the penalty may be less severe than the penalty applied. The penalties and scoring of the different potential routes may ensure safe and comfortable paths are generated and selected by the system. For example, a third plan that causes the vehicle 102A to gradually stop or change lanes to avoid an agent 104, 112 the predicted future may receive the highest score compared to paths with jarring movements and/or an increased chance of a collision event. Based on the assigned scores for the plans, the planning module 208 may select the best planned trajectory 214A to carry out. While the example above used collision as an example, the disclosure herein contemplates the use of any suitable scoring criterial, such as travel distance or time, fuel economy, changes to the estimated time of arrival at the destination, passenger comfort, proximity to other vehicles, the confidence score associated with the predicted contextual representation, and so forth.)
Therefore, it would have been obvious for one of the ordinary skill in the art before the effective filing date of the claimed invention to add displaying a to-be-recommended driving policy with a highest priority based on the priority information, as conceptually seen from the teaching of Burisch, into that of Park and Nakamura because this modification can help select a particular driving scenario among many other candidates from downloaded software update package to match with a corresponding recommendable driving policy using the external such as traffic information and internal environment such as a driver’s behavior or the vehicle’s ECU status to facilitate the autonomous driving for the user.
Re Claim 11, it is the system claim, having similar limitations of claim 3. Thus, claim 11 is also rejected under the similar rationale as cited in the rejection of claim 3.
Re Claim 18, it is the system claim, having similar limitations of claim 3. Thus, claim 18 is also rejected under the similar rationale as cited in the rejection of claim 3.
7. Claims 4, 12 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Park (US PGPub 20170267252), in view of Nakamura (US PGPub 20200331458), in view of Burisch (US PGPub 20220188667), and further in view of Jojo-Verge (US PGPub 20200363800).
As per Claim 4, none of Park, Nakamura and Burisch specifically teaches, however Jojo-Verge teaches of the method according to claim 3, wherein the at least one driving scenario comprises a single driving scenario and a composite driving scenario, and a priority of a to-be-recommended driving policy that corresponds to the composite driving scenario is higher than a priority of a to-be-recommended driving policy that corresponds to the single driving scenario. (Par 3, The action and scene value estimator may use a current driving scene and driving scene history to determine driving actions and estimated scene value and costs. The probabilistic exploration unit, the IIP, and the advanced vehicle motion model may use the driving actions, estimated scene value, and cost to determine estimated trajectories for the AV and other actors in the driving scene. Par 82, Based on these inputs, the system may use a single, combined policy (driving actions) and value (cost function) network, which may be implemented as residual networks and a Monte Carlo Tree Search (MCTS) which may not use randomized MC rollouts and may use a neural network to evaluate actions and value. The system may provide more generality in problem solving due to decreased system complexity. Par 89, The action and scene value estimator 7530 may combine a policy (driving actions) head and a value (driving scene value evaluated against the strategic goal provided the mission planner 5300 or mission planning unit 7400) head into a single network.)
Therefore, it would have been obvious for one of the ordinary skill in the art before the effective filing date of the claimed invention to add the at least one driving scenario comprises a single driving scenario and a composite driving scenario, and a priority of a to-be-recommended driving policy that corresponds to the composite driving scenario is higher than a priority of a to-be-recommended driving policy that corresponds to the single driving scenario, as conceptually seen from the teaching of Jojo-Verge, into that of Park, Nakamura and Burisch because this modification can help select a particular driving scenario among many other candidates when it’s combined with multiple scenarios for executing a corresponding recommendable driving policy in order to optimize the autonomous driving experience for the user.
Re Claim 12, it is the system claim, having similar limitations of claim 4. Thus, claim 12 is also rejected under the similar rationale as cited in the rejection of claim 4.
Re Claim 19, it is the system claim, having similar limitations of claim 4. Thus, claim 19 is also rejected under the similar rationale as cited in the rejection of claim 4.
8. Claims 5, 13 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Park (US PGPub 20170267252), in view of Nakamura (US PGPub 20200331458), and further in view of Aragon (US PGPub 20200192359).
As per Claim 5, none of Park, Burisch and Naik specifically teaches, however Argon teaches of the method according to claim 1, wherein the method further comprises: if an execution conflict exists between a plurality of to-be-recommended driving policies, executing part of the plurality of to-be-recommended driving policies according to the user instruction, wherein no execution conflict exists between the part of the plurality of to-be-recommended driving policies. (Par 38, Negotiation skills 290 may be learned through another neural network, which may be trained through some events, such as a cut-in maneuver from a surrounding vehicle (this may involve event detection as a sub-system). In this case, an event detection for a situation that might provoke conflict between the ego-driver and a surrounding driver may be detected 280. The difference between the event being considered here and the change in driving scenario mentioned above is that a conflict will be detected in this case by determining that the event generates an obstacle to the vehicle to achieve a planned goal. For instance, a cut-in maneuver could be in conflict with the goal of keeping a safe distance from a leader vehicle. A change in scenario may be more general and might not provoke a conflict. For instance, if the leader vehicle reduces speed, the ego-vehicle may reduce speed accordingly and this change in scenario may not provoke any conflict with the goal of maintaining a desired speed. In case of events, the outcome of such an event may be evaluated by the level of risk that developed during the driving scenarios that followed the event (for instance the level of proximity to surrounding vehicles through the driving scenarios following the event may be an indicator of risk). For example, whether the ego-driver allowed space for the other vehicle to merge in, or whether the ego-driver sped up—these are indications of the negotiation skill of the ego-driver. The neural network determining the negotiation skills 290 may be trained with inputs that encode the event (number of surrounding vehicles, positions, speeds, and accelerations of the surrounding vehicles, etc.), and a label for the neural network may be the level of risk that was achieved in the driving scenarios that followed the event, up to a certain prediction horizon (for instance a horizon of 10 or 20 seconds after the event).)
Therefore, it would have been obvious for one of the ordinary skill in the art before the effective filing date of the claimed invention to add if an execution conflict exists between a plurality of to-be-recommended driving policies, executing part of the plurality of to-be-recommended driving policies according to the user instruction, wherein no execution conflict exists between the part of the plurality of to-be-recommended driving policies, as conceptually seen from the teaching of Argon, into that of Park, Burisch and Naik because this modification can help update a particular driving features among many other candidates without any conflict among them for executing a corresponding recommendable driving policy using the external and internal environment information to optimize the autonomous driving experience for the user.
Re Claim 13, it is the system claim, having similar limitations of claim 5. Thus, claim 13 is also rejected under the similar rationale as cited in the rejection of claim 5.
Re Claim 20, it is the system claim, having similar limitations of claim 5. Thus, claim 20 is also rejected under the similar rationale as cited in the rejection of claim 5.
Response to Arguments
9. Applicant’s arguments with respect to claims 1, 9 and 16 and their dependent claims have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
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/JAE U JEON/Primary Examiner, Art Unit 2193
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