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 .
Election/Restrictions
This action is in reply to the election and amendment filed on 12 February 2026.
Claims 1, 2, and 4 have been amended and are hereby entered.
Claims 6-19 have been canceled.
Claims 1-5 and 20-21 are currently pending and have been examined.
Response to Amendments and Remarks
Claim Rejections - 35 USC § 101
Claims 1-5 and 20-21 were rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Applicant’s amendments and arguments, see pages 7-8, filed 12 February 2026, with respect to the rejection(s) of claim(s) 1-5 and 20-21 under 35 U.S.C. 101 have been fully considered and are persuasive.
Claim Rejections - 35 USC § 103
Claim(s) 1-4 and 20-21 were rejected under 35 U.S.C. 103 as being unpatentable over Hruschka (US Pub. No. 2022/0324484, hereinafter “Hruschka”) in view of Khonji et al.( "A Risk-Aware Architecture for Autonomous Vehicle Operation Under Uncertainty," 2020 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), Abu Dhabi, United Arab Emirates, 2020, pp. 311-317”, hereinafter “Khonji”) .
Claim(s) 5 was rejected under 35 U.S.C. 103 as being unpatentable over Hruschka in view of Khonji and in further view of Eggert et al. (US Pub. No. 2020/0231149, hereinafter “Eggert”).
Applicant's arguments filed 12 February 2026 have been fully considered but they are not persuasive.
Applicant argues:
Hruschka teaches risk-based trajectory planning using collision probability and severity (para. [0011]), but does not disclose detecting black swan events based on a combination of three distinct thresholds for a computed situation probability, a computed collision probability, and a computed collision severity. Thus, Hruschka focuses on risk value computation for trajectory planning instead on discrete event detection using a combination of multiple thresholds.
The examiner respectfully disagrees. As provided in the non-final office action, Applicant has defined a black swan event is an event in which there is unexpected or low situation probability events (see the instant application at [0039]). Accordingly, Hruschka teaches determining a situation probability (i.e. a high uncertainty in the situation) and further outputs a detection signal to a behavior planning system in response to an unexpected or black swan situation. As indicated by the examiner in the Office action, while Hruschka uses different nomenclature than “situation probability”, however, in Hruschka low uncertainty is equivalent to high situation probability and high uncertainty is equivalent to low situation probability. Further, the examiner notes that the other thresholds (collision probability and collision severity) are met by Hruschka to teach a black swan event, as cited in the non-final office action.
Further, Applicants argument indicating that Hruschka focuses on trajectory planning is not persuasive as the instant application itself is also identifying a possible collision and changing the trajectory accordingly.
Applicant further argues:
Office Action interprets an unexpected fatal crash of Khonji as black swan event as the situation probability is below a threshold (unexpected of unexpected or expected threshold), a probability higher than a second threshold (high probability or unavoidable, imminent collision), and a severity above a threshold (injuries or fatality). However, the applicant respectfully submits that Office Action's interpretation of Khonji as teaching or suggesting the specific detection of a potential black swan event based on the three-threshold logic (computed situation probability smaller than a first threshold, computed collision probability exceeding a second threshold, and determined collision severity exceeding a third threshold) for each predicted possible behavior, is overly broad and not supported by Khonji. Khonji addresses risk-aware planning and uncertaint, however, which are general descriptions to rare or unexpected events without a discrete detection mechanism using the claimed combined threshold logic. That is, Khonji also fails to teach a three-threshold based detection logic for detecting rare events in the environment.
The examiner respectfully disagrees. As appreciated by the Applicant, Khonji teaches the situation probability is below a threshold (unexpected of unexpected or expected threshold), a probability higher than a second threshold (high probability or unavoidable, imminent collision), and a severity above a threshold (injuries or fatality). While the Applicant indicates this is an overly broad reading, Applicant does not provide any support for such a conclusion. Just as in Hruschka, Khonji teaches a three threshold logic to determine a black swan event (i.e. an unexpected, unavoidable and severe collision.).
Hruschka was relied upon for showing determining the three threshold logic for determining a black swan event and further teaches controlling the vehicle in response. Khonji was relied upon to show detecting a potential black swan event (also using the three threshold logic) in the environment of the ego-agent for each predicted possible behavior.
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Further, Applicants argument indicating that Khonji is risk aware planning is not persuasive as the instant application itself is also identifying a possible risks.
Applicant further argues:
The applicant respectfully submits that the computer-implemented method of claim 1 introduces a specific detection mechanism including a three-threshold logic combined with automatic actuator control, which both prior art references fail to disclose. Instead, the prior art emphasizes trajectory optimization, and not discrete and reliable detection of the specific type of event (black swan event) and is silent with integrating appropriate immediate control action for detected black swan events.
The examiner respectfully disagrees. As noted above, the combination of Hruschka and Khonji teach making a determination based on a three threshold logic. While neither Hruschka nor Khonji use the term “black swan event” the combination does teach determining an unexpected, unavoidable and severe collision which corresponds to a black swan event.
Applicants rely upon the alleged deficiencies of claim 1 discussed and addressed above.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-4 and 20-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hruschka (US Pub. No. 2022/0324484, hereinafter “Hruschka”) in view of Khonji et al.( "A Risk-Aware Architecture for Autonomous Vehicle Operation Under Uncertainty," 2020 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), Abu Dhabi, United Arab Emirates, 2020, pp. 311-317”, hereinafter “Khonji”) .
Regarding claim 1, Hruschka teaches a computer-implemented method for detecting black-swan events in an assistance system for operation of an ego-agent, comprising:
sensing an environment of the ego-agent that includes at least one other agent (see at least Hruschka [0038] “ For this purpose, in a first step S1 at least the object 6 in the environment 5 of the motor vehicle 1 is detected by means of the environment detection device 3 and the uncertainty 7 with respect to the object 6 is determined by means of the electronic computing device 4 of the assistance system 2. A future environment 5 with the object 6 is predicted as a function of the detected environment 5 and the detected object 6 in a second step S2 by means of the electronic computing device 4.”);
predicting possible behaviors of the at least one other agent based on the sensed environment (see at least Hruschka [0038] “ For this purpose, in a first step S1 at least the object 6 in the environment 5 of the motor vehicle 1 is detected by means of the environment detection device 3 and the uncertainty 7 with respect to the object 6 is determined by means of the electronic computing device 4 of the assistance system 2. A future environment 5 with the object 6 is predicted as a function of the detected environment 5 and the detected object 6 in a second step S2 by means of the electronic computing device 4.”);
computing for each predicted possible behavior a situation probability and collision probability of a collision with the ego-agent (see at least Hruschka [0038] “ In a third step S3, a risk value R (FIG. 3) for a planned trajectory 8a, 8b, 8c (FIG. 4) is determined on the basis of a collision probability 9 determined by means of the electronic computing device 4 and a determined most probable impact constellation 10 and a determined accident severity 11 for the most probable impact constellation 10, wherein the collision probability 9 and the accident severity 11 are weighted in the risk value R on the basis of a weight 12, and wherein the trajectory 8 is determined as a function of the determined risk value R.” See also [0037] and [0040] regarding situation probability which takes into account uncertainty in pose and position. See also Figure 3 and 4 regarding traffic situation. See also [0044-0058] for details of probability );
determining, for each predicted possible behavior, a collision severity of the collision with the ego-agent (see at least Hruschka [0038] “ In a third step S3, a risk value R (FIG. 3) for a planned trajectory 8a, 8b, 8c (FIG. 4) is determined on the basis of a collision probability 9 determined by means of the electronic computing device 4 and a determined most probable impact constellation 10 and a determined accident severity 11 for the most probable impact constellation 10, wherein the collision probability 9 and the accident severity 11 are weighted in the risk value R on the basis of a weight 12, and wherein the trajectory 8 is determined as a function of the determined risk value R.” See additional details provided in [0043-0073] regarding severity);
[[detecting a potential black swan event in the environment of the ego-agent for each predicted possible behavior of the at least one other agent for which the computed situation probability is smaller than a first threshold, and the computed collision probability exceeds a second threshold, and the determined collision severity exceeds a third threshold]];
generating and outputting a detection signal including the detected at least one black swan event to a behavior planning system or a warning system of the ego-agent (see at least Hruschka [0038] “ In a third step S3, a risk value R (FIG. 3) for a planned trajectory 8a, 8b, 8c (FIG. 4) is determined on the basis of a collision probability 9 determined by means of the electronic computing device 4 and a determined most probable impact constellation 10 and a determined accident severity 11 for the most probable impact constellation 10, wherein the collision probability 9 and the accident severity 11 are weighted in the risk value R on the basis of a weight 12, and wherein the trajectory 8 is determined as a function of the determined risk value R.” See also Figure 4 and [0059-0061] ); and
in response to the detected black swan event, automatically generating and outputting a control signal for at least one actuator for assisting operation of the ego-agent based on a planned behavior for mitigating at least one risk in the environment when the computed situation probability of the detected black swan event exceeds a detection threshold (see at least Hruschka which teaches determining a situation probability and further outputs a detection signal to a behavior planning system in response to an unexpected or black swan situation. See at least Figure 2 and [0037] “FIG. 1 shows a schematic top plan view of a motor vehicle 1 including an assistance system 2 according to some aspects of the present disclosure. In this example, the motor vehicle 1 is at least partially assisted-operated. In particular, the assistance system 2 can thus intervene, for example, in a lateral control or in a longitudinal control of the motor vehicle 1. The motor vehicle 1 may also be fully automated. The assistance system 2 has at least one environment detection device 3 and an electronic computing device 4. An environment 5 and a further object 6, in this case a further motor vehicle, can be detected by means of the environment detection device 3.” See also [0038] “The determination of the trajectory 8 is shown in the present case in particular by the fourth step S4. In a fifth step S5, in turn, the determined emergency maneuver, i.e. the trajectory 8, can then be performed. Then, in a sixth step S6, a readjustment can be carried out, in which it is possible to go back to the first step S1, whereby a continuous optimization of the vehicle action can be realized.”)
Hruschka teaches detecting a black swan event including detecting an unexpected scenario with dire consequences and further includes determining that the computed collision probability exceeds a second threshold, and the determined collision severity exceeds a third threshold (see at least Hruschka [0043] “The scenario requires selecting between two unfavorable options. On the one hand, braking would mitigate the collision with object 6 but also cause a certain accident, thus forming a mitigation maneuver 8c. On the other hand, the evasive maneuver offers the chance of collision avoidance with both objects 6, 16, but if it fails, the collision occurs with even higher severity because of the high relative speed with respect to the oncoming traffic. … The decision depends on the potential accident severity 11 and its uncertainty 7 the risk value R. In other words, a low uncertainty 7 allows an informed decision for the avoidance maneuver 8a. However, if the uncertainty 7 is too high, collision mitigation is chosen.” See also [0059-0061] “[0059] FIG. 4 describes that the motor vehicle 1 is driving straight ahead, when “suddenly” a potential collision object, in this case the object 6, appears. This can, for example, occur when object 6 exits a parking space. Consequently, motor vehicle 1 must perform an emergency maneuver. … If the distance is great enough, the motor vehicle 1 is free to choose an appropriate evasive maneuver 8a to avoid a collision based on the risk assessment. If the distance decreases, a collision can only be avoided by steering. If the object 6 appears very suddenly, avoidance is no longer possible, but a mitigation maneuver 8c can still reduce impact severity….[0060] Furthermore, FIG. 4 shows a modification of the scenario presented above. Here, two objects 6, 16 appear in front of the motor vehicle 1. The further object 16 impedes a collision-free emergency evasive maneuver. Meaning, a collision becomes unavoidable earlier than in the scenario variant before. But even in this case, the motor vehicle 1 still has the option to reduce the collision severity to ensure maximum safety…[0061] While the further object 6 can legally drive on the adjacent lane, the object 6 disregards the right of way and drives out behind the obscuring parked cars, for example. In addition, these parked cars make a collision maneuver to the right difficult.).” The examiner notes that Applicant has indicated that a black swan event is an event in which there is unexpected or low situation probability events (see the instant application at [0039]. Further, the examiner notes that Hruschka teaches determining a situation probability and further outputs a detection signal to a behavior planning system in response to an unexpected or black swan situation. In Hruschka, low uncertainty is equivalent to high situation probability and high uncertainty is equivalent to low situation probability. )
However, Hruschka does not explicitly teach detecting a potential black swan event in the environment of the ego-agent for each predicted possible behaviors of the at least one other agent for which the computed situation probability is smaller than a first threshold, and the computed collision probability exceeds a second threshold, and the determined collision severity exceeds a third threshold.
Khonji teaches computing for each predicted possible behavior, a situation probability and a collision probability of a collision with the ego-agent (see at least Khonji Figure 6, page 314, left col. For situation probability “We define the set of predicted action maneuvers of dynamic object di∈D by Ai={a1i,a2i,…}, where each action aji=⟨pji, (xji(k), yji(k), Σji(k))Tk=1)⟩ is associated with a probability of occurrence pji, and a Gaussian process ((xji(k), yji(k)), Σji(k))Tk=1 representing location uncertainty over a horizon of T. For each action aji, we compute the corresponding shadows over time, denoted by (wji(k),ϵji(k)). The shadows are computed based on the aggregate uncertainty in both perception and prediction. The mean location of which the shadow is computed is given by (xji(k), yji(k)) and covariance by Σji(k):=Σji(k)+Σi, where Σi is the perception covariance of dynamic object di” The probability of occurrences corresponds to the situation probability. See also page 315, right col. for collision probability for each of the behaviors “As in [7], a library of maneuvers that respects traffic rules can be computed as reference trajectories. We leverage PFTs to capture the uncertainty in trajectory traversal for ego- and agent vehicles. As such, the overlap between two temporally aligned PFT trajectories represents the risk of collision. To construct PFTs for the ego-vehicle, we use vehicle dynamics and probabilistic priors about uncertainties in the environment and learning from tracking-error. By propagating the distributions of uncertainties through the vehicle's continuous dynamics, we construct probability distributions for the vehicle locations over a finite planning horizon (see [28] for more details)”. See also Figure 6 wherein the situation probability is shown in Figure 6b for each of the agents as a node and further, the determination of collision probability and severity as discussed in the corresponding description on page 316”).
Khonji further teaches detecting possible collisions for all of the probability situations including the unlikely events. Further, Khonji teaches detecting a potential black swan event in the environment of the ego-agent for each predicted possible behavior of the at least one other agent for which the computed situation probability is smaller than a first threshold, and the computed collision probability exceeds a second threshold, and the determined collision severity exceeds a third threshold (See at least Khonji page 311, right col. which discusses the need for determination of black swan or edge cases (unexpected or unlikely situations) with a likely collision with severe consequences. “Recent AV fatal crashes raise further debates among scholars and pioneers in the industry concerning how an autonomous vehicle should act when human safety is at risk. On a more philosophical level, a study [2] sheds light on the major challenges of understanding societal expectations about the principles that should guide the decision making in life-critical situations. As an illustrative example, suppose a self-driving vehicle, experiencing a partial system failure, forced into an ultimatum choice between running over pedestrians or sacrificing itself and its passenger to save them. What should be the reasoning behind such a situation, and more fundamentally, what should be the moral choice? Despite the profound philosophical dilemma and the impact on the public perception of AI as a whole and the regulatory aspects for AVs in particular, the current state-of-the-art of the technological stack of AVs does not explicitly capture and propagate uncertainty sufficiently well throughout decision processes in order to accurately assess these edge scenarios.” See also Figure 3 and corresponding discussion on page 313 “Any sensing modality has blind spots. For objects that lie beyond-line-of-sight, one can consider a communication network to improve upon the scene representation. Such an expanded field of view can be critical in certain edge scenarios. For example, in Fig. 3, the ego-vehicle (red) has two options: either maintain speed or overtake the vehicle ahead. Suppose that another agent vehicle is approaching from a distance that is not detected by onboard sensors of the ego-vehicle due to occlusion. In such a scenario, both the speed and location of the distant vehicle might not be accurately estimated, therefore maneuver A2 leading to a collision.” The examiner interprets an unexpected fatal crash as black swan event as the situation probability is below a threshold (unexpected of unexpected or expected threshold), a probability higher than a second threshold (high probability or unavoidable, imminent collision), and a severity above a threshold (injuries or fatality).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Hruschka to identify, situations that are unexpected in addition to the most likely situations as Khonji teaches, with a reasonable expectation of success, because as Khonji teaches the system captures uncertainly to accurately assess edge scenarios and plan for potential contingencies and uncertainties in human behavior (see at least Khonji 311, right col. 313, “II. B. Beyond Line of Sight” and page 315 “IV B. Short-Horizon Planner” and page 314, IV. Intention Recognition ) .
Regarding claim 2, the combination of Hruschka and Khonji teach the computer-implemented method for detecting black-swan events according to claim 1, wherein the method comprises monitoring the computed situation probability of the at least one detected black-swan event, and in case the computed situation probability of the detected black-swan event exceeds a detection threshold, changing a planning strategy for operating the ego-agent in the behavior planning system according to a different planning strategy (see at least Hruschka Figure 2 and [0038] which teaches continuous monitoring “Then, in a sixth step S6, a readjustment can be carried out, in which it is possible to go back to the first step S1, whereby a continuous optimization of the vehicle action can be realized.” And further teaches that in the loop updating the strategy when a collision is probable and with high risk or severity “In a third step S3, a risk value R (FIG. 3) for a planned trajectory 8a, 8b, 8c (FIG. 4) is determined on the basis of a collision probability 9 determined by means of the electronic computing device 4 and a determined most probable impact constellation 10 and a determined accident severity 11 for the most probable impact constellation 10, wherein the collision probability 9 and the accident severity 11 are weighted in the risk value R on the basis of a weight 12, and wherein the trajectory 8 is determined as a function of the determined risk value R. The determination of the trajectory 8 is shown in the present case in particular by the fourth step S4. In a fifth step S5, in turn, the determined emergency maneuver, i.e. the trajectory 8, can then be performed.” Further, the examiner notes that Khonji teaches detecting a black swan event and a detection threshold as applied in claim 1 above).
Regarding claim 3, the combination of Hruschka and Khonji teach the computer-implemented method for detecting black-swan events according to claim 2, wherein applying the different planning strategy for operating the ego-agent includes decreasing a preset velocity of the ego-agent (see at least Hruschka [0012-014] “For example, an emergency maneuver can then be performed in critical situations, wherein said emergency maneuver is performed based on a robust decision, based on probability theory, between evasion and mitigation under uncertainties in critical situations to ultimately perform combined steering and braking interventions to increase road safety.” See also [0042-43])
Regarding claim 4, the combination of Hruschka and Khonji teach the computer-implemented method for detecting black-swan events according to claim 1, further comprising:
planning a behavior of the ego-agent in a first behavior planning module, based on the predicted possible behaviors of the at least one other agent and the computed situation probability, the computed collision probability and the determined collision severity for the predicted possible behaviors (see at least Hruschka [0038] “For this purpose, in a first step S1 at least the object 6 in the environment 5 of the motor vehicle 1 is detected by means of the environment detection device 3 and the uncertainty 7 with respect to the object 6 is determined by means of the electronic computing device 4 of the assistance system 2. A future environment 5 with the object 6 is predicted as a function of the detected environment 5 and the detected object 6 in a second step S2 by means of the electronic computing device 4. In a third step S3, a risk value R (FIG. 3) for a planned trajectory 8a, 8b, 8c (FIG. 4) is determined on the basis of a collision probability 9 determined by means of the electronic computing device 4 and a determined most probable impact constellation 10 and a determined accident severity 11 for the most probable impact constellation 10, wherein the collision probability 9 and the accident severity 11 are weighted in the risk value R on the basis of a weight 12, and wherein the trajectory 8 is determined as a function of the determined risk value R. The determination of the trajectory 8 is shown in the present case in particular by the fourth step S4. In a fifth step S5, in turn, the determined emergency maneuver, i.e. the trajectory 8, can then be performed. Then, in a sixth step S6, a readjustment can be carried out, in which it is possible to go back to the first step S1, whereby a continuous optimization of the vehicle action can be realized.” See also Khonji Figure 1, high level planner and Page 314, “A. High Level Planner”, “High-Level Planner for High-level planning involves route planning, applying traffic rules, and consequently setting short-term objectives (aka set points), which will be fed into the Short-Horizon Planner (see Fig. 1). The planner adjusts those short-term objectives when no safe solution exists. To be able to model the feasibility of an obtained plan, …Existing high-level planners, such as Apollo's Route Planner [7], output primarily a reference trajectory from a start point to a destination that avoids all static obstacles. …).”;
assisting operation of the ego-agent based on the planned behavior (see at least Hruschka [0038] “The determination of the trajectory 8 is shown in the present case in particular by the fourth step S4. In a fifth step S5, in turn, the determined emergency maneuver, i.e. the trajectory 8, can then be performed. Then, in a sixth step S6, a readjustment can be carried out, in which it is possible to go back to the first step S1, whereby a continuous optimization of the vehicle action can be realized.”
monitoring whether the computed situation probability of the detected potential black- swan event exceeds a detection threshold in a second behavior planning module (see at least Hruschka Figure 2 and [0038] which teaches continuous monitoring and Khonji teaches detecting a black swan event and a detection threshold as applied in claim 1 above, In addition see at least Khonji Figure 1, wherein there is a “ “Short-Horizon Planner” separate from the “High-Level Planner”. See also Khonji page 315, “B. Short-term Planner” “For the application of AV, it is crucial to plan for potential contingencies instead of planning a single trajectory into the future. This often occurs in dynamic environments where the vehicle must react quickly (in milliseconds) to any potential event.”)
in case the computed situation probability of the detected black-swan event exceeds the detection threshold, switching assisting operation of the ego-agent from the planned behavior of the first behavior planning module assisting operation of the ego-agent to a second planned behavior determined by the second behavior planning module for mitigating effects of the detected black-swan event In addition see at least Khonji Figure 1, wherein there is a “ “Short-Horizon Planner” separate from the “High-Level Planner”. See also Khonji page 315, “B. Short-term Planner” “For the application of AV, it is crucial to plan for potential contingencies instead of planning a single trajectory into the future. This often occurs in dynamic environments where the vehicle must react quickly (in milliseconds) to any potential event.” See Short-Horizon Planner Algorithm as shown on page 315, right column, table).
Regarding claim 20, the combination of Hruschka and Khonji teach an advanced driver assistance system comprising a processing unit configured to execute the method according to claim 1 (see at least Hruschka Figure 1, vehicle 1 with assistance system 2. See also [0025] “A still further aspect of the present disclosure relates to an assistance system for an at least partially assisted-operated motor vehicle for determining a trajectory, at least one environment detection device comprising an electronic computing device, the assistance system being adapted to perform a method according to the preceding aspect. In particular, the method may be performed by means of the assistance system….[0026] In some examples, the electronic computing device includes electrical components, such as integrated circuits, processors and further electronic components, in order to be able to carry out a corresponding method.”).
Regarding claim 21, the combination of Hruschka and Khonji teach the vehicle comprising the advance driver assistance system according to claim 20 (see at least Hruschka Figure 1, vehicle 1 with assistance system 2. See also [0025] “A still further aspect of the present disclosure relates to an assistance system for an at least partially assisted-operated motor vehicle for determining a trajectory, at least one environment detection device comprising an electronic computing device, the assistance system being adapted to perform a method according to the preceding aspect. In particular, the method may be performed by means of the assistance system”).
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hruschka in view of Khonji and in further view of Eggert et al. (US Pub. No. 2020/0231149, hereinafter “Eggert”).
Regarding claim 5, the combination of Hruschka and Khonji teach the computer-implemented method for detecting black-swan events according to claim 1, but do not explicitly teach wherein the method comprises filtering out predicted behaviors of the at least one other agent from further consideration in the assistance system that have the computed situation probability smaller a fifth threshold and the computed collision probability below a sixth threshold..
Eggert teaches wherein the method comprises filtering out predicted behaviors of the at least one other agent from further consideration in the assistance system that have the computed situation probability smaller a fifth threshold and the computed collision probability below a sixth threshold (see at least Eggert [008-0010] “[0009] With the present invention, the most likely future behavior and, thus, trajectory and velocity of a traffic participant is iteratively predicted/calculated based on a prediction model selected based on the priority relationship between the ego-vehicle and the traffic participant. This enables to plan a future ego-vehicle behavior which is safe (low risks), useful (the ego-vehicle performs movement), and has a high comfort (low jerk, constrained acceleration). The method is computationally efficient because it uses only a single, iteratively changed prediction for the other traffic participant.” The examiner notes that by selecting the most likely future behavior, this is filtering out situation probabilities less than a fifth threshold and below collision probability below a sixth threshold (i.e. all situations of 100% probability and below) )
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JENNIFER M. ANDA whose telephone number is (571)272-5042. The examiner can normally be reached Monday-Friday 8:30 am-5pm MST.
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/JENNIFER M ANDA/Primary Examiner, Art Unit 3662
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