DETAILED ACTION
This Final Office Action is in response to the amendment filed 6/15/2026.
Claims 1 and 3-6 have been amended.
Claim 2 has been canceled.
Claims 1 and 3-6 are pending.
Response to Arguments
35 U.S.C. 112(f) limitations
Due to the amendment filed 6/15/2026, the limitations interpreted under 35 U.S.C. 112(f) no longer apply.
Rejections under 35 U.S.C. 102 and 103
The amendment filed 6/15/2026 overcomes the prior art rejections, and the claims contain allowable subject matter; however, outstanding 35 U.S.C. 112(b) rejections and claim objections remain to be resolved prior to allowance, as discussed below.
Key to Interpreting this Office Action
To enhance clarity, claim language is underlined throughout this Office action.
Citations to the prior art are provided in parentheses following each claim limitation, along with any necessary supplemental explanations.
Claim Objections
Claim 6 is objected to because of the following informalities:
Claim 6 recites calculate positions of the non-controlled objects and subsequently recites the position of non-controlled objects in the 15th line on page 6 and in the 3rd line on page 7 of the amendment filed 6/15/2026. For proper antecedent basis, the limitation should read “the positions of non-controlled objects.”
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1 and 3-6 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation of the attribute information in thirteenth line of claim 1. There is insufficient antecedent basis for this limitation. Specifically, “attribute information” cannot be considered an inherent feature of the object.
Claim 1 recites the limitation of evaluate a change in the position of the non-controlled object, as a movement trajectory of the non-controlled object, and calculate the movement trajectory of the non-controlled object based on time variation of the position of the non-controlled object. One of ordinary skill in the art cannot reasonably determine if two separate determinations of “movement trajectory” are claimed or if the claim uses redundant phrasing to determine the same “movement trajectory.” Specifically, the claim recites both “evaluate a change in the position…as a movement trajectory” and “calculate the movement trajectory…based on time variation of the position.” Both limitations determine the “movement trajectory” using a change (or variation) in position.
Claim 6 recites the limitation of evaluate respective changes in the positions of the non-controlled objects, as respective movement trajectories of the non-controlled objects, and calculate the movement trajectories of the non-controlled objects based on time variation of the position of the non-controlled objects. One of ordinary skill in the art cannot reasonably determine if two separate determinations of “movement trajectories” are claimed or if the claim uses redundant phrasing to determine the same “movement trajectories.” Specifically, the claim recites both “evaluate respective changes in the positions…as respective movement trajectories” and “calculate the movement trajectories…based on time variation of the position.” Both limitations determine the “movement trajectory” using a change (or variation) in position.
Claims 3-5 are rejected under 35 U.S.C. 112(b) for incorporating the errors of claim 1 by dependency.
Allowable Subject Matter
Claims 1 and 3-6 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
With respect to claim 1, the closest prior art of record, Strickland et al. (US 2015/0035685 A1), hereinafter Strickland, Kindo (US 2019/0302777 A1), hereinafter Kindo, and Zhu et al. (US 2012/0083960 A1), hereinafter Zhu, taken alone or in combination, does not teach the claimed autonomous control systems that, in an area where a controlled object that is a behavior-controllable mobile object and a non-controlled object that is a behavior-uncontrollable mobile object are mixed, controls behavior of the controlled object so that the controlled object and the non-controlled object do not come into contact with each other, the autonomous control system comprising:
a processor; and
a memory, coupled to the processor, storing instructions that when executed by the processor configures the processor to:
calculate a position of the controlled object,
identify an attribute of the non-controlled object based on the attribute information received from a device of the non-controlled object,
calculate a position of the non-controlled object based on environmental information acquired by an environment recognizer,
calculate a predicted movement trajectory by calculating an average value and variance of a trajectory on the basis of the identified attribute of the non-controlled object and the position of the non-controlled object calculated a predetermined time ago, and set a result as a candidate range of predicted movement trajectories of the non-controlled object,
evaluate a change in the position of the non-controlled object, as a movement trajectory of the non-controlled object, and calculate the movement trajectory of the non-controlled object based on time variation of the position of the non-controlled object,
evaluate a degree of deviation of the movement trajectory of the non-controlled object from the predicted movement trajectory of the non-controlled object corresponding to the identified attribute of the non-controlled object,
determine a safety level for the behavior of the controlled object on a basis of the identified attribute of the non-controlled object and the degree of deviation from the predicted movement trajectory, and
correct a behavior correction unit that corrects the behavior of the controlled object such that the higher the safety level, the less the controlled object approaches the non-controlled object on the basis of the position of the controlled object, the position of the non-controlled object, and the determined safety level.
Upon further search and consideration of the amendment filed 6/15/2026, Strickland, Kindo, and Zhu have been identified as most relevant prior art.
Specifically, Strickland teaches an autonomous control system that, in an area where a controlled object (i.e. vehicle 40) that is a behavior-controllable mobile object and a non-controlled object (i.e. pedestrian 10) that is a behavior-uncontrollable mobile object are mixed, controls behavior of the controlled object so that the controlled object and the non-controlled object do not come into contact with each other (see ¶0033, regarding a V2P communication system that addresses scenarios where a driver of a vehicle may come into contact with a pedestrian, where pedestrian 20 is associated with a V2P enabled device 10 and vehicle is associated with a V2P enabled device 10, as described in ¶0045, with respect to Figure 1; ¶0046, regarding that the V2P system may control vehicle 40 to automatically react, e.g., by engaging the braking system), the autonomous control system comprising a processor, and a memory, coupled to the processor, storing instructions (see ¶0087, regarding that the information in method 200 depicted in Figures 17 and 18 is communicated to a processor within the vehicle, where the safety algorithms are implemented on a V2P device associated with the vehicle), that when executed by the processor configures the processor to:
calculate a position of the controlled object (see ¶0087, with respect to steps 202 and 204 of Figure 17, regarding acquiring vehicle parameters including GPS coordinates of the vehicle, heading, speed, yaw rate, or brake state of the vehicle to predict a path of the vehicle),
identify an attribute of the non-controlled object based on the attribute information received from a device of the non-controlled object (see ¶0088, with respect to steps 206 and 208 of Figure 17, regarding receiving a message from a pedestrian equipped with a V2P device, where the message includes pedestrian parameters, such as GPS coordinates, heading, speed, or movement pattern of the pedestrian, the distraction level of the pedestrian, or other like parameters; ¶0047, regarding that device 10 carried by pedestrian 20 incorporates the type and disability classification of the user of device 10 into the broadcasting of a safety message received by vehicle 40).
While Strickland further teaches that the vehicle is configured with a RADAR/SONAR system or video sensor (i.e. “environment recognizer”) for the detection of objects (see ¶0076) and receives a position of the V2P device of a pedestrian (see ¶0038), Strickland does not explicitly disclose that the processor is further configured to calculate a position of the non-controlled object based on environmental information acquired by an environment recognizer. However, the technique of calculating a position of a pedestrian using onboard sensors of a vehicle is well known, and thus, it would be obvious to modify the position received by the vehicle of Strickland to instead be calculated by the vehicle based on data acquired from its onboard sensors, in light of Kindo that teaches a similar system (see ¶0024) that calculate[s] a position of a non-controlled object based on environmental information acquired by an environment recognizer (see ¶0038, regarding that moving object behavior detection unit 14 detects the position of the moving object from the result of recognizing the outside environment of the vehicle performed by vehicle outside environment recognition unit 12, defined as a camera and/or radar sensor in ¶0039). Thus, Strickland, as modified by Kindo, further teaches that the processor is configured to calculate a predicted movement trajectory on the basis of the identified attribute of the non-controlled object and the position of the non-controlled object calculated a predetermined time ago (see ¶0080, regarding that the vehicle may use the BSMs or other messages received from the V2P enabled device associated with a pedestrian to calculate the path history with respect to the class of the pedestrian, e.g., the last 300 meters of the pedestrian’s path, as described in ¶0079; ¶0035, regarding that different classes are associated with different average speeds, e.g., regular pedestrian has an average speed of 1-2 m/s, manual wheelchair user has an average speed of 0.3 m/s, powered wheelchair has an average speed of 0.4 m/s, as described in ¶0035), and set a result as a candidate range of predicted movement trajectories of the non-controlled object (see ¶0084, with respect to Figure 11, regarding that a pedestrian trajectory is predicted based on a current heading of a pedestrian 20 and includes a pedestrian zone 58 extending along a path of the pedestrian 20). This limitation does not influence (or is influenced by) other limitations; therefore, the pedestrian zone depicted in Figure 11 of Strickland reasonably teaches a “candidate range of predicted movement trajectories” of pedestrian 20.
Strickland does not teach that the “predicted movement trajectory” is calculated by calculating an average value and variance of a trajectory, and Strickland does not teach that the claimed processor is configured to evaluate a change in the position of the non-controlled object, as a movement trajectory of the non-controlled object, and calculate the movement trajectory of the non-controlled object based on time variation of the position of the non-controlled object, evaluate a degree of deviation of the movement trajectory of the non-controlled object from the predicted movement trajectory of the non-controlled object corresponding to the identified attribute of the non-controlled object, and determine a safety level for the behavior of the controlled object on a basis of the identified attribute of the non-controlled object and the degree of deviation from the predicted movement trajectory, so as to correct the behavior of the controlled object such that the higher the safety level, the less the controlled object approaches the non-controlled object on the basis of the position of the controlled object, the position of the non-controlled object, and the determined safety level.
Zhu teaches a similar system, described in ¶0018, with respect to Figure 1, that is configured to evaluate a change in the position of the non-controlled object, as a movement trajectory of the non-controlled object, and calculate the movement trajectory of the non-controlled object based on time variation of the position of the non-controlled object (see ¶0042, regarding that a pedestrian’s current movement is determined by computer 110 based on information provided by sensors, e.g., by comparing changes in the object’s position data over time). However, Zhu does not use the calculated “movement trajectory” in order to evaluate a degree of deviation of the movement trajectory of the non-controlled object from the predicted movement trajectory of the non-controlled object corresponding to the identified attribute of the non-controlled object, and determine a safety level for the behavior of the controlled object on a basis of the identified attribute of the non-controlled object and the degree of deviation from the predicted movement trajectory, so as to correct the behavior of the controlled object such that the higher the safety level, the less the controlled object approaches the non-controlled object on the basis of the position of the controlled object, the position of the non-controlled object, and the determined safety level, where the “predicted movement trajectory” is defined with respect to the “identified attribute of the non-controlled object,” taught by Strickland.
Kindo teaches that its system is configured to evaluate a degree of deviation of the movement trajectory of the non-controlled object from the predicted movement trajectory of the non-controlled object (see ¶0044, regarding that deviating moving object detection unit 16 detects a deviating moving object which is a moving object deviating from a standard state set in advance, where the deviating moving object includes a moving object predicted to deviate from the standard state within a time set in advance, as described in ¶0045, e.g., sudden braking or acceleration that does not conform to the traffic flow, as described in ¶0046). However, Kindo does not teach the “predicted movement trajectory” as corresponding to the identified attribute of the non-controlled object, so as to be calculated by calculating an average value and a variance of a trajectory on the basis of the identified attribute of the non-controlled object and the position of the non-controlled object calculated a predetermined time ago.
Kindo further does not teach that its system is configured to determine a safety level for the behavior of the controlled object on a basis of the identified attribute of the non-controlled object and the degree of deviation from the predicted movement trajectory, so as to correct the behavior of the controlled object such that the higher the safety level, the less the controlled object approaches the non-controlled object on the basis of the position of the controlled object, the position of the non-controlled object, and the determined safety level. While Strickland (see ¶0047), Zhu (see ¶0058), and Kindo (see ¶0048) teach a “safety level” with respect to a “non-controlled object,” none of these references use a “safety level” that is determined “on a basis of the identified attribute of the non-controlled object and the degree of deviation from the predicted movement trajectory,” in light of the overall claim.
No reasonable combination of prior art can be made to teach the claimed invention. The claimed invention would not have been obvious to one of ordinary skill in the art.
With respect to claim 6, the closest prior art of record, Strickland, Kindo, and Zhu, taken alone or in combination, does not teach the claimed autonomous control system that, in an area where a controlled object that is a behavior-controllable mobile object and a plurality of non-controlled objects, that are behavior-uncontrollable mobile objects, are mixed, controls behavior of the controlled object so that the controlled object and the first non-controlled object and the second non-controlled object do not come into contact with each other, the autonomous control system comprising:
a processor; and
a memory, coupled to the processor, storing instructions that when executed by the processor configures the processor to:
calculate a position of the controlled object,
identify respective attributes of the non-controlled objects based on respective attribute information received from respective devices of the non-controlled objects,
identify respective evaluation values for safe behavior from the respective devices of the non-controlled objects,
calculate positions of the non-controlled objects based on environmental information acquired by an environment recognizer,
calculate predicted movement trajectories by calculating an average value and variance of a trajectory on the basis of the respective identified attributes of the non-controlled objects and the positions of the non-controlled objects calculated a predetermined time ago, and set a result as a candidate range of predicted movement trajectories for each of the non-controlled objects,
evaluate respective changes in the positions of the non-controlled objects, as respective movement trajectories of the non-controlled objects, and calculate the movement trajectories of the non-controlled objects based on time variation of the position of the non-controlled objects,
evaluate respective degrees of deviation of the movement trajectories of the non-controlled objects from the predicted movement trajectories of the non-controlled objects corresponding to the respective identified attributes of the non-controlled object,
determine respective safety levels for the behavior of the controlled objects on a basis of the respective identified attributes of the non-controlled objects and the degrees of deviation from the predicted movement trajectories, and
correct the behavior of the controlled object such that the higher the safety level, the less the controlled object approaches the non-controlled objects on the basis of the position of the controlled object, the position of the non-controlled objects, and the determined safety levels,
wherein for non-controlled objects having a same identified attribute and a same degree of deviation from the predicted movement trajectory, if the evaluation values for safe behavior identified from the respective devices are different, different safety levels for the non-controlled objects are determined.
Specifically, claim 6 incorporates the allowable subject matter of claim 1 discussed above, as well as the further distinguishing features recited therein.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Specifically, Gulino et al. (US 2020/0004259 A1) teaches increasing a buffer around an object in which the autonomous will not encroach in response to higher levels of uncertainty associated with the object (see ¶0044), Green et al. (US 2021/0070286 A1) teaches determining corresponding vehicle models when the actual driving trajectory of a nearby vehicle deviates from the predicted trajectory (see ¶0027), which are used to adjust distances between vehicles (see ¶0056), Fonseca et al. (US 2022/0161822 A1) teaches the determination of collision between a vehicle and an object using an uncertainty model and radius of its disk (see ¶0050), Ghafarianzadeh et al. (US 2020/0307563 A1) teaches determining predicted locations associated with objects in an environment based on attributes, so as to alter a trajectory of the autonomous vehicle based on the predicted locations (see ¶0029), and Rowe (US 11,904,886 B1) teaches determining an error in distance between a predicted location of an object and an observed location of the object (see col. 14, lines 15-25).
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.
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/SARA J LEWANDROSKI/Examiner, Art Unit 3661
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