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
This action is in response to the applicant’s filing on December 31, 2025. Claims 26-49 are pending.
Response to Amendment and Arguments
In response to applicant's amendments, claims rejection under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph is hereby withdrawn.
Applicant's arguments with respect to 35 USC 103 rejections of said previous office action has been fully considered but they are not persuasive. Accordingly, said rejection is further expounded below.
Applicant argues that the present claims recite the feature of infrastructure assistance data signals that represent infrastructure assistance data for the infrastructure-supported assistance of the motor vehicle in an at least partially automated driving task, based on the analysis result. In contrast, nowhere do the cited references disclose or suggest this feature. For example, while LeBmann et al. may disclose determining occupancy maps for controlling the vehicle and Jha may disclose V2X communication, neither disclosures or suggests generating infrastructure assistance data signals based on the analysis result of object recognition and/or free-space recognition to provide infrastructure-supported assistance of a motor vehicle in an at least partially automated driving task.
Examiner’s responses: The argument and support are not persuasive. The limitation as recited is generically directed to a communication between a vehicle and infrastructure based on the object recognition from vehicle and communicate to the infrastructure. The infrastructure assistance data signals that represent infrastructure assistance data for the infrastructure-supported assistance of the motor vehicle as taught by Jha (Fig. 18). For example Jha discloses NAV 102 may be configured with computer vision to recognize stationary or moving objects (e.g., a pedestrian, another vehicle, or some other moving object) in an area surrounding vehicle 110, as it travels enroute to its destination [0037]. One such V2X application include Intelligent Transport Systems (ITS), which are systems to support transportation of goods and humans with information and communication technologies in order to efficiently and safely use the transport infrastructure and transport means (e.g., automobiles, trains, aircraft, watercraft, etc.) [0029-0030].
The Examiner believes that all the arguments of the Applicant have been properly addressed and explained. Thus, the rejections of all of the claims are maintained.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 26-49 are rejected under 35 U.S.C. 103 as being unpatentable over LeBmann et al. US2020/0363809 (“LeBamann”) in view of Jha et al. US2022/0332350 (“Jha”)
Regarding claim(s) 26, , 44, 48, 49. LeBmann discloses a method for the infrastructure-supported assistance of a motor vehicle in an at least partially automated driving task, comprising the following steps:
receiving environment signals that represent an environment of the motor vehicle ([0003] Sensors mounted on a vehicle are used in automotive applications to generate an occupancy map of the vehicle's environment. For example, radar sensors and/or lidar (light detection and ranging) sensors are used to provide sequences of measurements from which one or more occupancy maps are determined, each of the occupancy maps representing free and occupied space around a vehicle at a given time instance.);
analyzing the environment to ascertain an analysis result, wherein the analyzing includes: (i) an object recognition to detect an object in the environment of the motor vehicle, and/or (ii) a free space recognition to recognize an occupancy of an area in the environment of the motor vehicle to ascertain an occupancy status that indicates whether the area is free or occupied, wherein the analysis result indicates ([0024-0030] In another aspect, the present disclosure is directed at a method for controlling a vehicle on the basis of occupancy maps, the method comprising: determining a raw sequence of occupancy maps on the basis of consecutive sensor measurements, each of the sensor measurements capturing at least a portion of the vicinity of the vehicle and each the occupancy maps representing free and occupied space around the vehicle, wherein the occupancy maps of the raw sequence are associated with consecutive time instances; determining a filtered sequence of occupancy maps, wherein at least one member of the filtered sequence is a fused occupancy map determined by fusing two occupancy maps on the basis of said raw sequence of occupancy maps in accordance with the method of an embodiment of the fusion method described herein; and controlling the vehicle on the basis of the filtered sequence including said fused occupancy map.):
whether an object has been detected in the environment of the motor vehicle, and/or (ii) the ascertained occupancy status of the area in the environment of the motor vehicle ([0024-0030] );
LeBmann does not explicitly disclose:
Jha teaches another vehicle system and method that generating infrastructure assistance data signals that represent infrastructure assistance data for the infrastructure-supported assistance of the motor vehicle in an at least partially automated driving task, based on the analysis result; and outputting the infrastructure assistance data signals (fig. 18, [0029, 0058, 0059, ] The Vehicle-to-Everything (V2X) applications (referred to simply as “V2X”) include the following types of communications Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I) and/or Infrastructure-to-Vehicle (I2V), Vehicle-to-Network (V2N) and/or network-to-vehicle (N2V), Vehicle-to-Pedestrian communications (V2P), and ITS station (ITS-S) to ITS-S communication (X2X). V2X applications can use co-operative awareness to provide more intelligent services for end-users. This means that entities, such as vehicle stations or vehicle user equipment (vUEs) including such as CA/AD vehicles, roadside infrastructure or roadside units (RSUs), application servers, and pedestrian devices (e.g., smartphones, tablets, etc.), collect knowledge of their local environment (e.g., information received from other vehicles or sensor equipment in proximity) to process and share that knowledge in order to provide more intelligent services, such as cooperative perception, maneuver coordination, and the like, which are used for collision warning systems, autonomous driving, and/or the like.)
It would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify the system and method of LeBmann by incorporating the applied teaching of Jha to improve vehicle navigation guidance and one of ordinary skill art would have recognized that the results of the combination would have been predictable.
Regarding claim(s) 27. LeBmann in view of Jha further teaches wherein the infrastructure assistance data signals are generated based on the analysis result in such a way that the infrastructure assistance data include the analysis result (Jha: [0022] FIG. 18 depicts an example roadside ITS-S in a roadside infrastructure node according to various embodiments. ).
Regarding claim(s) 28. LeBmann in view of Jha further teaches wherein the analysis result is compared with a prior analysis result to ascertain one or more changes in relation to the prior analysis result, wherein the infrastructure assistance data signals are generated based on the one or more ascertained changes (Jha: [0243] For any of the embodiments discussed herein, any suitable data fusion or data integration technique(s) may be used to generate the composite information. For example, the data fusion technique may be a direct fusion technique or an indirect fusion technique. Direct fusion combines data acquired directly from multiple vUEs or sensors, which may be the same or similar (e.g., all vUEs or sensors perform the same type of measurement) or different (e.g., different vUE or sensor types, historical data, etc.).).
Regarding claim(s) 29. LeBmann in view of Jha further teaches wherein the infrastructure assistance data signals are generated based on the one or more ascertained changes in such a way that the infrastructure assistance data include the one or more ascertained changes and are free from the analysis result (Jha: [0246] Motion dynamic prediction includes a moving object trajectory resulting from the evolution of the successive mobile positions. A change of the moving object trajectory or of the moving object velocity (acceleration/deceleration) impacts the motion dynamic prediction. In most cases, when VRUs 116 are moving, they still have a large amount of possible motion dynamics in terms of possible trajectories and velocities. This means that motion dynamic prediction 1603, 1703, 1803 is used to identify which motion dynamic will be selected by the VRU 116 as quickly as possible and if this selected motion dynamic is subject to a risk of collision with another VRU or a vehicle.).
Regarding claim(s) 30. LeBmann in view of Jha further teaches wherein, when no occupancy of the area was able to be recognized using the free space recognition, the occupancy status indicates that the occupancy of the area is unknown (LeBmann: [0025-0028] In yet another aspect, the first occupancy map is divided into a plurality of cells, each of the cells being associated with a probabilistic value representing a ratio between the probability that the respective cell is occupied and the probability that the respective cell is free, wherein the second occupancy map and the fused occupancy map are structured corresponding to the first occupancy map, i.e. both the second map and the fused map are also divided into a plurality of cells with probabilistic values.).
Regarding claim(s) 31. LeBmann in view of Jha further teaches wherein, when using the free space recognition, and additionally using the object recognition, no clear result was able to be ascertained, the occupancy status indicates that the occupancy of the area is unknown (LeBmann: [0025-0028] In yet another aspect, the first occupancy map is divided into a plurality of cells, each of the cells being associated with a probabilistic value representing a ratio between the probability that the respective cell is occupied and the probability that the respective cell is free, wherein the second occupancy map and the fused occupancy map are structured corresponding to the first occupancy map, i.e. both the second map and the fused map are also divided into a plurality of cells with probabilistic values.).
Regarding claim(s) 32. LeBmann in view of Jha further teaches wherein when, using the free space recognition, and additionally using the object recognition, it is ascertained that the area cannot be examined, the occupancy status indicates that the occupancy of the area is unknown (LeBmann: [0025-0028] In yet another aspect, the first occupancy map is divided into a plurality of cells, each of the cells being associated with a probabilistic value representing a ratio between the probability that the respective cell is occupied and the probability that the respective cell is free, wherein the second occupancy map and the fused occupancy map are structured corresponding to the first occupancy map, i.e. both the second map and the fused map are also divided into a plurality of cells with probabilistic values.).
Regarding claim(s) 33. LeBmann in view of Jha further teaches wherein, based on the analysis result, a traffic scene in the environment of the motor vehicle is analyzed to see whether or not it is safe for the motor vehicle to travel along a determined trajectory, wherein the infrastructure assistance data signals are generated based on the analysis of the traffic scene, so that the infrastructure assistance data include the information of whether or not it is safe for the motor vehicle to travel along the determined trajectory (Jha: [0016] FIG. 12 illustrates an example trajectory planner using waypoints according to various embodiments. [0017] FIG. 13 illustrates an example of frenet frames according to various embodiments. [0018] FIGS. 14a and 14b illustrate an example procedure for the trajectory planning and convergence mechanism according to various embodiments.).
Regarding claim(s) 34. LeBmann in view of Jha further teaches wherein a behavior of the motor vehicle is predicted, and wherein the determined trajectory is ascertained based on the predicted behavior (Jha: [0141] The Planned Trajectory Container is used to share a planned/predicted trajectory as a cumulative result of one or more intentions over a predefined time in the future. Stations can send multiple trajectories with different priorities (e.g., two different trajectories for left lane change). This can help for maneuver coordination among neighboring vehicles.).
Regarding claim(s) 35. LeBmann in view of Jha further teaches wherein trajectory signals that represent a trajectory planned using the motor vehicle along which the motor vehicle is intended to be guided in an at least partially automated manner are received, the determined trajectory being ascertained based on the planned trajectory (Jha: [0141] The Planned Trajectory Container is used to share a planned/predicted trajectory as a cumulative result of one or more intentions over a predefined time in the future. Stations can send multiple trajectories with different priorities (e.g., two different trajectories for left lane change). This can help for maneuver coordination among neighboring vehicles.).
Regarding claim(s) 36. LeBmann in view of Jha further teaches wherein the traffic scene is analyzed from comfort travel aspects and/or from emergency reaction travel aspects to ascertain whether or not it is safe for the motor vehicle to travel along the determined trajectory based on the comfort travel aspects and/or the emergency reaction trave aspects, so that the infrastructure assistance data include the information of whether or not it is safe for the motor vehicle to travel along the determined trajectory from the comfort travel aspects and/or the emergency reaction trave aspects (Jha: [0059] Among the above mentioned use cases, unexpected emergency road situations present a unique set of challenges of group maneuver coordination and consensus. In this case, several vehicles (in one or more lanes) need to re-calculate urgent maneuver change (without conflicting with other vehicles' maneuvers)—demanding an agreed collective maneuver plan among these vehicles within very short time. Some challenges to be addressed in these cases include the following:).
Regarding claim(s) 37. LeBmann in view of Jha further teaches wherein in the case of unsafe travel along the predetermined trajectory, emergency signals that indicate that if the motor vehicle travels along the determined trajectory an emergency may occur for the motor vehicle are generated and output (Jha: [0059-0066]).
Regarding claim(s) 38. LeBmann in view of Jha further teaches wherein the emergency signals are generated in such a way that the emergency signals describe the emergency (Jha: [0059-0066] [0066] FIG. 2 shows an example scenario 200 of detection of a USCS 201 (also referred to as a “Emergency Maneuver Coordination Event”). In such USCS 201 cases, several vehicles 110 in one or more lanes (e.g., vehicles V1 through V9) need to re-calculate urgent maneuver change without conflicting with other vehicles' 110 maneuvers. A collective maneuver plan among these vehicles 110 can be used for this purpose, but such a collective maneuver plan would need to be agreed-to within very short time to avoid the USCS 201.).
Regarding claim(s) 39. LeBmann in view of Jha further teaches wherein action recommendation signals that represent one or more action recommendations for the motor vehicle are generated and output based on the analysis result (LeBmann: [0034] The computer system can comprise an input for receiving sensor measurements and an output for providing at least a fused occupancy map. The sensor measurements can be captured by the types of technologies stated further above. The output can be connected to another computer system arranged in the vehicle, thereby broadcasting the fused map to systems which may use this information. The fused map may also be broadcasted via a wireless network to other vehicles located in the vicinity. Spatial information on the environment can thus be distributed between different vehicles.).
Regarding claim(s) 40. LeBmann in view of Jha further teaches wherein a respective measure of confidence, indicating how accurate and/or reliable information represented by the output signals is, is ascertained for the output signals (Jha: [0152] In case the cost map is dedicated for a future specific time, and no convergence on the maneuver is reached, the confidence level layer (see e.g., [AC3302]) can be adjusted to reflect the multiple trajectories shared by neighboring stations and their priorities. For example, the cost map pixels with lower priority trajectories of neighboring stations can be considered as projected free space with a given confidence interval based on the number of stations (the higher the trajectories, the lower the confidence level).).
Regarding claim(s) 41. LeBmann in view of Jha further teaches, wherein: (i) the object recognition includes one or more object properties of a recognized object, so that a result of the object recognition indicates the one or more ascertained object properties, and/or (ii) the free space recognition includes ascertaining one or more area properties of the area, so that a result of the free space recognition indicates the one or more ascertained area properties (Jha: [0047] The number of VRUs 116 operating in a given area can get very high. In some cases, the VRU 116 can be combined with a VRU vehicle (e.g., rider on a bicycle or the like). In order to reduce the amount of communication and associated resource usage (e.g., spectrum requirements), VRUs 116 may be grouped together into one or more VRU clusters. A VRU cluster is a set of two or more VRUs 116 (e.g., pedestrians) such that the VRUs 116 move in a coherent manner, for example, with coherent velocity or direction and within a VRU bounding box. VRUs 116 with VRU Profile 3 (e.g., motorcyclists) are usually not involved in the VRU clustering.)
Regarding claim(s) 42. LeBmann in view of Jha further teaches wherein each of the one or more object properties is an element selected from the following group of object properties: position, dimension, color, speed, acceleration, nature (Jha: [0047] The number of VRUs 116 operating in a given area can get very high. In some cases, the VRU 116 can be combined with a VRU vehicle (e.g., rider on a bicycle or the like). In order to reduce the amount of communication and associated resource usage (e.g., spectrum requirements), VRUs 116 may be grouped together into one or more VRU clusters. A VRU cluster is a set of two or more VRUs 116 (e.g., pedestrians) such that the VRUs 116 move in a coherent manner, for example, with coherent velocity or direction and within a VRU bounding box. VRUs 116 with VRU Profile 3 (e.g., motorcyclists) are usually not involved in the VRU clustering.)
Regarding claim(s) 43. LeBmann in view of Jha further teaches wherein each of the one or more ascertained area properties is selected from the following group of area properties: position, dimension, color, nature (Jha: [0047] The number of VRUs 116 operating in a given area can get very high. In some cases, the VRU 116 can be combined with a VRU vehicle (e.g., rider on a bicycle or the like). In order to reduce the amount of communication and associated resource usage (e.g., spectrum requirements), VRUs 116 may be grouped together into one or more VRU clusters. A VRU cluster is a set of two or more VRUs 116 (e.g., pedestrians) such that the VRUs 116 move in a coherent manner, for example, with coherent velocity or direction and within a VRU bounding box. VRUs 116 with VRU Profile 3 (e.g., motorcyclists) are usually not involved in the VRU clustering.)
Regarding claim(s) 45. LeBmann in view of Jha further teaches wherein the received infrastructure assistance data signals are checked for how accurate and/or reliable information that the received infrastructure assistance data signals represent is, the control signals being generated based on a result of the check (Jha: [0029] The Vehicle-to-Everything (V2X) applications (referred to simply as “V2X”) include the following types of communications Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I) and/or Infrastructure-to-Vehicle (I2V), Vehicle-to-Network (V2N) and/or network-to-vehicle (N2V), Vehicle-to-Pedestrian communications (V2P), and ITS station (ITS-S) to ITS-S communication (X2X). )
Regarding claim(s) 46. LeBmann in view of Jha further teaches wherein the received signals are checked based on onboard data of the motor vehicle as a reference relative to the information that the corresponding received infrastructure assistance data signals represent (Jha: [0029] The Vehicle-to-Everything (V2X) applications (referred to simply as “V2X”) include the following types of communications Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I) and/or Infrastructure-to-Vehicle (I2V), Vehicle-to-Network (V2N) and/or network-to-vehicle (N2V), Vehicle-to-Pedestrian communications (V2P), and ITS station (ITS-S) to ITS-S communication (X2X).)
Regarding claim(s) 47. LeBmann in view of Jha further teaches wherein it is established that the received signals, except for emergency signals that indicate that in the event of the motor vehicle traveling along a determined trajectory an emergency may occur for the motor vehicle, are heartbeat signals, so that received signals, except for emergency signals, are checked to see whether they have been received in accordance with the heartbeat that is to be expected (Jha: [0005] ITS is currently developing Maneuver Coordination Service (MCS), which defines an interaction protocol and corresponding messages to coordinate maneuvers between two or more vehicles. The MSC is intended to support automatic driving as well as manual driven vehicles. The MCS is intended to reduce prediction errors by exchanging detailed information about intended manoeuvers between vehicles. Furthermore, the MCS provides possibilities to coordinate a joint maneuver if several vehicles intent to use the same space at the same time.)
Conclusion
THIS ACTION IS MADE FINAL. 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 extension fee 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.
Inquiry
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TRUC M DO whose telephone number is (571)270-5962. The examiner can normally be reached on 9AM-6PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ramón Mercado, Ph.D. can be reached on (571) 270-5744. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/TRUC M DO/Primary Examiner, Art Unit 3658
Prosecution Insights