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 .
Response to Amendment
The amendment filed 5/20/2026 overcomes the following objection(s)/rejection(s):
Objections to claims 1, 5, 11, 15, 20;
101 SME rejections of claims 1, 11, 20
101 rejection of claim 20 based on non-statutory status;
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-20 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.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-3, 10-13 and 20 are rejected under 35 U.S.C. 102(a)(1) as being clearly anticipated by Malhan et al (US 20210183244 A1).
(See (Malhan, Figs. 1, 4-5, 7-9; pars. 0028-30; “edge computer” 10 is a distributed system that includes road side unit (RSU) 20 or far edge and communicates with a distributed cloud networking system 40 or near edge; predicted trajectories and paths of VRUs are detected objects based on plural AI algorithms/models; a warning device 60 serves as “a notification” about a threat).
Regarding claim 1 which recites:
A method for threat notification, comprising: receiving, in a far edge device of a processor-based machine learning system, an image indicating at least a first object, wherein the first object comprises one of a vehicle and a vulnerable road user (VRU), the far edge device implementing a first machine learning model configured for object detection, the far edge device being coupled to a cloud device of the processor-based machine learning system via a near edge device of the processor-based machine learning system, the near edge device implementing a second machine learning model, different than the first machine learning model, the second machine learning model being configured for object classification; detecting, in the processor-based machine learning system, a first threat associated with the first object based on processing of at least portions of the image, utilizing the first machine learning model of the far edge device and the second machine learning model of the near edge device; and sending, in response to detection of the first threat, a notification about the first threat from the processor-based machine learning system to at least one of the first object and a second object associated with the first object (The limitations recited are clearly taught from the referenced citations below as noted in the rejection statement above).
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“[0028] The intersection infrastructure 1 includes an edge computing device 10, one or more sensors in a sensor array 50, and a warning device 60.
[0029] As compared to conventional computing systems, the edge computing device 10 has enhanced processing capabilities, lower latency, and faster response times. Based on these enhanced capabilities, the edge computing device 10 of the intersection infrastructure 1 can apply artificial intelligence (AI) algorithms to sensor data to determine a 360° analysis of an intersection and the surrounding vicinity to calculate the precise locations and paths of road users around the intersection, and calculate the predicted trajectories and paths of the road users around the intersection. Based on these calculations, the edge computing device 10 can determine multiple concurrent risk scenarios using AI algorithms and models to better identify at-risk VRUs while also identifying the vehicles posing the risk. By using the A algorithms, the edge computing device 10 of the intersection infrastructure 1 can increase the confidence level of the calculated predictions. The faster computational capabilities of the edge computing device 10 can increase the time horizon for warning a VRU to provide better real-time warnings to VRUs, mitigate danger, and increase safety margins around the intersection. The calculations by the edge computing device 10 can be used as the basis for outputting instructions to a warning device 60 to better warn, intercept, or direct one or more VRUs at the intersection in real-time to mitigate collisions between the VRUs and the vehicles around the intersection.
[0030] The edge computing device 10 is configured as a distributed computing system that includes a road side unit (RSU) 20 that networks and communicates with a distributed cloud networking system 40 (i.e., “the cloud”). The RSU 20 includes a graphics processing unit (GPU) 22, a central processing unit (CPU) 24, storage 26, and a communications module 30. The RSU 20 may be housed inside a traffic cabinet 21 at an intersection. The traffic cabinet 21 may include other hardware in addition to the RSU 20 for controlling the traffic signals at an intersection. The RSU 20 of the edge computing device 10, the sensor array 50, and the warning device 60 may be powered directly and/or indirectly from mains electricity used for powering other electric components at the intersection such as the control signals, the pedestrian signals, speakers for audible signals, street lights, electric signage, traffic control signal hardware, and the like. That is, the intersection infrastructure warning system 1 may be powered by the electric infrastructure already in place at the intersection. While the RSU 20 of the edge computing device 10 may be part of a vehicle-to-infrastructure (V2I) system, the RSU 20 of the present disclosure differs from conventional RSUs, in that the RSU 20 includes enhanced computational abilities for executing parallel computations using AI algorithms. The RSU 20 may also be a traditional intelligent transportation system (ITS) V2X device that is configured to coordinate with the edge computing device 10.
[0084] The image and motion data acquired by the sensor arrays 50 is used by the RSU 20 with AI algorithms to determine the predicted trajectories and paths of the road users 120, 122, and 130. That is, the sensor arrays 50 collect data to detect, localize, and track all road users 120, 122, and 130 in and around the intersection 100. The RSU 20 may use image processing and machine vision to identify and track the road users 120, 122, and 130. As shown in FIG. 4, the RSU 20 may superimpose boxes 140 on captured video from the cameras 52 to identify and track the road users 120, 122, and 130 around the intersection 100. The RSU 20 can then use the detection and ranging sensors 54 to acquire measurements for determining the spatial-temporal data of the road users 120, 122, and 130 such as trajectory, path, direction, speed, and acceleration.”
Regarding claim 2, Malhan et al teaches the method according to claim 1, wherein detecting the first threat associated with the first object comprises: tracking the first object to determine trajectory data of the first object, wherein the trajectory data comprises location, speed, and traveling direction of the first object; and determining the first threat based on the trajectory data of the first object (See rejection of claim 1 e.g., [0084] The image and motion data acquired by the sensor arrays 50 is used by the RSU 20 with AI algorithms to determine the predicted trajectories and paths of the road users 120, 122, and 130. That is, the sensor arrays 50 collect data to detect, localize, and track all road users 120, 122, and 130 in and around the intersection 100. The RSU 20 may use image processing and machine vision to identify and track the road users 120, 122, and 130. As shown in FIG. 4, the RSU 20 may superimpose boxes 140 on captured video from the cameras 52 to identify and track the road users 120, 122, and 130 around the intersection 100. The RSU 20 can then use the detection and ranging sensors 54 to acquire measurements for determining the spatial-temporal data of the road users 120, 122, and 130 such as trajectory, path, direction, speed, and acceleration.”).
Regarding claim 3, Malhan et al teaches the method according to claim 2, wherein detecting the first threat associated with the first object comprises: marking an identity (ID) of the first object in the image, and wherein sending the notification about the first threat to at least one of the first object and the second object associated with the first object comprises: sending, based on a mapping between the ID of the first object in the image and an ID of the first object in a communication layer (pars. 0083-85, 0092),
“[0083] The RSU 20 can determine the status of the traffic control signals 102 and the pedestrian control signals (i.e., ID)104 through a wired or wireless connection. That is, the RSU 20 is configured to receive SPaT messages from the control signals 102 and 104 to determine the status of the control signals 102 and 104. Alternatively, the RSU 20 may determine the status of the control signals 102 and 104 by the cameras 52.
[0084] The image and motion data acquired by the sensor arrays 50 is used by the RSU 20 with AI algorithms to determine the predicted trajectories and paths of the road users 120, 122, and 130. That is, the sensor arrays 50 collect data to detect, localize, and track all road users 120, 122, and 130 in and around the intersection 100. The RSU 20 may use image processing and machine vision to identify and track the road users 120, 122, and 130. As shown in FIG. 4, the RSU 20 may superimpose boxes 140 on captured video from the cameras 52 to identify and track the road users 120, 122, and 130 around the intersection 100. The RSU 20 can then use the detection and ranging sensors 54 to acquire measurements for determining the spatial-temporal data of the road users 120, 122, and 130 such as trajectory, path, direction, speed, and acceleration.
[0085] The data acquired by the sensor arrays 50 can be used by the RSU 20 to compute proxy basic safety messages (BSMs) from the motor vehicles 130 and to compute proxy personal safety messages (PSMs) for the pedestrians 120 and cyclists 122 (i.e., the vulnerable road users (VRUs)). That is, the RSU 20 can compute proxy spatial-temporal data for the road users 120, 122, and 130 in lieu sensors on the road users gathering the spatial-temporal data. The RSU 20 can then use the proxy spatial-temporal data with AI algorithms to predict the trajectories, paths, intent, and behavior patterns of the road users 120, 122, and 130.
[0092] After the RSU 20 identifies the vehicles 130 around the intersection 100 that pose a risk to the VRUs 120 and 122 at the intersection 100, the RSU 20 can output targeting instructions to the warning devices 60 to warn the at-risk VRUs 120 and 122 (i.e., the targeted VRUs 120 and 122). In this example embodiment shown in FIG. 4, the RSU 20 outputs flight instructions to one or more drones 70 at the intersection 100 to deploy the drones 70 and warn the targeted VRUs 120 and 122.”
the notification about the first threat to at least one of the first object and the second object associated with the first object (see above analysis e.g., VRUs in association with potential threat vehicles; see also Figs. 4, 7-9 and associated disclosure).
Regarding claim 10, Malhan et al further teach the method according to claim 1, wherein the image is captured by at least one of a roadside unit (RSU) and a sensor of the second object, and preprocessed to indicate the first object (Fig. 4, Pars. 0081-84; Note: pedestrian, cyclists, motor vehicles read on “first” and “second” objects).
“[0081] With reference to FIG. 4, an example intersection 100 where the infrastructure warning device 1 can be used is illustrated. The intersection 100 includes traffic control signals 102, pedestrian control signals 104, marked pedestrian crossing points (e.g., crosswalks) 106, and stop lines 108 for controlling the flow of pedestrians 120, cyclists 122, and motor vehicles 130 (collectively referred to as “road users”). The intersection 100 also includes the elements of the intersection infrastructure warning system 1 including the RSU 20 (not shown) housed in the traffic cabinet 21 mounted on a utility pole 103, the sensor arrays 50 (not shown) in housings 58, and the drones 70. In the example intersection warning infrastructure 1 shown in FIG. 4, the drones 70 are used as the warning device 60. The nests 90 for the drones 70 may include individual support poles 94. However, the nests 90 may also be installed on existing intersection infrastructure like utility pole 103.
[0082] While a four-way intersection is shown in FIG. 4, the infrastructure warning system 1 may also be used at more complex intersections with a greater number of junction roads, at roundabouts, and at intersections with less junction roads (e.g., three-way intersections).
[0083] The RSU 20 can determine the status of the traffic control signals 102 and the pedestrian control signals 104 through a wired or wireless connection. That is, the RSU 20 is configured to receive SPaT messages from the control signals 102 and 104 to determine the status of the control signals 102 and 104. Alternatively, the RSU 20 may determine the status of the control signals 102 and 104 by the cameras 52.
[0084] The image and motion data acquired by the sensor arrays 50 is used by the RSU 20 with AI algorithms to determine the predicted trajectories and paths of the road users 120, 122, and 130. That is, the sensor arrays 50 collect data to detect, localize, and track all road users 120, 122, and 130 in and around the intersection 100. The RSU 20 may use image processing and machine vision to identify and track the road users 120, 122, and 130. As shown in FIG. 4, the RSU 20 may superimpose boxes 140 on captured video from the cameras 52 to identify and track the road users 120, 122, and 130 around the intersection 100. The RSU 20 can then use the detection and ranging sensors 54 to acquire measurements for determining the spatial-temporal data of the road users 120, 122, and 130 such as trajectory, path, direction, speed, and acceleration.”
Regarding claims 11-13, claims 11-13 rejected for the same reasons as claims 1-3 above, respectively.
Regarding claim 20, claim 20 rejected for the same reasons as claim 1 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.
Claims 4-9 and 14-19 are rejected under 35 U.S.C. 103 as being unpatentable over Malhan et al (US 20210183244 A1) in view of Beauchamp et al (US 20210287529 A1).
Regarding claim 4, Malhan et al does not teach, the method according to claim 1, wherein detecting the first threat associated with the first object comprises: determining, in a first application scenario, a first zone where the first threat exists, the first zone being associated with the second object, wherein the first zone is in a traveling direction of the second object and is a sector-shaped zone centered on the second object, and wherein the first zone is a collision zone.
In a similar field of endeavor, Beauchamp et al teaches the method according to claim 1, wherein detecting the first threat associated with the first object comprises: determining, in a first application scenario, a first zone where the first threat exists, the first zone being associated with the second object, wherein the first zone is in a traveling direction of the second object and is a sector-shaped zone centered on the second object, and wherein the first zone is a collision zone (Fig 26 and Para 175, method for collision avoidance between VRUs and vehicles, wherein the method comprises a set of rules for providing a danger notification that may relate to a proximity range shaped like an ellipse. When the vehicle is notified of a danger, the danger notification may include a prescription for collision avoidance including (dx/dt.sup.2 braking-terms and (dy/dt.sup.2 swerving-terms in the predicted spatiotemporal trajectory of the notified UE terminal belonging to the vehicle, which relates approximately to the shape of an ellipse on the road. i.e. see Fig 27 which demonstrates the sector-shaped zone which is in the traveling direction of the second object (towards the front of the car where it is moving) and the zone is a collision zone centered on a moving threat (the vehicle), also see 175 - a moderate or medium level warning may be given to the VRU and/or the vehicle may be controlled to slow down or to prepare for slowing down).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date to incorporate the teachings of Malhan et al (US 20240246479 A1) in view of Beauchamp et al (US 20210287529 A1) so that detecting the first threat associated with the first object comprises: determining, in a first application scenario, a first zone where the first threat exists, the first zone being associated with the second object. Doing so could provide danger notifications pertaining to the field of road safety, and pertaining to collision avoidance before accidents happen (Beauchamp et al., Abst).
Regarding claim 5, Malhan et al does not teach, the method according to claim 4, wherein the first zone is divided into a plurality of sub-zones according to a distance from the second object, and wherein sending the notification about the first threat to at least one of the first object and the second object associated with the first object comprises: sending, according to a detection that the first threat is in a corresponding sub-zone, a corresponding notification to at least one of the first object and the second object associated with the first object.
In a similar field of endeavor, Beauchamp et al teaches the method according to claim 4, wherein the first zone is divided into a plurality of sub-zones according to a distance from the second object, and wherein sending the notification about the first threat to at least one of the first object and the second object associated with the first object comprises: sending, according to a detection that the first threat is in a corresponding sub-zone, a corresponding notification to at least one of the first object and the second object associated with the first object (Fig 26 and Para 175, the zones are divided into a plurality of sub-zones 1-9, and as is stated in Para 175: the danger notification may indicate that level 5 or 6 may be a moderate threat to the VRU. In these embodiments, a moderate or medium level warning may be given to the VRU and/or the vehicle may be controlled to slow down or to prepare for slowing down. i.e. the notification regarding the first threat is sent according to a detection that a threat is in a corresponding sub-zone (level or zone 5-6 may be a moderate threat to the VRU)).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date to incorporate the teachings of Malhan et al (US 20240246479 A1) in view of Beauchamp et al (US 20210287529 A1) so that the first zone is divided into a plurality of sub-zones according to a distance from the second object, and wherein sending the notification about the first threat to at least one of the first object and the second object associated with the first object comprises: sending, according to a detection that the first threat is in a corresponding sub-zone, a corresponding notification to at least one of the first object and the second object associated with the first object. Doing so could provide danger notifications pertaining to the field of road safety, and pertaining to collision avoidance before accidents happen (Beauchamp et al., Abst).
Regarding claim 6, Malhan et al does not teach, the method according to claim 5, wherein the plurality of sub-zones comprise a first sub-zone, a second sub-zone, and a third sub-zone, and wherein a distance from a boundary between the first sub-zone and the second sub-zone to the second object is determined according to a braking distance of the second object, and a distance from a boundary between the second sub-zone and the third sub-zone to the second object is determined according to a speed and a reaction time of the second object.
In a similar field of endeavor, Beauchamp et al teaches the method according to claim 5, wherein the plurality of sub-zones comprise a first sub-zone, a second sub-zone, and a third sub-zone, and wherein a distance from a boundary between the first sub-zone and the second sub-zone to the second object is determined according to a braking distance of the second object, and a distance from a boundary between the second sub-zone and the third sub-zone to the second object is determined according to a speed and a reaction time of the second object (Para 175 and Fig 26, the method for collision avoidance between VRUs and vehicles, wherein the method comprises a set of rules for providing a danger notification that may relate to a proximity range shaped like an ellipse. When the vehicle is notified of a danger, the danger notification may include a prescription for collision avoidance including (dx/dt.sup.2 braking-terms and (dy/dt.sup.2 swerving-terms in the predicted spatiotemporal trajectory of the notified UE terminal belonging to the vehicle, which relates approximately to the shape of an ellipse on the road. Since the capacity to brake is higher than the capacity to swerve (e.g., μ.sub.x>μ.sub.y), the predicted spatiotemporal trajectory of the notified UE terminal belonging to the vehicle may exhibit a higher trajectory probability along the direction of driving in order to maintain vehicle control, and a progressively lower trajectory probability transversally given the standard deviations (σ) for t.sub.r, μ.sub.x and, μ.sub.y. Therefore, according to one aspect of the described technology, the proximity range may have the shape of an ellipse, wherein the major axis of the ellipse is coincident with the predicted spatiotemporal trajectory of the notified UE terminal belonging to the vehicle. This two-dimensional gradient for the trajectory probability may relate to a collision-probability assessment and/or Confidence factor, within a PathPrediction danger notification. i.e. there are at least 3 sub-zones as can be seen in the figure and the distance from the boundary of each sub-zone to the vehicle is determined according to a braking ability of the vehicle, and also the boundary between subzones is determined based on a spatiotemporal trajectory and reaction time of the vehicle (see more details in Para 134-146)).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date to incorporate the teachings of Malhan et al (US 20240246479 A1) in view of Beauchamp et al (US 20210287529 A1) so that the plurality of sub-zones comprise a first sub-zone, a second sub-zone, and a third sub-zone, and wherein a distance from a boundary between the first sub-zone and the second sub-zone to the second object is determined according to a braking distance of the second object, and a distance from a boundary between the second sub-zone and the third sub-zone to the second object is determined according to a speed and a reaction time of the second object. Doing so could provide danger notifications pertaining to the field of road safety, and pertaining to collision avoidance before accidents happen (Beauchamp et al., Abst).
Regarding claim 7, Malhan et al does not teach, the method according to claim 6, wherein sending the notification about the first threat to at least one of the first object and the second object associated with the first object comprises: sending, in response to detection that the first threat is in the first sub-zone, a notification of a first urgency level to at least one of the first object and the second object associated with the first object; sending, in response to detection that the first threat is in the second sub-zone, a notification of a second urgency level to at least one of the first object and the second object associated with the first object; and sending, in response to detection that the first threat is in the third sub-zone, a notification of a third urgency level to at least one of the first object and the second object associated with the first object, wherein urgency degrees of the first urgency level, the second urgency level, and the third urgency level descend gradually.
In a similar field of endeavor, Beauchamp et al teaches the method according to claim 6, wherein sending the notification about the first threat to at least one of the first object and the second object associated with the first object comprises: sending, in response to detection that the first threat is in the first sub-zone, a notification of a first urgency level to at least one of the first object and the second object associated with the first object; sending, in response to detection that the first threat is in the second sub-zone, a notification of a second urgency level to at least one of the first object and the second object associated with the first object; and sending, in response to detection that the first threat is in the third sub-zone, a notification of a third urgency level to at least one of the first object and the second object associated with the first object, wherein urgency degrees of the first urgency level, the second urgency level, and the third urgency level descend gradually (Fig 26 and Para 175, this two-dimensional gradient for the trajectory probability may relate to a collision-probability assessment and/or Confidence factor, within a PathPrediction danger notification. In some embodiments, the danger notification may be different depending on the distance (or proximity range) between the VRU and the vehicle. In level 1, the distance between the vehicle and the VRU is farthest where the danger notification may indicate that there is a relatively low risk of collision. In level 9, the distance between the vehicle and the VRU is closest where the danger notification may indicate that there is a very high risk of collision. In some embodiments, the danger notification may indicate that levels 5-9 may be more dangerous than levels 1-4, and the VRU may be appropriately warned and/or the vehicle may be controlled to slow down or stop. In some embodiments, the danger notification may indicate that level 8 or 9 may be extremely dangerous. In these embodiments, the vehicle may be immediately stopped and/or the VRU may be alerted with an extreme danger. In some embodiments, the danger notification may indicate that level 1 or 2 may not be an immediate threat to the VRU. In these embodiments, a low risk warning may be given to the VRU and/or the vehicle. In some embodiments, the danger notification may indicate that level 5 or 6 may be a moderate threat to the VRU. In these embodiments, a moderate or medium level warning may be given to the VRU and/or the vehicle may be controlled to slow down or to prepare for slowing down. i.e. for the at least three different sub-zones, a notification of a threat based on the corresponding sub-zone has a corresponding urgency to the sub-zone, and the urgency levels descend gradually from 3-1).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date to incorporate the teachings of Malhan et al (US 20240246479 A1) in view of Beauchamp et al (US 20210287529 A1) so that sending the notification about the first threat to at least one of the first object and the second object associated with the first object comprises: sending, in response to detection that the first threat is in the first sub-zone, a notification of a first urgency level to at least one of the first object and the second object associated with the first object, and so on for the second and third sub-zone. Doing so could provide danger notifications pertaining to the field of road safety, and pertaining to collision avoidance before accidents happen (Beauchamp et al., Abst).
Regarding claim 8, Malhan et al does not teach, the method according to claim 1, wherein detecting the first threat associated with the first object comprises: determining, in a second application scenario, a second zone associated with a road, the second zone having the first threat indicating a threatening road participant, wherein the second zone is a monitoring zone, and the first threat comprises at least one of a large vehicle and a speeding vehicle,
and wherein sending the notification about the first threat to at least one of the first object and the second object associated with the first object comprises: sending, in response to detection of the first threat, the notification about the first threat to at least one of the first object and the second object located in the second zone.
In a similar field of endeavor, Beauchamp et al teaches the method according to claim 1, wherein detecting the first threat associated with the first object comprises: determining, in a second application scenario, a second zone associated with a road, the second zone having the first threat indicating a threatening road participant, wherein the second zone is a monitoring zone, and the first threat comprises at least one of a large vehicle and a speeding vehicle (Fig 27 and Para 176, collision avoidance between VRUs and vehicles, wherein the method comprises a set of rules for providing a danger notification that may relate to a proximity range shaped like an ensemble of n concatenated ellipses, wherein smaller ellipses relate to higher collision-probability assessments. According to one aspect of the described technology, the dimensional safety margin M may relate to a collision-probability assessment and/or a Confidence factor, such that if the dimensional safety margin M is set at a small value, the probability of collision will be higher. In the illustration of FIG. 18, the proximity range R (212) of the first VRU (202) is smaller than the proximity range R (211) of the second VRU (201), with respect to the same vehicle (301). Therefore, the proximity range R (212) may be labelled as a relatively unsafe close approach between VRU (202) and vehicle (301) at future time t, as compared to the moderate close approach between VRU (201) and vehicle (301) at a different future time t. The communications server (10), acting as a cloud-component of a collision-avoidance system (60), may then implement the provision of the danger notification including a prescription for collision avoidance to VRU (202), and of a warning message to VRU (201), and of a prescription for applying brakes to slow down or to stop for vehicle (301). Other danger notification may be implemented depending on the road context in order to optimize the collision avoidance. i.e. determining a zone associated with the road, which has a first threat (vehicle) that is threatening a road participant(s) 202, and is a monitoring zone (zones 1-9 are monitored for presence of VRUs). The vehicle is moving at a speed which is calculated to be at risk for the VRUs (see Para 134-135)),
and wherein sending the notification about the first threat to at least one of the first object and the second object associated with the first object comprises: sending, in response to detection of the first threat, the notification about the first threat to at least one of the first object and the second object located in the second zone (Fig 27 and Para 176, collision avoidance between VRUs and vehicles, wherein the method comprises a set of rules for providing a danger notification that may relate to a proximity range shaped like an ensemble of n concatenated ellipses, wherein smaller ellipses relate to higher collision-probability assessments. […] the proximity range R (212) of the first VRU (202) is smaller than the proximity range R (211) of the second VRU (201), with respect to the same vehicle (301). Therefore, the proximity range R (212) may be labelled as a relatively unsafe close approach between VRU (202) and vehicle (301) at future time t, as compared to the moderate close approach between VRU (201) and vehicle (301) at a different future time t. The communications server (10), acting as a cloud-component of a collision-avoidance system (60), may then implement the provision of the danger notification including a prescription for collision avoidance to VRU (202), and of a warning message to VRU (201), and of a prescription for applying brakes to slow down or to stop for vehicle (301). Other danger notification may be implemented depending on the road context in order to optimize the collision avoidance. i.e. sending the notification to the first and second (VRU 202 and vehicle 301) regarding the threat).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date to incorporate the teachings of Malhan et al (US 20240246479 A1) in view of Beauchamp et al (US 20210287529 A1) so that detecting the first threat associated with the first object comprises: determining, in a second application scenario, a second zone associated with a road, the second zone having the first threat indicating a threatening road participant. Doing so could provide danger notifications pertaining to the field of road safety, and pertaining to collision avoidance before accidents happen (Beauchamp et al., Abst).
Regarding claim 9, Malhan et al does not teach, the method according to claim 8, wherein the second object comprises a vehicle, and the method further comprises: sending the notification to a VRU different from the first object and the second object, wherein the VRU is located in the second zone.
In a similar field of endeavor, Beauchamp et al teaches the method according to claim 8, wherein the second object comprises a vehicle, and the method further comprises: sending the notification to a VRU different from the first object and the second object, wherein the VRU is located in the second zone (Fig 27 and Para 176, collision avoidance between VRUs and vehicles, wherein the method comprises a set of rules for providing a danger notification that may relate to a proximity range shaped like an ensemble of n concatenated ellipses, wherein smaller ellipses relate to higher collision-probability assessments. According to one aspect of the described technology, the dimensional safety margin M may relate to a collision-probability assessment and/or a Confidence factor, such that if the dimensional safety margin M is set at a small value, the probability of collision will be higher. In the illustration of FIG. 18, the proximity range R (212) of the first VRU (202) is smaller than the proximity range R (211) of the second VRU (201), with respect to the same vehicle (301). Therefore, the proximity range R (212) may be labelled as a relatively unsafe close approach between VRU (202) and vehicle (301) at future time t, as compared to the moderate close approach between VRU (201) and vehicle (301) at a different future time t. The communications server (10), acting as a cloud-component of a collision-avoidance system (60), may then implement the provision of the danger notification including a prescription for collision avoidance to VRU (202), and of a warning message to VRU (201), and of a prescription for applying brakes to slow down or to stop for vehicle (301). Other danger notification may be implemented depending on the road context in order to optimize the collision avoidance. i.e. sending the notification to a VRU (201) different from the first object (VRU 202) and the second object (vehicle 301), wherein the VRU is located in the zone).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date to incorporate the teachings of Malhan et al (US 20240246479 A1) in view of Beauchamp et al (US 20210287529 A1) so that the method comprises sending the notification to a VRU different from the first object and the second object, wherein the VRU is located in the second zone. Doing so could provide danger notifications pertaining to the field of road safety, and pertaining to collision avoidance before accidents happen (Beauchamp et al., Abst).
Regarding claims 14-19, claims 14-19 rejected for the same reasons as claims 4-9 above, respectively.
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.
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/VU LE/Supervisory Patent Examiner, Art Unit 2668
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