Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/01/2025 has been entered.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 1, 3-9, and 11-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Independent claim recites “detecting first trajectories of a plurality of vehicles over one or more first time intervals; identifying first paths, on the basis of a plurality of first trajectories in each case; and providing the first paths and/or first information derived from the first paths as first traffic information, wherein identifying the first paths comprises identifying the first paths on the basis of the plurality of first trajectories during a plurality of first time intervals; wherein the first paths are averages of the first trajectories and indicate which trajectories are typically driven; wherein the first traffic information is transmitted to and/or used by a vehicle controller to control vehicle operations or transmitted to and/or used by a traffic monitoring system to control traffic flow”. This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. Nothing in the claim precludes the detecting, identifying, and providing from practically being performed in the human mind. If a claim limitation under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” groupings of abstract ideas. Accordingly, the claims recite an abstract idea.
Specifically, independent claim 1 recites “detecting first trajectories of a plurality of vehicles over one or more first time intervals; identifying first paths, on the basis of a plurality of first trajectories in each case; and providing the first paths and/or first information derived from the first paths as first traffic information, wherein identifying the first paths comprises identifying the first paths on the basis of the plurality of first trajectories during a plurality of first time intervals; wherein the first paths are averages of the first trajectories and indicate which trajectories are typically driven; wherein the first traffic information is transmitted to and/or used by a vehicle controller to control vehicle operations or transmitted to and/or used by a traffic monitoring system to control traffic flow”, which is merely just a concept can be performed in the human mind. Thus, all of the limitations can be performed mentally. As such, the claim is directed solely to perform mental processes that fall into the “Mental Processes” groupings of abstract ideas and is directed to a judicial exception.
This judicial exception is not integrated into a practical application. In particular, the claim does not recite any generic processor. Therefore, the claim does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to the abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Claims 3-9 and 11-14 recite limitations adding specific information to the abstract idea. The additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are not patent eligible.
Claim Rejections - 35 USC § 103
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.
Claim(s) 1 and 3-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Raamot (US 20160027300 A1) in view of Ramamurthy et al. (US 20230316921 A1).
In regard to claim 1, Raamot teaches a method for delivering traffic information at an intersection (Raamot, Fig. 1A), the method comprising: detecting first trajectories of a plurality of vehicles over one or more first time intervals (Raamot, Para. 63, the traffic controller is run for the first time at block 304. With the traffic controller running, the traffic controller measures traffic trajectories at block 306 using, for example, the trajectory sensors described above); identifying first paths, on the basis of a plurality of first trajectories in each case (Raamot, Para. 60, The trajectory calculator 212 can compute vehicle trajectories or a trajectory framework (described below) based on data received from trajectory sensors 220, in-road sensors 222, and adjacent intersections' traffic controllers 260. The trajectory calculator 212 may also base the trajectory information off of features of the intersection, including the geometry of the intersection, stored in a geographic information description 252 (which may be a database or the like) and/or in a trajectory framework database 254. The geographic information description 252 may include map components (such as data on the stop line, lane segment points, and the like). The geographic location of relevant attributes of the intersection may be extracted from various map data sources and stored in a geographic information description (GID) 252. This GID may then be converted by the ATMS 242 to reveal geometric constraints that will affect the vehicle trajectories as they drive through the roadway network. The geometric properties of the roadway network may be stored in a data structure referred to as the trajectory framework. This trajectory framework can include data that supports overlay of vehicle trajectory data relative to the roadway geometries, allowing modeling of past, present, and/or future vehicle trajectories relative to the traffic signalization; Para. 211, the traffic controller 210 obtains data regarding vehicle trajectories from one or more data sources. At block 804, the traffic controller 210 uses geographic information description (GID) to convert sensor data to a GID frame of reference or coordinate system. At block 806, the traffic controller 210 uses converted sensor data to calculate traffic flow parameters); and providing the first paths and/or first information derived from the first paths as first traffic information (Raamot, Para. 176, traffic flow parameters can be derived from highway capacity manual and other industry standardized estimates for stage 1 configuration. Under stage 2 operation, these parameters can be verified by real world measurement from the vehicle trajectories within the traffic controller 210. These real world parameters can then be fed back into to re-compute the pre-configuration data by the traffic controller 210 Configuration Generator. These assumed traffic flow parameters may include startup lost time, vehicle acceleration rate, and vehicle deceleration rate; Para. 248, the traffic controller 210 can provide a fusion of these detection inputs and other data sources into a collective set of vehicle trajectories within the GID); wherein identifying the first paths comprises identifying the first paths on the basis of the plurality of first trajectories during a plurality of first time intervals (Raamot, Para. 251, The traffic controller 210 begins in one embodiment by determining the historic average volume per cycle for each approach. This volume can be defined as a rolling average volume taken over the prior 5 cycles (other numbers may be chosen). In the event that this data does not exist (controller restart) the average volume can be taken by averaging the approach volume for the prior 4 weeks (for example) during the same day of week and hour of day).
Raamot does not specifically teach wherein the first paths are averages of the first trajectories and indicate which trajectories are typically driven (Ramamurthy, Para. 142, The turning paths may be determined based on stored locations of vehicles in the intersection moving from the ingress lanes 1406, 1408 to the egress lanes 1410, 1412. The paths may be generated based on nodal points and/or radii of curvature of lines (or paths) connecting the nodal points. The control module may average trajectories of the vehicles to create a set of nodes or a turning radius to determine each of the dynamic paths. The control module is configured to adjust a window duration for tracking the vehicles to determine the dynamic paths); wherein the first traffic information is transmitted to and/or used by a vehicle controller to control vehicle operations or transmitted to and/or used by a traffic monitoring system to control traffic flow (Ramamurthy, Para. 159, Predicted path information and/or nodal positions on the static or dynamic paths are broadcast over air in basic safety messages to other vehicles to prevent collisions. Path data may be sent in map messages to other vehicles to prevent collisions. The collision warning may include forward collision warning, pedestrian collision warning, and/or other collision warning (e.g., side collision warning). The collision warning operations are performed based on a predicted path of the host vehicle as determined by the control module).
Raamot and Ramamurthy are analogous art because they both pertain to collecting vehicle trajectories at an intersection.
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to use average vehicle trajectories to determine vehicle paths (as taught by Ramamurthy) resulting in predictable result of providing paths of vehicles on roadways and between intersections.
In regard to claim 3, Combination of Raamot and Ramamurthy teach the method as claimed in claim 1, further comprising: detecting second trajectories of a plurality of vehicles over second time intervals that do not overlap with the plurality of first time intervals (Raamot, Fig. 1A, Trajectory sensors 120, each trajectory sensors monitors different sequence of phases; Para. 54, sequence of phases may be: phases 1&5 active at the same time, followed by phases 2&6 active at the same time, followed by phases 3&7 active at the same time, followed by phases 4&8 active at the same time, which completes a cycle (which then repeats possibly indefinitely); Para. 4, obtain, from the trajectory sensors, vehicle trajectory data associated with a plurality of vehicles approaching and traversing the intersection, the vehicle trajectory data including data regarding position, velocity, and acceleration of the plurality of vehicles; transform the vehicle trajectory data into data relative to a coordinate system derived from geometric information about the intersection stored in a memory device at the traffic controller; compute, from at least the vehicle trajectory data, a delay factor representing delay of the vehicles at the intersection, a stop factor representing a number of vehicles stopped at the intersection, a capacity of the intersection reflecting a number of vehicles per minute passing through green lights in each lane, estimated emissions of the vehicles, and a safety factor); identifying second paths, on the basis of a plurality of second trajectories in each case (Raamot, Para. 60, The trajectory calculator 212 can compute vehicle trajectories or a trajectory framework (described below) based on data received from trajectory sensors 220, in-road sensors 222, and adjacent intersections' traffic controllers 260. The trajectory calculator 212 may also base the trajectory information off of features of the intersection, including the geometry of the intersection, stored in a geographic information description 252 (which may be a database or the like) and/or in a trajectory framework database 254. The geographic information description 252 may include map components (such as data on the stop line, lane segment points, and the like). The geographic location of relevant attributes of the intersection may be extracted from various map data sources and stored in a geographic information description (GID) 252. This GID may then be converted by the ATMS 242 to reveal geometric constraints that will affect the vehicle trajectories as they drive through the roadway network. The geometric properties of the roadway network may be stored in a data structure referred to as the trajectory framework. This trajectory framework can include data that supports overlay of vehicle trajectory data relative to the roadway geometries, allowing modeling of past, present, and/or future vehicle trajectories relative to the traffic signalization; Para. 211, the traffic controller 210 obtains data regarding vehicle trajectories from one or more data sources. At block 804, the traffic controller 210 uses geographic information description (GID) to convert sensor data to a GID frame of reference or coordinate system. At block 806, the traffic controller 210 uses converted sensor data to calculate traffic flow parameters); and providing the second paths and/or second information derived from the second paths as second traffic information (Raamot, Para. 176, traffic flow parameters can be derived from highway capacity manual and other industry standardized estimates for stage 1 configuration. Under stage 2 operation, these parameters can be verified by real world measurement from the vehicle trajectories within the traffic controller 210. These real world parameters can then be fed back into to re-compute the pre-configuration data by the traffic controller 210 Configuration Generator. These assumed traffic flow parameters may include startup lost time, vehicle acceleration rate, and vehicle deceleration rate; Para. 248, the traffic controller 210 can provide a fusion of these detection inputs and other data sources into a collective set of vehicle trajectories within the GID).
In regard to claim 4, Combination of Raamot and Ramamurthy teach the method as claimed in claim 3, wherein identifying the second paths comprises identifying the second paths on the basis of the plurality of second trajectories detected during a plurality of second time intervals (Raamot, Para. 251, The traffic controller 210 begins in one embodiment by determining the historic average volume per cycle for each approach. This volume can be defined as a rolling average volume taken over the prior 5 cycles (other numbers may be chosen). In the event that this data does not exist (controller restart) the average volume can be taken by averaging the approach volume for the prior 4 weeks (for example) during the same day of week and hour of day).
In regard to claim 5, Combination of Raamot and Ramamurthy teach the method as claimed in claim 4, wherein the plurality of first time intervals correspond to a first traffic light phase and the plurality of second time intervals correspond to a second traffic light phase (Raamot, Fig. 1A, Trajectory sensors 120, each trajectory sensors monitors different sequence of phases; Para. 54, sequence of phases may be: phases 1&5 active at the same time, followed by phases 2&6 active at the same time, followed by phases 3&7 active at the same time, followed by phases 4&8 active at the same time, which completes a cycle (which then repeats possibly indefinitely);.
In regard to claim 6, Combination of Raamot and Ramamurthy teach the method as claimed in claim 5, wherein the first time intervals and the second time intervals are assigned to a time of day and/or to a time span extending over more than one day (Raamot, Para. 251, The traffic controller 210 begins in one embodiment by determining the historic average volume per cycle for each approach. This volume can be defined as a rolling average volume taken over the prior 5 cycles (other numbers may be chosen). In the event that this data does not exist (controller restart) the average volume can be taken by averaging the approach volume for the prior 4 weeks (for example) during the same day of week and hour of day).
In regard to claim 7, Combination of Raamot and Ramamurthy teach the method as claimed in claim 5, wherein the plurality of first time intervals are assigned to a first time of day and/or to a first time span extending over more than one day, and wherein the plurality of second time intervals are assigned to a second time of day and/or to a second time span extending over more than one day (Raamot, Para. 251, The traffic controller 210 begins in one embodiment by determining the historic average volume per cycle for each approach. This volume can be defined as a rolling average volume taken over the prior 5 cycles (other numbers may be chosen). In the event that this data does not exist (controller restart) the average volume can be taken by averaging the approach volume for the prior 4 weeks (for example) during the same day of week and hour of day).
Claim(s) 8-9 and 11-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Raamot (US 20160027300 A1) in view of Ramamurthy et al. (US 20230316921 A1) and further in view of Herson et al. (US 20200388150 A1).
In regard to claim 8, Combination of Raamot and Ramamurthy do not teach the method as claimed in claim 7, wherein intersection points of paths are identified on the basis of the paths as derived information.
However, Herson teaches wherein intersection points of paths are identified on the basis of the paths as derived information (Herson, Para. 40, the simulation platform may be configured to monitor whether the distance between vehicles 1, vehicle 2, and/or vehicle 3 is within a threshold distance (e.g., a distance that indicates that a collision occurred and/or nearly occurred). The simulation platform may determine the distance between the vehicles by determining the differences between the simulation coordinates for the vehicles at particular times t of the event. If a difference between simulation coordinates for at least two of the vehicles (e.g., vehicle 1 and vehicle 2) at a particular time are within a threshold distance (e.g., a threshold collision distance), the simulation platform may detect that an incident occurred (e.g., a collision between vehicle 1 and vehicle 2). Additionally, or alternatively, the simulation platform may analyze the paths of the vehicles to determine whether at least two of the paths include a point of intersection).
Raamot, Ramamurthy, and Herson are analogous art because they both pertain to vehicle trajectory monitoring system.
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to analyze the paths of the vehicle to determine two of the paths include a point of intersection (as taught by Herson) in order to determine a collision occurred.
In regard to claim 9, Combination of Raamot, Ramamurthy, and Herson teach the method as claimed in claim 8, wherein the intersection points are provided as traffic information or as part of the traffic information (Herson, Para. 44-45, the simulation platform may perform one or more actions based on detecting an incident. For example, based on detecting the collision between vehicle 1 and vehicle 2, the simulation platform may control a traffic control device (e.g., a traffic light, a roadside sign, and/or the like) in an area of the intersection. More specifically, the simulation platform may cause a traffic light at the intersection to display a hazard signal, or cause a sign along one or more of the roadways of the intersection to display a warning that a collision occurred in the upcoming intersection).
In regard to claim 11, Combination of Raamot, Ramamurthy, and Herson teach the method as claimed in claim 9, wherein the traffic information is provided to one or more vehicles and/or to a database (Herson, Para. 45, the simulation platform may cause a traffic light at the intersection to display a hazard signal, or cause a sign along one or more of the roadways of the intersection to display a warning that a collision occurred in the upcoming intersection).
In regard to claim 12, Combination of Raamot, Ramamurthy, and Herson teach the method as claimed in claim 11, wherein the trajectories are detected by means of one or more environment sensors (Raamot, Fig. 1A, Trajectory sensors 120; Para. 56, The trajectory sensors may be radar (e.g., microwave), ultrasound, video camera, infrared sensors, or hybrid sensors).
In regard to claim 13, Combination of Raamot, Ramamurthy, and Herson teach the method as claimed in claim 12, wherein the one or more environment sensors are cameras, radars, lidars, or contact loops (Raamot, Fig. 1A, Trajectory sensors 120; Para. 56, The trajectory sensors may be radar (e.g., microwave), ultrasound, video camera, infrared sensors, or hybrid sensors).
In regard to claim 14, Combination of Raamot, Ramamurthy, and Herson teach the method as claimed in claim 13, wherein the trajectories are detected by means of vehicle-to-X communication (Raamot, Fig. 2, Connected vehicles 224; Para. 59, the traffic controller 210 can receive trajectory information from connected vehicles 224 and user devices 226 of drivers or pedestrians (such as cell phones, smartphones, tablets, laptops, smart watches, other wearable computing devices, and the like).
Response to Arguments
Applicant's arguments filed on 12/01/2025 have been fully considered but they are not persuasive. In that remarks, applicant argues in substance:
Applicant argues: " As mentioned, the claims have been amended to recite the first traffic information is transmitted to and/or used by a vehicle controller to control vehicle operations or is transmitted to and/or used by a traffic monitoring system to control traffic flow. These features ensure that any alleged exception is used with specific devices or systems (i.e., vehicle controllers to control vehicle operations or traffic monitoring systems to control traffic flow). These are not generic processing devices or general purpose computer systems but are specifically tailored systems associated with vehicles or with traffic control devices (e.g., traffic lights).
The claimed approach also provides meaningful technical advantages in the technical field of vehicle control and safety because the claimed approach is implemented in real time, is automated, and effectively controls the operation of vehicles or the operation of traffic control devices associated with traffic monitoring systems. This, in turn, increases the safety of the occupants of vehicles in meaningful ways.”
Examiner's Response: Examiner respectfully submits that the added claim limitations are broad and are interpreted as wherein the first traffic information is transmitted to a vehicle or transmitted to a traffic monitoring system which would be just extra solution activity instead of providing additional elements that are sufficient to amount to significantly more.
Response to amended claims is considered above in claim Rejections.
Conclusion
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/SHARMIN AKHTER/
Examiner, Art Unit 2689