DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Claims 1, 3, 5-15, 17, 19-24 filed on 10/29/2025 are presently examined. Claims 2, 4, 16, and 18 are cancelled. Claims 1, 15, 20, and 21 are amended.
Response to Arguments
In regards to 35 USC 112(a), the new limitations in the independent claims do not have support in Applicant’s specification, and are rejected under 112(a).
Regarding 35 USC 101, Applicant's amendments filed 10/29/2025 result in the withdrawal of the 101 rejection.
Regarding the 35 USC 103 rejections, Applicant’s arguments filed 10/29/2025 have been fully considered but they moot. The amendments changed the scope of the invention requiring an updated grounds of rejection. New reference Kulkarni teaches using a GPS to determine a first and second location of a target vehicle at an intersection, and obtaining the distance travelled between said first and second locations at the intersection, and subsequently updating the probabilities of the exit candidates of the target vehicle based on its travel through the intersection. Thus, the 35 USC 103 rejection will be maintained.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 15, and 20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Applicants’ claims recite “obtaining, via a global positioning system (GPS), geographic coordinates corresponding to a first location of a target vehicle at a target intersection and geographic coordinates corresponding to a second location of the target vehicle”
Applicant’s specification describes in [0073] that the positioning system 121 may contain a GPS. This is the only mention of GPS in Applicant’s specification. Positioning system 121 is only recited 3 times in the specification. [0074] estimates the geographical location of the ego vehicle, not target vehicle. [0084] determines the driving route for the ego vehicle using positioning system 121. [0137] obtains location information of the host vehicle. There is no support for collecting geographical positions for the first and second locations of the target vehicle using GPS.
Applicants’ claims recite “obtaining, via an inertial sensor, acceleration data of the target vehicle to obtain a driving direction of the target vehicle.”
The only paragraphs in Applicant’s specification that describe an inertial measurement unit are [0073] and [0075] and they collect data on the ego vehicle, not the target vehicle. One of ordinary skill in the art understands that an IMU can only collect data on the object it is physically placed on. The only possibility the ego vehicle could determine the heading of a target vehicle via an acceleration obtained via an IMU is that the target vehicle shares its own collected acceleration data with the ego vehicle using a communication technology such as V2V, however Applicant’s specification does not provide support for this. There is no support for obtaining vehicle an inertial sensor, acceleration data of a target vehicle to obtain a driving direction of the target vehicle. Using its own sensors, the ego vehicle could determine a heading of the target vehicle by tracking the angle the target vehicle travels between the first and second locations, as a non-limiting example.
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 1, 5, 7-10, 14-15, 19-20, and 22-23 are rejected under 35 U.S.C. 103 as being unpatentable over Fang et al. (US 20190333373 A1), in view of Kulkarni et al. (US 20200255027 A1), Hong et al. (US 11195418 B1), Garcia et al. (US 11529961 B2), hereinafter referred to as Fang, Kulkarni, Hong, and Garcia respectively.
Regarding claims 1, 15, and 20, Fang discloses A vehicle driving exit prediction method, the method comprising:
obtaining N reference points respectively associated with N driving exits of the target intersection, wherein N is a positive integer ([see at least FIG. 6] [0053] “the route prediction unit 42 extracts multiple route candidates on which target-vehicle M2 may travel based on the position of target-vehicle M2 and the road structure.” The end of each candidate route corresponds to an exit from the intersection, and is the reference point Reference point is interpreted with broadest reasonable interpretation.);
wherein for a driving exit K, the driving exit K indicates a Kth driving exit in the N driving exits, K is a positive integer less than or equal to N, and the method further comprising ([see at least FIG. 6] [0053] “the route prediction unit 42 extracts multiple route candidates on which target-vehicle M2 may travel based on the position of target-vehicle M2 and the road structure.” Each candidate route is an exit from the intersection. Analysis is performed on each candidate route.):
generating a predicted track K with reference to the driving direction of the target vehicle by using the first location as a start point and a reference point K associated with the driving exit K as an end point ([0033] “the predicted route of the target vehicle includes the direction, area, traffic lane, and the like in which the target vehicle will travel from” [0041] “At step S103, the GPS receiver 20 detects the current position of host-vehicle M1 to acquire the road structure at the current position of host-vehicle M1. Then, vehicle behavior prediction apparatus 1 detects the relative position of target-vehicle M2 with respect to host-vehicle M1.” [see FIG. 3] the position of the target vehicle is determined and each candidate route is based on that and the direction of the target vehicle.) ;
obtaining N likelihoods respectively corresponding to the N driving exits based on the first location, the driving direction of the target vehicle, and the N reference points ([0054] “calculating the likelihood (possibility) that target-vehicle M2 may travel on each candidate route and adjusting the likelihood.”),
wherein a given likelihood of the N likelihoods indicates a probability that the target vehicle travels out of the target intersection from a corresponding given driving exit ([0054] “The route on which target-vehicle M2 will travel may be predicted by calculating the likelihood (possibility) that target-vehicle M2 may travel on each candidate route and adjusting the likelihood.”); and
Fang discloses using a GPS to detect a current position of a host and target vehicle and the consideration of movement over time of the target vehicle ([0041] “At step S103, the GPS receiver 20 detects the current position of host-vehicle M1 to acquire the road structure at the current position of host-vehicle M1. Then, vehicle behavior prediction apparatus 1 detects the relative position of target-vehicle M2 with respect to host-vehicle M1.” [0033] “the predicted route of the target vehicle includes the direction, area, traffic lane, and … where the target vehicle will travel from this time on.” [0036] “the lower the vehicle speed is, the smaller the moving distance is, making it more difficult to calculate the moving direction” [0059] “That even so target-vehicle M2 slows down outside the intersection means that the possibility that the route that target-vehicle M2 wants to take is right-turn-route R5 is high”). Fang fails to explicitly disclose obtaining via a global positioning system (GPS), geographic coordinates corresponding to a first location of a target vehicle at a target intersection and geographic coordinates corresponding to a second location of the target vehicle, wherein the first and second locations are actual locations of the target vehicle at two different time moments.
However, Kulkarni teaches disclose obtaining via a global positioning system (GPS), geographic coordinates corresponding to a first location of a target vehicle at a target intersection and geographic coordinates corresponding to a second location of the target vehicle, wherein the first and second locations are actual locations of the target vehicle at two different time moments ([0003] “positions of other road participants is based on data obtained by a plurality of sensors of the ego vehicle for perceiving its surroundings … GPS … the sensors can detect the positions and headings of other road participants and derive probabilities of the road participants making a turn at an intersection.” [0045] “Using the detected position and heading of the white car V, the turn probability prediction unit of the ego vehicle EV calculates second turn probabilities. In FIG. 3a the white car V is about to enter the intersection.” [0046] FIG. 3b illustrates a situation shortly after the situation of FIG. 3a. Here, the white car V has entered the intersection and changed its heading toward the right R).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Fang with the teaching from Kulkarni of using at least GPS to determine two positions, subsequent from each other, of a target vehicle at an intersection. One would be motivated with a reasonable expectation of success to use GPS to determine subsequent positions of a target vehicle in order to determine an updated probability of the exit the target vehicle will take based on the new position among other information (Kulkarni [0046] “turn probability prediction unit therefore predicts a new value for the second turn probability based on position and heading of the white car V”).
Fang discloses obtaining … a driving direction of a target vehicle ([0069] “The information on vehicles M3 and M4 includes their positions, speeds, accelerations, and traveling directions.”). Fang also discloses inter vehicle communication to obtain data ([0029] “acquired by sensors provided in host-vehicle M1 or may be acquired using inter-vehicle communication or road-vehicle communication.”). Fang fails to explicitly disclose obtaining, via an inertial sensor, acceleration data of the target vehicle to obtain a driving direction of the target vehicle.
However, it would not be possible without inter-vehicle communication for an ego vehicle to obtain, via an inertial sensor, acceleration and driving direction data of a target vehicle. Inertial sensors only capture data of the object they are on. Therefore, an ego vehicle’s inertial sensor could not capture a target vehicle’s acceleration and driving direction. Applicant’s specification only provides an inertial sensor on the ego vehicle 100. Fang discloses obtaining information including acceleration and traveling directions of target vehicles as well as inter-vehicle communication. It would be obvious to one of ordinary skill in the art to obtain acceleration data and determine driving direction of target vehicles using inter-vehicle communication and inertial sensors. It is merely one more of a limited number of ways to acquire acceleration and driving direction information of target vehicles.
Fang fails to explicitly disclose obtaining, based on the first location and the second location, a distance S that the target vehicle moves from the first location to the second location on an actual track, and obtaining, based on the distance S, a track point K that is on the predicted track K
However, Kulkarni teaches obtaining, based on the first location and the second location, a distance S that the target vehicle moves from the first location to the second location on an actual track, and obtaining, based on the distance S, a track point K that is on the predicted track K ([0003] “detect the positions and headings of other road participants and derive probabilities of the road participants making a turn at an intersection.” [0045] “Using the detected position and heading of the white car V, the turn probability prediction unit of the ego vehicle EV calculates second turn probabilities. In FIG. 3a the white car V is about to enter the intersection. Its heading can be expressed as an angle with regard to the line markings of the lane it is currently driving on. Thus, in FIG. 3a the heading of the white car is zero degrees. The turn probability prediction unit thus assumes an equal probability of one third for the white car V to turn left L, continue straight ahead S, or turn right R.” [0046] “FIG. 3b illustrates a situation shortly after the situation of FIG. 3a. Here, the white car V has entered the intersection and changed its heading toward the right R. Based on the heading, the likelihood for taking a right turn R has increased significantly. The turn probability prediction unit therefore predicts a new value for the second turn probability based on position and heading of the white car V. For example, the likelihood for a right turn R has doubled and is now calculated as 6/9 (two thirds), the likelihood for continuing straight ahead S is 2/9, and the likelihood for a left turn L is 1/9.”).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Fang with the teaching from Kulkarni of determining a first and second position of a target vehicle, and basing an updated probability of the candidate exits of the intersection on the distance travelled between the two points as shown in FIGs 3a and 3b. One would be motivated with a reasonable expectation of success to update the probabilities of the exit candidates in order to enable the ego vehicle to better adapt to other road participants and improve the performance and safety of the vehicle (Kulkarni [0052] “The described method for controlling a trajectory of an ego vehicle thus allows to reliably predict the turn probabilities of road participants. This allows to better adapt the behavior of the ego vehicle EV to the predicted behavior of other vehicles V, in particular at intersections. The described method can therefore greatly improve the performance and operation safety of autonomous vehicles, especially in urban environments.”).
Fang fails to explicitly disclose calculating a distance K between the track point K and the second location; and calculating the likelihood K based on the distance K.
However, Hong teaches calculating a distance K between the track point K and the second location; and calculating the likelihood K based on the distance K ([see FIGs 1, 3, and 5] In FIG. 3 points 316 and 318 are chosen between. [column 12, lines 15-27] “costs may include, but are not limited to, a positional based cost (e.g., limiting the distance allowed between predicted points) … the probability associated with the cell may be multiplied with the cost (which, in at least some examples, may be normalized) such that the point (e.g., a candidate point) associated with the highest value of the cost times probability is selected as the predicted point.”)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Fang with the teaching from Hong of determining the probability of a route being taken by using the distance between predicted points, where the last point is the point on the exit. One would be motivated with a reasonable expectation of success to use the closer points for predicted trajectories in order to only apply points along a trajectory that would have reasonable motion (Hong [column 4, lines 26-33] “generating a predicted trajectory and/or selecting the predicted points can be based at least in part on one or more costs and/or constraints associated with vehicle dynamics to prevent or reduce an occurrence where the predicted trajectory represents an unreasonable trajectory (e.g., involving “teleportation” of the vehicle where subsequent points comprise physically impossible motion of the vehicle).”).
Fangs fails to explicitly disclose calculating, based on the likelihood K and historical posterior probabilities corresponding to the N driving exits respectively, a posterior probability K corresponding to the driving exit K, wherein a given historical posterior probability indicates a posterior probability that corresponds to a given driving exit and that is obtained through previous calculation; and obtaining the driving exit corresponding to a largest posterior probability in the N driving exits as the driving exit of the target vehicle ([0054] “the route prediction unit 42 narrows down the three extracted candidate routes using the traffic rules and the traffic conditions.” There are other ways to narrow down the candidate routes, including, but not limited to pedestrian information, calculated distances, amount of traffic, and traffic signals. [0055] “since two candidates of the three candidate routes have been excluded through these processes, the route prediction unit 42 predicts that left-turn-route R4, which is the remaining candidate route, is the route on which target-vehicle M2 will travel.”). Fang does not explicitly use historical probabilities, but does determine the most likely exit.
However, Garcia teaches calculating, based on the likelihood K and historical posterior probabilities corresponding to the N driving exits respectively, a posterior probability K corresponding to the driving exit K, wherein a given historical posterior probability indicates a posterior probability that corresponds to a given driving exit and that is obtained through previous calculation ([column 3, lines 11-15] “a probability layer added to the map can include historical probability information, where the probability information can be the basis for a prediction of other vehicle behaviors (based on an analysis of historical data that indicates systemic behaviors)” [column 8 lines 62-67 through column 9 lines 1-3] “when mapping the historical information on the map, can include an index of observed vehicle paths within an area. Statistical analysis of the historical information can determine a prediction of a path of the vehicle based on that index of observed vehicle paths. Lane IDs can be provided for each lane so that the path from one lane to the other lane can be indexed easily, and a probability can be assigned to each lane ID.”); and
obtaining the driving exit corresponding to a largest posterior probability in the N driving exits as the driving exit of the target vehicle ([See FIG. 5] [column 10, lines 19-22] “at intersection 500, vehicles originating from lane 502 have a 77% chance of driving into lane 506 and a 23% chance of driving into lanes 508 and/or 510.” [at least column 10, lines 57-64] “According to historical data, past vehicles have taken the path 708 (e.g., an illegal u-turn) about 46% of the time. Therefore, based on the historical data and/or any other indications that vehicle 704 will take path 708 (e.g., such as drifting to the far right of the lane to provide more of a turning radius), AV 710 can wait before it proceeds through the four way stop in case vehicle 704 takes path 708, therefore avoiding a collision.”).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Fang with the teaching from Garcia of using historical probability information to determine which lanes have probabilities for each potential exit from the intersection. One would be motivated with a reasonable expectation of success to use this historical probability in order to enable vehicles to accurately predict what other vehicles will do in order to improve safety ((column 2, lines 57-67 through column 3, lines 1-10]) “The disclosed technology addresses the need in the art for accurately predicting what other vehicles … will do. Other vehicles may, for example, deviate from expected or conventional rules of the road … Being able to predict these types of behaviors can enrich safety protocols the AV uses to anticipate and/or avoid otherwise unexpected and potentially dangerous behavior from other vehicles on the road. In order to do so, the set of rules the AV uses to drive should be informed and/or enhanced by historical knowledge of any systemic behavior of other vehicles on the road.”).
Regarding claim 5, 19, and 23, Fang fails to explicitly disclose The method according to claim 1, further comprising:
predicting an intermediate probability K based on the historical posterior probabilities corresponding to the N driving exits; and
updating the intermediate probability K based on the likelihood K and an association probability K corresponding to the driving exit K to obtain the posterior probability K.
However, Garcia teaches predicting an intermediate probability K based on the historical posterior probabilities corresponding to the N driving exits ([column 13, lines 63-66] “Based on this mapped historical data 206, map service 214 can determine, for each potential path, a prediction about a future behavior of the object while the object is at a specific position in the lane.”); and
updating the intermediate probability K based on the likelihood K and an association probability K corresponding to the driving exit K to obtain the posterior probability K ([column 6, lines 40-44] “Map update service 160 takes the prediction from prediction service 122 and implements the statistical behavior of the objects at a mapped location (e.g., updates the map with associated statistical information that prediction service 122 can utilize to make predictions).” This prediction the vehicle performs of the objects is updated to the historical statistical information. [at least column 10, lines 57-64] “According to historical data, past vehicles have taken the path 708 (e.g., an illegal u-turn) about 46% of the time. Therefore, based on the historical data and/or any other indications that vehicle 704 will take path 708 (e.g., such as drifting to the far right of the lane to provide more of a turning radius), AV 710 can wait before it proceeds through the four way stop in case vehicle 704 takes path 708, therefore avoiding a collision.”).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Fang with the teaching from Garcia of using historical probability information to predict the potential exits of a detected object. One would be motivated with a reasonable expectation of success to use this historical probability in order to provide faster predictions and avoid collisions ([column 6, lines 50-55] “prediction service 122 on autonomous vehicle 102 may provide faster predictions for objects on the road in real time or near real time, such that the autonomous vehicle 102 may detect object positions within the lane, apply the one or more models to determine future behavior of the object” [column 10, lines 62-64] “AV 710 can wait before it proceeds through the four way stop in case vehicle 704 takes path 708, therefore avoiding a collision”).
Regarding claims 7 and 22, Fang discloses The method according to claim 1, further comprising:
for each driving exit among the N driving exits, obtaining a location point at the driving exit as a reference point associated with the driving exit ([see at least FIG. 6] [0053] “the route prediction unit 42 extracts multiple route candidates on which target-vehicle M2 may travel based on the position of target-vehicle M2 and the road structure … the route prediction unit 42 extracts, as candidate routes, routes within a certain distance from target-vehicle M2.” A distance is determined for each possible exit for any potential routes, in which each distance determined corresponds to the end of each candidate route to each exit of the intersection.).
Regarding claim 8, Fang discloses The method according to claim 1, further comprising:
obtaining N reference lanes from a target driving entrance to the N driving exits, wherein the target driving entrance is a driving entrance through which the target vehicle travels into the target intersection ([FIG. ] each candidate trajectory follows the curve of the lane for each exit through the intersection. This is similar to the reference lanes seen in FIG. 9 in Applicant’s drawings.); and
obtaining a location point at each of the N reference lanes as a reference point associated with a driving exit corresponding to the reference lane ([see at least FIG. 6] [0053] “the route prediction unit 42 extracts multiple route candidates on which target-vehicle M2 may travel based on the position of target-vehicle M2 and the road structure … the route prediction unit 42 extracts, as candidate routes, routes within a certain distance from target-vehicle M2.” A distance is determined for each possible exit for any potential routes, in which each distance determined corresponds to the end of each candidate route to each exit of the intersection.).
Regarding claim 9, Fang discloses The method according to claim 8, further comprising:
for any reference lane H in the N reference lanes, obtaining a lane center point sequence H of the reference lane H, and selecting a point in the lane center point sequence H as a reference point associated with a driving exit H corresponding to the reference lane H ([See FIGs 12 and 16] [0063] “the route prediction unit 42 calculates distance D from center-line CL of the traffic lane where target-vehicle M2 is positioned to the center position of target-vehicle M2. The center position of target-vehicle M2 means the center position of the vehicle width. The route prediction unit 42 calculates distance D using the vehicle width of target-vehicle M2 acquired by the object detection unit 10.”).
Regarding claim 10, Fang discloses The method according to claim 8, further comprising:
obtaining the N reference lanes from prior reference lanes of map data ([0037] “the route prediction unit 42 can judge that the traffic lane on which target-vehicle M2 is positioned is a left-turn-only lane from the road structure acquired from the map database 30.” [0037] “the traffic rules prohibit target-vehicle M2 from going in any direction except turning left. This enables the route prediction unit 42 to judge that the route on which target-vehicle M2 will travel is left-turn-route R1 as an arrow indicates in FIG. 2. As just described, use of the traffic rules when the route prediction unit 42 predicts the route on which target-vehicle M2 will travel improves the accuracy in predicting the route on which target-vehicle M2 will travel.” [see FIG. 2] The route of the trajectory is the reference late to be taken by the target vehicle for each exit in the intersection. This is similar to the reference lanes seen in FIG. 9 from the Applicant’s drawings.).
Regarding claim 14, Fang fails to explicitly disclose The method according to claim 1, wherein the generating the predicted track K comprises:
generating the predicted track K based on a Bezier curve with reference to the driving direction of the target vehicle by using the first location as the start point and the reference point K associated with the driving exit K as the end point (the predicted routes appear to be curves based on a starting point, direction, and end point (intersection exit within a distance of the vehicle), but Fang does not explicitly disclose a Bezier curve.).
However, Hong teaches generating the predicted track K based on a Bezier curve with reference to the driving direction of the target vehicle by using the first location as the start point and the reference point K associated with the driving exit K as the end point ([FIG. 5] see scenario 504, predicted trajectory 514 is based on target vehicle’s position, direction, and at least end position being the last point on the trajectory. [column 4, lines 21-25] “The predicted points can be used to determine a predicted trajectory. In some cases, the predicted trajectory can be determined by interpolating between the points or fitting a curve to the points (e.g., fitting one or more of a polynomial curve, a Bezier curve” [column 29, lines 57-62] “Examples 1002, 1004, 1006, and 1008 illustrate Gaussian regression and GMM-CVAE trajectories, with uncertainty depicted as ellipses. In some examples, uncertainty ellipses can be larger when the target entity is turning than when the target entity is continuing straight, and often follow the direction of velocity.”)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Fang with the teaching from Hong of using a Bezier curve based on the starting location, direction, and end point for predicted trajectories. One would be motivated with a reasonable expectation of success to use Bezier curves for the predicted trajectories in order to only apply points along a curve that would have reasonable trajectories (Hong [column 4, lines 26-33] “generating a predicted trajectory and/or selecting the predicted points can be based at least in part on one or more costs and/or constraints associated with vehicle dynamics to prevent or reduce an occurrence where the predicted trajectory represents an unreasonable trajectory (e.g., involving “teleportation” of the vehicle where subsequent points comprise physically impossible motion of the vehicle).”).
Claims 3 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Fang in view of Kulkarni, Hong, and Garcia, further in view of Lee (KR 101889085 B1), hereafter referred to as Lee.
Regarding claims 3 and 17, Fang discloses The method according to claim 1, further comprising:
calculating, based on the distance K, the likelihood K corresponding to the driving exit K ([0063] “Then, the route prediction unit 42 narrows down candidate routes using the calculated distance D … if distance D is a predetermined value (for example, 0.3 m) or less, the route prediction unit 42 predicts that the route on which target-vehicle M2 will travel is straight-route R6.””).
Fang fails to explicitly disclose calculating an included angle K between a tangent direction of the track point K and the driving direction of the target vehicle; and calculating, based on the included angle K, the likelihood K corresponding to the driving exit K.
However, Lee teaches calculating an included angle K between a tangent direction of the track point K and the driving direction of the target vehicle ([0012] “estimate a target lane of the vehicle by comparing the directions and angles of the plurality of virtual trajectories with the estimated direction and angle of the vehicle's progress.”); and
calculating, based on the included angle K, the likelihood K corresponding to the driving exit K.
([0055] “if the vehicle's progress angle is in the range of 40 to 60°, the target lane can be estimated as lane 1, if it is in the range of 60 to 70°, the target lane can be estimated as lane 2, and if it is in the range of 70 to 80°, the target lane can be estimated as lane 3.”).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Fang with the teaching from Lee of comparing the angle of the virtual trajectory with the driving direction of the vehicle to estimate which lane is the target lane. One would be motivated with a reasonable expectation of success to use the angle comparison at intersections in order to determine when vehicles are committing traffic violations ([0072] “determine whether a vehicle has committed a traffic violation while using the intersection.”).
Claims 6, 21, and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Fang in view of Kulkarni, Hong, and Garcia, further in view of Maischberger et al. (US 20160370193 A1), hereafter referred to as Maischberger.
Regarding claims 6, 21, and 24, Fang fails to explicitly disclose The method according to claim 5, further comprising: calculating the association probability K by using the following formula: P(zk) = 1/N
wherein P(zk) represents the association probability K, and N represents a quantity of the N driving exits.
However, Maischberger teaches calculating the association probability K by using the following formula: P(zk) = 1/N
wherein P(zk) represents the association probability K, and N represents a quantity of the N driving exits ([0070] “The probability of the end-user taking the correct outlet at the new intersection is 1/N.” The probability is calculated according to the number of outlets.).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Fang with the teaching from Maischberger of using the equation P = 1/N. One would be motivated with a reasonable expectation of success to use this equation in order to incorporate the standard definition of probability of each individual event corresponding to one of a total number of possibilities.
Claims 11 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Fang in view of Kulkarni, Hong, and Garcia, further in view of Asaoka (JP 2008256620 A), hereafter Asaoka.
Regarding claim 11, Fang fails to explicitly disclose The method according to claim 8, further comprising:
obtaining the N reference lanes based on dynamic reference lanes that are from the target driving entrance to the N driving exits and that are generated based on a vehicle flow at the target intersection.
However, Asaoka teaches obtaining the N reference lanes based on dynamic reference lanes that are from the target driving entrance to the N driving exits and that are generated based on a vehicle flow at the target ([0009] “the driving lane estimation unit compares data representing the distance of the driving trajectory that the vehicle actually traveled between the intersection start position and the intersection end position on the map with data representing the distance between the intersection start position and the intersection end position on the map obtained from the map data storage unit, and estimates the driving trajectory with the distance difference as the driving lane.” The estimated lane for the trajectory from start to end of intersection is determined via vehicle flow at the intersection.),
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Fang with the teaching from Asaoka of using the start and end point lane trajectory from the map data and comparing it to the estimated lane for the start to end point trajectory from a vehicle travelling through the intersection in order to correct map data (Asaoka [0015] “it is possible to provide a map data correction device, a map data correction method, and a map data correction program that are capable of correcting map data related to driving lanes,”).
Regarding claim 12, Fang discloses The method according to claim 8, further comprising:
generating, based on a vehicle flow at the target intersection, dynamic reference lanes from the target driving entrance to the N driving exits, obtaining prior reference lanes of map data, and correcting the prior reference lanes by using the dynamic reference lanes, to obtain the N reference lanes.
However, Asaoka teaches The method according to claim 8, further comprising:
generating, based on a vehicle flow at the target intersection, dynamic reference lanes from the target driving entrance to the N driving exits ([0009] “the driving lane estimation unit compares data representing the distance of the driving trajectory that the vehicle actually traveled between the intersection start position and the intersection end position on the map with data representing the distance between the intersection start position and the intersection end position on the map obtained from the map data storage unit, and estimates the driving trajectory with the distance difference as the driving lane.” The estimated lane for the trajectory from start to end of intersection is determined via vehicle flow at the intersection.), and
obtaining prior reference lanes of map data ([0007] “identify an intersection start position and an intersection end position on the map; a traveling trajectory distance calculation unit that calculates the distance of a traveling trajectory actually traveled by the vehicle between the intersection start position and the intersection end position on the map identified by the intersection position identification unit” a trajectory, or reference lane, provided by the map.); and
correcting the prior reference lanes by using the dynamic reference lanes, to obtain the N reference lanes ([0007] “a traveling lane estimation unit that estimates a traveling lane by comparing data representing the distance of the traveling trajectory calculated by the traveling trajectory distance calculation unit with data representing the distance between the intersection start position and the intersection end position on the map obtained from the map data storage unit; and a map data correction unit that corrects the map data stored in the map data storage unit based on the data representing the traveling lane estimated by the traveling lane estimation unit.” Comparing the trajectory, or reference lane, provided by the map, with the lane resulting from traffic flow, and correcting the map with the traffic flow determined lane.).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Fang with the teaching from Asaoka of using the start and end point lane trajectory from the map data and comparing it to the estimated lane for the start to end point trajectory from a vehicle travelling through the intersection in order to correct map data (Asaoka [0015] “it is possible to provide a map data correction device, a map data correction method, and a map data correction program that are capable of correcting map data related to driving lanes,”).
Claim 13 are rejected under 35 U.S.C. 103 as being unpatentable over Fang in view of Hong and Garcia, further in view of Lee and Ohara et al. (US 20190225231 A1), hereafter Ohara.
Regarding claim 13, Fang discloses The method according to claim 8, further comprising:
obtaining an association probability K based on a transverse distance between the target vehicle and a reference lane K ([0063] “as illustrated in FIG. 12, the route prediction unit 42 calculates distance D from center-line CL of the traffic lane where target-vehicle M2 is positioned to the center position of target-vehicle M2.”), wherein the reference lane K is a reference lane from the target entrance to the driving exit K ([see at least FIG. 6] each reference lane is the route which vehicle M2 may take to exit the intersection, each route corresponding to a lane of each exit.).
Fang fails to explicitly disclose a relative angle between the target vehicle and the reference lane K, and a distance between the target vehicle and a center point of the target intersection.
However, Lee teaches a relative angle between the target vehicle and the reference lane K ([0012] “estimate a target lane of the vehicle by comparing the directions and angles of the plurality of virtual trajectories with the estimated direction and angle of the vehicle's progress.”).
Further, Ohara teaches a distance between the target vehicle and a center point of the target intersection ([0075] “the running locus predictor 140 determines whether or not the subject vehicle M has approached the intersection (Step S100). Here, “approaching”, for example, means that the subject vehicle becomes less than a predetermined distance or a predetermined time to the entrance CI or the center point of the intersection.”).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Fang with the teachings from Lee and Ohara of using relative angles between the driving direction of the target vehicle and virtual trajectory and the distance between the vehicle the center of the intersection. One would be motivated with a reasonable expectation of success to use the angle comparison at intersections in order to determine when vehicles are committing traffic violations ([0072] “determine whether a vehicle has committed a traffic violation while using the intersection.”) and the distance to the center of the intersection in order to only begin the prediction processing when the target vehicle has approached the intersection (Ohara [0075] “In a case in which the subject vehicle M has not approached the intersection, one routine of this flowchart ends.”).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARK R HEIM whose telephone number is (571)270-0120. The examiner can normally be reached M-F 9-6 EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Fadey Jabr can be reached at 571-272-1516. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/M.R.H./Examiner, Art Unit 3668
/Fadey S. Jabr/Supervisory Patent Examiner, Art Unit 3668