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 .
Prior arts cited in this office action:
Jin et al. (CN 111314857 A, hereinafter “Jin”)
Chiba et al. (JP 3965679 B2, hereinafter “Chiba”)
Zheng et al. (US 20220035374 A1, hereinafter “Zheng”)
Response to Arguments
Applicant’s Arguments/Remarks files on 10/13/2025 have been fully considered and are moot in view of the new ground of rejection set forth below.
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.
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, 8-12, and 15-19 are rejected under 35 U.S.C. 103 as being unpatentable over Jin et al. (CN 11314857 A, hereinafter “Jin”) in view of Zheng et al. (US 20220035374 A1, hereinafter “Zheng”).
Regarding claims 1, 8 and 15:
Jin teaches a track restoration method (Jin Abstract [0023]-[0025], where Jin teaches. processor (s), storage device, computer system and program for performing the invention, wherein to a certain degree can solve the vehicle cannot due to lack of GPS device obtaining the travel track, not capable of realizing the dynamic updating of the vehicle trip chain, calculating problem trip chain the consumed system resource is too large. the method comprises: optimal adjacency matrix for constructing road according to the city road net topological information; the car video data from the detection point to analyze to obtain the vehicle driving information, the vehicle running information comprises a snapshot time, the snapshot point, license plate number, based on the optimal adjacency matrix, historical travel chain to the vehicle running information and the comparing and judging the vehicle, constructing a real-time travel trajectory of the vehicle), comprising:
acquiring vehicle-passing data in a target area, wherein the vehicle-passing data is generated based on shooting data, and the shooting data is obtained by shooting a vehicle in the target area by a shooting device in the target area (Jin [0035]-[0036], [0041], [0060]-[0065], fig. 3, where Jin teaches obtaining the real-time travel path of the vehicle needs improvement of city road net topological structure, an intersection detection point information, and finally the track information of the vehicle on the map is exhibited. The invention based on the video data of the vehicle real-time travel track acquisition that is the vehicle track information between many intersections of the city road net extracted in real time, and displaying it on a map, so as to obtain plenty of vehicle running state information, is the traffic manager to provide effective data support, alleviating urban traffic congestion problems. will obtain adjacency matrix city road net optimal for detailed below );
constructing a driving track of the vehicle shot by the shooting device, based on the vehicle-passing data (Jin [0068]-[0071], fig. 3, where Jin teaches In step 303, based on the optimal adjacency matrix, to trip chain of the vehicle travel history information and the comparing and judging the vehicle, constructing a real-time travel trajectory of the vehicle. based on the vehicle driving information, can obtain the space information of the current vehicle, the space information of the spatio-temporal information can be finally recorded and historical trip chain of the vehicle for comparison determination, to determine the relationship between the current stroke and the historical travel. the updating history trip chain of the vehicle, the new trip chain respectively be described below).
Jen teaches wherein the vehicle-passing data comprises at least two target sub-data, the at least two target sub-data are in a one-to one correspondence to at least two location points in a road network corresponding to the target area, the target sub-data is the vehicle-passing data generated when a target vehicle passes through the corresponding location point, and the restoring the driving track of the vehicle shot by the shooting device based on the vehicle-passing data comprises:
determining a passing sequence of the target vehicle between the at least two location points, based on the at least two target sub-data;
determining a connecting path between adjacent location points in the at least two location points, based on the passing sequence, to obtain a target driving track of the target vehicle (Jin [0068]-[0071], where Jin teaches based on the vehicle driving information, can obtain the space information of the current vehicle, the space information of the spatio-temporal information can be finally recorded and historical trip chain of the vehicle for comparison determination, to determine the relationship between the current stroke and the historical travel. the updating history trip chain of the vehicle, the new trip chain respectively be described below. In some embodiments, when the last recording point of the shooting point of the vehicle and its historical travel chain bit are the same, it can be considered the same intersection of intersection vehicle is currently located last recording point in the historical travel chain. In this case, the vehicle travel information and historical trip chain of the vehicle for comparison determination, it is necessary to research for the snapshot time interval, making the following determination: if the vehicle current snapshot difference value of the last recording time travel chain and its history is greater than a first threshold, then the vehicle adds a trip chain, otherwise, updating the historical travel chain).
Jin fails to explicitly use the word restoring the driving track
However, constructing the real-time travel trajectory of the vehicle would be considered to be equivalent to restoring the driving track by one of ordinary skill in the art since as disclosed by Jin constructing the path traveled by the vehicle has the same meaning as restoring the path traveled by the vehicle based on the images captured as different locations which is in essence reconstructing the route traveled by the vehicle.
Jen fails to teach explicitly wherein the obtaining a target driving track of the target vehicle comprises: obtaining all possible driving tracks of the target vehicle, selecting at least two driving tracks with the shortest path length as the target driving track at a same time.
However, Jen teaches the path finding process described as follows: starting from any one of the single paths, the distance between the two points is the edge, if no edges are connected between two points, then weight is infinite, for every pair of vertices u and v, searching whether there is a vertex w, so that shorter than the known path then from u to w to v, if there is a shorter path, then updating the shortest path. For example, from any node i to any node of the shortest path j includes two possible, wherein a scene is another scene is through several k-node finally reaches the node j from node i directly from node i to node j. algorithm assuming Dis (i, j) is the node i to the node j of the shortest distance of the path, for each node k, the algorithm checks the Dis (i, k) + Dis (k, j) <Dis (i, j) is correct, if so, short path shows a path to node j from node i to node k to node j of node i, updating the Dis (i, j) = Dis (i, k) + Dis (k, j), when the traversing algorithm computing all the node k, final Dis (i, j) recorded in it is a shortest path from node i to node j. therefore, since all possible paths are calculate and ranked in order to find the shortest path or path with the shortest time selecting the top to paths to display to a user is trivial to one of ordinary skill in the art. Furthermore, Zheng teaches the initial path construction module is configured for inserting new transportation tasks into the possible positions in all the existing wAGV paths according to the paths calculated in the previous step but has not been completely executed within a rolling horizon framework, calculating the insertion cost and selects paths and corresponding positions with the lowest insertion cost, selecting the paths and positions corresponding to the minimum cost, inserting the task into updated paths, and sequentially processing all new transportation tasks and obtaining updated initial paths; the real-time optimization module is configured by adopting a heuristic algorithm based on tabu search, combining the initial paths obtained by the initial path construction module and constraints of the dynamic scheduling model to obtain a quasi-optimal scheduling result within the rolling horizon framework, and realizing the real-time scheduling system; and the scheduling module is configured for controlling, according to the scheduled paths obtained by the real-time optimization module, the wAGVs to visit specific terminals at specific times, picking up or unloading a specific number of containers, and completing the assigned transportation task in an approximately shortest driving path with the minimum waiting time and delay time (Zheng [0014]-[0019]).
Therefore, taking the teachings of Jen and Zheng as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the application to generate all possible paths and select at least the two best paths such that alternative can be presented to ensure flexibility of the route taken and certain routes might be preferred for certain reason other than shortest or fastest.
Regarding claims 3, 10 and 17:
Jin in view of Zheng teaches wherein the target sub-data comprises: identity information of the target vehicle, shooting time information and shooting location information of the shooting device for shooting the target vehicle (Jin [0060]-[0067], where Jin teaches With continued reference to FIG. 3, in step 302, the video data from the detection point to analyze to obtain the vehicle driving information, the vehicle running information comprises a snapshot time, the snapshot point, license plate number. In some embodiments, the detection point, that is, each intersection is provided with a vehicle information collecting device. a vehicle information collecting device for vehicle travel through the intersection of the obtained information, a small private included by each direction of the intersection to the vehicle, motorcycle, or large passenger car and so on. For example, the inlet passage of each intersection of city road provided with an electronic police detector (referred to as electronic police) can realize the vehicle function. Therefore, the invention based on real time the receiving of electric alarm to dynamically update the travel trajectory of the vehicle by vehicle data. information about vehicle travel information further comprises a snapshot time, the snapshot point, license plate number, in some embodiments, the vehicle running information further comprises vehicle driving direction, lane information, vehicle type, etc. the unique identifier is a vehicle license plate number, according to the license plate number can be in many historical travel chain retrieving historical trip chain of a vehicle, so as to judge whether the vehicle with a trip record, and according to the last recording history information of the travel chain obtained by searching and researching the stroke).
Regarding claims 4, 11 and 18:
Jin in view of Zheng teaches wherein the determining the connecting path between adjacent location points in the at least two location points based on the passing sequence comprises:
in a case that there is one candidate path between a first location point and a second location point is 1, determining the candidate path as a connecting path between the first location point and the second location point, wherein the first location point and the second location point are any two adjacent location points of the at least two location points (Jin [0086]-[0089], where Jin teaches In step 603, by querying the optimal adjacency matrix to obtain the shortest path between the first intersection and the second intersection. obtain the first intersection and the second intersection, if the deleted path of the vehicle is still not complete, then it needs to continue carrying out supplementing the missing path in between the first intersection and the second intersection. In the preferred adjacency matrix using the routing algorithm to the path between the said two intersections to calculate completion processing. The best adjacency matrix is based on Floyd algorithm to construct. the algorithm is to find the shortest path between the two detection point. there are generally two kinds of conditions when searching for the shortest path between the two detection point, one is directly from point i to point j; another one is from the point i by a plurality of node k, k belongs to K, then to point j, wherein K is the set of all middle node).
Regarding claims 5, 12 and 19:
Jin in view of Zheng teaches wherein the determining the connecting path between adjacent location points in the at least two location points based on the passing sequence comprises:
in a case where a quantity of candidate paths between the first location point and the second location point is greater than 1, determining the connection path between the first location point and the second location point as any one of:
the candidate path with a shortest length in the candidate paths between the first location point and the second location point;
the candidate path with a shortest passing time of the target vehicle in the candidate paths between the first location point and the second location point;
the candidate path with fewest turns in the candidate paths between the first location point and the second location point; or
the candidate path with a maximum historical passing times of the target vehicle in the candidate paths between the first location point and the second location point (Jin [0086]-[0089], where Jin teaches In step 603, by querying the optimal adjacency matrix to obtain the shortest path between the first intersection and the second intersection. obtain the first intersection and the second intersection, if the deleted path of the vehicle is still not complete, then it needs to continue carrying out supplementing the missing path in between the first intersection and the second intersection. In the preferred adjacency matrix using the routing algorithm to the path between the said two intersections to calculate completion processing. The best adjacency matrix is based on Floyd algorithm to construct. the algorithm is to find the shortest path between the two detection point. there are generally two kinds of conditions when searching for the shortest path between the two detection point, one is directly from point i to point j; another one is from the point i by a plurality of node k, k belongs to K, then to point j, wherein K is the set of all middle node).
Claims 6-7, 13-14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Jin et al. (CN 11314857 A, hereinafter “Jin”) in view of Zheng et al. (US 20220035374 A1, hereinafter “Zheng”) and in view of Chiba et al. (JP 3965679 B2, hereinafter “Chiba”).
Regarding claims 6, 13 and 20:
Jin in view of Zheng fails to explicitly teach wherein a traffic intersection in the target area forms a connecting node in the road network, a segment in the target area connecting two adjacent traffic intersections forms a connecting edge in the road network, the candidate path comprises at least one of the connecting edges, the method further comprising:
determining an average passing time of each connecting edge in the road network;
determining a passing time of the target vehicle to pass through the candidate path, based on the average passing time
However, Jin teaches In step 603, by querying the optimal adjacency matrix to obtain the shortest path between the first intersection and the second intersection. obtain the first intersection and the second intersection, if the deleted path of the vehicle is still not complete, then it needs to continue carrying out supplementing the missing path in between the first intersection and the second intersection. In the preferred adjacency matrix using the routing algorithm to the path between the said two intersection to calculate completion processing. The best adjacency matrix is based on Floyd algorithm to construct. the algorithm is to find the shortest path between the two detection point. there are generally two kinds of conditions when searching for the shortest path between the two detection point, one is directly from point i to point j; another one is from the point i by a plurality of node k, k belongs to K, then to point j, wherein K is the set of all middle node (Jin [0060]-[0067], [0086]-[0089]). Chiba further teaches (2) Time: In the morning and evening when there is a lot of traffic, the priority of route search via an intersection where information can be provided is increased.
(3) On nights when there are many oversights and misjudgments, the priority of route search via an intersection where information can be provided is increased.
(4) Weather: In the case of bad weather such as rain and snow, the priority of route search via an intersection where information can be provided is increased.
(5) Road conditions: When traveling on a road with a large number of lanes, the priority of route search via an intersection where information can be provided is increased.
(6) When there are a plurality of route candidates, the priority of route search via an intersection whose intersection angle is close to 90 ° (that is, the traveling route of the host vehicle is substantially in a straight line) is increased.
That is, a route search via an intersection (FIG. 5 (a)) connected to a curve and an intersection (FIG. 5 (b), (c)) where the traveling path opposite to the traveling path of the host vehicle is not on the same straight line. Lower priority or prohibit route search.
Therefore, taking the teachings of Jin, Zheng and Chiba as a whole, it would have been obvious to one or ordinary sill in the art before the effective filing data of the application do determine the best route based on the road condition, the traffic conditions and to perform connection between intersection point (distance) using edges or middle or any suitable reference to determine the shortest, the most economical, or the optimal rout desired for a particular vehicle, such that the route taking be the vehicle can be optimal or tailored to a particular situation. see also the book ("Training Tutorial for the Practice of Data Structures", Lewis editor, Oxford: Southern University Press, 2009.04, pp. 127-128, "5.3. 2 Shortest Pathway Navigational Query System")
Regarding claims 7 and 14:
Jin in view of Zheng and in view of Chiba teaches wherein the determining the average passing time of each connecting edge in the road network comprises at least one of:
when road condition information of a target connecting edge is acquired, determining an average passing time of the target connecting edge based on the road condition information, wherein the target connecting edge is any connecting edge in the road network, and the road condition information comprises an actual passing time of a vehicle to passes through the target connecting edge;
when road condition information of a target connecting edge is not acquired, determining an average passing time of the target connecting edge based on a road grade of the target connecting edge, wherein different road grades correspond to different average passing time.
The same rational given above with regard to claim 6 is also applicable here to claim 7 AND 14.
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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/WEDNEL CADEAU/Primary Examiner, Art Unit 2632 December 16, 2025
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