DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. This is in response to the applicant response filed on 07/28/2026. In the applicant’s response, claims 1 and 5-9 were amended. Accordingly, claims 1 and 5-9 are pending and being examined. Claims 1, 8, and 9 are independent form.
Claim Interpretation Under 35 USC § 112(f)
3. The claim’s interpretation as invoking 35 USC § 112(f) made in the previous action has been removed in view of the applicant’s amendment.
Claim Rejections - 35 USC § 101
4. The claim’s rejection under 35 USC § 101 make in the previous office action has been withdrawn in view of the applicant’s amendment.
Claim Rejections - 35 USC § 103
5. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
6. Claim Claims 1, 5, and 7-9 are rejected under 35 U.S.C. 103 as being unpatentable over Wu et al (CN113345237, hereinafter “Wu”).
Regarding claim 1, Wu discloses an information processing device comprising:
at least one processor; and at least one memory storing instructions that, when executed by the at least one processor (the method, system, and CRM for “lane change recognition and prediction based on roadside lidar for extracting vehicle trajectories”; see par.0001 (i.e., the English version provided in the previous office action, referred to the same hereinafter)), cause the information processing device to:
acquire point cloud data, which is a set of data for each point measured by a
measurement device (see step 1-A and par.[n0041]: “Scan the entire point cloud map obtained [by the LIDAR associated a vehicle] after background filtering, find any core point, and expand the core point, that is, find all density-connected data points originating from the core point”);
acquire movement region information (see par.[n0060]: “In a two-dimensional plane, the lanes are divided into small squares. Based on the point cloud density of the small squares, the sparsest point cloud density is selected to divide different lanes. Continuous nonlinear lane lines are displayed in the point cloud map through multi-segment linear fitting. In addition, the number and direction of lanes are estimated based on the direction of vehicle movement.”), wherein the movement region information is a map indicating positions of a plurality of lanes and moving directions on the lanes on a horizontal plane in a measurement range of the measurement device (ibid., wherein the lane lines “are displayed in the point cloud map” obtained [by the LIDAR associated a vehicle] after background filtering, find any core point, and expand the core point, that is, find all density-connected data points originating from the core point”; see step 1-A and par.[n0041]); and
detect, based on the movement region information, a cluster of the data for each vehicle on the plurality of lanes from the point cloud data see par.[n0167]: “When the lidar scans a vehicle target, [...], and calculate the number of adjacent lanes L of the vehicle. The calculation method for the number of adjacent lanes L is as follows: based on the coordinates of the front key point in the two-dimensional top view of the vehicle point cloud cluster, find the small square where the coordinates of the front key point are located in the lane line storage matrix. After the position of the small square is determined, determine the lane where the vehicle is located and the direction of movement of the vehicle in the lane, and determine how many lanes in the same direction are on both sides of the lane.”),
wherein the instructions cause the information processing device to prohibit the data
existing on lanes having different moving directions from being detected as the cluster representing a same vehicle (see par.[n0162]: “When driving normally on a highway, vehicles do not change lanes continuously, but drive in the middle of the same lane. This means that the point cloud information features of the vehicle are concentrated within a range of 1.5m on both sides of the center of the lane; the point cloud data above the lane line is sparse”.).
As explained above, even though Wu does not explicitly disclose “to prohibit the data existing on lanes having different moving directions from being detected as the cluster representing a same vehicle”, Wu appreciates that “the point cloud information features of the vehicle are [only] concentrated within a range of 1.5m on both sides of the center of the lane” instead of point cloud within other lanes different from the same driving lane. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have been equally interchangeable between “prohibit[ing] the data existing on lanes having different moving directions from being detected as the cluster representing a same vehicle” and mere concentrating the data existing within the same driving lane of the vehicle to cluster the vehicle. Claim 1 thus is unpatentable over Wu.
Regarding claim 5, Wu discloses the information processing device according to claim 1, wherein, in a case of detecting, based on a predicted position of a tracked vehicle
detected at a past processing time, the cluster corresponding to the tracked vehicle at a current processing time, cluster corresponding to the tracked vehicle at a current processing time, the instructions cause the information processing device to exclude the data existing in a different movement region from the predicted position and detect the cluster (see par.[n0213]: “[...] acquire point cloud data of on-road targets and the background; the data processing module is used to: filter out background point cloud data, filter out pedestrian point cloud data, cluster vehicle point clouds, track vehicle targets in different frames [i.e., different times], divide lanes, and match vehicle targets to the corresponding lanes”. In other words, wherein a targeted vehicle is determined by clustering the vehicle points on same lane/region and excluding that located on another lanes/regions.).
Regarding claim 7, Wu discloses the information processing device according to claim 1, wherein the instructions further cause the information processing device to extract segments representing sets of data, and detect one or more segments for each
vehicle as the cluster (see fig.2(a)-(c) and par.[n0164]: “In a two-dimensional plane, the lanes are divided [segmented] into small squares. Based on the point cloud density of the small squares, the sparsest point cloud density is selected to divide different lanes. The continuous nonlinear lane lines are displayed in the point cloud map through multi-segment linear fitting...”).
Regarding claim 8, 9, each of them is an inherent variation of claim 1, thus it is interpreted and rejected for the reasons set forth in the rejection of claim 1.
7. Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Wu in view of Zeng et al (US2016/0203374, hereinafter “Zeng”).
Regarding claim 6, Wu discloses the claimed invention except for detecting the cluster corresponding to another object newly detected at a current processing time recited by the claim. However, in the same field of endeavor, Zeng teaches identifying new targets (i.e., new vehicles)) based on any clusters of measurement points which do not group with known targets at the previous step (see 412 of fig.10 and par.62, lines 5-7). It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the teachings of Zeng into the teachings of Wu and identify new vehicles based on any clusters of measurement points which do not group with known targets at the previous step taught by Zeng. Suggestion or motivation for doing so would have been to establish “collision warning and collision avoidance systems” as taught by Zeng, cf., Par.62. Therefore, the claim is unpatentable over Wu in view of Zeng.
Response to Arguments
8. Applicant's arguments with respect to claim 1 have been considered but are moot in view of the new ground(s) of rejection. Specifically, on pages 6-7, applicant argues:
Wu does not teach or suggest, "acquire movement region information, wherein the
movement region information is a map indicating positions of a plurality of lanes and moving
directions on the lanes on a horizontal plane in a measurement range of the measurement device... wherein the instructions cause the information processing device to prohibit the data
existing on lanes having different moving directions from being detected as the cluster
representing a same vehicle...
The examiner respectfully disagrees with the arguments. As explained above, to cluster point clous as a vehicle driving within a lane of a map, Wu, par.[n0060], states “In a two-dimensional plane, the lanes are divided into small squares. Based on the point cloud density of the small squares, the sparsest point cloud density is selected to divide different lanes. Continuous nonlinear lane lines are displayed in the point cloud map through multi-segment linear fitting. In addition, the number and direction of lanes are estimated based on the direction of vehicle movement.”. Wu clearly points out “the number and direction of lanes are estimated based on the direction of vehicle movement”. Besides, par.[n0162], Wu teaches: “When driving normally on a highway, vehicles do not change lanes continuously, but drive in the middle of the same lane. This means that the point cloud information features of the vehicle are concentrated within a range of 1.5m on both sides of the center of the lane; the point cloud data above the lane line is sparse”. In other words, to cluster point cloud as a vehicle driving within a lane of a map, Wu does not only discloses referencing the lane's moving direction information during the clustering process but also teaches limiting the point cloud calculations within the driving lane of the map only. The applicant’s arguments are unpersuasive.
Conclusion
9. 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 extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
10. Any inquiry concerning this communication or earlier communications from the examiner should be directed to RUIPING LI whose telephone number is (571)270-3376. The examiner can normally be reached 8:30am--5:30pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, HENOK SHIFERAW can be reached on (571)272-4637. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RUIPING LI/Primary Examiner, Ph.D., Art Unit 2676
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