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 .
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-4, 9-12, and 17 are rejected under 35 U.S.C. 102(a)(1) as being unpatentable by Luo (CN 113074744 A).
Regarding Claim 1, representative of Claims 9 and 17, Luo teaches a road information identification method, comprising:
receiving road environment data of a plurality of modalities for a road environment, wherein the road environment comprises an environment of a lane area and an environment of a lane-free area ([pg.1, last paragraph]: S1, collect road data according to the field industry, and obtain road information through internal data processing, so as to obtain the road topology relationship in the lane-free zone; See Fig. 2, depicting lane area and intersection/lane free area, [pg.3, last paragraph]: collect laser point cloud and image data around the road);
performing topology parsing based on the road environment data of the plurality of modalities, to obtain a lane-level topology connection relationship of a road, wherein the lane-level topology connection relationship of the road indicates a mutual location relationship between lanes on the road and a connection status of the lanes ([pg.2, paragraph1-2]: s2, according to the road topological relation, the end node coordinate of an exit lane and the start node coordinate of the corresponding lane, calculating the mapping distance between the exit lane and the corresponding lane, and taking the lane corresponding to the minimum mapping distance as the entry lane, thereby establishing a lane mapping relation; and S3, generating a virtual connecting line according to the lane mapping relation); and
determining road information of the road based on the lane-level topology connection relationship of the road ([pg.3, paragraph 1]: improve the efficiency and aesthetics of virtual connection lines, and further improve the efficiency of high-precision map road topology construction).
Regarding Claim 2, representative of Claim 10, Luo teaches the method of claim 1. In addition, Luo teaches wherein the lane-level topology connection relationship of the road comprises:
a topology connection relationship between lanes in the lane area ([pg. 4, paragraph 3]: as shown in Figure 2, the dashed box is a lane-free area. The first road area includes lanes L1, L2, L3, L4, and L5. L1 and L2 are the exit lanes, and L3, L4, and L5 are the entry lanes), a topology connection relationship between virtual lanes in the lane-free area, and a topology connection relationship between a lane in the lane area and a virtual lane in the lane-free area ([pg.2, paragraph1-2]: s2, according to the road topological relation, the end node coordinate of an exit lane and the start node coordinate of the corresponding lane, calculating the mapping distance between the exit lane and the corresponding lane, and taking the lane corresponding to the minimum mapping distance as the entry lane, thereby establishing a lane mapping relation; and S3, generating a virtual connecting line according to the lane mapping relation).
Regarding Claim 3, representative of Claim 11, Luo teaches the method of claim 2. In addition, Luo teaches wherein before the topology connection relationship between the virtual lanes in the lane-free area is obtained, and before the topology connection relationship between the lane in the lane area and the virtual lane in the lane-free area is obtained, the method further comprises: determining the virtual lane in the lane-free area based on the road environment data of the plurality of modalities ([pg.4, paragraph 5]: S3: Generate a virtual connecting line according to the lane mapping relationship. S3-1: The end node of the exit lane is p1(x1,y1,z1), and the start node of the entry lane is p2(x2,y2,z2), as shown in Figure 3, calculate the mapping distance).
Regarding Claim 4, representative of Claim 12, Luo teaches the method of claim 1. In addition, Luo teaches wherein the road environment data of the plurality of modalities comprises at least one of raw data of the road environment, sensing data of the road environment, and prior data of the road environment ([pg.3, last paragraph]: collect laser point cloud and image data around the road, and obtain result point cloud and result image data through internal processing, [pg.1, last paragraph]: S1, collect road data according to the field industry, and obtain road information through internal data
processing, so as to obtain the road topology relationship in the lane-free zone).
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) 5 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Luo (CN 113074744 A) in view of Shi (WO 2019086055 A2).
Regarding Claim 5, representative of Claim 13, Luo teaches the method of claim 1. However, Luo does not explicitly teach the remaining limitations of Claim 5.
Shi teaches wherein the performing topology parsing based on the road environment data of the plurality of modalities, to obtain the lane-level topology connection relationship of the road comprises: fusing the road environment data of the plurality of modalities, to obtain fused data ([pg. 9, paragraph 1]: data collection device 301 performs calibration registration on the data collected by the sensor, and fuses the data collected by the different sensors); and performing topology parsing on the fused data to obtain the lane-level topology connection relationship of the road ([pg. 9, paragraph 1]: the data collection device 301 performs calibration registration on the data collected by the sensor, and fuses the data collected by the different sensors to generate the sensor data after calibration registration. The data collection device 301 transmits the generated sensor data or the updated sensor data to the electronic map generation device 302).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to have modified the teachings of Luo to include the teachings of Shi to include multiple sensor data that is fused together prior to map generation. Doing so would improve the accuracy of map generation as multiple sensors may compensate for where one sensor lacks.
Claim(s) 6 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Luo (CN 113074744 A) in view of Cao (CN 109991984 A).
Regarding Claim 6, representative of Claim 14, Luo teaches the method of claim 1. However, Luo does not explicitly teach the remaining limitations of Claim 6.
Cao teaches wherein the determining road information of the road based on the lane-level topology connection relationship of the road comprises:
performing semantic parsing based on the road environment data of the plurality of modalities, to obtain lane-level semantic information of the road ([pg.2, last paragraph]: obtaining a point cloud map related to the surrounding environment…determining a semantic recognition object related to road traffic by the vertical feature point cloud); and
combining the lane-level topology connection relationship of the road with the lane-level semantic information of the road, to obtain the road information of the road ([pg. 1, paragraph 5]: merging the road topology map and the lane topology map with a semantic recognition object to generate a high precision map).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to have modified Luo to include the teachings of Cao by including a semantic information gathering and fusion with topology information. Doing so would improve the accuracy of the generated map.
Claim(s) 7-8 and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Luo (CN 113074744 A) in view of Wette (US 20190266417 A1).
Regarding Claim 7, representative of Claim 15, Luo teaches the method of claim 1. However, Luo does not explicitly teach the remaining limitations of Claim 7.
Wette teaches wherein the road information of the road is obtained by using a road information model, and the road information model is obtained through training based on a neural network ([0055] as an alternative, where the neural network maps input matrix m.sup.(i,j) on one option as to how two roadways p.sub.i, p.sub.j may be connected to one another).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to have modified Luo to include the teachings of Wette by substituting the generation of a connection between two lanes by the generation of a connection between two lanes via a neural network. Doing so would provide the predictable result of obtaining road connectivity.
Regarding Claim 8, representative of Claim 16, the Luo and Wette combination teaches the method of claim 7. In addition, Wette teaches wherein that the road information model is obtained through training based on the neural network comprises:
obtaining the road environment data and the road information corresponding to the road environment data in a training sample, wherein the road information is obtained through pre-labeling ([0050] This matrix is now used as input for a previously trained neural network, which from this matrix ascertains the connectivity of the roads s.sub.i and s.sub.j with respect to one another and thereafter outputs them, [0052]: neural network thus maps the input matrix M.sup.(i-j) on one of the options, in which way two roadways p.sub.i, p.sub.j may be connected to one another. These options are completely and unambiguously enumerated even before the training phase of the neural network and are each assigned to exactly one output node); and
training the road information model by using the road environment data in the training sample as input data for training the road information model, and by using the road information corresponding to the road environment data as expected output data for training the road information model, to obtain the road information model ([0022]: lane change matrices are created from the data, which indicate from which lane-road combination the vehicle enters another lane-road combination, the lane change matrices being provided to a neural network as input data [0025] the neural network provides the data regarding connectivity in the form of a number or in the form of an adjacency matrix. This advantageously provides different options for outputting the connectivity data, [0057] The training of the neural network may take place in a variety of ways).
Conclusion
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/JANICE E. VAZ/Examiner, Art Unit 2667
/MATTHEW C BELLA/Supervisory Patent Examiner, Art Unit 2667