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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on June 21, 2024 was filed on the filing date of the application on June 21, 2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings were received on June 21, 2024. These drawings are accepted.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Harada (US 10,217,232) in view of JIN et al. (US 2023/0349720)(each cited in the Information Disclosure Statement (IDS) filed June 21, 2024).
As to claim 1, Harada discloses a method (Figure 3, method 300) comprising: segmenting vehicle trace data (e.g. segmenting map data 260, e.g. obtained from vehicles, column 6, lines 49-60) into a first set of tiles (e.g. into a first set of tiles)(step 310, column 7, lines 23-38 notes segmenting module 220 retrieves map data 260 of the map and stores the map data 260; step 320, column 7, lines 39-49 notes once the segmenting module 220 retrieves the map data 260, segmenting module 220 segments the map data 260 using a first grid and a second grid, e.g. divides the map using the first grid to produce a first set of tiles, where Figures 4-6 illustrates tile division); applying an optimization algorithm to the first set of tiles (step 330, column 8, lines 20-35 notes alignment module 230 identifies internal misalignments for the tiles of the first set (and the second set) and executes one or more optimization processes to correct the misalignments within the data that is present from layering data gathered from separate scans of the various locations within each tile, and additionally performing steps 340-360, column 8, lines 36 thru column 9, lines 45); segmenting the vehicle trace data (e.g. segmenting map data 260) into a second set of tiles (e.g. into a second set of tiles), wherein geospatial arrangement of the second set of tiles is shifted with respective to geospatial arrangement of the first set of tiles (e.g. the second set of tiles is an offset of the first set of tiles)(step 320, column 7, lines 39-49 notes segmenting module 220 segments the map data 260 using a first grid and a second grid, e.g. divides the map using the first grid to produce a first set of tiles, then divides the map again using the second grid which is an offset from the first grid to produce a second set of tiles that overlap with the first set of tiles and are offset, where Figures 4-6 illustrates tile division); applying the optimization algorithm to the second set of tiles (step 330, column 8, lines 20-35 notes alignment module 230 identifies internal misalignments for the tiles of (the first set and) the second set and executes one or more optimization processes to correct the misalignments within the data that is present from layering data gathered from separate scans of the various locations within each tile, and additionally performing steps 340-360, column 8, lines 36 thru column 9, lines 45); and generating a digital representation of an environment based on an application of the optimization algorithm to the first and second sets of tiles (e.g. Figures 10A and 10B, column 11, lines 5-18 notes before and after views of a zoomed in portion of the patch tile 930 from Figure 9, where Figure 10B illustrates view 1010 with an improved resolution/alignment after the alignment module 230 corrects the internal misalignment; Figures 12A and 12B, column 11, lines 46 thru column 12, lines 13 notes before and after views of misalignments, where Figure 12B illustrates view 1210 where double lines and redundant objects are resolved into a locally aligned form after the alignment module 230 analyzes neighboring tiles 1130 and adjusts an alignment of the neighbor tile 1130 in relation to the patch tile 930, which improves the local accuracy of map data to within, e.g. at least 0.15 meters for objects and other features of the map).
As noted above, Harada describes segmenting vehicle trace data into a first set of tiles and a second set of tiles, and applying optimization algorithms to the first set of tiles and the second set of tiles, but do not explicitly disclose the sets of tiles as “tile groups.”
JIN et al. also disclose a method comprising: segmenting vehicle trace data into a first set of tile groups ([0023], [0024] notes an annotated high-definition (HD) map to navigate surrounding roads and highways to reach a destination, these HD maps may be broken into tiles, such as hexagonal tiles, each of these tiles may include cells that define features within the tiles, such as locations of roads, where [0060] notes tile group formation module 312 configured to group neighboring tiles of the electronic map into tile groups, e.g. a first tile group, where [0025] notes there may be seven groups of tiles, thus may be considered the first tile group may consist of a “first set of tile groups”); applying an optimization algorithm to the first set of tile groups ([0060] notes based on this grouping of the electronic map, the neighbor tile group selection module 314 configured to select a first tile group (and a second tile group that border one another on a first tile in the first tile group and a second tile in the second tile group), and based on this selection, the neighbor tile group synchronization module 316 configured to independently optimize the first file group (and the second tile group if a feature crosses between the first tile group and the second tile group)); segmenting the vehicle trace data into a second set of tile groups (e.g. as noted above, [0060] notes tile group formation module 312 configured to group neighboring tiles of the electronic map into tile groups, e.g. a second tile group, where [0025] notes there may be seven groups of tiles, thus may be considered the second tile group may consist of a “second set of tile groups”), wherein geospatial arrangement of the second set of tile groups is shifted with respective to geospatial arrangement of the first set of tile groups ([0060] notes group border shift module 318 configured to shift a border of the first tile group and a border of the second tile group to join the first tile and the second tile in the second tile group or the first tile group); applying the optimization algorithm to the second set of tile groups (e.g. as noted above, [0060] notes neighbor tile group synchronization module 316 configured to independently optimize the first file group and the second tile group if a feature crosses between the first tile group and the second tile group).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify Harada’s system and method of optimizing sets of tiles with JIN et al.’s method of optimizing sets of tile groups to match features across neighboring tiles, thus improving the system (see [0023]-[0025] of JIN et al.).
As to claim 2, Harada modified with JIN et al. disclose applying the optimization algorithm to the first set of tile groups comprises independently applying the optimization algorithm to individual tile groups in the first set of tile groups (modified with JIN, e.g. as noted in claim 1, the first tile group and the second tile group are optimized independently).
As to claim 3, Harada modified with JIN et al. disclose the first and second tile groups are arranged on a geospatial grid of hexagonal tiles; and a respective tile group comprises a cluster of adjacent hexagonal tiles on the geospatial grid of hexagonal tiles (modified with JIN, e.g. at least Figure 5, [0063] notes tiles have a hexagonal shape, where Figures 6, [0065] notes first tile group 630 and second tile group 640 comprise adjacent neighboring tiles).
As to claim 4, Harada modified with JIN et al. disclose a respective hexagonal tile on the geospatial grid of hexagonal tiles corresponds to a contiguous geographic region of the environment (modified with JIN, [0063] notes first tile 530 and second tile 540 each include cells that define features within the first tile 530 and second tile 540, e.g. indicate the position and location of roads shown by factor graph 500, the information may be collected via sensor regarding the environment surrounding a vehicle).
As to claim 5, Harada modified with JIN et al. disclose the cluster of adjacent hexagonal tiles comprises a central hexagonal tile surrounded by six hexagonal tiles adjacent the central hexagonal tile (modified with JIN, Figure 6 illustrates each of first tile group 630 and second tile group comprising a central hexagon tile surrounded by six hexagonal tiles).
As to claim 6, Harada modified with JIN et al. disclose central hexagon tiles for the second set of tile groups are shifted by at least one tile position on the geospatial grid of hexagonal tiles with respect to central hexagon tiles for the first set of tile groups (Harada, column 7, lines 39-49 notes second set of tiles are an offset of the first set of tiles; modified with JIN, Figure 8, [0069] notes shifting the borders of the tiles groups to ensure that each tile undergoes a joint optimization with its neighbors).
As to claim 7, Harada modified with JIN et al. disclose segmenting the vehicle trace data into a third set of tile groups (Harada, segmenting map data 260 into additional sets of tiles; modified with JIN, e.g. grouping tiles into a third tile group), wherein geospatial arrangement of the third set of tile groups is shifted with respective to geospatial arrangement of the first and second sets of tile groups (Harada, e.g. additional set of tiles may be shifted from the first and second sets of tiles; modified with JIN, e.g. additional tile group may be shifted from first and second tile groups); applying the optimization algorithm to the third set of tile groups (Harada, execute one or more optimization processes to correct misalignments for additional set of tiles; modified with JIN, independently optimize the additional tile group); segmenting the vehicle trace data into a fourth set of tile groups (Harada, segmenting map data 260 into additional sets of tiles; modified with JIN, e.g. grouping tiles into a fourth tile group), wherein geospatial arrangement of the fourth set of tile groups is shifted with respective to geospatial arrangement of the first, second, and third sets of tile groups (Harada, e.g. additional set of tiles may be shifted from the first, second, and third sets of tiles; modified with JIN, e.g. additional tile group may be shifted from first, second and third tile groups); applying the optimization algorithm to the fourth set of tile groups (Harada, execute one or more optimization processes to correct misalignments for additional set of tiles; modified with JIN, independently optimize the additional tile group); segmenting the vehicle trace data into a fifth set of tile groups (Harada, segmenting map data 260 into additional sets of tiles; modified with JIN, e.g. grouping tiles into a fifth tile group), wherein geospatial arrangement of the fifth set of tile groups is shifted with respective to geospatial arrangement of the first, second, third, and fourth sets of tile groups (Harada, e.g. additional set of tiles may be shifted from the first, second, third, and fourth sets of tiles; modified with JIN, e.g. additional tile group may be shifted from first, second, third, and fourth tile groups); and applying the optimization algorithm to the fifth set of tile groups (Harada, execute one or more optimization processes to correct misalignments for additional set of tiles; modified with JIN, independently optimize the additional tile group)(Harada, modified with JIN, [0043] notes dividing tiles into at least seven groups of tiles, thus the process may be repeated, as described in claim 1, for additional tile groups as noted above).
As to claim 8, Harada modified with JIN et al. disclose generating the representation of the environment based on the application of the optimization algorithm to the first and second sets of tile groups comprises: generating the representation of the environment based on the application of the optimization algorithm to the first, second, third, fourth, and fifth sets of tile groups (Harada, e.g. as noted in claim 1, generating views with corrected misalignments, e.g. after optimization process of the first set of tiles and second set of tiles; modified with JIN, e.g. as noted in claim 7, additional groups of tiles, thus process may be repeated, as described in claim 1, for additional tile groups).
As to claim 9, Harada modified with JIN et al. disclose the optimization algorithm comprises a simultaneous localization and mapping (SLAM) algorithm (Harada, column 5, lines 50-61 notes alignment module 230 initiates optimization when then tiles are divided into grid structures defined by the first grid and the second grid, where the alignment module 230 first identifies internal misalignments within each of the tiles and separately adjusts each tile to at least partially correct the internal misalignments, and may further apply a simultaneous localization and alignment (SLAM) process, a smoothing and alignment (SAM) process or another suitable process to each of the tiles to identify and correct the internal misalignments).
As to claim 10, Harada modified with JIN et al. disclose the vehicle trace data is obtained from connected vehicles (Harada, column 6, lines 49-60 notes map alignment system 170 obtains map data 260 through a secondary service that collects information about various geographic locations and/or acquires at least a portion of the map data 260 using various sensors integrated with vehicle 100; modified with JIN, [0044] notes connected vehicle applications support vehicle-to-vehicle (V2V) communications and vehicle-to-infrastructure (V2I) communications with wireless technology).
As to claim 11, Harada modified with JIN et al. disclose the vehicle trace data comprises at least one of: data related to three-dimensional (3D) trajectories of the connected vehicles; or data related to landmarks observed by the connected vehicles along their 3D trajectories (Harada, column 6, lines 29-48 notes map data 260 defines features and other elements that comprise a map, and/or is data of a topological map that includes lane markers, roadways, traffic signs, geographic elements, and other geospatial elements, in general, elements included within the map data 260 can be elements that are generally useful when, e.g. autonomously controlling a vehicle, thus the map data 260 includes objects and features such as obstacles, indications of lanes and roadways, traffic lights and signs, and so on; modified with JIN, [0022] notes HD map may provide for autonomous vehicles and ADAS systems to perform prediction and planning of, for example, a trajectory of an ego vehicle).
As to claim 12, Harada modified with JIN et al. disclose a system (Harada, Figure 1, vehicle 100, with map alignment system 170 of Figure 2; modified with JIN, neighboring tile synchronization system 300) comprising: one or more processing resources (Harada, processor 110; modified with JIN, processor 320); and non-transitory computer-readable medium (Harada, memory 210, column 5, lines 8-17; modified with JIN, computer-readable medium 322), coupled to the one or more processing resources (Harada, e.g. coupled to processor 110; modified with JIN, coupled to processor 320), comprising stored therein instructions (Harada, e.g. at least segmenting module 220, alignment module 230, and mapping module 240, where column 5, lines 14-17 notes module 220, 230, and 240 are computer-readable instructions; modified with JIN, [0038] notes computer-readable medium 322 stores software, e.g. for executing tile group formation module 312, neighbor tile group selection module 314, neighbor tile group synchronization module 316, and tile group border shift module 318) that when executed by the one or more processing resources (Harada, e.g. executed by processor 110; modified with JIN, processor 320) cause the system to perform the steps as outlined in the method of claim 1 (Harada, column 5, lines 14-17 further notes processor 110 executes modules 220, 230, and 240, to perform the various operations disclosed; modified with JIN, processor 320 executes modules 312, 314, 316, and 318 to perform the various operations disclosed). Please see the rejection and rationale of claim 1.
Claims 13-18 are similar in scope to claims 3-8, respectively, and are therefore rejected under similar rationale.
As to claim 19, Harada modified with JIN et al. disclose a method comprising: segmenting vehicle trace data into a first set of tile groups (Harada, segmenting map data 260 into a first set of tiles; modified with JIN, e.g. grouping tiles into a first tile group, see claim 1); independently applying an optimization algorithm to individual tile groups in the first set of tile groups (Harada, execute one or more optimization processes to correct misalignments for the first set of tiles; modified with JIN, independently optimize the first tile group, see claims 1 and 2); segmenting the vehicle trace data into a second set of tile groups (Harada, segmenting map data 260 into a second set of tiles; modified with JIN, e.g. grouping tiles into a second tile group, see claim 1), wherein: the first and second tile groups are arranged on a geospatial grid of hexagonal tiles (modified with JIN, see claim 3), a respective tile group comprises a central hexagonal tile surrounded by six hexagonal tiles adjacent the central hexagonal tile (modified with JIN, see claim 5), and central hexagon tiles for the second set of tile groups are shifted by at least one tile position on the grid of hexagonal tiles with respect to central hexagon tiles for the first set of tile groups (modified with JIN, see claim 6); independently applying the optimization algorithm to individual tile groups in the second set of tile groups (Harada, execute one or more optimization processes to correct misalignments for the second set of tiles; modified with JIN, independently optimize the second tile group, see claims 1 and 2); and generating a representation of an environment based on the application of the optimization algorithm to the first and second sets of tile groups (Harada, generating a corrected aligned view, see claim 1).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify Harada’s system and method of optimizing sets of tiles with JIN et al.’s method of optimizing sets of tile groups to match features across neighboring tiles, thus improving the system (see [0023]-[0025] of JIN et al.).
Claim 20 is similar in scope to claim 4, and is therefore rejected under similar rationale.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JACINTA M CRAWFORD whose telephone number is (571)270-1539. The examiner can normally be reached 8:30a.m. to 4:30p.m.
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, King Y. Poon can be reached at (571)272-7440. 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.
/JACINTA M CRAWFORD/Primary Examiner, Art Unit 2617
Prosecution Insights