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 .
Priority
Examiner acknowledges Applicant’s claim for priority to Chinese Patent Application No. 202210287032.9, filed under 35 U.S.C. 119 and receipt of the priority document filed on 03/23/2022.
Information Disclosure Statement
The information disclosure statement(s) (IDS)(s) submitted on 09/23/2024, 03/28/2025, 12/31/2025 and 05/18/2026 has/have been received, considered, and is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDS(s) has/have been considered by the Examiner.
Claim Objections
Claim 29 is objected to because of the following informalities: Claim 29 recites in part, “causing the processor to perform he method for marking.”
Examiner notes correcting the term “he” to “the” so the corrected claim would then read in part, “causing the processor to perform the method for marking.”
Appropriate correction is required.
Claim Rejections - 35 USC § 102
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 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 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US. 20210312197 A1 to Zhong et al. (Zhong).
Regarding claim 1, Zhong discloses method for marking an obstacle in a robot map (see Figs. 1, 2 & 4(b)), comprising:
acquiring a probability map (Zhong discloses a probability map (P map) (see Figs. 1 & 2, P map) and a height map (Zhong discloses a probability map (P map) (see Figs. 1 & 2, H map), the probability map being configured to identify a probability distribution of obstacles at a plurality of positions in a robot operating environment, and the height map being configured to identify a height distribution of the obstacles corresponding to the plurality of positions in the robot operating environment (Zhong discloses where P represents a probabilistic grid map and H represents an elevation grid map, both corresponding to the plurality of positions in the robot’s environment ([0030] (there is a spatial rectangular coordinate system XYZ, the X axis is horizontal to the right, the Y axis is upright, and the Z axis is forward…setting that the grid map is created on the XOZ plane, and XOZ reflects the current horizontal plane...P represents a probabilistic grid map, and H represents an elevation grid map)));
performing data grouping according to the probability map and the height map to obtain a plurality of data sets, each data set identifying obstacle information of an obstacle (Zhong discloses data grouping according to grid map P and height map H to detect and identify obstacle information regarding an obstacle(s) (see Figs. 1 & 2; [0020] (The present invention designs a grid map obstacle detection method fusing probability and height information, which uses the Bayesian inference and clustering algorithm to fuse probabilistic and height information to detect obstacles in a space, and completes obstacle selection in combination with the rigorous screening and merging processes))), the obstacle information comprising at least obstacle position information and obstacle edge information (Zhong discloses the obstacle information comprising at least obstacle position and edge information of an obstacle(s) (see Fig. 4(a); [0040] (3-3) Clustering and Extracting Bounding Boxes; [0041] (Entering the clustering stage after selecting the initial values, using the KMeans clustering algorithm for all virtual points, calculating the weighted distance P.sub.k|C.sub.i−C.sub.k|.sub.p from all virtual points to the clustering center C.sub.k in the clustering process, ∥.sub.p being a p norm, if the weighted distance from a certain virtual point to any clustering center exceeds d.sub.max, eliminating the virtual point…when updating the clustering center, calculating the weighted mean of all samples in the category as a new clustering center…extracting bounding boxes of all categories after clustering, the bounding boxes being described respectively by 7 values: clustering center C.sub.k, probability P.sub.k, maximum Y coordinate Y.sub.max, maximum and minimum grid serial numbers in the X direction X.sub.max and X.sub.min, and maximum and minimum grid serial numbers in the Z direction Z.sub.max and Z.sub.min; visualizing the bounding boxes on the XOZ plane))); and
marking the obstacle in the robot map according to the obstacle information (Zhong discloses in Fig. 4(b) marking the obstacle in the robot map according to the obstacle information obtained in the grid map obstacle detection method shown in Fig. 2 (see Figs. 2 & 4(b))).
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.
Claim(s) 2-9 are rejected under 35 U.S.C. 103 as being unpatentable over anticipated by US. 20210312197 A1 to Zhong in view of U.S. 20190025838 A1 to Artes et al. (Artes).
Regarding claim 2, Zhong discloses the method according to claim 1, further comprising:
acquiring an initial probability map and an initial height map of the obstacles (in claim 1, e.g. Zhong);
collecting captured data of a robot during working and parsing the captured data to obtain obstacle data, the captured data comprising at least image data and height data (in claim 1, e.g. Zhong).
However, Zhong does not appear to further disclose:
adjusting the initial probability map and the initial height map according to the obstacle data, determining the adjusted initial probability map as the probability map, and determining the adjusted initial height map as the height map.
Artes, in the same field of endeavor, further discloses:
adjusting the initial probability map and the initial height map according to the obstacle data, determining the adjusted initial probability map as the probability map, and determining the adjusted initial height map as the height map (Artes disclose making adjustments to the robot, adjustments to relevant parameters of floor plans and mapped areas, ([0053] (the border of a difficult-to-pass zone can be adjusted over time so that…all chairs will be found within the zone determined to be difficult to pass…this means that, based on the previously saved data on the position and size of obstacles, the frequency, and…probability…of finding an obstacle at a particular location can be determined…the frequency with which a chair leg appears in a certain area can be measured...additionally or alternatively, the density of chair legs in a given area, determined by means of numerous measurements, can be evaluated…based on the probability model, the zone determined to have a cluster of obstacles can be adapted such that obstacles will be found, with a specified degree of probability, within this zone…this will result…in an adjustment of the borders of the “difficult-to-pass” zone containing the cluster of…obstacles); [0069] (A further possibility is for the user to influence the automatic sectoring process by confirming…or rejecting hypotheses made by the robot…the user may “instruct” the robot to carry out the sectoring of an area of operation…by designating doors on the map or by eliminating doors incorrectly identified by the robot…following this the robot…can automatedly carry out a new sectoring of the map… the user can adjust relevant parameters (e.g. typical door widths, thickness of the inner walls, floor plan of the apartment, etc.) so that the robot can use these parameters to generate an adjusted sectoring of its area of operation))).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the grid map obstacle detection method of Zhong to incorporate the optical triangulation sensor of Artes to include the ability to adjust the values and parameters surrounding the robot, robot sensor(s), vehicle environment, as well as for viewing sight or area maps for which a robot is maneuvering along a path, with predictable results, with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to combine Zhong and Artes for the express benefit of including adjustments to the initial probability map (s) and height map(s) as well as an effort to avoid obstacles when obstacles are present, as explained in Artes [0053] and [0069].
Regarding claim 3, the combination of Zhong and Artes discloses the method of claim 1 in for example the obviousness to combine in the rejection of corresponding parts of claim(s) 1 above incorporated herein by reference.
Artes further discloses, further comprising:
extracting a plurality of candidate points in combination with the probability map and the height map and according to a preset probability threshold (Artes discloses using a SLAM algorithm and extracting candidate points along boundaries, areas and in combination with the probability map and height map of Zhong ([0036] (a hypothesis is credited with a previously specified number of points for every confirming sensor measurement…when…a certain hypothesis achieves a minimum number of points, it is regarded as plausible…a negative total number of points could result in the hypothesis being rejected); [0038] (the rectangles are selected in such a manner so that a rectangle may be clearly assigned to every point on the map of the area of robot operation that is accessible to the robot…it is not to be excluded that a rectangle may contain points that are not accessible to the robot…in order to determine the orientation and size of the individual rectangles, long boundary lines in the map of the area of robot operation…are employed such as those e.g. that run along a wall (see, e.g., FIG. 1, straight line through the points L′ and K′, straight line through the points P and P′ as well as P″ and P′″); [0041] (In the upper right hand part of FIG. 5 in zone 301 of the room 300, numerous small obstacles are designated on the robot's map that are only a few centimeters large…the robot can be configured to analyze the map in order to recognize an area on the map containing a cluster of obstacles that are distributed throughout the area such that the straight-line progression of the robot through the area is blocked by the obstacles)));
wherein performing data grouping according to the probability map and the height map to obtain the plurality of data sets comprises:
clustering the plurality of candidate points to obtain a plurality of groups of clustered data, each group of clustered data identifying one of the plurality of data sets (Artes discloses clustering pluralities of candidate points based on groups of clustered data identifying a plurality of datasets (see Fig. 5; [0041] (in zone 301 of the room 300, numerous small obstacles are designated on the robot's map that are only a few centimeters large…the zone designated 320 contains a great number (a cluster) of smaller obstacles…which prevent the robot from rapidly moving forward…it would be useful to define areas which are difficult to pass through because of the presence of a large number of small obstacles as separate zones...the robot can be configured to analyze the map in order to recognize an area on the map containing a cluster of obstacles that are distributed throughout the area such that the straight-line progression of the robot through the area is blocked by the obstacles…once such an area containing a cluster of obstacles is recognized, the robot defines a zone such that the first zone contains the detected cluster))).
It would have been obvious to combine for the reasons set forth in the rejection of corresponding parts of claim 1 above incorporated herein by reference.
Regarding claim 4, the combination of Zhong and Artes discloses the method of claim 1 in for example the obviousness to combine in the rejection of corresponding parts of claim(s) 1 above incorporated herein by reference.
Artes further discloses, further comprising:
acquiring a simultaneous localization and mapping (SLAM) map of the robot operating environment, the SLAM map comprising an unknown region identified as a region where a robot is prohibited from passing (Artes discloses utilizing SLAM in order to output a raster map(s) of the area(s) containing obstacles (see Figs.1 & 4-6; [0030] (FIG. 1 shows…a map of an area of robot operation as compiled by the robot, e.g. by means of sensors and a SLAM algorithm…the robot measures the distance to obstacles…and calculates line segments that define the borders of its area of operation based on the measurement data (usually a point cloud)…the area of robot operation may be defined…by a closed chain of line segments…wherein every line segment comprises a starting point, an end point and…a direction…the direction of the line segment indicates which side of the line segment is facing the inside of the area of operation…one alternative to the aforementioned chain of line segments is a raster map, in the case of which a raster of, e.g. 10×10 cm is placed over the area of robot operation and every cell (i.e. every 10×10 cm box) is marked if it is occupied by an obstacle); [0053] (Doors may be used…to specify the border between two rooms (zones) by comparing open and closed states…the robot should take note of a zone that is temporally inaccessible due to a closed door and…inform the user of this…a zone that was not accessible during a first exploratory run of the robot because of a closed door but which is newly identified during a subsequent run is added to the map as a new zone))).
calculating a first region area and a second region area respectively according to the SLAM map and the data set, the first region area indicating a largest continuous unknown region in the SLAM map, and the second region area being an obstacle floor area identified by the data set (Artes discloses calculating a first region area and second region floor area respectively according to the slam map (see Figs. 1 & 2; [0038] (the area of robot operation is overlaid with rectangles of various sizes that are intended to represent the rooms…the rectangles are selected in such a manner so that a rectangle may be clearly assigned to every point on the map of the area of robot operation that is accessible to the robot…the area designated by the rectangles may thus be larger and of a simpler geometric form than the actual area of robot operation…to determine the orientation and size of the individual rectangles, long boundary lines in the map of the area of robot operation…are employed…various criteria are used for the selection of the boundary lines to be used…to determine the specific form and position of the rectangles…points are given to the boundary lines for fulfilled criteria…the boundary line with the highest number of points will then be used as the border between two rectangles); [0039] (the robot can supplement the outer boundary lines of the map of boundary lines…to form a rectilinear polygon…it is also possible to place a rectangle through the outer boundary lines of the apartment and to remove from them inaccessible areas))); and
identifying a floor-type obstacle according to the first region area and the second region area, wherein the robot is prohibited from shuttling inside and outside the floor-type obstacle (Artes discloses floor-type obstacles such as carpet floor 303 and tiled floor 302 in Figs. 4-6 ([0040] (room 300 can be sectored based on the sensor data recorded by the robot…one criterion for a further sectoring may be the floor covering…with the aid of sensors, the robot can distinguish…between a tiled floor, a wooden floor or a carpet...usually two different floor coverings are separated by a slightly uneven…border and the slipping behavior of the wheels may differ for different floor coverings…various floor coverings also differ from each other with regard to their optical characteristics (color, reflection, etc.)…the robot recognizes in room 300 a zone 302 with a tiled floor and a zone 303 with a carpet…the remaining zone 301 has a wooden floor); [0079] (the living room 300 is again sectored into zones according to the two different types of floor coverings, here a carpet (number 303) and a tiled floor (number 302)…a building with different floors can be schematically displayed on the HMI…when the user selects…by tapping, a floor, a simplified map that was stored for this floor will be displayed))).
It would have been obvious to combine for the reasons set forth in the rejection of corresponding parts of claim 1 above incorporated herein by reference.
Regarding claim 5, the combination of Zhong and Artes discloses the method of claim 4 in for example the obviousness to combine in the rejection of corresponding parts of claim(s) 1 and 4 above incorporated herein by reference, further comprising:
collecting regions of which the second region area is less than a preset area threshold and which are adjacent in the SLAM map (in claim 3, e.g. Artes).
Artes further discloses, further comprising:
generating a smallest circumscribed polygon corresponding to the collected regions, and determining a region identified by the smallest circumscribed polygon as the floor-type obstacle (in claim 4, e.g. Artes) (Furthermore, Artes discloses generating polygon regions and identifying them as floor-type obstacles (see Figs. 8A-E; [0045] (FIG. 8 shows a further example of the further sectoring of a zone (a room) into smaller zones…FIG. 8B shows the map compiled by a robot with boundary lines similar to those of…FIG. 2…at the table's position the robot “sees” a number of smaller obstacles (table and chair legs) that prevent the robot from moving in a straight line…to begin with, the robot identifies the relatively small obstacles…puts them into groups and surrounds them with a polygon that is as small as possible (see FIG. 8C)…in order to assign the simplest geometric form possible to the demarcated zone “dinette”, the robot attempts to arrange a rectangle around the polygon …wherein the rectangle must maintain a minimum distance to the polygon…this minimum distance should be large enough so that the robot need not move out of the zone “dinette” while cleaning it…the minimum distance will be at least as large as the diameter…of the robot…other possibilities for determining an optimum orientation of the rectangle include selecting a minimal surface area of a rectangle or orienting the rectangle in accordance with the main axes of inertia…of the distribution of the smaller obstacles))).
It would have been obvious to combine for the reasons set forth in the rejection of corresponding parts of claim(s) 1 and 4 above incorporated herein by reference.
Regarding claim 6, the combination of Zhong and Artes discloses the method of claim 3 in for example the obviousness to combine in the rejection of corresponding parts of claim(s) 1 and 3-4 above incorporated herein by reference, further comprising:
grouping according to positions of the candidate points in the groups of clustered data, and extracting a first candidate point group, the first candidate point group comprising a largest number of candidate points (in claim(s) 3 & 4, e.g. Artes);
calculating a smallest circumscribed rectangle corresponding to the candidate points in the first candidate point group (in claim(s) 3 & 4, e.g. Artes); and
updating each group of clustered data according to the smallest circumscribed rectangle for correcting the obstacle information identified by the clustered data (in claim(s) 3 & 4, e.g. Artes).
It would have been obvious to combine for the reasons set forth in the rejection of corresponding parts of claim(s) 1 & 3-4 above incorporated herein by reference.
Regarding claim 7, the combination of Zhong and Artes discloses the method of claim 4 in for example the obviousness to combine in the rejection of corresponding parts of claim(s) 1-2 and 4 above incorporated herein by reference, further comprising:
parsing the SLAM map to obtain an obstacle orientation (in claim(s) 1, 2 & 4, e.g. Zhong & Artes); and
marking the obstacle in the map according to the obstacle information comprises: marking the obstacle in the robot map according to an obstacle position, an obstacle edge and the obstacle orientation (in claim(s) 1, 2 & 4, e.g. Zhong & Artes).
It would have been obvious to combine for the reasons set forth in the rejection of corresponding parts of claim(s) 1-2 & 4 above incorporated herein by reference.
Regarding claim 8, the combination of Zhong and Artes discloses the method of claim 4 in for example the obviousness to combine in the rejection of corresponding parts of claim(s) 1 and 4 above incorporated herein by reference.
Artes further discloses, further comprising:
when second region areas have an overlap, determining whether the obstacles corresponding to the overlapped second region areas have an enclosing relationship according to the overlapped second region areas (Artes discloses regions that have overlap and determining obstacles within the overlap region areas based on plotting points along boundary lines to create a region to search for obstacles (see Figs. 1-3; [0038] (Based on the assumption of rectangular rooms, the area of robot operation is overlaid with rectangles of various sizes that are intended to represent the rooms…the rectangles are selected in such a manner so that a rectangle may be clearly assigned to every point on the map of the area of robot operation that is accessible to the robot…to determine the orientation and size of the individual rectangles, long boundary lines in the map of the area of robot operation…are employed such as those…that run along a wall (see, e.g., FIG. 1, straight line through the points L′ and K′, straight line through the points P and P′ as well as P″ and P′″)…various criteria are used for the selection of the boundary lines to be used); [0039] (Based on detected doors (see FIG. 1, door threshold between the points O and A, as well as between P′ and P″)…the apartment can be automatedly sectored into three rooms 100, 200 and 300))); and
when the obstacles corresponding to the overlapped second region areas do not have the enclosing relationship, optimizing the data sets of the obstacles corresponding to the overlapped second region areas (In Figs. 5 and 6, Artes discloses obstacles in overlapped second region 310 and optimizing the datasets of said obstacles (see Figs. 3-5; [0039] (the robot can supplement the outer boundary lines of the map of boundary lines (see FIG. 1) to form a rectilinear polygon...it is also possible to place a rectangle through the outer boundary lines of the apartment…and to remove from them inaccessible areas (see FIG. 2, area X)…based on detected doors (see FIG. 1, door threshold between the points O and A, as well as between P′ and P″) and inner walls (see FIG. 1, antiparallel boundary lines in the distance d.sub.w), the apartment can be automatedly sectored into three rooms 100, 200 and 300 (see FIG. 3)…inaccessible areas within the rooms can be interpreted by the robot to be pieces of furniture or other obstacles and can be correspondingly designated on the map))).
It would have been obvious to combine for the reasons set forth in the rejection of corresponding parts of claim(s) 1 & 4 above incorporated herein by reference.
Regarding claim 9, the combination of Zhong and Artes discloses the method of claim 8 in for example the obviousness to combine in the rejection of corresponding parts of claim(s) 1, 4 and 8 above incorporated herein by reference, wherein marking the obstacle in the map according to the obstacle information comprises:
when the obstacles corresponding to the overlapped second region areas have the enclosing relationship, marking the obstacle in the map according to the enclosing relationship and the obstacle information (in claim(s) 8, e.g. Artes).
It would have been obvious to combine for the reasons set forth in the rejection of corresponding parts of claim(s) 1, 4 & 8 above incorporated herein by reference.
Claim(s) 19-29 are rejected under 35 U.S.C. 103 as being unpatentable over anticipated by US. 20210312197 A1 to Zhong in view of U.S. 20190025838 A1 to Artes, as applied to the claims above, in further view of US. 11416003 B2 to Whitman et al. (Whitman).
Regarding claim 19, Zhong discloses a non-transitory storage medium configured to store at least one executable instruction therein, wherein the at least one executable instruction causes a processor to;
acquire a probability map and a height map, the probability map being configured to identify a probability distribution of obstacles at a plurality of positions in a robot operating environment, and the height map being configured to identify a height distribution of the obstacles corresponding to the plurality of positions in the robot operating environment (in claim 1, e.g. Zhong);
perform data grouping according to the probability map and the height map to obtain a plurality of data sets, each data set identifying obstacle information of an obstacle, the obstacle information comprising at least obstacle position information and obstacle edge information (in claim 1, e.g. Zhong); and
mark the obstacle in the robot map according to the obstacle information (in claim 1, e.g. Zhong).
However, Zhong does not further expressly disclose:
a non-transitory storage medium configured to store at least one executable instruction therein, wherein the at least one executable instruction causes a processor to.
Whitman, in the same field of endeavor, further discloses:
a non-transitory storage medium configured to store at least one executable instruction therein, wherein the at least one executable instruction causes a processor to (Whitman discloses a non-transitory storage medium configured to store executable instruction via interface/controller 1040, wherein processor 1010 processes the instruction within computing device 1000 (¶ (96) (computing device 1000 includes a processor 1010…, memory 1020…, a storage device 1030, a high-speed interface/controller 1040 connecting to the memory 1020 and high-speed expansion ports 1050, and a low speed interface/controller 1060 connecting to a low speed bus 1070 and a storage device 1030…each of the components 1010, 1020, 1030, 1040, 1050, and 1060, are interconnected using various busses, and may be mounted on a common motherboard or in other manners as appropriate…processor 1010 can process instructions for execution within the computing device 1000, including instructions stord in the memory 1020 or on the storage device 1030 to display graphical information for a graphical user interface (GUI) on an external input/output device, such as display 1080 coupled to high speed interface 1040…multiple processors and/or multiple buses may be used…along with multiple memories and types of memory))).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the system of the combination of Zhong and Artes to incorporate the mapping method of Whitman to include at least a processor, memory, interface/controller, communication bus system and storage device that communicate with each other via the communication bus in order to store and process executable instruction and record data pertaining to the probability map(s), height map(s) and SLAM, with predictable results, with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to combine Zhong, Artes and Whitman for the express benefit of including at least a processor, memory, interface/controller and storage device to communicate and/or transfer data and instruction for generating probability map(s) and height map(s) using SLAM technique(s), as explained in Whitman ¶ (96).
Regarding claim 20, the combination of Zhong, Artes and Whitman discloses: a terminal, comprising a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other through the communication bus (in claim(s) 19, e.g. Whitman); and
the memory is configured to store at least one executable instruction, the at least one executable instruction causing the processor to perform the method for marking an obstacle in a robot map according claim 1 (in claim(s) 1 & 19, e.g. Zhong & Whitman).
It would have been obvious to combine for the reasons set forth in the rejection of corresponding parts of claim(s) 1 & 19 above incorporated herein by reference.
Regarding claim 21, the combination of Zhong, Artes and Whitman discloses the non-transitory storage medium of claim 19 in for example the obviousness to combine in the rejection of corresponding parts of claim(s) 2 and 19 above incorporated herein by reference, wherein the processor is further configured to:
acquire an initial probability map and an initial heightmap of the obstacles (in claim(s) 1, e.g. Zhong);
collect captured data of a robot during working and parse the captured data to obtain obstacle data, the captured data comprising at least image data and height data (in claim(s) 1, e.g. Zhong); and
adjust the initial probability map and the initial height map according to the obstacle data, determine the adjusted initial probability map as the probability map, and determine the adjusted initial height map as the height map (in claim(s) 2, Artes).
It would have been obvious to combine for the reasons set forth in the rejection of corresponding parts of claim(s) 1 & 19 above incorporated herein by reference.
Regarding claim 22, the combination of Zhong, Artes and Whitman discloses the non-transitory storage medium of claim 19 in for example the obviousness to combine in the rejection of corresponding parts of claim(s) 2 and 19 above incorporated herein by reference, wherein the processor is further configured to:
extract a plurality of candidate points in combination with the probability map and the height map and according to a preset probability threshold (in claim(s) 3 & 6, e.g. Artes); and
cluster the plurality of candidate points to obtain a plurality of groups of clustered data, each group of clustered data identifying one of the plurality of data sets (in claim(s) 3 & 6, e.g. Artes).
It would have been obvious to combine for the reasons set forth in the rejection of corresponding parts of claim(s) 1, 3, 6 & 19 above incorporated herein by reference.
Regarding claim 23, the combination of Zhong, Artes and Whitman discloses the non-transitory storage medium of claim 19 in for example the obviousness to combine in the rejection of corresponding parts of claim(s) 4 and 19 above incorporated herein by reference, wherein the processor is further configured to:
acquire a simultaneous localization and mapping (SLAM) map of the robot operating environment, the SLAM map comprising an unknown region identified as a region where a robot is prohibited from passing (in claim(s) 4, e.g. Artes);
calculate a first region area and a second region area respectively according to the SLAM map and the data set, the first region area indicating a largest continuous unknown region in the SLAM map, and the second region area being an obstacle floor area identified by the data set (in claim(s) 4, e.g. Artes); and
identify a floor-type obstacle according to the first region area and the second region area, wherein the robot is prohibited from shuttling inside and outside the floor-type obstacle (in claim(s) 4-5, e.g. Artes).
It would have been obvious to combine for the reasons set forth in the rejection of corresponding parts of claim(s) 4, 5 & 19 above incorporated herein by reference.
Regarding claim 24, the combination of Zhong, Artes and Whitman discloses the non-transitory storage medium of claim 23 in for example the obviousness to combine in the rejection of corresponding parts of claim(s) 5 and 23 above incorporated herein by reference, wherein the processor is further configured to:
collect regions of which the second region area is less than a preset area threshold and which are adjacent in the SLAM map (in claim(s) 5, e.g. Artes); and
generate a smallest circumscribed polygon corresponding to the collected regions, and determine a region identified by the smallest circumscribed polygon as the floor-type obstacle (in claim(s) 5, e.g. Artes).
It would have been obvious to combine for the reasons set forth in the rejection of corresponding parts of claim(s) 5 & 23 above incorporated herein by reference.
Regarding claim 25, the combination of Zhong, Artes and Whitman discloses the non-transitory storage medium of claim 22 in for example the obviousness to combine in the rejection of corresponding parts of claim(s) 6 and 22 above incorporated herein by reference, wherein the processor is further configured to:
group according to positions of the candidate points in the groups of clustered data, and extract a first candidate point group, the first candidate point group comprising a largest number of candidate points (in claim(s) 6, e.g. Artes);
calculate a smallest circumscribed rectangle corresponding to the candidate points in the first candidate point group (in claim(s) 6, e.g. Artes); and
update each group of clustered data according to the smallest circumscribed rectangle for correcting the obstacle information identified by the clustered data (in claim(s) 6, e.g. Artes).
It would have been obvious to combine for the reasons set forth in the rejection of corresponding parts of claim(s) 6 & 22 above incorporated herein by reference.
Regarding claim 26, the combination of Zhong, Artes and Whitman discloses the non-transitory storage medium of claim 23 in for example the obviousness to combine in the rejection of corresponding parts of claim(s) 7 and 23 above incorporated herein by reference, wherein the processor is further configured to:
parse the SLAM map to obtain an obstacle orientation (in claim(s) 1, 2, 4, 7, e.g. Artes); and
mark the obstacle in the robot map according to an obstacle position, an obstacle edge and the obstacle orientation (in claim(s) 1, 2, 4, 7, e.g. Artes).
It would have been obvious to combine for the reasons set forth in the rejection of corresponding parts of claim(s) 1-2, 4, 7 & 23 above incorporated herein by reference.
Regarding claim 27, the combination of Zhong and Artes discloses the non-transitory storage medium of claim 23 in for example the obviousness to combine in the rejection of corresponding parts of claim(s) 1, 4, 8-9 and 23 above incorporated herein by reference, wherein the processor is further configured to:
when second region areas have an overlap, determine whether the obstacles corresponding to the overlapped second region areas have an enclosing relationship according to the overlapped second region areas (in claim(s) 8 & 9, e.g. Artes); and
when the obstacles corresponding to the overlapped second region areas do not have the enclosing relationship, optimize the data sets of the obstacles corresponding to the overlapped second region areas (in claim(s) 8 & 9, e.g. Artes).
It would have been obvious to combine for the reasons set forth in the rejection of corresponding parts of claim(s) 1, 4, 8-9 & 27 above incorporated herein by reference.
Regarding claim 28, the combination of Zhong and Artes discloses the non-transitory storage medium of claim 27 in for example the obviousness to combine in the rejection of corresponding parts of claim(s) 1, 4 and 8-9 above incorporated herein by reference, wherein the processor is further configured to:
when the obstacles corresponding to the overlapped second region areas have the enclosing relationship, mark the obstacle in the map according to the enclosing relationship and the obstacle information (in claim(s) 8 & 9, e.g. Artes).
It would have been obvious to combine for the reasons set forth in the rejection of corresponding parts of claim(s) 1, 4, 8-9 & 27 above incorporated herein by reference.
Regarding claim 29, the combination of Zhong, Artes and Whitman discloses a terminal, comprising a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other through the communication bus (in claim(s) 1, 2, 19 & 20, e.g. Zhong & Whitman); and
the memory is configured to store at least one executable instruction, the at least one executable instruction causing the processor to perform he method for marking an obstacle in a robot map according to claim 2 (in claim(s) 1, 2, 19 & 20, e.g. Zhong & Whitman).
It would have been obvious to combine for the reasons set forth in the rejection of corresponding parts of claim(s) 1-2 & 19-20 above incorporated herein by reference.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure as teaching the state of the art of method(s) and apparatus for making obstacle(s) in robot map(s), at the time of filing. For example:
US 20220083076 A1 to Maeda; Keisuke teaches, inter alia ACTION PLANNING APPARATUS, ACTION PLANNING METHOD, AND PROGRAM in for example the ABSTRACT, Figures and/or Paragraphs below:
“An action planning apparatus includes an action planning section that creates, to control action of a robot apparatus, on the basis of an outside world map, a higher action plan including an optimal solution achieving an action target and enabling derivation of a suboptimal solution different from the optimal solution.”
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US 20210356972 A1 to Kwon; Soonbeom teaches, inter alia ELECTRONIC APPARATUS AND CONTROLLING METHOD THEREOF in for example the ABSTRACT, Figures and/or Paragraphs below:
“An electronic apparatus for providing a traversability map of a robot and a controlling method thereof are provided. The electronic apparatus includes a transceiver, a memory configured to store feature information of each of a plurality of robots, and at least one processor configured to receive sensing data obtained by sensing vicinity by at least one external device from the external device from the at least one external device, through the transceiver, generate at least one map with respect to a space where the at least one external device is positioned based on the received sensing data, generate a traversability map for traversal of a robot based on feature information of at least one robot among the plurality of robots and the generated at least one map, and control the transceiver to transmit the traversability map to the robot.”
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US 12572148 B2 to Xie; Haojian teaches, inter alia Method For Detecting Obstacle, Self-moving Robot, And Non-transitory Computer Readable Storage Medium in for example the ABSTRACT, Figures and/or Paragraphs below:
“A method for detecting an obstacle, applied to a self-moving robot, including: transforming obstacle information into depth information; converting the depth information into a point cloud map, and determining coordinate data of a reference point on the obstacle; determining a valid analysis range in a height direction in the point cloud map; and determining, based on the coordinate data of the reference point, whether an obstacle is present within the valid analysis range.”
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/ROBERT L PINKERTON/Examiner, Art Unit 3665
/HUNTER B LONSBERRY/Supervisory Patent Examiner, Art Unit 3665
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