DETAILED CORRESPONDENCE
This Office action is in response to the application filed 10/11/2024.
Claim Status
Claims 1-13, 15-18 and 20-22 are pending.
Claim 14 and 19 are canceled.
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
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. SG10202203787, filed on April 12, 2023.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 10/11/2024 and 10/15/2024 complies with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 3-5, 8-10, 13, 15-18, 20 and 22 are rejected under 35 U.S.C. 102 (a)(1) are as being anticipate by Y. Zhu et al., “Wheelchair Automatic Docking Method for Body-Separated Nursing Bed Based on Grid Map,” in IEEE Access, vol. 9, pp. 79549-79561, 2021 hereinafter “Zhu”
Claims 1 and 20. Zhu teaches a method of docking a wheelchair relative to an object, the method comprising:
converting a user-selected 2D point in a two-dimensional (2D) image of a scene into a three-dimensional (3D) point in a 3D point cloud of the scene (pg. 79551, col. 1, sec. 2 teaches a scenario that reads on this element as such—“In the positioning process, the current relative pose between the bed and wheelchair can be obtained by determining the two-sided linear structural equation and solving the corner position, and then controlling the wheelchair to dock. In addition, the results obtained can be verified by using a priori knowledge with 90°, which greatly simplifies the difficulty of Lidar data processing and improves the robustness of the positioning algorithm. In order to obtain the characteristic lines of V-shaped artificial landmark, Split-merge method is used to extract the features of Lidar point cloud, and the problem of finding a plane in 3D space is transformed into the problem of finding a straight line in 2D space [14]. Through the manual selection method, we analyze the graphics to be fitted. V-shaped artificial landmark to be fitted is an isosceles right triangle with a side length of 0.6m, so the distance threshold is set to 0.6m [15]. The algorithm flow of the Split-merge method is shown in Fig. 4);
determining a first group of edges from the 3D point cloud based on the 3D point, the first group of edges including an approximated reference edge of the object (Figure 2 illustrates “system scheme diagram of docking method”. Here figure 2 along with figure 3 illustrates 3D “point cloud matching”. To continue, figure 4 illustrates fitting edges as a straight line. While figure 5 illustrates V-shape artificial landmark in which the line Y is a reference edge. Figure 1 clarifies );
defining an intermediate dock pose spaced apart from the approximated reference edge by a spacing (pg. 79552, col. 1, para. 1 along with figure 5 reads on this element as such—“The relative posture between the intelligent wheelchair and the bed can be
obtained, and wheelchair/nursing-bed automatic docking can be realized through the motion control algorithm” Thus, taken together the cited section reads on this element. );
determining a second group of edges from the 3D point cloud, the second group of edges including at least one potential reference edge, the at least one potential reference edge being based on a plurality of intersections between the second group of edges and respective projection lines between a plurality of random points of the 3D point cloud from within the second group of edges and the intermediate dock pose (see figure 4 which at least illustrates two edges being fitted from the cloud points. While figure 9 illustrates path planning that has a plurality of fitted projection lines that correlates with the second group of edges see figure 4. Thus, taken together the cited section reads on this element. The relative position is of the wheelchair is also illustrated in figures 5-7);
determining a dock pose based on one of the at least one potential reference edge (figures 5 and 7 illustrates the Y-line as a potential reference edge for determining the docking pose); and
moving the wheelchair towards the dock pose (pg. 79552, section 4 teaches the motion model of the wheelchair. While figure 11 further illustrates moment of the wheelchair.).
Claims 3 and 22. Zhu teaches the method as recited in claim 1 and further teaches, wherein the 2D image comprises a partial image of the object (figure 9 illustrates an image of an object).
Claim 4. Zhu teaches the method as recited in claim 1, and further teaches wherein the determining the dock pose comprises:
fitting the at least one potential reference edge with a geometric model of the object (best illustrated in figure 4);
projecting the intermediate dock pose onto the geometric model to form a projection (best illustrated in figure 9); and
adding an offset to the projection, such that the dock pose is spaced apart from the object by at least the offset (pg. 79555, col. 1 describe this element as such—“The computer control, a kind of sampling control which only calculates the control quantity based on the deviation of the sampling time, cannot continuously output the control quantity like analog control for continuous control. According to the sampling time of the motor encoder, we set the sampling time to 100ms, calculate the coordinate error between the current value and the expected value, form the PID closed-loop feedback through the measuring components, and gradually adjust the yaw angle of the wheelchair so that the wheelchair can move stably.”).
Claim 5. Zhu teaches the method as recited in claim 1, further comprising: determining the dock pose from a plurality of search poses responsive to a sum of cost within a footprint of the wheelchair exceeding a threshold (pg. col. 2, pg. 79557- col. 1, pg. 79558 reads on this element as such teaches comparing the yaw angle during the docking process in which the yaw angle exceeds 2.5◦ which describes this element).
Claim 8. Zhu teaches the method as recited in claim 1, further comprising: iteratively updating the 3D point cloud based on a plurality of 2D images acquired at various time instants concurrently with the moving of the wheelchair (Zhu on pg. 79554, col. 1, section B teaches a scenario reads on this element as such—“Then search the next grid cell that the wheelchair will pass through according to the optimal path, adjust the wheel chair posture to
Make the wheelchair move to this cell at the shortest distance. Repositioning the wheelchair, reconnecting two points and to determine the next cell, and repeat the above steps until the wheelchair reaches the target position. The specific method is shown in Fig. 9.”).
Claim 9. Zhu teaches the method as recited in claim 8, further comprising: iteratively updating the intermediate dock pose in response to the updating of the 3D point cloud (pg. 79554-79555, section B reads on this element as such— “[r]epositioning the wheelchair, reconnecting two points and to determine the next cell, and repeat the above steps until the wheelchair reaches the target position….After adjusting the posture of the wheelchair, drive the wheelchair to move to the next grid cell nearby along the median line of BC. But there may be deviation during the movement. After the wheelchair enters the next cell, reposition the wheelchair and repeat the above steps until the coordinates of point A and point D coincide.).
Claim 10. Zhu teaches the method as recited in claim 8, further comprising: iteratively fitting the at least one potential reference edge with the geometric model based on an updated 3D point cloud (pgs. 79554-79555, section B, sub-section (5) reads on this element as such “After the coordinates of point A and point D coincide, adjust the wheelchair posture to make the median line BC coincide with the angle bisector LP of V-shaped artificial landmark. The wheelchair gradually approaches the artificial landmark along the angle bisector until the docking is completed”).
Claim 13. Zhu teaches the method as recited in claim 1, further comprising: determining the first group of edges based on a convex hull of a first group of points from the 3D point cloud, wherein the first group of points are within a predetermined range of height values (figure 7 best illustrates the height on the X-axis).
Claim 15. Zhu teaches the method as recited in claim 1, further comprising: determining the second group of edges based on a concave hull of a second group of points of the 3D point cloud, wherein the second group of points are within a sampled region, wherein the intermediate dock pose faces the sampled region (figure 11(a)-11(d) best illustrates this element.).
Claim 16. Zhu teaches the method as recited in claim 1, further comprising: determining the 3D point cloud based on a spatio-temporal voxel layer accumulating a plurality of 3D points from multiple time instants (pg. 79555 describes this element as such—“The coordinates of Band C can be obtained from (5) (6) (7) (8). (4) After adjusting the posture of the wheelchair, drive the wheelchair to move to the next grid cell nearby along the median line of BC. But there may be deviation during the movement. After the wheelchair enters the next cell, reposition the wheelchair and repeat the above steps until the coordinates of point A and point D coincide. (5) After the coordinates of point A and point D coincide, adjust the wheelchair posture to make the median line BC coincide with the angle bisector LP of V-shaped artificial landmark. The wheelchair gradually approaches the artificial landmark along the angle bisector until the docking is completed”).
Claim 17. Zhu teaches the method as recited in claim 1, further comprising:
segmenting the 2D image to obtain a segmented 2D image (figure 2 illustrates “Lidar data processing”); and
filtering the 3D point cloud to retain a plurality of 3D points corresponding to the object based on the segmented 2D image (figure 2 illustrates “point cloud filtering”).
Claim 18. Zhu teaches the method as recited in claim 1, wherein converting the user-selected 2D point into the three-dimensional (3D) point comprises determining the 3D point based on an intersection between a de-projection line and the 3D point cloud (col. 1, section 2 teaches “Lidar point cloud matching is mainly used to locate the bed and wheelchair, usually using efficient and accurate ICP for matching….” which reads on this element).
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.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 2 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Zhu in view of S. Yang, S. Yang and X. Yi, “An Efficient Spatial Representation for Path Planning of Ground Robots in 3D Environments”, in IEEE Access, vol. 6, pp. 41539-41550, 2018 hereinafter “Yang”.
Claims 2 and 21. Zhu teaches the method as recited in claim 1; however, Zhu is silent on RGB. Yet, Yang teaches comprising:
presenting the 2D image via a user interface as an RGB (Red Green Blue) image as viewed from the wheelchair, the 2D image being made up of a plurality of pixels, any of the plurality of pixels being available for selection as the user-selected 2D point; and
receiving the user-selected 2D-point as user input before obtaining the 3D point cloud, wherein the 3D point cloud is generated about the 3D point (Figures 5 (a)-(j) best illustrate the RGG images with a 3D point cloud).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Yang with the Zhu because such combination would provide spatial representation for path planning.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Zhu in view of Zheng et al, GB 2636687 hereinafter Zheng.
Claim 6. Zhu teaches the method as recited in claim 1 and teaches determining the path of motion for the wheelchair from a current position to the dock as illustrated in figures 7 and 9; however, Zhu is silent on the term costmap. Yet, the Zheng further comprising: via a 2D costmap ([0026]-[0027] along with [0031] and [0036] reads on the claim term via a 2D costmap as such—“As a result, a binary costmap 226 is generated, where each region is classified as either passable or impassable. The binary costmap 226 is a 2D map, that may be used as an input to 2D navigation algorithms….The outputs of the traversability analysis module 220 include the 2D costmap 226, also referred herein as a traversability map 226. The traversability map 226 may be provided to a path planner 230. The path planner 230 may include a global planner 232 and a local planner 234. The global planner 232 may receive positional information of a destination 208, and may receive the robot pose 216. The global planner 230 may generate a global path 236 based on these information.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Zheng with the Zhu because such combination would provide an accurately identify whether the surroundings are passable or impassable regions (Zheng, [0041]).
Claims 7, 11 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Zhu in view Zheng and in further view of H. Vadivel, K. S. Priyanayana and A. G. B. P. Jayasekara, “Enhancing the Capabilities of Approaching to Service Scenarios and Settling for Intelligent Wheelchair Robots,” 2021 Moratuwa Engineering Research Conference (MERCon), Moratuwa, Sri Lanka, 2021, pp. 711-716, hereinafter “Vadivel”.
Claim 7. Zhu is silent on term costmap. However, Zheng teaches the method as recited in claim 6 and teaches the concept of the 2D costmap; however, Zheng is silent on teaching movement under the object. Yet, Vadivel teaches further comprising: removing a portion of the object from the 2D costmap, wherein a width of the portion of the object corresponds to a width of the wheelchair moving under the object (pg. 713, col. 2, section B reads on this element as such—“… to use as a launching pad for the quest for safe docking spots, and (ii) to exclude corners from the list of potential locations (since it was difficult for a customer to choose to dock in a corner).” While fig. 4 teaches the wheelchair approaching an object with a width that corresponds to the width of the wheelchair for docking under a table in a writing scenario.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Zheng with Vadivel because such combination would provide support for everyday transactional activities that pose challenges to steering and operation in tight, narrow, or cluttered spaces (Vadivel, pg. 711, col. 1, para. 2).
Claim 11. Zhu teaches the method as recited in claim 1; however, Zhu is silent on the term perpendicular. Yet, Vadivel teaches further comprising:
determining a respective perpendicular pose for each of the at least one potential reference edge (figure 2-4 illustrates the height perpendicular pose); and
selecting one of the at least one potential reference edge having a corresponding perpendicular pose within a threshold angle relative to the intermediate dock pose (pg. 713, section B describes this element as such—“Sequential search methods were proposed that allow the use of an aligned 3D box that was placed or slid underneath the table structure. The number of cloud points that come within this 3D box were countered and the knowledge to make safety decisions was used. The box's dimensions were determined by the Americans with Disabilities Act's table requirements.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Zheng with Vadivel because such combination would provide support for everyday transactional activities that pose challenges to steering and operation in tight, narrow, or cluttered spaces (Vadivel, pg. 711, col. 1, para. 2).
Claim 12. Vadivel the method as recited in claim 11, wherein the threshold angle is one selected from a range from 25 degrees to 65 degrees, and wherein the predetermined range of height values is from 0.75 m to 1.2 m (On pg. 715, col. 1 reads on this element as such—“ According to the boxplot, the optimized distance between wheelchair back end & table for eating (29cm), writing (27.75cm), reading (27.25 cm), using a laptop (40cm), and the optimized height difference between table surface & wheelchair seat for all scenarios were obtained as in same difference (Same value = 32.25 cm).” Furthermore, “…dimensions were determined by the Americans with Disabilities Act's table requirements.” as taught on pg. 713, section B).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Zheng with Vadivel because such combination would provide support for everyday transactional activities that pose challenges to steering and operation in tight, narrow, or cluttered spaces (Vadivel, pg. 711, col. 1, para. 2).
Conclusion
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/A.D.T/Examiner, Art Unit 3661
/RUSSELL FREJD/Primary Examiner, Art Unit 3661
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