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 .
Status of Application
This action is in reply to the after final filed 01/23/2026.
Claims 1 – 4, 6, and 10 – 24 are pending and elected for examination.
Claims 16 & 22 have been amended.
Claims 5, and 7 – 9 have been previously cancelled by the applicant.
The Examiner respectfully rescinds the objection to claims 16 & 22 in view of the amendments.
Response to Arguments
Applicant’s arguments, see pages 2-5, filed 01/23/2026, with respect to the rejection(s) of claim(s) 1 & 6 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Dooley et al. US 2021/0164785 A1.
Examination According to Amendments
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 USC §119 (a)-(d). The certified copy has been filed in the present application.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on November 22, 2023, has been considered by the examiner.
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.
1, 6, 10, 14, 15, 19, 21 & 22: Cao + Dooley
Claim(s) 1, 6, 10, 14, 15, 19, 21 & 22 are rejected under 35 U.S.C. 103 as being unpatentable over CN 107966150 A, hereinafter Cao, in view of the Dooley et al. US 2021/0164785 A1, hereafter Dooley.
Regarding Claim 1, Cao teaches A computer-implemented autonomous operation method for a robot, comprising: moving the robot, under a control of a manual guide method performed by a user, along a guide path in an operation scene (see at least Cao P0069: Control the robot to move along the road in the substation so that the lidar can scan all areas that need to be inspected); generating a map including the guide path by positioning and mapping during the robot being moved along the guide path in the operation scene (see at least Cao P0069: then use the scanned lidar data and inertial measurement unit data to build map data that matches the substation environment); generating a plurality of operation points on the guide path in the map (see at least Cao P0069: a substation environment map and Figs. 2 and 3 showing substations, the examiner interprets substations as an example of operation points); generating an operation path, wherein the operation path passes through all of the unpassed operation points and has a shortest total distance (see at least Cao P0079: find a route that passes through all inspection locations and has the shortest path); and moving the robot, according to the operation path, to each of the unpassed operation points so as to perform an operation (see at least Cao P0019: moves along this path to the target inspection point; autonomous navigation of multiple inspection points: given a set of all inspection points to be inspected, the robot calculates the path passing through all inspection points, and then stops at all inspection points in sequence along this path).
Cao does not explicitly teach that applied force through pushing or pulling is used in the manual guide control. However, Dooley teaches wherein the manual guide method comprises: moving the robot along the guide path in the operation scene in response to force applied by the user to the robot through pushing or pulling (see at least Dooley para. 0102: FIG. 4A is a top plan view illustration of a physical map of a home environment 150 with superimposed identification of desired robot destinations 152 and individual routes 154 taken by a robot within the home environment 150 between pairs of the desired robot destinations 152 for creation of a route map useable by a mobile robot and/or training of a mobile robot according to certain embodiments. Each desired robot destination 152 may be selected by an operator, and individual routes 154 may be established during a training method by which an operator provides input signals configured to start and stop recording of defined routes used during a robot guiding step, such that when the mobile robot is guided (e.g., pushed by the operator) along paths between different pairs of the desired robot destinations 152, routes 154 are generated for paths imaged while recording is active).
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 Cao to incorporate the method of Dooley in the robot control field of invention to use Dooley’s guided push technique on Cao’s autonomous robot for the advantage of rapidly training a mobile robot to autonomously navigate an unstructured residential environment (see at least Dooley para. 0008).
Regarding Claim 6, the combination of Cao, Dooley teaches the limitations of claim 1 and moving the robot, according to the operation path, to each of the unpassed operation points so as to perform the operation includes: moving the robot, according to the operation path, to a target operation point among all of the unpassed operation points (see at least Cao P0083 – P0088: find out the road section L2 where the next inspection point NP is located… find the shortest path from SP to NP;… S7.8 Repeat the above operation until the robot can pass through all designated inspection points in sequence… S8, robot autonomous navigation: including autonomous navigation of designated location points); moving the robot to the target operation point so as to perform the operation, in response to the target operation point being reachable (see at least Cao P0084 – P0088: Find the head endpoint P21 of L2; S7.6 Find the end point P22 of L2; S7.7 Use Dijkstra algorithm or A* algorithm combined with binary tree structure to screen the above endpoints and find the shortest path from SP to NP… autonomous navigation of designated inspection points: given any inspection point, the robot calculates the path to the inspection point, and then moves along this path to the target inspection point the examiner interprets use of Dijkstra or A* algorithms for shortest path calculation as indicating that the point is reachable); skipping the target operation point, in response to the target operation point being unreachable (see at least Cao P0091 – P0092: S9.2 detects that the path ahead is impassable; S9.3 selects "stop", "one-key reverse" or "bypass" according to the preset execution strategy. If it is "stop", execute S9.4; if it is "one-key reverse", execute S9.5; if it is "bypass", execute S9.6); ending the operation, in response to the target operation point being the last unpassed operation point (see at least Cao P0095: cancels the current navigation task, recalculates a path to the target navigation point, moves to the target navigation point along the path, and then continues to execute the remaining navigation tasks the examiner notes that if no remaining navigation tasks exist, execution of the remaining tasks includes only returning to the charging points (see at least Cao Fig. 2)); and returning to generating the operation path, in response to the target operation point being not the last unpassed operation point (see at least Cao P0091: detects that the path ahead is impassable and P0095: cancels the current navigation task, recalculates a path to the target navigation point, moves to the target navigation point along the path, and then continues to execute the remaining navigation tasks).
Regarding Claim 10, Cao teaches moving the robot, under a control of a manual guide method performed by a user, along a guide path in an operation scene (see at least Cao P0069: Control the robot to move along the road in the substation so that the lidar can scan all areas that need to be inspected); instructions for generating a map including the guide path by positioning and mapping during the robot being moved along the guide path in the operation scene (see at least Cao P0069: then use the scanned lidar data and inertial measurement unit data to build map data that matches the substation environment); instructions for generating a plurality of operation points on the guide path in the map (see at least Cao P0069: a substation environment map and Figs. 2 and 3 showing substations, the examiner interprets substations as an example of operation points); instructions for generating an operation path, wherein the operation path passes through all of the unpassed operation points and has a shortest total distance (see at least Cao P0079: find a route that passes through all inspection locations and has the shortest path); and instructions for moving the robot, according to the operation path, to each of the unpassed operation points so as to perform an operation (see at least Cao P0019: moves along this path to the target inspection point; autonomous navigation of multiple inspection points: given a set of all inspection points to be inspected, the robot calculates the path passing through all inspection points, and then stops at all inspection points in sequence along this path).
Cao does not explicitly teach the following. However, Dooley teaches:
A robot, comprising: a processor; a memory coupled to the processor and one or more computer programs stored in the memory and executable on the processor wherein, the one or more computer programs comprise: instructions for moving the robot, along a guide path in an operation scene (see at least Dooley para. 0114: “In certain embodiments, the mobile robot comprises (i) at least one first camera configured to detect infrared wavelengths, (ii) a first infrared illumination source configured to provide substantially non-structured, flood illumination of surroundings within a field of view of the at least one first camera, and (iii) at least one processor configured to control operation of the at least one first camera. In such an embodiment, the recording of images of surroundings experienced by the mobile robot comprises illuminating objects within a field of view of the at least one first camera during a plurality of first illumination intervals, and recording images during each illumination interval of the plurality of first illumination intervals using the at least one first camera.); instructions for generating a map including the guide path by positioning and mapping during the robot being moved along the guide path in the operation scene instructions for generating a plurality of operation points on the guide path in the map (see at least Dooley para. 0105: In many applications, it may be beneficial to generate maps of an environment and/or record routes through the environment during or following a single pass of a robot along the desired route. In such a scenario, one or more cameras of a robot camera will be exposed to limited view of the environment. As a result, a visual SLAM map will have visual features limited to those identified by the one or more cameras during passage of the robot through the route. para. 0106: One aspect of the disclosure relates to a method for rapidly training a mobile robot configured for autonomous operation to navigate an unstructured residential environment, wherein the method comprises multiple steps. A first step includes identifying a set of desired robot destinations within the unstructured residential environment, wherein the set of desired robot destinations comprises at least four desired robot destinations (e.g., to form at least two pairs of desired robot destinations). Such identification may be performed by an operator to correspond to destinations where the mobile robot is likely to be able to provide assistance to a human user. A second step includes guiding the mobile robot (e.g., by an operator pulling or pushing the mobile robot) along paths between at least a minimum number of different pairs of desired robot destinations to enable establishment of full connectivity between each different pair of desired robot destinations;
wherein the instructions for moving the robot, under the control of the manual guide method performed by the user, along the guide path in the operation scene comprise: instructions for moving the robot along the guide path in the operation scene in response to force applied by the user to the robot through pushing or pulling (see at least Dooley para. 0104 : FIG. 4A is a top plan view illustration of a physical map of a home environment 150 with superimposed identification of desired robot destinations 152 and individual routes 154 taken by a robot within the home environment 150 between pairs of the desired robot destinations 152 for creation of a route map useable by a mobile robot and/or training of a mobile robot according to certain embodiments. Each desired robot destination 152 may be selected by an operator, and individual routes 154 may be established during a training method by which an operator provides input signals configured to start and stop recording of defined routes used during a robot guiding step, such that when the mobile robot is guided (e.g., pushed by the operator) along paths between different pairs of the desired robot destinations 152, routes 154 are generated for paths imaged while recording is active.).
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 Cao to incorporate the method of Dooley in the robot control field of invention to use Dooley’s guided push technique on Cao’s autonomous robot for the advantage of rapidly training a mobile robot to autonomously navigate an unstructured residential environment (see at least Dooley para. 0008).
Claim 19 is directed toward non-transitory computer readable medium that performs the steps recited in the systems of claims 10. The cited portions of the reference(s) used in the rejections of claims 10 teach the steps directed toward the non-transitory computer readable medium of claims 19. Therefore, claim 19 is rejected under the same rationale used in the rejections of claim 10.
Regarding Claim 14, the combination of Cao and Dooley teaches the limitations of claim 10 and Cao teaches after generating the map including the guide path by positioning and mapping during the robot being moved along the guide path in the operation scene (see at least Cao P0013: Control the robot to move along the road in the substation so that the lidar can scan all areas that need to be inspected), further comprising: removing signal noise from the guide path in the map (see at least Cao P0071: Setting thresholds to process noise clutter, edges and color scale in the substation environment map).
Regarding Claim 15, the combination of Cao and Dooley teaches the limitations of claim 10 and Cao teaches moving the robot, according to the operation path, to each of the unpassed operation points so as to perform the operation includes: moving the robot, according to the operation path, to a target operation point among all of the unpassed operation points (see at least Cao P0083 – P0088: find out the road section L2 where the next inspection point NP is located… find the shortest path from SP to NP;… S7.8 Repeat the above operation until the robot can pass through all designated inspection points in sequence… S8, robot autonomous navigation: including autonomous navigation of designated location points); moving the robot to the target operation point so as to perform the operation, in response to the target operation point being reachable (see at least Cao P0084 – P0088: Find the head endpoint P21 of L2; S7.6 Find the end point P22 of L2; S7.7 Use Dijkstra algorithm or A* algorithm combined with binary tree structure to screen the above endpoints and find the shortest path from SP to NP… autonomous navigation of designated inspection points: given any inspection point, the robot calculates the path to the inspection point, and then moves along this path to the target inspection point the examiner interprets use of Dijkstra or A* algorithms for shortest path calculation as indicating that the point is reachable); skipping the target operation point, in response to the target operation point being unreachable (see at least Cao P0091 – P0092: S9.2 detects that the path ahead is impassable; S9.3 selects "stop", "one-key reverse" or "bypass" according to the preset execution strategy. If it is "stop", execute S9.4; if it is "one-key reverse", execute S9.5; if it is "bypass", execute S9.6); ending the operation, in response to the target operation point being the last unpassed operation point (see at least Cao P0095: cancels the current navigation task, recalculates a path to the target navigation point, moves to the target navigation point along the path, and then continues to execute the remaining navigation tasks the examiner notes that if no remaining navigation tasks exist, execution of the remaining tasks includes only returning to the charging points (see at least Cao Fig. 2)); and returning to generating the operation path, in response to the target operation point being not the last unpassed operation point (see at least Cao P0091: detects that the path ahead is impassable and P0095: cancels the current navigation task, recalculates a path to the target navigation point, moves to the target navigation point along the path, and then continues to execute the remaining navigation tasks).
Regarding Claim 21, the combination of Cao, & Dooley teaches the limitations of claim 1 and the shortest total distance refers to a shortest total distance from a starting position, via all of the unpassed operation points, back to the starting position, or to an end position (see at least Cao P0018: calculate all inspection points and find a route that passes through all inspection locations and has the shortest path, Fig. 3 and P0083: find the shortest path from SP to NP), and wherein the end position comprises a location of a charging device of the robot (see at least Cao P0098: it will directly calculate the shortest path to the charging point from the robot's location, and then move to the charging point along this path).
Regarding Claim 22, the combination of Cao and Dooley teaches the limitations of claim 1 and during the robot moves along the guide path in the operation scene, while the robot locates each path point on the guide path in the operation scene and records a position of each path point on the guide path in the operation scene (see at least Cao P0013: Control the robot to move along the road in the substation so that the lidar can scan all areas that need to be inspected, and then use the scanned lidar data and inertial measurement unit data to build map data that matches the substation environment the examiner interprets substations as examples of path points), the robot obtains data around each path point on the guide path in the operation scene (see at least Cao P0022: The laser radar in the robot scans the surrounding environment), and records a pose of the robot at a moment obtaining the data (see at least Cao P0023: The inertial measurement unit in the robot feeds back its own posture the examiner interprets posture as an example of pose); and wherein the data obtained at each path point on the guide path in the operation scene includes at least one image or radar data frame (see at least Cao P0022: The laser radar in the robot scans the surrounding environment), and the at least one image or radar data frame obtained at each path point on the guide path in the operation scene includes one image or radar data frame as a key frame of the map (see at least Cao P0013: use the scanned lidar data and inertial measurement unit data to build map data that matches the substation environment and P0031: The robot needs to stop at the inspection point and perform inspection operations).
2: Cao + Dooley + Baldwin
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Cao, and Dooley in view of publication “Laser-only road-vehicle localization with dual 2D push-broom LIDARS and 3D priors”, hereinafter Baldwin.
Regarding Claim 2, the combination of Cao and Dooley teaches the limitations of claim 1 and generating the map including the guide path by positioning and mapping during the robot being moved along the guide path in the operation scene includes: obtaining, through a positioning sensor of the robot, positioning data during the robot being moved along the guide path in the operation scene, wherein the positioning sensor includes a dual laser radar; and generating the map including the guide path by processing the positioning data using a simultaneous localization and mapping technology (see at least Cao P0022: The laser radar scans the surrounding environment at a high frequency, thereby calculating the distance between the robot itself and the objects in the surrounding environment, and using the characteristics of the surrounding environment and map data to calculate the robot's own position and direction in the map).
Cao does not explicitly teach a dual laser radar. However, dual laser would be a simple and obvious substitution for the existing single laser. As taught by Baldwin, the Dual-LIDAR system exhibits less displacement to the reference trajectory on multiple loops than the INS (see at least Baldwin Pg2496 end of Section V). The substitution of a dual LIDAR system for Cao’s single LIDAR system requires minimal modification and yields predictable localization results, motivated by better maintained calibration and decreased displacement over time.
3: Cao + Dooley + Huo
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Cao and Dooley in view of CN 111802978 A, hereinafter Huo.
Regarding Claim 3, the combination of Cao and Dooley teaches the limitations of claim 1 but does not explicitly teach a disinfection robot. However, Huo teaches the robot is a disinfection robot (see at least Huo P0010: controlling the cleaning robot to perform a cleaning operation the examiner interprets a cleaning robot as an example of a disinfection robot), and generating the plurality of operation points on the guide path in the map (see at least Huo P0014: The area to be cleaned is divided according to the preset distance, the measured distance and the current position to obtain a sub-area to be cleaned) includes: generating the plurality of operation points evenly on the guide path in the map at a preset interval (see at least Huo P0018: the line connecting the points extending from the vertex along the first set direction to the first preset distance).
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 Cao to incorporate the method of Huo in the robotic path planning and control field of invention to use a cleaning robot with preset cleaning intervals for the advantage of completing cleaning coverage of an entire area (see at least Huo P0011).
4: Cao +Dooley + Carlson
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Cao and Dooley in view of the Encyclopedia Britannica entry for “graph theory” revised for content in 2008 but attached in the current version for web reference completeness, hereinafter Carlson.
Regarding Claim 4, the combination of Cao and Dooley teaches the limitations of claim 1 and shortest path planning using Dijkstra or A* algorithms (see at least Cao P0078). Cao does not explicitly teach generating the operation path includes: generating the operation path using one of a Chinese Postman Problem algorithm and a Traveling Salesman Problem algorithm. However, the Traveling Salesman and Chinese Postman Problems are commonly known algorithms in the art for finding the shortest paths, and it would be obvious to combine them with the method of Cao to find the shortest path. Carlson, in Encyclopedia Brittanica further confirms the usage of these algorithms in their article on Graph Theory: Among the current interests in graph theory are problems concerning efficient algorithms for finding optimal paths (depending on different criteria) in graphs. Two well-known examples are the Chinese postman problem (the shortest path that visits each edge at least once), which was solved in the 1960s, and the traveling salesman problem (the shortest path that begins and ends at the same vertex and visits each edge exactly once), which continues to attract the attention of many researchers because of its applications in routing data, products, and people demonstrating the motivation to combine such algorithms for the advantage of using efficient algorithms to find optimal routing paths of products and people.
16 23 & 24: Cao + Dooley + Fong
Claims 16, 23 & 24 are rejected under 35 U.S.C. 103 as being unpatentable over Cao and Dooley in view of US 8,798,840 B2, hereinafter Fong.
Regarding Claim 16, the combination of Cao and Dooley teaches the limitations of claim 10 and Cao teaches during the robot movement along the guide path in the operation scene, while the robot locates each path point on the guide path in the operation scene and records a position of each path point on the guide path in the operation scene (see at least Cao P0013: Control the robot to move along the road in the substation so that the lidar can scan all areas that need to be inspected, and then use the scanned lidar data and inertial measurement unit data to build map data that matches the substation environment the examiner interprets substations as examples of path points), the robot obtains data around each path point on the guide path in the operation scene (see at least Cao P0022: The laser radar in the robot scans the surrounding environment), and records a pose of the robot at a moment obtaining the data (see at least Cao P0023: The inertial measurement unit in the robot feeds back its own posture the examiner interprets posture as an example of pose); wherein the data obtained at each path point on the guide path in the operation scene includes at least one image or radar data frame (see at least Cao P0022: The laser radar in the robot scans the surrounding environment), and the at least one image or radar data frame obtained at each path point on the guide path in the operation scene includes one image or radar data frame as a key frame of the map (see at least Cao P0013: use the scanned lidar data and inertial measurement unit data to build map data that matches the substation environment and P0031: The robot needs to stop at the inspection point and perform inspection operations); and wherein after generating the map including the guide path by positioning and mapping during the robot being moved along the guide path in the operation scene (see at least Cao P0013: Control the robot to move along the road in the substation so that the lidar can scan all areas that need to be inspected), further comprising: updating a pose of the robot at each key frame of the map and a position of each path point on the guide path in the map (see at least Cao P0024: calculate the robot's position and direction in space the examiner interprets position and direction as an example of calculating location and viewing direction/frame view and P0070 – P0073: map data optimization: The substation environment map constructed by the above step S2 has some distorted and messy areas. These data are not conducive to the subsequent robot positioning processing. Therefore, the map data needs to be processed accordingly to eliminate isolated points of the map and adjust the distorted areas… Adjust the distorted areas and continuous interrupted areas in the substation environment map… Robot positioning: including preliminary positioning and precise positioning the examiner interprets processing and adjusting to eliminate distortions as an example of updating).
Cao teaches an initial map building process (see at least P0013) and adjustment of the map during the later operation (see at least P0014) but does not explicitly teach in response to a loop of the map being detected. However, this repetition would be obvious to one of ordinary skill in the art because it is common in the field of robotic monitoring to perform regular, repeated maintenance checks. Furthermore, Fong teaches in response to the loop of the map being detected (see at least C7 Ln20: by looping back, the robot is able to collect additional sensor data that can be used to update one or more previous grids and even modify sensor data used to populate the old version of the same grid).
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 Cao to incorporate the method of
Fong in the robotic mapping field of invention to use processor and memory resources
for the advantage of implementing on a plurality of platforms (see at least Fong C9
Ln16).
Regarding Claim 23, the combination of Cao, and Dooley teaches the limitations of claim 22 and after generating the map including the guide path by positioning and mapping during the robot being moved along the guide path in the operation scene (see at least Cao P0069: Control the robot to move along the road in the substation so that the lidar can scan all areas that need to be inspected, and then use the scanned lidar data and inertial measurement unit data to build map data that matches the substation environment),
Cao does not explicitly teach loop detection and control. However, Fong teaches updating a pose of the robot at each key frame of the map and the position of each path point on the guide path in the map (see at least Fong C7 Ln20: the robot is able to collect additional sensor data that can be used to update one or more previous grids and even modify sensor data used to populate the old version), in response to a loop of the map being detected (see at least Fong C7 Ln17: the trajectory of the mobile robot has circled back on itself. In doing so, the robot traverses an area that it previously traversed earlier in its trajectory).
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 Cao to incorporate the method of Fong in the robotic mapping field of invention to use repeated routing for the advantage of updating previous grids and creating cohesive maps (see at least Fong C7 Ln21 and C8 Ln23).
Regarding Claim 24, the combination of Cao, Dooley, and Fong teaches the limitations of claim 23 and Fong teaches before updating the pose of the robot at each key frame of the map and the position of each path point on the guide path in the map, in response to the loop of the map being detected, further comprising: establishing and storing a transformation relationship between the position of each path point on the guide path in the map and the pose of the robot at the key frame of the map that is obtained at each path point (see at least Fong C4 Ln54: the robotic system explores its environment, takes numerous images of its environment, makes a map depicting landmarks in the environment, and estimates the location of the robot relative to those landmarks. In the preferred embodiment, landmarks are visually identified using visual features from the image data are extracted and matched using a Scale Invariant Feature Transform (SIFT)); updating the pose of the robot at each key frame of the map and the position of each path point on the guide path in the map (see at least Fong C4 Ln7: Estimates of the locations of the anchor nodes within the global reference frame are continually updated as the SLAM module 134 refines the localization map characterizing the environment), in response to the loop of the map being detected includes: updating the pose of the robot at each key frame of the map, in response to the loop of the map being detected; and updating, according to the updated pose of the robot at each key frame of the map and the transformation relationship, the position of each path point on the guide path in the map (see at least Fong C7 Ln20: the robot is able to collect additional sensor data that can be used to update one or more previous grids and even modify sensor data used to populate the old version of the same grid. If the current pose of the robotic system is known with sufficient certainty relative to a prior pose, the anchor node associated with the prior pose is retrieved and the new sensor mapped to the grid associated with the prior anchor node and C8 Ln17: The localization system may update the estimated locations of the anchor nodes to generate an occupancy map, for example, in the global reference frame).
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 Cao to incorporate the method of Fong in the robotic mapping field of invention to use repeated routing for the advantage of updating previous grids and creating cohesive maps (see at least Fong C7 Ln21 and C8 Ln23).
11: Cao + Dooley + Baldwin
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Cao, Dooley, in further view of Baldwin.
Regarding Claim 11, the combination of Cao, Dooley teaches the limitations of claim 10 and generating the map including the guide path by positioning and mapping during the robot being moved along the guide path in the operation scene includes: obtaining, through a positioning sensor of the robot, positioning data during the robot being moved along the guide path in the operation scene, wherein the positioning sensor includes a dual laser radar; and generating the map including the guide path by processing the positioning data using a simultaneous localization and mapping technology (see at least Cao P0022: The laser radar scans the surrounding environment at a high frequency, thereby calculating the distance between the robot itself and the objects in the surrounding environment, and using the characteristics of the surrounding environment and map data to calculate the robot's own position and direction in the map).
Cao does not explicitly teach a dual laser radar. However, dual laser would be a simple and obvious substitution for the existing single laser. As taught by Baldwin, the Dual-LIDAR system exhibits less displacement to the reference trajectory on multiple loops than the INS (see at least Baldwin Pg2496 end of Section V). The substitution of a dual LIDAR system for Cao’s single LIDAR system requires minimal modification and yields predictable localization results, motivated by better maintained calibration and decreased displacement over time.
12: Cao + Dooley + Huo
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Cao, Groover, Nakagawa, and Fong, in further view of Huo.
Regarding Claim 12, the combination of Cao & Dooley teaches the limitations of claim 10 but does not explicitly teach a disinfection robot. However, Huo teaches the robot is a disinfection robot (see at least Huo P0010: controlling the cleaning robot to perform a cleaning operation the examiner interprets a cleaning robot as an example of a disinfection robot), and generating the plurality of operation points on the guide path in the map (see at least Huo P0014: The area to be cleaned is divided according to the preset distance, the measured distance and the current position to obtain a sub-area to be cleaned) includes: generating the plurality of operation points evenly on the guide path in the map at a preset interval (see at least Huo P0018: the line connecting the points extending from the vertex along the first set direction to the first preset distance).
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 Cao to incorporate the method of Huo in the robotic path planning and control field of invention to use a cleaning robot with preset cleaning intervals for the advantage of completing cleaning coverage of an entire area (see at least Huo P0011).
13: Cao + Dooley + Carlson
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Cao, Dooley, in further view of Carlson.
Regarding Claim 13, the combination of Cao and Dooley teaches the limitations of claim 10 and Cao teaches shortest path planning using Dijkstra or A* algorithms (see at least Cao P0078). Cao does not explicitly teach generating the operation path includes: generating the operation path using one of a Chinese Postman Problem algorithm and a Traveling Salesman Problem algorithm. However, the Traveling Salesman and Chinese Postman Problems are commonly known algorithms in the art for finding the shortest paths, and it would be obvious to combine them with the method of Cao to find the shortest path. Carlson, in Encyclopedia Brittanica further confirms the usage of these algorithms in their article on Graph Theory: Among the current interests in graph theory are problems concerning efficient algorithms for finding optimal paths (depending on different criteria) in graphs. Two well-known examples are the Chinese postman problem (the shortest path that visits each edge at least once), which was solved in the 1960s, and the traveling salesman problem (the shortest path that begins and ends at the same vertex and visits each edge exactly once), which continues to attract the attention of many researchers because of its applications in routing data, products, and people demonstrating the motivation to combine such algorithms for the advantage of using efficient algorithms to find optimal routing paths of products and people.
17: Cao + Dooley + Fong + Demir
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Cao, Dooley, and Fong, in further view of Demir.
Regarding Claim 17, the combination of Cao, Dooley and Fong teaches the limitations of claim 16 and Cao teaches before updating the pose of the robot at each key frame of the map and the position of each path point on the guide path in the map (see at least Cao P0024: calculate the robot's position and direction in space the examiner interprets position and direction as an example of calculating location and viewing direction/frame view and P0070 – P0073: map data optimization: The substation environment map constructed by the above step S2 has some distorted and messy areas. These data are not conducive to the subsequent robot positioning processing. Therefore, the map data needs to be processed accordingly to eliminate isolated points of the map and adjust the distorted areas… Adjust the distorted areas and continuous interrupted areas in the substation environment map… Robot positioning: including preliminary positioning and precise positioning the examiner interprets processing to eliminate distortions as an example of updating), in response to the loop of the map being detected, further comprising: establishing and storing a transformation relationship between the position of each path point on the guide path in the map and the pose of the robot at the key frame of the map that is obtained at each path point (see at least Cao P0066: using the characteristics of the surrounding environment and map data to calculate the robot's own position and direction in the map the examiner interprets this calculation as an example of a transformation); updating the pose of the robot at each key frame of the map and the position of each path point on the guide path in the map, in response to the loop of the map being detected includes: updating the pose of the robot at each key frame of the map (see at least Cao P0067: The inertial measurement unit in the robot feeds back its own posture: The inertial measurement unit detects the linear acceleration and angular velocity of the robot in the three directions of X/Y/Z in space in real time, and preliminarily calculates the robot's own posture information in space based on the integral and P0069: use the scanned lidar data and inertial measurement unit data to build map data that matches the substation environment), in response to the loop of the map being detected; and updating, according to the updated pose of the robot at each key frame of the map and the transformation relationship, the position of each path point on the guide path in the map (see at least Cao P0070 – P0072: the map data needs to be processed accordingly to eliminate isolated points of the map and adjust the distorted areas… Adjust the distorted areas and continuous interrupted areas in the substation environment map the examiner interprets processing and adjusting to eliminate distortions as an example of updating).
Cao teaches calculation of position and direction and adjustment of the map to remove distortions from previous scanning but does not explicitly teach a specific transformation calculation and storage. However, Demir teaches before updating the pose of the robot at each key frame of the map and the position of each path point on the guide path in the map (see at least Demir P0024: The HD map may be stored in a computer storage device of a vehicle and updated as needed), in response to the loop of the map being detected, further comprising: establishing and storing a transformation relationship between the position of each path point on the guide path in the map and the pose of the robot at the key frame of the map that is obtained at each path point (see at least Demir P0034: The Transition Map 144 may be a data structure that includes transformations between the LiDAR Map 142 and the HD Map 148 for a plurality of locations); updating the pose of the robot at each key frame of the map and the position of each path point on the guide path in the map, in response to the loop of the map being detected includes: updating the pose of the robot at each key frame of the map (see at least Demir P0033: The LiDAR Map 142 may contain the poses of the LiDAR system 152 with timestamp information), in response to the loop of the map being detected; and updating, according to the updated pose of the robot at each key frame of the map and the transformation relationship, the position of each path point on the guide path in the map (see at least Demir P0047: the Transition Map 144 may store a transformation for each of a plurality of grid locations in order to get a faster access to the desired transformation in the runtime. The transformation may be based on one or more transformations of identified features from the perspective of a vehicle in the grid location. The HD Localization module 230 may determine the transformation for the current location based on the grid location corresponding to the current location).
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 Cao to incorporate the method of Demir in the robotic path planning and control field of invention to store transformations for the advantage of a quicker runtime execution (see at least Demir P0047).
Cao teaches an initial map building process (see at least P0013) and adjustment of the map during the later operation (see at least P0014) but does not explicitly teach repeating the travel inspection process in a loop. However, this repetition would be obvious to one of ordinary skill in the art for the reasons described in Claim 16. Furthermore, Fong teaches in response to the loop of the map being detected (see at least C7 Ln20: by looping back, the robot is able to collect additional sensor data that can be used to update one or more previous grids and even modify sensor data used to populate the old version of the same grid).
18 & 20: Cao + Dooley + Shao
Claims 18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Cao, Groover, Nakagawa, and Fong, in further view of Shao.
Regarding Claim 18, the combination of Cao and Dooley teaches the limitations of claim 10 and Shao further teaches the manual control methods including instructions for receiving, through a human-computer interaction equipment of the robot, a first motion control instruction input by the user for moving the robot along the guide path in the operation scene according to the first motion control instruction (see at least Shao P0063: user inspecting the substation through remote control. The user can remotely control the inspection robot 33 to leave the magnetic track and walk freely and P0049: The base station system area 1 in the present invention exists as a control center, and the mobile station system area 2 arranged on the inspection robot is used to detect the comprehensive parameters of the substation and can communicate with the base station system area 1 the examiner interprets mobile station system area 2 as an example of human-computer interaction equipment of the robot); and instructions for receiving, by the robot, a second motion control instruction from a user terminal and moving the robot along the guide path in the operation scene according to the second motion control instruction (see at least Shao P0060: the inspection robot 33 can effectively communicate with the control center anywhere in the inspection site, interact with the control center, receive instructions and feedback information).
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 Cao to incorporate the method of manual remote control in the robotic control and substation inspection field of invention to Shao for the advantage of responding to emergency situations (see at least Shao P0063).
The limitation of claim 18 is considered disclosed as only one limitation is required to be performed per the claimed “at least one of” statement. As the prior art discloses as least receiving motion control instructions, further limiting steps of claim 18 are not required to be performed as it is conditional.
Regarding Claim 20, the combination of Cao and Dooley teaches the limitations of claim 19 but does not explicitly name the manual control methods. However, Shao teaches instructions for moving the robot, under the control of the manual guide method performed by the user (see at least Cao P0069: Control the robot to move along the road in the substation so that the lidar can scan all areas that need to be inspected), along the guide path in the operation scene further comprise: instructions for receiving, through a human-computer interaction equipment of the robot, a motion control instruction input by the user for moving the robot along the guide path in the operation scene according to the motion control instruction (see at least Shao P0063: user inspecting the substation through remote control. The user can remotely control the inspection robot 33 to leave the magnetic track and walk freely and P0049: The base station system area 1 in the present invention exists as a control center, and the mobile station system area 2 arranged on the inspection robot is used to detect the comprehensive parameters of the substation and can communicate with the base station system area 1 the examiner interprets mobile station system area 2 as an example of human-computer interaction equipment of the robot); and receiving, by the robot, a motion control instruction from a user terminal for moving the robot along the guide path in the operation scene according to the motion control instruction (see at least Shao P0060: the inspection robot 33 can effectively communicate with the control center anywhere in the inspection site, interact with the control center, receive instructions and feedback information).
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 Cao to incorporate the method of manual remote control in the robotic control and substation inspection field of invention to Shao for the advantage of responding to emergency situations (see at least Shao P0063).
The limitation of claim 20 is considered disclosed as only one limitation is required to be performed per the claimed “at least one of” statement. As the prior art discloses as least receiving motion control instructions, further limiting steps of claim 20 are not required to be performed as it is conditional.
Conclusion
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/KITO R ROBINSON/Supervisory Patent Examiner, Art Unit 3664