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 .
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.
Joint Inventors
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.
Response to Arguments and Amendments
Applicant’s arguments and amendments, filed April 15th, 2026, with respect to the rejections of claims 1-6, 8 and 12-21 under 35 U.S.C. 103 have been fully considered but they are not persuasive.
Applicant argues the combination of Watts and Pardi fails to teach or suggest controlling an arm of the mobile robot or its end effector based on the ceiling estimate, wherein controlling the arm or end effector involves simply avoiding collision with the ceiling. Examiner understands the Applicant’s emphasis on Pardi regarding the secondary reference being “not concerned with the typical formulation of the path-planning problem, i.e., finding a shortest collision-free path. Instead we wish to plan non-shortest paths which are optimal in other ways […]”. Examiner respectfully disagrees with the Applicant’s assertion that this portion of Pardi is necessarily leading away from the claimed invention. Examiner notes that even within the statement of Pardi concerning not finding a shortest collision-free path, it does not mention that it is avoiding finding collision-free paths altogether. While the Applicant emphasizes the “not concerned […] collision-free path” portion of Pardi, Examiner emphasizes the “shortest collision-free path […] non-shortest paths which are optimal in other ways” portion. Given the broadest reasonable interpretation, Pardi does not lead away from controlling the robot arm or end effector from avoiding collisions, which the Examiner further notes is not the basis for patentability in these claims.
Furthermore, as stated in the Advisory Action sent on March 25th, 2026, Examiner reiterates that the secondary reference of Pardi is utilized in combination with Watts to show the control of a mobile robot arm in response to environmental data. Controlling a robot arm involves simple data gathering and processing steps to determine the trajectory of the robot arm, a well-known concept in the art. Merely claiming a ceiling as the obstacle to avoid does not limit the scope of the invention as written. Examiner suggests incorporating functionality of the robot that is specific to and necessitates a ceiling of a container as the obstacle into the claim language to distinguish from common obstacle and collision-avoidance.
For at least for the reasons stated above, along with the primary reference of Watts clearly disclosing “planning collision-free trajectories for the one or more robotic arms” as stated in the rejections below, the 35 U.S.C. 103 rejections are maintained and updated to address amended limitations.
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.
Claims 1-6, 8, 12-14, 16-17 and 19-21 are rejected under 35 U.S.C. 103 as being obvious over Watts (US Patent No. 9,632,504 B1) in view of Pardi et al. (“Maximally Manipulable Vision-Based Motion Planning for Robotic Rough-Cutting on Arbitrarily Shaped Surfaces”), herein “Pardi”.
Regarding Claim 1, Watts discloses a method of estimating a ceiling location of a container within which a mobile robot is configured to operate (See Fig. 4C below and Col. 3 Lines 47-58, “[…] a robotic device may use contours of a surface of a building, such as a ceiling, to help with localization. In particular, the ceiling of a building in which the robotic device is operating may have varying depth texture, which may include different shapes, structures, and/or patterns […] a robotic device may perform warehouse operations requiring navigation within a warehouse that contains a ceiling […] may be used to help a robotic device determine its precise location within the building.”), the method comprising:
sensing distance measurement data associated with a ceiling of the container using one or more distance sensors arranged on an end effector of the mobile robot (See Fig. 2A and 4C below, and also Col. 4 Lines 1-30, “The initial contour map may be generated using one or more depth sensors […] a mapping robot may be assigned to navigate through the building with a stereo camera that collects depth images of the ceiling at different points […] sensor devices that provide enough distance measurements from the robot to different points on the ceiling to identify one or more of the surface contours.” See also Col. 10 Line 67 to Col. 11 Lines 1-4, “[…] may use one or more sensors attached to a robotic arm 202, such as sensor 206 and sensor 208, which may be two-dimensional (2D) sensors and/or 3D depth sensors […]”);
determining a ceiling estimate of the container based on the distance measurement data (See Col. 4 Lines 55-63, “[…] the robot may be able to use a rough estimate of its current position to determine a relatively small search area on the map of ceiling contours to look for matching contours […] first determining a rough estimate of the robot's location […] collect the sensor data for identifying ceiling contours.”).
wherein controlling the arm of the mobile robot and/or an end effector coupled to the arm of the mobile robot comprises controlling the arm and/or the end effector to move without colliding with the ceiling of the container (See Col. 10 Lines 49-52, “[…] reconstructed environment may then be used for identifying objects to pick up, determining pick positions for objects, and/or planning collision-free trajectories for the one or more robotic arms and/or a mobile base.”).
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But does not explicitly disclose controlling an arm of the mobile robot and/or an end effector coupled to the arm of the mobile robot based, at least in part, on the ceiling estimate.
Pardi, in a similar field of endeavor, teaches controlling an arm of the mobile robot and/or an end effector coupled to the arm of the mobile robot based, at least in part, on the ceiling estimate (See Abstract, “[…] method for constrained motion planning from vision, which enables a robot to move its end-effector over an observed surface, given start and destination points […] finding robot trajectories which maximize the robot’s manipulability throughout the motion, while also minimizing surface-distance travelled between the two points.” Examiner notes the motion planner uses visual surface observation to generate trajectories for the robot’s end-effector over an arbitrary surface, with emphasis on surface-based planning).
In view of Pardi’s teachings, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include, with the mobile robot measuring distances to building surfaces including ceilings and using representations of such surfaces for navigation, localization, and motion planning within a container or building environment as disclosed by Watts, the ability to use the distance measurements to generate and control trajectories of the robot’s arm or end-effector, with a reasonable expectation of success, since both robots rely on sensor-derived surface estimates and their combination predictably improves safety and motion planning in confined robotic environments.
Regarding Claim 2, Watts further discloses the method of claim 1, wherein the one or more distance sensors include a first distance sensor arranged on a first side of the end effector, the first distance sensor being configured to sense a first distance in a first direction (See Fig. 4C above and Col. 16 Lines 18-20, “[…] a first sensor 452 on the robot 450 may detect a number of points 464 as the first sensor 452 is directed at a particular orientation towards the ceiling 400.”), the method further comprising:
orienting, prior to sensing the distance measurement data, the first distance sensor such that the first direction is toward the ceiling of the container (See Fig. 4C and Col. 16 Lines 18-20 as referenced above).
Regarding Claim 3, Watts further discloses the method of claim 2, wherein the one or more distance sensors include a second distance sensor arranged on a second side of the end effector, the second distance sensor being configured to sense a second distance in a second direction, the second direction being opposite the first direction (See Fig. 4C above and Col. 16 Lines 34-48, “[…] a second sensor 454 on robot 450 may detect a separate set of points 462 while the second sensor 454 is oriented in a different direction towards ceiling 400.”),
wherein sensing distance measurement data comprises sensing distance measurement data including the first distance and the second distance (See Col. 16 Lines 38-41, “By orienting the second sensor 454 in a different upward direction, additional data points may be collected to identify additional contours for more precise localization.” Examiner notes the collection of “additional” contours to increase measurement precision means both the data from the first sensor and second sensor are being used together).
Regarding Claim 4, Watts further discloses the method of claim 2, wherein sensing distance measurement data comprises:
controlling the arm of the mobile robot to move the arm through a scan trajectory (See Col. 10 Lines 43-52, “The sensors may scan an environment containing one or more objects in order to capture visual data and/or three-dimensional (3D) depth information. Data from the scans may then be integrated into a representation of larger areas in order to provide digital environment reconstruction […] planning collision-free trajectories for the one or more robotic arms and/or a mobile base.” See also Col. 11 Lines 9-18, “[…] scans from one or more 2D or 3D sensors with fixed mounts on a mobile base, such as a front navigation sensor 216 and a rear navigation sensor 218, and one or more sensors mounted on a robotic arm, such as sensor 206 and sensor 208, may be integrated to build up a digital model of the environment, including the sides, floor, ceiling, and/or front wall of a truck or other container. Using this information, the control system may cause the mobile base to navigate into a position […]” Examiner notes the sensors on the robot arm scan the environment while operating and are able to integrate the information into trajectories for the mobile base or robot arm); and
sensing the distance measurement data as the arm is moved through the scan trajectory (See Col. 10 Lines 43-52 and Col. 11 Lines 9-18 as referenced above).
Regarding Claim 5, Watts further discloses the method of claim 4, wherein controlling an arm of the mobile robot to move the arm through a scan trajectory comprises moving the arm through a scan trajectory that includes a first direction and a second direction at an angle to the first direction (See Col. 15 Lines 4-12, “[…] a mobile robot may collect data from one or more sensors directed towards the surface as the robot is operating. Different types and numbers of sensors may be used […] the robot use may two or more fixed depth sensors oriented in different directions to provide different views of the surface for collection of depth data. For instance, a robot may be equipped with three laser rangefinders angled upward in order to collect data from different areas on the ceiling.” See also Col. 5 Lines 3-9, “[…] a robot may be equipped with one or more upward facing sensors that can change orientation when their view of the ceiling is obstructed as well […] one or more of the upward facing sensors may also periodically or continuously change orientation as the robot operates to collect more ceiling contour data for precise navigation.” Examiner notes the sensors on the robot arm able to continuously change orientation as the robot operates means it is capable of moving the arm through a scan trajectory including multiple directions at different angles).
Regarding Claim 6, Watts further discloses the method of claim 5, wherein the scan trajectory includes a first segment along the first direction, a second segment along the first direction, and a third segment along the second direction, the third segment connecting the first and second segments (See Col. 16 Lines 58-67 to Col. 17 Lines 8-39, “[…] contours in different areas on the ceiling or other surface may each be identified from sensor data collected by the robot from the different areas. Identifying multiple contours may provide improved precision as well as redundancy. Multiple contours may also enable accurate localization when the surface has certain repeating contours (e.g., resulting from a number of identical features that are positioned at multiple points on the surface) […] one contour corresponds to the outer boundary of lighting fixture 408. In this case, a contour line around the edge of lighting fixture 408 indicates both the area covered by the lighting fixture 408 on the depth map as well as the depth difference between lighting fixture 408 and the ceiling 400 directly above it. The contour line includes points of equal depth around the circle […] A surface contour follows this shape from one edge of the light to the other. Contours showing the changing surface depth from the lighting fixture 408, including the area, surface curves, and/or depth difference from the ceiling, may be used in order to align the detected points 464 at a position which corresponds to the contours on the depth map […] one contour corresponds to the outer boundary of air duct 402. A contour line around the boundary of air duct indicates both the width spanned by the air duct 402 on the depth map as well as the depth difference between air duct 402 and the ceiling 400 directly above it […] including the width, surface curves, and/or depth differences, may be used in order to align the detected points 462 at a position which corresponds to the contours on the depth map.” Examiner notes all the reference points collected with the sensors are aligned by connecting various segments of contours shown on the depth map. Each segment represents a distance/depth collected by a scan, and there exists multiple segments collected in multiple directions of the environment with reference to the ceiling above the detected area).
Regarding Claim 8, Watts further discloses the method of claim 1, wherein determining a ceiling estimate based on the distance measurement data comprises:
fitting a plane based on at least one datum in the distance measurement data (See Col. 13 Lines 35-57, “[…] depth map includes indications of depth at different points on the surface […] A surface contour includes a group of connected points on the surface representative of the depth of the surface at each of the points. More specifically, surface contours represent depth outlines of one or more features of a surface of the building. Surface features with irregular or curving shapes may be particularly helpful for robotic localization […] some of the contours may correspond to components attached to the surface […] some of the contours may correspond to the shape of the surface itself […] a building may have a curved or arching ceiling, or a ceiling with two or more planes aligned at different angles to one another. Some of the contours may be connected points with the same depth (e.g., the outline of the outer edge of a ceiling light). Other contours may be connected points with depth that changes in a linear way, or according to some predictable function (e.g., a part of a ceiling that curves with a consistent degree of curvature).” Examiner notes the measurement data takes into account the possibility of either a single plane or multiple planes to represent the surface contour of the ceiling, hence the generation of a depth map showing outlines at different points on the surface that may include more than one plane or other protruding shapes and features); and
determining the ceiling estimate based on the plane (See Col. 4 Lines 55-63 and Col. 13 Lines 35-57 as referenced above).
Regarding Claim 12, Watts further discloses the method of claim 8, wherein fitting a plane comprises fitting a flat plane to the at least one datum (See Col. 4 Lines 55-63 and Col. 13 Lines 35-57 as referenced above. Examiner notes the surface of the ceiling is the datum, and a flat ceiling is the same as fitting a flat plane to the datum since the robot is able to determine the surface to be lacking contours or depth).
Regarding Claim 13, Watts further discloses the method of claim 8, wherein fitting a plane comprises fitting a curved plane using at least two pieces of data of the distance measurement data (See Col. 13 Lines 49-57, “[…] may have a curved or arching ceiling, or a ceiling with two or more planes aligned at different angles to one another. Some of the contours may be connected points with the same depth (e.g., the outline of the outer edge of a ceiling light). Other contours may be connected points with depth that changes in a linear way, or according to some predictable function (e.g., a part of a ceiling that curves with a consistent degree of curvature).” See also Col. 17 Lines 20-25, “Contours showing the changing surface depth […] including the area, surface curves, and/or depth difference from the ceiling, may be used in order to align the detected points 464 at a position which corresponds to the contours on the depth map.” Examiner notes the degrees of curvature and angular alignment of two or more planes relative to each other are two pieces of data regarding a curved plane).
Regarding Claim 14, Watts further discloses the method of claim 8, wherein determining a ceiling estimate based on the distance measurement data further comprises assigning a shape primitive to the plane (See Col. 14 Lines 42-44, “[…] some contours may be identified within the depth map based on the shape or curvature of individual components or groups of connected components.” See also Col. 16 Lines 52-65, “[…] detected points that fall within one or more contours stored in the map may be identified by looking for matching shapes, curves, and/or changes in depth […] just one uniquely identifiable surface contour may be identified from sensor data and used to align the position of the robot with the depth map […] Identifying multiple contours may provide improved precision as well as redundancy. Multiple contours may also enable accurate localization when the surface has certain repeating contours (e.g., resulting from a number of identical features that are positioned at multiple points on the surface).”), and
controlling at least one operation of the mobile robot based, at least in part, on the shape primitive (See Col. 14 Lines 42-44 and Col. 16 Lines 52-65 as referenced above. Examiner notes controlling the robot to align with the depth map based on the shape or curvatures in the detected surface contour(s) is controlling an operation of the mobile robot).
Regarding Claim 16, Watts further discloses the method of claim 1, wherein controlling the arm and/or the end effector to move without colliding with the ceiling of the container comprises:
determining a trajectory of the arm of the mobile robot based, at least in part, on the ceiling estimate (See Fig. 2A and Col. 10 as referenced above); and
moving the arm in accordance with the trajectory (See Fig. 2A and Col. 10 as referenced above).
Regarding Claim 17, Watts further discloses the method of claim 1, wherein controlling the arm and/or the end effector to move without colliding with the ceiling of the container comprises:
determining a grasp strategy for grasping an object in the container based, at least in part, on the ceiling estimate (See Col. 11 as referenced above, as well as Lines 41-58, “[…] positioned within an environment such as a warehouse environment and used to pick up, move, and/or otherwise manipulate objects within reach […] may include a robotic arm 222 with an end-effector-mounted gripper 224, which may be of the same type as the robotic manipulator 202 and gripper 204 described with respect to the robotic truck unloader 200. The robotic arm 222 may be mounted on a pedestal 226, which may allow the robotic arm 222 to easily pick up and move nearby packages, such as to distribute packages between different mobile robots […] may include an actuator to allow a control system to change the height of the robotic arm 222.” Examiner notes manipulating the robot arm with the attached gripper to appropriately grasp an object is a grasp strategy, which uses the distance measurements gathered from the sensors when localizing the robot in the environment); and
grasping the object using the grasp strategy (See Col. 11 as referenced above).
Regarding Claim 19, Watts further discloses the method of claim 1, wherein the end effector comprises a suction-based gripper (See Col. 11 Lines 19-23, “[…] the robotic arm 202 may be equipped with a gripper 204, such as a digital suction grid gripper. In such embodiments, the gripper may include one or more suction valves […]”).
Regarding Claim 20, Watts further discloses a mobile robot, comprising:
a mobile base (See Fig. 2A above and Col. 10 Lines 51-52, “[…] planning collision-free trajectories for the one or more robotic arms and/or a mobile base.”);
an arm coupled to the mobile base (See Fig. 2A and Col. 10 Lines 51-52 as referenced above);
a gripper coupled to the arm, wherein the gripper includes one or more distance sensors arranged thereon (See Col. 10 Lines 53-55, “[…] may include a robotic arm 202 with a gripping component 204 for gripping objects […]” See also Col. 10 Line 67 to Col. 11 Lines 1-4, “[…] may use one or more sensors attached to a robotic arm 202, such as sensor 206 and sensor 208, which may be two-dimensional (2D) sensors and/or 3D depth sensors […]”); and
a controller configured to:
determine a ceiling estimate of a container based on distance measurement data sensed by the one or more distance sensors (See Col. 4 Lines 55-63 as referenced above. See also Col. 2 Lines 14-36, “[…] when executed by a control system of a robotic device, cause the control system to perform functions.”).
wherein controlling the arm and/or the gripper comprises controlling the arm and/or the gripper to move without colliding with the ceiling of the container (See Col. 10-11 as referenced above).
But does not explicitly disclose the controller configured to control the arm and/or the gripper based, at least in part, on the ceiling estimate.
Pardi, in a similar field of endeavor, teaches the controller configured to control the arm and/or the gripper based, at least in part, on the ceiling estimate.
In view of Pardi’s teachings, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include, with the mobile robot measuring distances to building surfaces including ceilings and using representations of such surfaces for navigation, localization, and motion planning within a container or building environment as disclosed by Watts, the ability to use the distance measurements to generate and control trajectories of the robot’s arm or end-effector, with a reasonable expectation of success, since both robots rely on sensor-derived surface estimates and their combination predictably improves safety and motion planning in confined robotic environments.
Regarding Claim 21, Watts further discloses a component of a mobile robot, the component comprising:
an arm configured to couple to a base of the mobile robot (See Fig. 2A, 4C along with Col. 4, Col. 10 and Col. 11 as referenced above); and
an end effector coupled to the arm, the end effector including one or more distance sensors (See Fig. 2A, 4C along with Col. 4, Col. 10 and Col. 11 as referenced above), wherein
the arm is configured to move through a scan trajectory (See Col. 10 Lines 43-52 and Col. 11 Lines 9-18 as referenced above),
the end effector is configured to orient a first distance sensor of the one or more distance sensors toward a ceiling of a container (See Fig. 4C and Col. 16 Lines 18-20 as referenced above),
the first distance sensor is configured to capture first distance measurements as the arm is moved through the scan trajectory (See Fig. 4C and Col. 16 Lines 18-20 as referenced above), and
a ceiling height of the container is determined based, at least in part, on the first distance measurements (See Col. 4 Lines 55-63 and Col. 13 Lines 35-57 as referenced above), and
the arm and/or the end effector is controlled to move without colliding with the ceiling of the container (See Col. 10-11 as referenced above).
But does not explicitly disclose the arm and/or the end effector is controlled to move without colliding with the ceiling of the container based, at least in part, on the ceiling height.
Pardi, in a similar field of endeavor, teaches the arm and/or the end effector is controlled to move without colliding with the ceiling of the container based, at least in part, on the ceiling height (See Abstract as referenced above).
In view of Pardi’s teachings, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include, with the mobile robot measuring distances to building surfaces including ceilings and using representations of such surfaces for navigation, localization, and motion planning within a container or building environment as disclosed by Watts, the ability to use the distance measurements to generate and control trajectories of the robot’s arm or end-effector, with a reasonable expectation of success, since both robots rely on sensor-derived surface estimates and their combination predictably improves safety and motion planning in confined robotic environments.
Claim 7 is rejected under 35 U.S.C. 103 as being obvious over Watts (US Patent No. 9,632,504 B1) in view of Pardi et al. (“Maximally Manipulable Vision-Based Motion Planning for Robotic Rough-Cutting on Arbitrarily Shaped Surfaces”) as applied to claim 1 above, and further in view of Cheah et al. (“MIRRAX: A Reconfigurable Robot for Limited Access Environments”).
Regarding Claim 7, Watts in view of Pardi teaches the method of claim 1, but does not explicitly teach wherein sensing distance measurement data is performed while a base of the mobile robot is outside of the container.
Cheah, in a similar field of endeavor, teaches sensing distance measurement data is performed while a base of the mobile robot is outside of the container (See Abstract, “[…] conventional mobile robots are unable to address the challenge of operating in extreme environments where the robot is required to traverse narrow gaps in highly cluttered areas with restricted access […] a robot designed to meet these challenges by way of its reconfigurable capability.” See also Pg. 1341, “[…] navigating through constrained and cluttered areas, and another is that access points may be restricted […]” Examiner emphasizes the design and control of this robot for approaching and entering narrow or restricted environments for inspection and mapping, demonstrating a conventional robotics strategy of approaching a confined space from outside and planning entry before physically entering).
In view of Cheah’s teachings, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include, with the mobile robot measuring distances to building surfaces including ceilings and using representations of such surfaces for navigation, localization, and motion planning of both the robot and it’s end effector within a container or building environment as disclosed by Watts in view of Pardi, the ability to perform sensing while the robot base is outside the container, with a reasonable expectation of success, since it is common practice in mobile robotics to perceive and approach cluttered or restricted access environments prior to and during operation. The stated need to traverse narrow gaps and restricted environments for the MIRRAX robot shows that environment sensing and mapping must occur as the robot approaches these regions, thus supporting the performance of distance measurements while the base remains outside prior to full entry. Therefore, combining this common robotics practice with a mobile robot already capable of distance sensing and ceiling estimation would have been obvious to allow sensing while the robot base remains outside the container for improved planning and control.
Claims 9-11 are rejected under 35 U.S.C. 103 as being obvious over Watts (US Patent No. 9,632,504 B1) in view of Pardi et al. (“Maximally Manipulable Vision-Based Motion Planning for Robotic Rough-Cutting on Arbitrarily Shaped Surfaces”) as applied to claims 1 and 8 above, and further in view of Gutmann et al. (EP Patent Pub. No. 2 776 216 B1), herein “Gutmann”.
Regarding Claim 9, Watts in view of Pardi teaches the method of claim 8, but does not explicitly teach wherein determining a ceiling estimate based on the distance measurement data further comprises:
determining the at least one datum as a datum having a minimum distance in the distance measurement data.
Gutmann, in a similar field of endeavor, teaches determining a ceiling estimate based on the distance measurement data (See 0011, “[…] indoor navigation which employs active beacons in the form of navigation cubes that project two patterns onto the ceiling in the area […]”) further comprises:
determining the at least one datum as a datum having a minimum distance in the distance measurement data (See 0247, “[…] after traveling a certain minimum distance and integrating a certain minimum number of measurements, it can be concluded that the tracking filter succeeded. The process can then switch back to Vector Field SLAM […]” See also 0279, “[…] a required minimum tracking distance also enforces an adequate minimum number of readings.”).
In view of Gutmann’s teachings, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include, with method of estimating a ceiling location using sensors attached to a robot arm as disclosed by Watts, the distance measurement data to include having a minimum distance, with a reasonable expectation of success, since the invention in Watts involves analyzing variations in detected distances to the ceiling at different points of measurement to determine contour outlines, so the system is already capable of measuring and comparing different distances to determine a minimum value, and it has been held that where the general conditions of a claim are disclosed in the prior art, discovering the working ranges involves only routine skill in the art.
Regarding Claim 10, Watts further discloses the method of claim 8, wherein determining a ceiling estimate based on the distance measurement data further comprises:
determining the ceiling estimate based on the filtered distance measurement data (See Col. 4 Lines 55-63 as referenced above).
But does not explicitly disclose filtering the distance measurement data to generate filtered distance measurement data.
Gutmann, in a similar field of endeavor, teaches filtering the distance measurement data to generate filtered distance measurement data (See 0247-0248, “[…] after traveling a certain minimum distance and integrating a certain minimum number of measurements, it can be concluded that the tracking filter succeeded […] the tracking filter ignores to covariances associated with the nodes in the map […] used over a short distance of robot travel and only to verify our pose hypothesis. Thus, the consistency of this filter is not a primary concern. A property of the filter is that we only need to maintain the robot pose […]”).
In view of Gutmann’s teachings, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include, with method of estimating a ceiling location using sensors attached to a robot arm as disclosed by Watts, the distance measurement data to include having filtered distance data, with a reasonable expectation of success, since the invention in Watts involves analyzing variations in detected distances to the ceiling at different points of measurement to determine contour outlines, so the system is already capable of measuring and comparing different distances to determine variations, and implementing a filter for statistical outliers in the gathered measurements improves the accuracy of the data over longer times and distances of operation.
Regarding Claim 11, Watts in view of Pardi does not explicitly teach the method of claim 10, wherein filtering the distance measurement data comprises:
sorting the distance measurement data in order of distance to generate sorted data; and
excluding from the filtered distance measurement data, a threshold amount of the sorted data with the smallest distances.
Gutmann, in a similar field of endeavor, teaches filtering the distance measurement data comprises:
sorting the distance measurement data in order of distance to generate sorted data (See 0095, “[…] initial sequence typically contains a minimum or relatively low number of sensor samples (e.g., 2 to 50) while the mobile device 100 moves a certain distance. This distance is usually proportional to the chosen cell size such that enough samples are available that cover a reasonable fraction of the cell […] for a cell size of 1 meter, the distance threshold may be selected within the range of 0.5 m to 1 meter. More generally, some embodiments may be configured to travel a distance of 1/3 to 2/3 of the cell size.”); and
excluding from the filtered distance measurement data, a threshold amount of the sorted data with a smallest distances (See 0245, “[…] distance falls above a threshold, i.e. D ( z t ) > Dmax, then the measurement z t is rejected. A typical threshold is D max = 3. Otherwise, the measurement z t is accepted, and the robot pose is updated […]”).
In view of Gutmann’s teachings, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include, with method of estimating a ceiling location using sensors attached to a robot arm as disclosed by Watts, the distance measurement data to be sorted and exclude a certain threshold of distances, with a reasonable expectation of success, since the invention in Watts involves analyzing variations in detected distances to the ceiling at different points of measurement to determine contour outlines, so the system is already capable of measuring and comparing different distances to determine variations, and excluding a threshold of distances can improve the operational efficiency of generating the depth map by eliminating irrelevant or outlying distance measurements that would not affect the robot’s function.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Bryant Tang whose telephone number is (571)270-0145. The examiner can normally be reached M-F 8-5 CST.
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/BRYANT TANG/Examiner, Art Unit 3658
/JASON HOLLOWAY/Primary Examiner, Art Unit 3658
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