DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The amendment filed 02/27/2026 is being entered. Claims 1-28 are canceled. Claims 29, 30, 31, 32, 37, 38, 39, 40, 47, 48, and 49 are amended. claims 29-50 are pending. Claims 29-50 are pending, and rejected as detailed below. This action is made final.
35 U.S.C. 112(b) Claim Rejections
Amendments to claims 30, 31, 38, and 39 are entered. Therefore the 35 U.S.C. 112(b) claim rejection for claims 30, 31, 38, and 39 have been withdrawn.
35 U.S.C. 112(d) Claim Rejections
Amendment to claim 49 is entered. Therefore the 35 U.S.C. 112(d) claim rejection for claim 49 has been withdrawn.
Response to Arguments
Claim Rejections under 35 U.S.C. §102
Applicant argues that Bukhari fails to disclose a drop zone obstruction detection system configured to "process at least some of the sensor data at the target drop zone to determine if an obstruction is detected within the volume of interest" as claimed in claims 29 and 37. The Office Action maps Bukhari to claims 29 and 37 based on its detection of rack elements and rack components and its determination of whether a rack volume is large enough to fit an element. However, Bukhari's 3-D / point-cloud teachings are used for element and rack-structure detection, available volume sizing within racks, and comparing the available volume above a region of interest to the volume of an element. See Bukhari para. [0044]. This is distinct from the determination of obstructions in the drop zone as claimed in claims 29 and 37, where an obstruction detection analysis at a "target drop zone" determines whether the volume of interest at the target drop zone contains obstructions. Given that Bukhari does not disclose this obstruction-detection analysis for a target drop zone, Bukhari fails to show every limitation of claims 29 and 37, as claimed, as required for anticipation.
Applicant also argues that Bukhari fails to disclose a drop zone obstruction detection method comprising "performing an object detection analysis based on the point cloud data to determine if there are obstructions in the drop zone" as claimed in claim 47. The Office Action maps Bukhari to claim 47 based on its detection of rack elements and rack components and its determination of whether a rack volume is large enough to fit an element. However, Bukhari's 3-D / point-cloud teachings are used for element and rack-structure detection, available volume sizing within racks, and comparing the available volume above a region of interest to the volume of an element. See Bukhari para. [0044]. This is distinct from the object detection analysis based on point cloud data to determine obstructions in the drop zone, as claimed in claim 47, where the determination is whether the drop zone contains obstructions. Given that Bukhari does not disclose this obstruction-detection analysis for a target drop zone, Bukhari fails to show every limitation of claim 47 as claimed, as required for anticipation.
Applicant’s arguments, "process at least some of the sensor data at the target drop zone to determine if an obstruction is detected within the volume of interest" as claimed in claims 29 and 37 and "performing an object detection analysis based on the point cloud data to determine if there are obstructions in the drop zone" as claimed in claim 47 under 35 U.S.C. §102 have been fully considered and not persuasive. More specifically, Bukhari explains that the volume of interest at the target drop zone is directly related to the obstruction components 94-97. In other words, each of the volume of interests in the rack 81 determined via the obstruction components 94-97 thus allowing the vehicle to place an element with one of the volume of interest in the rack 81. Furthermore, Bukhari (para. 0060) also disclose how the control system is able to determine each of the volume of interests in the rack 81 so that the control system can determine opened and closed volumes. In other words, when a robot fills up a first volume from each of the volume of interests in the rack 81 with a first element, the control system is able to determine that the first volume is now obstructed with the first element. As a result, subsequent elements will only able to be placed within the remaining volumes from each of the volumes of interests in the rack 81, wherein the first volume is no longer identified as one of the volume of interests in the rack 81. Therefore, it is inherent that the control system is required to determined obstruction(s) within each of the volume of interests so that available spaces can be recognized within the rack 81 to load subsequent elements.
Applicant also argues that Bukhari fails to disclose a "volume of interest" as claimed in claims 29 and 37. At most, Bukhari discloses detecting a volume based on 3D data within a first part of the region of interest and a lack of 3D data within a second part of the region of interest (see Bukhari para. [0044]). Whereas the "volume of interest" of the present invention, as claimed in claims 29 and 37, is based upon the payload size. See Specification para [0088]. While Bukhari detects a volume of available space and compares it to the dimensions of the element, Bukhari does not form the volume based upon the payload size. See Bukhari para [0044]. This is distinct from the "volume of interest" of the claimed invention, as claimed in independent claims 29 and 37, which is based upon the 3D size of the payload. See Specification para. [0088]; Fig 4. Given that the "volume of interest" of the invention as claimed in claims 29 and 37 is distinct from the volume formed from the region of interest in Bukhari, Bukhari fails to disclose a "volume of interest" as claimed in claims 29 and 37.
Applicant also argues Bukhari fails to disclose a "volume of interest" as claimed in claim 49. At most, Bukhari discloses detecting a volume based on 3D data within a first part of the region of interest and a lack of 3D data within a second part of the region of interest (see Bukhari para. [0044]). Whereas the "volume of interest" of the present invention, as claimed in claim 49, is generated based upon the payload size. See Specification para [0088]. While Bukhari detects a volume of available space and compares it to the dimensions of the element, Bukhari does not form the volume based upon the payload size. See Bukhari para [0044]. This is distinct from the "volume of interest" of the claimed invention, as claimed in independent claim 49, which is based upon the 3D size of the payload. See Specification para. [0088]; Fig 4. Given that the "volume of interest" of the invention as claimed in claim 49 is distinct from the volume formed from the region of interest in Bukhari, Bukhari fails to disclose a "volume of interest" as claimed in claim 49.
Applicant’s arguments, "volume of interest" as claimed in claims 29 and 37 and "volume of interest" as claimed in claim 49 under 35 U.S.C. §102 have been fully considered and not persuasive. More specifically, according to the BRI of claims of the instant application, claimed language does not specifically mentioned that the “volume of interest” is based upon the 3D size of the payload. However, claimed language states that “volume of interest” is generated at the identified target drop zone as Bukhari 0044 is able to anticipate the “volume of interest” of the instant application. In other words, it is improper to import claim limitations from the specification into the claims (see MPEP 2111.01(II)).
Applicant also argues that Claims 30-36, 38-46, and 48-50 depend on and contain the limitations of claims 29, 37, and 47, respectively, and are allowable for at least the same reasons as claims 29, 37, and 47, along with their own patentably distinct features.
Applicant’s arguments, in relation to dependent claims 30-36, 38-46, and 48-50 under 35 U.S.C. §102 have been fully considered and not persuasive as the independent claims 29, 37, and 47 are rejected according to Bukhari and aforementioned responses.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 29-50 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Bukhari (US 20210349468 A1).
Regarding claim 29, Bukhari teaches (Currently Amended) A robotic vehicle (Bukhari, at least one para. 0001; “This specification relates generally to examples of a mobile robot configured to detect elements in an environment.”), comprising:
a chassis and a manipulatable payload engagement portion (Bukhari, at least one para. 0031; “Robot 10 includes a body 12 having wheels (not shown) to enable robot 10 to travel across a surface, such as the floor of a warehouse, a factory, or other terrain. Robot 10 also includes a support area 15 configured to support the weight of an element, such as a pallet, a container, or any other device to be manipulated, using an end-effector 16.”);
sensors configured to acquire real-time sensor data (Bukhari, at least one para. 0038; “Referring also to the block diagram of FIG. 3, one or more sensors 36a, 36b, 36c, 36d, 36e, and 36f are located on robot 10 for use in detecting the location of the robot itself, for detecting an element to pick-up, and/or for detecting a location on which to place—for example, to stack—the element. ”); and
a drop zone obstruction detection system comprising computer program code executable by at least one processor to evaluate the sensor data to (Bukhari, at least one para. 0029; “A control system, which may include one or more processing devices examples of which are described herein, is configured—for example programmed—to control the end-effector, the robot body, or both the end-effector and the robot body to move in three or more degrees of freedom—for example, in at least four degrees of freedom—to stack the first element on top of the second element or to lift the first element off of the second element and move it away from the second element.”):
identify a target drop zone (Bukhari, at least one para. 0044; “The operations may include detecting a rack containing a volume in which to place or from which to remove an element, pointing a sensor above a bottom of the rack to a region of interest”);
generate a volume of interest (VOI) at the target drop zone (Bukhari, at least one para. 0044; “detecting the volume based on 3D data within a first part of the region of interest and a lack of 3D data within a second part of the region of interest, and determining, based on the 3D data and dimensions of the element, whether the volume is large enough to fit the element.”); and
process at least some of the sensor data at the target drop zone to determine if an obstruction is detected within the volume of interest (Bukhari, at least one para. 0049; “a rack, such as rack 81 of FIG. 6, may include vertical pillars such as 94, 95 and horizontal beams such as 96, 97. The techniques described herein may be used to detect those components, volumes bounded by those components, or both the components and volumes. The information—for example, data—representing the components, volumes, or both are used for detection, including identification.”, wherein components 94-97 are the obstructions) and (Bukhari, at least one para. 0060; “Operations 110 are described with respect to a rack and a pallet element; however, they may be performed for any appropriate type of volume that holds any appropriate type of element. Operations 110 include detecting (111) a rack containing the volume. The rack may be detected as described by executing operations 70 of FIG. 4. For example, a sensor on a robot captures image data representing components of the rack in a region of interest containing the rack; the image data is filtered to produce filtered data having less of an amount of data than the image data; components of the rack—such as horizontal and vertical beams—are identified by analyzing the filtered data using a deterministic process; and the rack is detecting based on the components. For example, the components are compared to predefined rack configurations in the database and the one that matches is identified as the rack. This information may be used to determine where to look for a volume in which to place an element. In this regard, the rack may be partially full and, therefore, there is still a need to identify regions of the rack that are available to receive an element.”).
Regarding claim 30, Bukhari teaches (Currently Amended) The vehicle of claim 29, wherein the drop zone obstruction detection system is configured to generate control signals to cause the payload engagement portion to drop a pallet in the target drop zone when no obstruction in the target drop zone is determined (Bukhari, at least one para. 0062; “Operations 110 include determining (114), based on the 3D data that was received and the lack thereof in a region and dimensions of the element, whether the volume is large enough to fit the element. In this regard, the dimensions of the element may be detected as described herein or may be programmed into the robot or otherwise known beforehand. The dimensions of the element are compared to the size of the volume. If the volume is larger than the element, then it is determined that the element will fit within the volume. The element may be aligned (116) within the volume.”).
Regarding claim 31, Bukhari teaches (Currently Amended) The vehicle of claim 29, wherein the drop zone obstruction detection system is configured to generate control signals to cause the payload engagement portion to hold a pallet when an obstruction in the target drop zone is determined (Bukhari, at least one para. 0062; “If the rack is empty, this may be done based on knowledge of the rack's geometry. If the rack is not empty—for example, the rack may contain a stack of pallets—the top pallet in the stack may be identified using operations 70 or any other appropriate methodology. The robot then moves (117) the element into the rack and places it at an appropriate location, for example, on the rack itself or a stack held in the rack.”).
Regarding claim 32, Bukhari teaches (Currently Amended) The vehicle of claim 29, wherein the drop zone obstruction detection system is configured to extract and segment at least one feature within the drop zone based on at least some of the sensor data to determine whether an obstruction is within the VOI (Bukhari, at least one para. 0049; “a rack, such as rack 81 of FIG. 6, may include vertical pillars such as 94, 95 and horizontal beams such as 96, 97. The techniques described herein may be used to detect those components, volumes bounded by those components, or both the components and volumes. The information—for example, data—representing the components, volumes, or both are used for detection, including identification.”, wherein each of the components 94-97 are the obstructions segments features).
Regarding claim 33, Bukhari teaches (Previously Presented) The vehicle of claim 29, wherein the robotic vehicle is an autonomous mobile robot forklift, an autonomous mobile robot tugger, or autonomous mobile pallet truck (Bukhari, at least one para. 0027; “Autonomous vehicles used as examples herein include mobile robots (or simply “robots”); however, any appropriate type of autonomous vehicle may be used including, but not limited to, self-driving machinery or stationary robots. The elements used as examples herein include pallets and containers; however, any appropriate types of elements may be used including, but not limited to, boxes, racks, crates, or bins. As noted, an example pallet includes a flat transport structure that supports goods during lifting. A pallet also includes a mechanism called a socket that the robot's end-effectors may enter and that are used to connect to, and to lift, the pallet. ”).
Regarding claim 34, Bukhari teaches (Previously Presented) The vehicle of claim 29, wherein the sensors include payload area sensors and/or fork tip sensors (Bukhari, at least one para. 0040; “sensors 36a, 36c may be located on, and movable with, the end-effector. A sensor located here enables the robot to detect and to image elements, such as a pallet or container, that are located directly in front of the end-effector. Data from such sensors enables the end-effector to identify sockets in the element and, therefore, facilitates entry of the end-effector into the sockets in the element. In some examples, one or more sensors may be located on the body of the robot. For example, one or more sensors 36d may be located at a mid-point of the robot, one or more sensors 36b may be located at a bottom of the robot, and/or one or more sensors 36e may be located above sensors 36d. One or more sensors 36f may be located at the top of the robot.”).
Regarding claim 35, Bukhari teaches (Previously Presented) The vehicle of claim 29, wherein the sensor data includes point cloud data (Bukhari, at least one para. 0051; “one or more of sensors 36a, 36b, 36c, 36d, 36e, or 36f may be directed at the element to obtain image data representing components of the element. In some implementations, the image data may be 3D data captured by sensors such as a 3D camera or multiple LIDAR sensors. In an example, the image data defines a point cloud.”).
Regarding claim 36, Bukhari teaches (Previously Presented) The vehicle of claim 29, wherein the drop zone is a floor, a drop table or conveyor, rack shelving, a top of a pallet already dropped, or a bed of an industrial cart (Bukhari, at least one para. 0060; “Operations 110 are described with respect to a rack and a pallet element; however, they may be performed for any appropriate type of volume that holds any appropriate type of element.”).
Regarding claim 37, Bukhari teaches (Currently Amended) A drop zone obstruction detection method for use by a robotic vehicle (Bukhari, at least one para. 0001; “This specification relates generally to examples of a mobile robot configured to detect elements in an environment.”), comprising:
providing a robotic vehicle having a chassis and a manipulatable payload engagement portion (Bukhari, at least one para. 0031; “Robot 10 includes a body 12 having wheels (not shown) to enable robot 10 to travel across a surface, such as the floor of a warehouse, a factory, or other terrain. Robot 10 also includes a support area 15 configured to support the weight of an element, such as a pallet, a container, or any other device to be manipulated, using an end-effector 16.”), sensors configured to acquire real-time sensor data (Bukhari, at least one para. 0038; “Referring also to the block diagram of FIG. 3, one or more sensors 36a, 36b, 36c, 36d, 36e, and 36f are located on robot 10 for use in detecting the location of the robot itself, for detecting an element to pick-up, and/or for detecting a location on which to place—for example, to stack—the element. ”), and a drop zone obstruction detection system comprising computer program code executable by at least one processor (Bukhari, at least one para. 0029; “A control system, which may include one or more processing devices examples of which are described herein, is configured—for example programmed—to control the end-effector, the robot body, or both the end-effector and the robot body to move in three or more degrees of freedom—for example, in at least four degrees of freedom—to stack the first element on top of the second element or to lift the first element off of the second element and move it away from the second element.”); and
the drop zone obstruction system:
identifying the target drop zone (Bukhari, at least one para. 0044; “The operations may include detecting a rack containing a volume in which to place or from which to remove an element, pointing a sensor above a bottom of the rack to a region of interest”);
generating a volume of interest (VOI) at the target drop zone (Bukhari, at least one para. 0044; “detecting the volume based on 3D data within a first part of the region of interest and a lack of 3D data within a second part of the region of interest, and determining, based on the 3D data and dimensions of the element, whether the volume is large enough to fit the element.”); and
processing at least some of the sensor data at the target drop zone to determine if an obstruction is detected within the volume of interest (Bukhari, at least one para. 0049; “a rack, such as rack 81 of FIG. 6, may include vertical pillars such as 94, 95 and horizontal beams such as 96, 97. The techniques described herein may be used to detect those components, volumes bounded by those components, or both the components and volumes. The information—for example, data—representing the components, volumes, or both are used for detection, including identification.”, wherein the components 94-97 are the obstructions).
Regarding claim 38, Bukhari teaches (Currently Amended) The method of claim 37, further comprising the drop zone obstruction detection system generating control signals to cause the payload engagement portion to drop the pallet in the target drop zone in response to determining that there is no obstruction in the target drop zone (Bukhari, at least one para. 0062; “Operations 110 include determining (114), based on the 3D data that was received and the lack thereof in a region and dimensions of the element, whether the volume is large enough to fit the element. In this regard, the dimensions of the element may be detected as described herein or may be programmed into the robot or otherwise known beforehand. The dimensions of the element are compared to the size of the volume. If the volume is larger than the element, then it is determined that the element will fit within the volume. The element may be aligned (116) within the volume.”).
Regarding claim 39, Bukhari teaches (Currently Amended) The method of claim 37, further comprising the drop zone obstruction detection system generating control signals to cause the payload engagement portion to hold a pallet in response to determining that there is at least one obstruction in the target drop zone (Bukhari, at least one para. 0062; “If the rack is empty, this may be done based on knowledge of the rack's geometry. If the rack is not empty—for example, the rack may contain a stack of pallets—the top pallet in the stack may be identified using operations 70 or any other appropriate methodology. The robot then moves (117) the element into the rack and places it at an appropriate location, for example, on the rack itself or a stack held in the rack.”).
Regarding claim 40, Bukhari teaches (Currently Amended) The method of claim 37, further comprising the drop zone obstruction detection system extracting and segmenting features within the drop zone based on at least some of the sensor data and determining whether an obstruction is within the VOI (Bukhari, at least one para. 0049; “a rack, such as rack 81 of FIG. 6, may include vertical pillars such as 94, 95 and horizontal beams such as 96, 97. The techniques described herein may be used to detect those components, volumes bounded by those components, or both the components and volumes. The information—for example, data—representing the components, volumes, or both are used for detection, including identification.”, wherein the components 94-97 are the obstructions segments features).
Regarding claim 41, Bukhari teaches (Previously Presented) The method of claim 37, wherein the robotic vehicle is an autonomous mobile robot forklift, an autonomous mobile robot tugger, or an autonomous mobile robot pallet truck (Bukhari, at least one para. 0027; “Autonomous vehicles used as examples herein include mobile robots (or simply “robots”); however, any appropriate type of autonomous vehicle may be used including, but not limited to, self-driving machinery or stationary robots. The elements used as examples herein include pallets and containers; however, any appropriate types of elements may be used including, but not limited to, boxes, racks, crates, or bins. As noted, an example pallet includes a flat transport structure that supports goods during lifting. A pallet also includes a mechanism called a socket that the robot's end-effectors may enter and that are used to connect to, and to lift, the pallet. ”).
Regarding claim 42, Bukhari teaches (Previously Presented) The method of claim 37, wherein the one or more sensors comprises at least one LiDAR scanner (Bukhari, at least one para. 0038; “robot 10 may include one or more 3D cameras, one or more light detection and ranging (LIDAR) scanners, one or more optical sensors, one or more sonar sensors, one or more time-of-flight (TOF) sensors, one or more radar sensors, one or more 2D camera sensors, one or more ultrasonic sensors, or any appropriate multiple numbers and/or combination thereof.”).
Regarding claim 43, Bukhari teaches (Previously Presented) The method of claim 37, wherein the one or more sensors comprises at least one stereo camera (Bukhari, at least one para. 0038; “robot 10 may include one or more 3D cameras, one or more light detection and ranging (LIDAR) scanners, one or more optical sensors, one or more sonar sensors, one or more time-of-flight (TOF) sensors, one or more radar sensors, one or more 2D camera sensors, one or more ultrasonic sensors, or any appropriate multiple numbers and/or combination thereof.”).
Regarding claim 44, Bukhari teaches (Previously Presented) The method of claim 37, wherein the sensors include payload area sensors and/or fork tip sensors (Bukhari, at least one para. 0040; “sensors 36a, 36c may be located on, and movable with, the end-effector. A sensor located here enables the robot to detect and to image elements, such as a pallet or container, that are located directly in front of the end-effector. Data from such sensors enables the end-effector to identify sockets in the element and, therefore, facilitates entry of the end-effector into the sockets in the element. In some examples, one or more sensors may be located on the body of the robot. For example, one or more sensors 36d may be located at a mid-point of the robot, one or more sensors 36b may be located at a bottom of the robot, and/or one or more sensors 36e may be located above sensors 36d. One or more sensors 36f may be located at the top of the robot.”).
Regarding claim 45, Bukhari teaches (Previously Presented) The method of claim 37, wherein the sensor data includes point cloud data (Bukhari, at least one para. 0051; “one or more of sensors 36a, 36b, 36c, 36d, 36e, or 36f may be directed at the element to obtain image data representing components of the element. In some implementations, the image data may be 3D data captured by sensors such as a 3D camera or multiple LIDAR sensors. In an example, the image data defines a point cloud.”).
Regarding claim 46, Bukhari teaches (Previously Presented) The method of claim 37, wherein the drop zone is a floor, a drop table or conveyor, rack shelving, a top of a pallet already dropped, or a bed of an industrial cart (Bukhari, at least one para. 0060; “Operations 110 are described with respect to a rack and a pallet element; however, they may be performed for any appropriate type of volume that holds any appropriate type of element.”).
Regarding claim 47, Bukhari teaches (Currently Amended) A drop zone obstruction detection method (Bukhari, at least one para. 0001; “This specification relates generally to examples of a mobile robot configured to detect elements in an environment.”) and (Bukhari, at least one para. 0029; “A control system, which may include one or more processing devices examples of which are described herein, is configured—for example programmed—to control the end-effector, the robot body, or both the end-effector and the robot body to move in three or more degrees of freedom—for example, in at least four degrees of freedom—to stack the first element on top of the second element or to lift the first element off of the second element and move it away from the second element.”), comprising:
providing mobile robot (Bukhari, at least one para. 0031; “Robot 10 includes a body 12 having wheels (not shown) to enable robot 10 to travel across a surface, such as the floor of a warehouse, a factory, or other terrain. Robot 10 also includes a support area 15 configured to support the weight of an element, such as a pallet, a container, or any other device to be manipulated, using an end-effector 16.”) with one or more sensors (Bukhari, at least one para. 0038; “Referring also to the block diagram of FIG. 3, one or more sensors 36a. 36b, 36c, 36d, 36e, and 36f are located on robot 10 for use in detecting the location of the robot itself, for detecting an element to pick-up, and/or for detecting a location on which to place—for example, to stack—the element.”);
identifying a target drop zone (Bukhari, at least one para. 0044; “The operations may include detecting a rack containing a volume in which to place or from which to remove an element, pointing a sensor above a bottom of the rack to a region of interest”);
using the one or more sensors (Bukhari, at least one para. 0038; “sensors 36a. 36b, 36c, 36d, 36e, and 36f ”), collecting point cloud data from locations at or near a drop zone (Bukhari, at least one para. 0051; “one or more of sensors 36a, 36b, 36c, 36d, 36e, or 36f may be directed at the element to obtain image data representing components of the element. In some implementations, the image data may be 3D data captured by sensors such as a 3D camera or multiple LIDAR sensors. In an example, the image data defines a point cloud.”);
performing an obstruction detection analysis based on the point cloud data to determine if there are obstructions in the drop zone (Bukhari, at least one para. 0049; “a rack, such as rack 81 of FIG. 6, may include vertical pillars such as 94, 95 and horizontal beams such as 96, 97. The techniques described herein may be used to detect those components, volumes bounded by those components, or both the components and volumes. The information—for example, data—representing the components, volumes, or both are used for detection, including identification.”, wherein the components 94-97 are the obstructions).
Regarding claim 48, Bukhari teaches (Currently Amended) The method of claim 47, further comprising generating a signal corresponding to a presence or absence of at least one obstruction in the target drop zone (Bukhari, at least one para. 0062; “Operations 110 include determining (114), based on the 3D data that was received and the lack thereof in a region and dimensions of the element, whether the volume is large enough to fit the element. In this regard, the dimensions of the element may be detected as described herein or may be programmed into the robot or otherwise known beforehand. The dimensions of the element are compared to the size of the volume. If the volume is larger than the element, then it is determined that the element will fit within the volume. The element may be aligned (116) within the volume. If the rack is empty, this may be done based on knowledge of the rack's geometry. If the rack is not empty—for example, the rack may contain a stack of pallets—the top pallet in the stack may be identified using operations 70 or any other appropriate methodology. The robot then moves (117) the element into the rack and places it at an appropriate location, for example, on the rack itself or a stack held in the rack.”).
Regarding claim 49, Bukhari teaches (Currently Amended) The method of claim 47, wherein performing the obstruction detection analysis further comprises:
determining boundaries of the target drop zone by extracting features from the point cloud data (Bukhari, at least one para. 0049; “a rack, such as rack 81 of FIG. 6, may include vertical pillars such as 94, 95 and horizontal beams such as 96, 97. The techniques described herein may be used to detect those components, volumes bounded by those components, or both the components and volumes. The information—for example, data—representing the components, volumes, or both are used for detection, including identification. (wherein 94-97 are the obstructions segments features)”);
determining a volume of interest (VOI) at the target drop zone (Bukhari, at least one para. 0044; “detecting the volume based on 3D data within a first part of the region of interest and a lack of 3D data within a second part of the region of interest, and determining, based on the 3D data and dimensions of the element, whether the volume is large enough to fit the element.”); and
comparing the VOI to the boundaries of the drop zone to determine the presence or absence of potential obstructions in the target drop zone (Bukhari, at least one para. 0062; “Operations 110 include determining (114), based on the 3D data that was received and the lack thereof in a region and dimensions of the element, whether the volume is large enough to fit the element. In this regard, the dimensions of the element may be detected as described herein or may be programmed into the robot or otherwise known beforehand. The dimensions of the element are compared to the size of the volume. If the volume is larger than the element, then it is determined that the element will fit within the volume. The element may be aligned (116) within the volume. If the rack is empty, this may be done based on knowledge of the rack's geometry. If the rack is not empty—for example, the rack may contain a stack of pallets—the top pallet in the stack may be identified using operations 70 or any other appropriate methodology. The robot then moves (117) the element into the rack and places it at an appropriate location, for example, on the rack itself or a stack held in the rack.”).
Regarding claim 50, Bukhari teaches (Previously Presented) The method of claim 47, further comprising determining if an object to be delivered fits within the drop zone based on a comparison of dimensions of the object and the obstruction detection analysis (Bukhari, at least one para. 0062; “Operations 110 include determining (114), based on the 3D data that was received and the lack thereof in a region and dimensions of the element, whether the volume is large enough to fit the element. In this regard, the dimensions of the element may be detected as described herein or may be programmed into the robot or otherwise known beforehand. The dimensions of the element are compared to the size of the volume. If the volume is larger than the element, then it is determined that the element will fit within the volume.”).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to UPUL P CHANDRASIRI whose telephone number is (703)756-5823. The examiner can normally be reached M-F 8.30 am to 5pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Christian Chace can be reached at 571-272-4190. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/U.P.C./ Examiner, Art Unit 3665 /CHRISTIAN CHACE/Supervisory Patent Examiner, Art Unit 3665
Prosecution Insights