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 .
Drawings
The drawings as submitted by Applicant on 12/07/2023 have been accepted.
Disposition of Claims
Claims 1-20 are pending in the instant application. No claims have been added. No claims have been cancelled. No claims have been amended. Claims 1-13 have been withdrawn from consideration. Claims 14-20 are rejected herein. The rejection of the pending claims is hereby made non-final.
Election/Restrictions
Applicant has elected claims 14-20 without traverse.
Double Patenting
The non-statutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A non-statutory double patenting rejection is appropriate where the claims at issue are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a non-statutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
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Claims 14-20 are rejected on the ground of non-statutory double patenting as being unpatentable over claims 1-7 of co-pending application No 18/808156. Although the claims at issue are not identical, they are not patentably distinct from each other because both applications are directed to systems and methods for the autonomous loading and unloading of pallets, as outlined below:
Application number 18/532783
Application number 18/808156
14. A system comprising: an autonomous forklift comprising: a plurality of sensors mounted on the autonomous forklift and forks configured to be inserted into pockets of a tail-adjacent pallet, the plurality of sensors being configured to obtain data comprising a geometry of a ramp, a location of the tail-adjacent pallet, and a location of pallet pockets in the tail-adjacent pallet; the ramp operatively connecting a trailer floor to a warehouse floor associated with operation of the autonomous forklift; and the tail-adjacent pallet being located on the trailer floor, wherein the tail-adjacent pallet is configured to be picked up and moved by the autonomous forklift using an inserting trajectory of the forks of the autonomous forklift, wherein a configuration of the forks of the autonomous forklift to pick up the tail-adjacent pallet is based on the geometry of the ramp and the location of the tail-adjacent pallet, and the location of pallet pockets in the tail-adjacent pallet.
15. The system of claim 14, wherein the plurality of sensors include an Inertial Measurement Unit (“IMU”), a Light Detection and Ranging (“LiDAR,”) a plurality of encoders, and a camera system.
16. The system of claim 14, wherein determining the configuration of the forks of the autonomous forklift comprises: detecting, using a computer processor and the plurality of sensors, the location of the tail-adjacent pallet and the location of pallet pockets of the tail-adjacent pallet; and adjusting, using the computer processor and based on the obtained data, a plurality of degrees of freedom the forks of the autonomous forklift to enable collision-free trajectory for insertion of the forks of the autonomous forklift into the pallet pockets, the plurality of degrees of freedom including a lift, a tilt, a side shift, and a spread of the forks.
17. The system of claim 14, wherein the geometry of the ramp comprises a fixed geometry of the ramp and a variable geometry of the ramp, wherein the fixed geometry of the ramp includes a length of the ramp, a width of the ramp, an angle between the ramp and a ramp lip, steepness of the ramp lip, and a shape of the ramp lip, and wherein the variable geometry of the ramp includes an angle of the ramp.
18. The system of claim 17, wherein the fixed geometry of the ramp is obtained, using a computer processor and a machine learning model, by generating a full surface contour model based on a plurality of sensor measurements.
19. The system of claim 17, wherein the fixed geometry of the ramp is obtained using manual measurements.
20. The system of claim 17, wherein the variable geometry of the ramp is obtained using the plurality of sensors mounted on the autonomous forklift.
1. An autonomous forklift, comprising: a controller; a fork assembly having an actuator coupled to the controller, the actuator configured to adjust the fork assembly; and a sensor coupled to the controller, the sensor positioned to capture (i) a view above a path of the autonomous forklift, and (ii) a view of a load, wherein the controller is configured to: (1) receive data from the sensor, (2) analyze the data to detect an obstacle above the path of the autonomous forklift, (3) analyze the data to determine a height of the obstacle, (4) analyze the data to determine if the obstacle is deformable or non-deformable, (5) analyze the data to determine a height of the load, (6) if the height of the load is greater than the height of the obstacle, then (i) command the autonomous vehicle to travel through the obstacle if the obstacle is deformable, or (ii) command the autonomous vehicle to avoid travel through the obstacle if the obstacle is non-deformable, and (7) if the height of the load is smaller than the height of the obstacle, command the autonomous vehicle to travel through the obstacle.
2. The system of claim 1, wherein the sensor includes at least two sensing devices, each sensing device selected from a group consisting of a Light Detection and Ranging (“LiDAR”) system and a camera.
3. The system of claim 1, wherein the controller is further configured to notify personnel if (i) the height of the load is greater than the height of the obstacle and (ii) the obstacle is non-deformable.
4. The system of claim 1, wherein the controller is further configured to analyze the data to detect a height of a surface traversed by the autonomous forklift.
5. The system of claim 4, wherein the controller is further configured to determine the height of the obstacle relative to the height of the surface traversed by the autonomous forklift.
6. The system of claim 1, wherein the controller is further configured to command the actuator to adjust a height or tilt of the fork assembly based on the height of the obstacle or based on the height of the load.
7. The system of claim 1, wherein the controller is further configured to command the actuator to adjust a height or tilt of the fork assembly based on a difference between the height of the obstacle and the height of the load.
The examiner submits that the language as recited in the pending application is similar to that as recited in co-pending application No 18/808156 as shown in the table above, which shows exemplary claims 14-20 of the pending application in view of claims 1-7 of co-pending application No 18/808156. This is a provisional non-statutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
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.
Claims 14-20 are rejected under 35 U.S.C. 103(a) as being unpatentable over Wehner et al (US 2023/0106126) in view Janekovic et al(US 2025/0128924).
Regarding claim 14, the prior art discloses a system comprising: an autonomous forklift comprising: a plurality of sensors mounted on the autonomous forklift (see at least paragraph [0024] to Wehner et al, wherein sensors (e.g., optical, radar, and ultrasonic sensors or rangefinders, etc.) that are located at the tip of each fork and/or adjacent to a mast that may be used to detect fork and vehicle location relative to cargo or dunnage) and forks configured to be inserted into pockets of a tail-adjacent pallet, , a location of the tail-adjacent pallet, and a location of pallet pockets in the tail-adjacent pallet (see at least paragraph [0031] to Wehner et al, wherein the vehicle may sense palletized cargo. The sensing of palletized cargo may use a one or a combination of sensors of the sensor suite, that may be used to generate a 3D image of the cargo that may be compared to 3D models 1230 to classify the cargo and detect proper lift orientation and lift points); the ramp operatively connecting a trailer floor to a warehouse floor associated with operation of the autonomous forklift (see at least paragraph [0040] to Wehner et al, wherein Ramp detection of an aircraft or vehicle ramp, cargo bay, or cargo door - which may be calculated based on LIDAR data to detect ramp edges); and the tail-adjacent pallet being located on the trailer floor, wherein the tail-adjacent pallet is configured to be picked up and moved by the autonomous forklift using an inserting trajectory of the forks of the autonomous forklift (see at least paragraph [0031] to Wehner et al, wherein the cargo that may be compared to 3D models 1230 to classify the cargo and detect proper lift orientation and lift points. In this example, the cargo is on a pallet that is located on dunnage, although non-palletized cargo may also be handled in other cases), wherein a configuration of the forks of the autonomous forklift to pick up the tail-adjacent pallet is based on the geometry of the ramp and the location of the tail-adjacent pallet, and the location of pallet pockets in the tail-adjacent pallet (see at least paragraphs [0040]-[0044] to Wehner et al, wherein there is ramp detection… and forklift conveyor speed and position - which may be calculated based on data from encoders, proximity sensors, pressure sensors, and/or any of the other data, as discussed above; [0045] Anti-tip system speed, position, and orientation - which may be calculated based on data from encoders, proximity sensors, pressure sensors, and/or any of the other data).
Wehner et al does not appear to explicitly disclose wherein the plurality of sensors being configured to obtain data comprising a geometry of a ramp.
However Janekovic et al discloses a model based autonomous mobile robot operation system and method, wherein the plurality of sensors being configured to obtain data comprising a geometry of a ramp (see at least paragraph [0096] to Janekovic et al, wherein he robot 140 receives a model of an environment, including models of each of the piecewise segments from the central communication system 130. The models including angles and geometries of each piecewise segment and their interconnections… warehouse floor, ramp segment 1, ramp segment 2, etc.). The robot 140 also determines how far it has traveled along a segment and where a next transition will occur).
The examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). The examiner submits that the combination of the teaching of the cargo transport system and method, as disclosed by Wehner et al and model based autonomous mobile robot operations system and method as taught by Janekovic, in order to enable autonomous operation of a mobile vehicle in a warehouse environment, could have been readily and easily implemented, with a reasonable expectation of success. As such, the aforementioned combination is found to be obvious to try, given the state of the art at the time of filing.
Regarding claim 15, the prior art discloses the system of claim 14, wherein the plurality of sensors include an Inertial Measurement Unit (“IMU”), a Light Detection and Ranging (“LiDAR,”) a plurality of encoders, and a camera system (see at least paragraph [0024] to Wehner et al, wherein sensors may include, for example, positioning sensors, Global Positioning System (GPS) sensors, inertial measurement units (IMUs), proximity detectors, cameras, stereographic imaging sensors, ultrasonic sensors, 3D flash LIDAR systems, LIDAR systems, and 3D Time of Flight (TOF) cameras, to name a few).
Regarding claim 16, the prior art discloses the system of claim 14, wherein determining the configuration of the forks of the autonomous forklift comprises: detecting, using a computer processor and the plurality of sensors (see at least paragraph [0024] to Wehner et al, wherein the suite uses a suite of sensors), the location of the tail-adjacent pallet and the location of pallet pockets of the tail-adjacent pallet (see at least paragraph [0032] to Wehner et al, wherein where a container has integrated lift points (e.g., for fork placement at the top, bottom, or sides of the container)); and adjusting, using the computer processor and based on the obtained data, a plurality of degrees of freedom the forks of the autonomous forklift to enable collision-free trajectory for insertion of the forks of the autonomous forklift into the pallet pockets, the plurality of degrees of freedom including a lift, a tilt, a side shift, and a spread of the forks (see at least paragraph [0031] to Wehner et al, wherein the vehicle may sense palletized cargo. The sensing of palletized cargo may use a one or a combination of sensors of the sensor suite, that may be used to generate a 3D image of the cargo that may be compared to 3D models 1230 to classify the cargo and detect proper lift orientation and lift points).
Regarding claim 17, the prior art discloses the system of claim 14, wherein the geometry of the ramp comprises a fixed geometry of the ramp and a variable geometry of the ramp, wherein the fixed geometry of the ramp includes a length of the ramp, a width of the ramp, an angle between the ramp and a ramp lip, steepness of the ramp lip, and a shape of the ramp lip, and wherein the variable geometry of the ramp includes an angle of the ramp (see at least paragraph [0096] and [0116] Janekovic et al).
Regarding claim 18, the prior art discloses the system of claim 17, wherein the fixed geometry of the ramp is obtained, using a computer processor and a machine learning model, by generating a full surface contour model based on a plurality of sensor measurements (see at least paragraphs [0024] to Wehner et al, wherein the term dense 3D sensor units may be used to refer to units that may provide data that may be used for 3D sensing around a cargo system, such as stereographic imaging sensors, ultrasonic sensors, 3D flash LIDAR, LIDAR, radar, and cameras coupled with image processing and recognition, for example. Further, aspects discussed herein may also have cargo detection and identification sensors, such as sensors (e.g., optical, radar, and ultrasonic sensors or rangefinders, etc.)).
Regarding claim 19, the prior art discloses the system of claim 17, wherein the fixed geometry of the ramp is obtained using manual measurements (see at least paragraph [0128] to Janekovic et al).
Regarding claim 20, the prior art discloses the system of claim 17, wherein the variable geometry of the ramp is obtained using the plurality of sensors mounted on the autonomous forklift (see at least paragraph [0036] to Wehner et al, wherein track angle - which may be calculated with data from one or more tilt sensors on the track of each propulsion unit).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
The examiner has considered all references listed on the Notice of References Cited, PTO-892.
The examiner has considered all references cited on the Information Disclosure Statement submitted by Applicant, PTO-1449.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TALIA F CRAWLEY whose telephone number is (571)270-5397. The examiner can normally be reached on Monday thru Thursday; 8:30 AM-4:30 PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Fahd A Obeid can be reached on 571-270-3324. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TALIA F CRAWLEY/ Primary Examiner, Art Unit 3627