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 .
This is a Non-Final rejection on the merits of this application. Claims 16-33 are currently pending, as discussed below.
Examiner Notes that the fundamentals of the rejections are based on the broadest reasonable interpretation of the claim language. Applicant is kindly invited to consider the reference as a whole. References are to be interpreted as by one of ordinary skill in the art rather than as by a novice. See MPEP 2141. Therefore, the relevant inquiry when interpreting a reference is not what the reference expressly discloses on its face but what the reference would teach or suggest to one of ordinary skill in the art.
Information Disclosure Statement
The information disclosure statement (IDS) filed on 10/29/2025 is being considered by the examiner.
Priority
Acknowledgement is made that the present application is a national stage entry of PCT/EP2024/056501 filed on 03/12/2024 which claims foreign priority to Japanese Patent Application to DE10 2023 106 517.3 filed on 03/15/2023.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 16-25, 27 and 29-33 are rejected under 35 U.S.C. 103 as being unpatentable over Murli et al. (US 2023/0174358 A1 hereinafter Murli) in view of Thode et al. (US 2020/0247612 A1 hereinafter Thode).
Regarding Claim 16, Murli teaches A method for automatically controlling an industrial truck when entering a rack aisle and when driving along at least one rack of a racking system (see at least Abstract), the method comprising:
driving over a predefined entry region of at least one rack aisle; (see at least Fig. 8-10 [0063-0082]: At step 802, an operator can control the MHV 104 to approach an aisle, e.g. the operator may maneuver the MHV 104 to be within a certain proximity to a side of an aisle and/or to be at angle with respect to the aisle that is less than a predetermined angle limit.)
detecting an environment adjacent to the industrial truck via at least one optical and/or run-time measuring sensor; (see at least Fig. 2 , 8-10 [0041-0042, 0063-0082]: The sensor unit 160 may be installed on the MHV 103 and includes one or more sensors 172 that can be configured to detect objects in the surrounding environment, in particular, storage racking, objects being stored in the storage racking, walks and the like. The sensor may comprises, e.g., 3D lidar sensor, 3D depth camera, radar, stereo cameras, time of flight sensors, ultrasonic sensors, or the like. At step 804, the guidance system 100 can be activated to automatically detect an aisle nearby and may acquire and process sensor output from the sensor unit 160 to determine the absolute/relative location of one or more aisle features with respect to the MHV 104.) and
upon a detection of at least one predefined feature of an entrance into a selected rack aisle of the at least one rack aisle, enabling an automatic alignment of the industrial truck into a predefined position relative to the selected rack aisle. (see at least Fig. 8-10 [0032, 0043-0052, 0063-0082]: At step 804, the guidance system 100 can be activated to automatically detect an aisle nearby and may acquire and process sensor output from the sensor unit 160 to determine the absolute/relative location of one or more aisle features (e.g. crossbeam, proxy crossbeam, wall, railing, or other features that may define an aisle) with respect to the MHV 104. The guidance system may determine the distance of the left and/or right side of the MHV from the aisle features on the left and/or right side of the MHV 104. The Guidance system may perform path planning to determine a guidance instruction to guide the MHV onto the desired travel path (e.g. desired position with respect to the aisle features) through the aisle and navigate the MHV within the aisle by centering the MHV approximately equidistant from each of the respective sides of the aisle, aligning the MHV offset from the center of the aisle by a predefined distance, aligning the MHV at a minimum, or an offset from the minimum, distance from a side of the aisle, or the like.. The MHV may be a fully autonomously AGV vehicle.)
Examiner notes that Murli discloses that when the material handling vehicle detects external object via onboard sensors to identify aisle features to generate an aisle model and subsequently control a steering angle of the material handling vehicle based on the detected location of aisle feature (but not expressly a predefined feature). For more clarification, Examiner further introduces prior art Thode, who is directed to system and method for providing and updating localization for industrial vehicles based on a racking systems in a warehouse environment, Thode teaches upon a detection of at least one predefined feature of an entrance into a selected rack aisle of the at least one rack aisle, enabling an automatic alignment of the industrial truck into a predefined position relative to the selected rack aisle. (see at least Fig. 14-15 [0172-0184]: A position of the vehicle is determined at an end of aisle through capturing images of the rack leg identifiers 302 (i.e. predefined features of an entrance of rack aisle) on rack legs at aisle ends to update a position of the vehicle. The vehicle position processor is configured to (i) generate end-of-aisle positional data and aisle-specific rack leg spacing data from an image of an aisle entry identifier 302, as captured by the camera 304, (ii) generate an initial position of the materials handling vehicle along the inventory transit surface using the end-of-aisle positional data, and (iii) generate an expected position of the materials handling vehicle as the vehicle travels down a racking system aisle corresponding to the aisle entry identifier from the end-of-aisle positional data and the aisle-specific rack leg spacing data.)
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Murli’s material handling vehicle’s guidance system and method to incorporate the technique of enabling an automatic alignment of the industrial truck into a predefined position relative to the selected rack aisle upon a detection of at least one predefined feature of an entrance into a selected rack aisle of the at least one rack aisle as taught by Thode with reasonable expectation of success to improve positioning accuracy, faster and more reliable operations and reduce operator dependency.
Regarding Claim 17, The combination of Murli in view of Thode teaches The method as recited in claim 16, wherein,
Murli further teaches the environment adjacent to the industrial truck comprises at least one object which is/are located adjacent to the industrial truck, (see at least Fig. 5A-C [0043]: The guidance system 100 may be configured to determine the position of externa objects within a field of view of the sensor 172 and provide guidance instructions to the MHV 104 according to a determined distance from such objects.) and
the at least one predefined feature is a geometric feature. (see at least Fig. 8-10 [0032, 0043-0052, 0063-0082]: At step 804, the guidance system 100 can be activated to automatically detect an aisle nearby and may acquire and process sensor output from the sensor unit 160 to determine the absolute/relative location of one or more aisle features (e.g. crossbeam, proxy crossbeam, wall, railing, or other features that may define an aisle) with respect to the MHV 104.
Regarding Claim 18, The combination of Murli in view of Thode teaches The method as recited in claim 17,
Murli further teaches wherein the at least one object which is/are located adjacent to the industrial truck is a lateral boundary of the at least one rack aisle. (see at least Fig. 5A-C [0053, 0061]: FIG. 5 A representatively illustrates an environment comprising the side of an aisle having racking and various objects stored therein, from the perspective of a sensor 172 mounted to a MHV 104 , and FIG. 5 B illustrates a 3D point cloud of the detected environment as generated by a LiDAR sensor, color coded based on distance from the sensor. The guidance system 100 may determine the distance of a reference frame (e.g., a plane passing through the predetermined MHV 104 reference point and parallel with a respective side of the MHV 104 ) from the one or more determined objects (e.g., a crossbeam) as the MHV 104 moves along the aisle.)
Regarding Claim 19, The combination of Murli in view of Thode teaches The method as recited in claim 17, wherein, after the environment adjacent to the industrial truck has been detected via the detection of the at least one object, the method further comprises:
Murli further teaches determining or ascertaining a distance and/or a position of the industrial truck relative to the at least one object detected. (see at least Fig. 8-10 [0032, 0043-0052, 0063-0082]: At step 804, the guidance system 100 can be activated to automatically detect an aisle nearby and may acquire and process sensor output from the sensor unit 160 to determine the absolute/relative location of one or more aisle features (e.g. crossbeam, proxy crossbeam, wall, railing, or other features that may define an aisle) with respect to the MHV 104. The guidance system may determine the distance of the left and/or right side of the MHV from the aisle features on the left and/or right side of the MHV 104. The Guidance system may perform path planning to determine a guidance instruction to guide the MHV onto the desired travel path (e.g. desired position with respect to the aisle features) through the aisle and navigate the MHV within the aisle by centering the MHV approximately equidistant from each of the respective sides of the aisle, aligning the MHV offset from the center of the aisle by a predefined distance, aligning the MHV at a minimum, or an offset from the minimum, distance from a side of the aisle, or the like.. The MHV may be a fully autonomously AGV vehicle. )
Regarding Claim 20, The combination of Murli in view of Thode teaches The method as recited in claim 17, wherein, after or during the detecting of the environment adjacent to the industrial truck, the method further comprises:
Murli further teaches effecting a computer-aided generation of a segmentation of the environment detected and/or of a segmentation of the at least one object of the environment detected. (see at least [0057-0060]: the guidance system 100 may be configured (e.g., via one or more operating routines stored in the processor unit 164 memory) to carry out one or more methods (e.g., pattern recognition algorithms, machine learning models, or the like) on the transformed sensor output to differentiate between and/or identify crossbeams of the storage racking, uprights of the storage racking, the objects being stored in the storage racking, walls, and the like.)
Regarding Claim 21, The combination of Murli in view of Thode teaches The method as recited in claim 20,
Murli further teaches where the effecting of the computer- aided generation comprises creating and saving the segmentation of the environment detected and/or of the segmentation of the at least one object of the environment detected. (see at least Fig. 8-10 [0032, 0043-0052, 0063-0082]: At step 812 , the guidance system 100 may perform pattern recognition on the transformed sensor output, for example as described above, to identify one or more aisle features. In some embodiments, the guidance system 100 may identify and discard sensor output representing the floor. If an aisle feature is identified, then the guidance system 100 may, at step 814 , create a virtual (i.e., software-based) model of the aisle (herein referred to as an “aisle model”). In some embodiments, the aisle model may be stored in the memory of the processor unit 164 . The aisle model may comprise information corresponding to the aisle feature. The modeled aisle feature may be referred to herein as a “projected reference.” The aisle model may comprise information such as the type of aisle feature (horizontal crossbeam, vertical crossbeam, proxy crossbeam, etc.) being modeled, the number of projected references, the location along the Y-axis or Z-axis of each projected reference (e.g., height of a projected reference representing a horizontal crossbeam), start position of each projected reference, slope of each projected reference, figure of merit of each projected reference and/or the model, confidence interval of each projected reference and/or the model, or the like.)
Regarding Claim 22, The combination of Murli in view of Thode teaches The method as recited in claim 20,
Murli further teaches wherein the effecting of the segmentation is performed via setting coordinates and/or via measurement points. (see at least Fig. 8-10 [0032, 0043-0052, 0063-0082]: Each new distance determination may require a new sensor output transform, as described above. In some embodiments, the sensor output may comprise a 3D point cloud (e.g., from a LiDAR sensor) having hundreds of thousands of points for each LiDAR scan. Transforming and/or performing feature identification (e.g., pattern matching) using all of the sensor data for each scan, such as all the points in the point cloud, may be compute intensive and may result in a lower rate of coordinate transform and/or feature identification and thus a lower rate of distance determination and guidance updates. Sensor output from a plurality of sensors 172 may be transformed to a uniform reference coordinate system (e.g., an MHV reference frame), individual reference frames for each sensor 172 (e.g., reference frames oriented on the left side and right side of the MHV 104 ), or any other suitable reference frame(s) from which the guidance system 100 can determine the absolute or relative location of one or more aisle features with respect to the MHV 104.)
Regarding Claim 23, The combination of Murli in view of Thode teaches The method as recited in claim 17, wherein, after the at least one object of the environment has been detected, the method further comprises:
Murli further teaches effecting a computer-aided generation of a grouping of control-relevant environment objects and non-control-relevant environment objects. (see at least Fig. 8-10 [0032, 0043-0052, 0063-0082]: the guidance system 100 may be configured to carry out one or more methods (e.g., pattern recognition algorithms, machine learning models, or the like) on the transformed sensor output to differentiate between and/or identify crossbeams of the storage racking, uprights of the storage racking, the objects being stored in the storage racking, walls, and the like. The guidance system 100 may perform pattern recognition on the transformed sensor output to identify one or more aisle features and may identify and discard sensor output representing the floor.)
Regarding Claim 24, The combination of Murli in view of Thode teaches The method as recited in claim 16,
Murli further teaches wherein the at least one optical and/or run-time measuring sensor via which the detecting of the environment is performed is a camera, a lidar, an electromagnetic radar-sensor, and/or an acoustic ultrasonic sensor. (see at least Fig. 2 , 8-10 [0041-0042]: The sensor unit 160 may be installed on the MHV 103 and includes one or more sensors 172 that can be configured to detect objects in the surrounding environment, in particular, storage racking, objects being stored in the storage racking, walks and the like. The sensor may comprises, e.g., 3D lidar sensor, 3D depth camera, radar, stereo cameras, time of flight sensors, ultrasonic sensors, or the like.)
Regarding Claim 25, The combination of Murli in view of Thode teaches The method as recited in claim 16, wherein, immediately following the enabling of the automatic alignment of the industrial truck into the predefined position relative to the selected rack aisle, the method further comprises:
Murli further teaches effecting an automatic implementation of the enabled automatic alignment, or indicating the enabling of the automatic alignment to an operator of the industrial truck so that the operator, via a control command, can manually select the automatic alignment of the industrial truck. (see at least Fig. 8-10 [0032, 0043-0052, 0063-0082]: if an aisle feature is identified and an aisle model is successfully created, the guidance system 100 may, at step 816 , remove the speed limit of step 806 or otherwise relinquish control of the speed of the MHV 104 to the operator, AGV (e.g., via an autonomous operation module 450 ), warehouse management system, or the like. As the operator continues moving the MHV 104 toward or into the aisle, the guidance system 100 can then navigate the MHV 104 within the aisle, for example centering the MHV 104 approximately equidistant from each of the respective sides of the aisle, aligning the MHV 104 offset from the center of the aisle by a predefined distance, aligning the MHV 104 at a minimum, or an offset from the minimum, distance (e.g., a buffer distance) from a side of the aisle, or the like. In some embodiments, the operator may control the speed of the MHV 104 while the guidance system 100 aligns the MHV 104 within the aisle.)
Regarding Claim 27, The combination of Murli in view of Thode teaches The method as recited in claim 16, wherein, upon the detection of the at least one predefined feature of the entrance, the method further comprises:
Murli further teaches determining or ascertaining a distance of the industrial truck from the entrance detected. (see at least [0043]: The guidance system 100 may be configured to determine the position of external objects within a field of view of the sensor 172 and provide guidance instructions to the MHV 104 according to a determined distance from such objects. In some embodiments, the sensor 172 can detect the relative position of storage racking, objects stored in the storage racking, walls, rails, and other objects that can define an aisle along which the MHV 104 can travel. Determining the position of external objects such as storage racking, walls, etc., may include classifying detected objects as a specific type of object, for example, classifying a detected object as storage racking or a crossbeam. The sensor 172 can be used for both aligning the MHV 104 with an aisle (i.e., prior to entering the aisle) and for keeping the MHV 104 centered or otherwise aligned within the aisle as it moves along the aisle.)
Regarding Claim 29, The combination of Murli in view of Thode teaches The method as recited in claim 16, wherein, after the detection of the least one predefined feature of the entrance into the at least one rack aisle and the enabling of the automatic alignment of the industrial truck into the predefined position relative to the selected rack aisle, the method further comprises:
Murli further teaches automatically driving the industrial truck into the selected rack aisle. (see at least Fig. 8-10 [0032, 0043-0052, 0063-0082]: if an aisle feature is identified and an aisle model is successfully created, the guidance system 100 can then navigate the MHV 104 within the aisle, for example centering the MHV 104 approximately equidistant from each of the respective sides of the aisle, aligning the MHV 104 offset from the center of the aisle by a predefined distance, aligning the MHV 104 at a minimum, or an offset from the minimum, distance (e.g., a buffer distance) from a side of the aisle, or the like. In some embodiments, the operator may control the speed of the MHV 104 while the guidance system 100 aligns the MHV 104 within the aisle.)
Regarding Claim 30, The combination of Murli in view of Thode teaches The method as recited in claim 16, wherein, after the detection of the least one predefined feature of the entrance into the selected rack aisle, the method further comprises:
Murli further teaches determining or ascertaining a center line which extends along a center of the selected rack aisle; and/or determining or ascertaining a predefined distance from a rack wall of the selected rack aisle. (see at least Fig. 8-10 [0032, 0043-0052, 0063-0082]: At step 804, the guidance system 100 can be activated to automatically detect an aisle nearby and may acquire and process sensor output from the sensor unit 160 to determine the absolute/relative location of one or more aisle features (e.g. crossbeam, proxy crossbeam, wall, railing, or other features that may define an aisle) with respect to the MHV 104. The guidance system may determine the distance of the left and/or right side of the MHV from the aisle features on the left and/or right side of the MHV 104. The Guidance system may perform path planning to determine a guidance instruction to guide the MHV onto the desired travel path (e.g. desired position with respect to the aisle features) through the aisle and navigate the MHV within the aisle by centering the MHV approximately equidistant from each of the respective sides of the aisle, aligning the MHV offset from the center of the aisle by a predefined distance, aligning the MHV at a minimum, or an offset from the minimum, distance from a side of the aisle, or the like.. The MHV may be a fully autonomously AGV vehicle. )
Regarding Claim 31, The combination of Murli in view of Thode teaches The method as recited in claim 16, wherein, after or during the detecting of the environment, the method further comprises:
Murli further teaches effecting a computer-aided generation of a time-current local map of the environment. (see at least [0084]: the guidance system 100 may temporarily store the aisle model as it navigates an aisle but may not permanently store the aisle model. In other words, the guidance system 100 may create a new aisle model ( 800 ) each time it begins traversing a given aisle, even if it has traversed the aisle previously. The guidance system 100 may accordingly be flexible to handle changes in an aisle without prior training or mapping of the aisle.)
Regarding Claim 32, The combination of Murli in view of Thode teaches The method as recited in claim 16,
Murli further teaches wherein the automatic controlling of the industrial truck relates exclusively to an operational control of a steering. (see at least Fig. 8-10 [0032, 0043-0052, 0063-0082]: if an aisle feature is identified and an aisle model is successfully created, the guidance system 100 can then navigate the MHV 104 within the aisle, for example centering the MHV 104 approximately equidistant from each of the respective sides of the aisle, aligning the MHV 104 offset from the center of the aisle by a predefined distance, aligning the MHV 104 at a minimum, or an offset from the minimum, distance (e.g., a buffer distance) from a side of the aisle, or the like. In some embodiments, the operator may control the speed of the MHV 104 while the guidance system 100 aligns the MHV 104 within the aisle.)
Regarding Claim 33, The combination of Murli in view of Thode teaches the method as recited in claim 16,
Murli further teaches An industrial truck which is configured to perform the method as recited in claim 16. (see at least Fig. 1)
Claim(s) 26 is rejected under 35 U.S.C. 103 as being unpatentable over Murli in view of Thode and Volker (DE102015111697A1_English Translation).
Regarding Claim 26, The combination of Murli in view of Thode teaches The method as recited in claim 25, further comprising:
It may be alleged that the combination of Murli in view of Thode does not explicitly teach detecting a plurality of possible entrances;
displaying the plurality of possible entrances to the operator for a selection of the automatic alignment of the industrial truck; and either,
the operator, via the control command, manually selecting one of the plurality of possible entrances in front of which the automatic alignment of the industrial truck is to be effected, or
automatically selecting an entrance of the plurality of possible entrances which is located closest to the industrial truck.
Volker is directed to system and method for controlling forklift truck for order picking, Volker teaches detecting a plurality of possible entrances; (see at least Fig.1-3 [0026-0028]: The industrial trucks may detect both a left shelf edge 2 and a right shelf edge 3 (corresponds to possible entrances) through the pallets 5 and shelf supports 6.)
displaying the plurality of possible entrances to the operator for a selection of the automatic alignment of the industrial truck (see at least Fig.1-3 [0026-0028]: Both options are offered to the operator as possibilities for guide lines 3 and 10 (corresponds to possible entrances). In the present example, the operator has chosen a route in the middle of the shelf aisle 11 formed by the shelf edges 2 , 9, in which the forklift truck 1 is guided along both guide lines 3 , 10.) ; and either,
the operator, via the control command, manually selecting one of the plurality of possible entrances in front of which the automatic alignment of the industrial truck is to be effected, (see at least Fig.1-3 [0026-0028]: Both options are offered to the operator as possibilities for guide lines 3 and 10 (corresponds to possible entrances). In the present example, the operator has chosen a route (corresponds to operator selection) in the middle of the shelf aisle 11 formed by the shelf edges 2 , 9, in which the forklift truck 1 is guided along both guide lines 3 , 10 (corresponds to automatic alignment).) or automatically selecting an entrance of the plurality of possible entrances which is located closest to the industrial truck.
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Murli and Thode to incorporate the technique of detecting multiple possible alignment features, presenting the alignment options for operator selection and automatically aligning the industrial truck based on operator selection as taught by Volker with reasonable expectation of success and doing so would improve picking accuracy and reduce alignment errors while ensuring faster operation.
Claim(s) 26 is rejected under 35 U.S.C. 103 as being unpatentable over Murli in view of Thode and Kim et al. (US 2022/0161432 A1 hereinafter Kim).
Regarding Claim 28, The combination of Murli in view of Thode teaches The method as recited in claim 27,
It may be alleged that the combination of Murli and Thode does not explicitly teach wherein the enabling of the automatic alignment of the industrial truck is effected when the distance of the industrial truck from the entrance detected is less than a predefined distance.
Kim is directed to system and method for controlling autonomous mobile robot, Kim teaches wherein the enabling of the automatic alignment of the industrial truck is effected when the distance of the industrial truck from the entrance detected is less than a predefined distance. (see at least [0022-0023, 0080-0081, 0102-0105]: A control method of an autonomous mobile robot may further include, determining whether a current position of the autonomous mobile robot is within an effective range of the accuracy zone, and driving the autonomous mobile robot toward the destination point without further moving toward the waypoint when the controller determines that the current position of the autonomous mobile robot is within the effective range of the accuracy zone.)
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Murli and Thode to incorporate the technique of enabling automatic control of the AMR toward a predetermined location is effected when the distance of the AMR is within a predetermined distance from a feature as taught by Kim with reasonable expectation of success to improve efficiency and operational repeatability.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANA F ARTIMEZ whose telephone number is (571)272-3410. The examiner can normally be reached M-F: 9:00 am-3:30 pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Faris S. Almatrahi can be reached at (313) 446-4821. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DANA F ARTIMEZ/Examiner, Art Unit 3667
/FARIS S ALMATRAHI/Supervisory Patent Examiner, Art Unit 3667
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