DETAILED ACTION
Response to Amendment
Applicant’s response to the last Office Action, filed on 4/20/2026 has been entered and made of record.
Applicant’s amendments necessitated the new ground of rejection set forth herein; therefore, this action is made Final.
Response to Arguments
Applicant's arguments filed on 4/20/2026 have been fully considered but they are not persuasive.
The Sun reference is added to the rejection of the independent claims in view of amendments. See detailed analysis below.
In the field of semantic segmentation and stereo matching Sun teaches that the evaluation system is trained with a plurality of training images to simultaneously generate both the distance image and the estimation to identify the location of the object. (¶ 0007-0008 and 0016 introduce Sun’s technique for acquiring binocular stereo imaging and accomplishing simultaneous semantic segmentation and distance image generation using end-to-end deep learning. ¶ 0063, “Depth estimation, semantic segmentation, and 3D object detection are then trained, for example, trained substantially simultaneously, in an end-to-end manner.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the above combination cargo container detection which uses joint semantic segmentation and distance image generation with Sun’s technique for simultaneous joint semantic segmentation and distance image generation. Liu teaches a stereo camera system and computer hardware for processing the camera output. Semantic segmentation is performed to segment the cargo container and distance image generation is jointly performed to obtain the 3D point cloud data of the cargo container corners. However, as Liu describes this process the point cloud extraction may occur after the segmentation. Sun teaches simultaneous semantic segmentation and distance image generation using end-to-end deep learning for stereo imaging. This offers reduced computational intensity. This cannot be considered a non-obvious improvement in view of the relevant prior art here. Using known engineering design, no “fundamental” operating principle of the teachings are changed; they continue to perform the same functions as originally taught prior to being combined.
Double Patenting
The nonstatutory 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 nonstatutory double patenting rejection is appropriate where the conflicting claims 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); 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 nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1-17 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-21 of copending Application No. 18/065,881 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other:
Regarding claim 1, Application 18/065,881 discloses a system for image-based identification of the position of a cargo container comprising edges and corners, the system comprising: (claim 1)
an electro-optical unit, which is or can be directed on the cargo container and is configured to provide an image signal which contains distance information, and (stereo camera at claim 6.)
an electronic evaluation unit, which is connected in a signal-transmitting manner to the electro-optical unit and is configured to generate, on the basis of the image signal, a distance image with respect to the spatial location of the edges of the cargo container and a two-dimensional estimation of the position of the corners of the cargo container within the image and to provide a position signal with respect to the spatial position of the corners of the cargo container on the basis of the distance image and the estimation. (claims 1 and 6)
This is a provisional nonstatutory 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.
Claim(s) 1-2, 4-5, 7-8, and 10-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Liu (“Trailer hopper automatic detection method for silage harvesting based improved U-Net”) in view of Bonefas (US PGPub 2017/0206645) and Sun (US PGPub 2023/0222817)
Regarding claim 1, Liu discloses a system for image-based identification of the position of a cargo container comprising edges and corners, the system comprising: (Liu teaches a system for automatic detection of a trailer hopper driving alongside a harvester from a stereo camera system on a harvester, See Abstract. A U-net neural network algorithm is proposed to detect the spatial position of the edges and corners of the trailer hopper during harvesting.)
an electro-optical unit, which is or can be directed on the cargo container and is configured to provide an image signal which contains distance information, and (Pg. 2, right column, ¶ 3, “the imaging acquisition system is shown in Fig. 1. ZED 2 camera [stereo camera] was installed on the throwing arm of Wuzheng 4QZ-450 self-propelled silage harvester.” Pg. 7, Section “2.2.3. Trailer hopper spatial positioning algorithm” teaches that the ZED 2 camera provides distance information in the form of 3D point cloud information from camera’s ZED API library to generate the point cloud.)
an electronic evaluation unit configured to implement an evaluation system, wherein the electronic evaluation unit, is connected in a signal-transmitting manner to the electro-optical unit and the evaluation system is configured to generate, on the basis of the image signal, a distance image with respect to the spatial location of the edges of the cargo container and a two-dimensional estimation using semantic segmentation to identify the position of the corners of the cargo container within the image signal and to provide a position signal with respect to the spatial position of the corners of the cargo container on the basis of the distance image and the estimation. (Pg. 7, Section “2.2.3. Trailer hopper spatial positioning algorithm”, “trailer hopper spatial positioning algorithm for obtaining the spatial coordinates of the trailer hopper corner point. Firstly, pixel coordinates of four hopper corner points are extracted based on the trailer hopper fitting algorithm results. Secondly, call the point cloud extraction function in the ZED API library to generate the point cloud containing trailer hopper. Finally, taking the corner pixel coordinates as input, the designated point coordinate acquisition function is called to extract the corner point spatial coordinates and realize the spatial positioning of the trailer hopper.” Also see Algorithm 1 and Figs. 11, 13 and 15. See hardware in Fig. 4. See hopper positioning signal at pg. 11, right column, last paragraph.)
In the field of image-based identification of the position of a cargo container Bonefas teaches an electronic evaluation unit, which is connected in a signal-transmitting manner to the electro-optical unit. (Bonefas teaches a system for automatic detection of the trailer hopper from a stereo camera system on a harvester, See Abstract. Figs. 1 and 2 and ¶ 0024-0028 show connection in a signal transmission manner between image processing module/electronic evaluation unit and the stereo camera/electro-optical unit.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Liu’s cargo container image detection with Bonefas’ cargo container image detection. Liu teaches a stereo camera system and computer hardware for processing the camera output. Bonefas expressly teaches a signal transmission connection between the camera and the computer hardware. This cannot be considered a non-obvious improvement in view of the relevant prior art here. Using known engineering design, no “fundamental” operating principle of the teachings are changed; they continue to perform the same functions as originally taught prior to being combined.
In the field of semantic segmentation and stereo matching Sun teaches that the evaluation system is trained with a plurality of training images to simultaneously generate both the distance image and the estimation to identify the location of the object. (¶ 0007-0008 and 0016 introduce Sun’s technique for acquiring binocular stereo imaging and accomplishing simultaneous semantic segmentation and distance image generation using end-to-end deep learning. ¶ 0063, “Depth estimation, semantic segmentation, and 3D object detection are then trained, for example, trained substantially simultaneously, in an end-to-end manner.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the above combination cargo container detection which uses joint semantic segmentation and distance image generation with Sun’s technique for simultaneous joint semantic segmentation and distance image generation. Liu teaches a stereo camera system and computer hardware for processing the camera output. Semantic segmentation is performed to segment the cargo container and distance image generation is jointly performed to obtain the 3D point cloud data of the cargo container corners. However, as Liu describes this process the point cloud extraction may occur after the segmentation. Sun teaches simultaneous semantic segmentation and distance image generation using end-to-end deep learning for stereo imaging. This offers reduced computational intensity. This cannot be considered a non-obvious improvement in view of the relevant prior art here. Using known engineering design, no “fundamental” operating principle of the teachings are changed; they continue to perform the same functions as originally taught prior to being combined.
Regarding claim 2, the above combination discloses the system as claimed in claim 1, wherein the evaluation system is trained on the plurality of training images and associated information with respect to the location of the cargo container and is configured to learn solutions to multiple problems simultaneously. (Liu Pg. 2 right column, ¶ 3 shows training image-based learning, including learning the recognition solutions to many different trailer types simultaneously.)
Regarding claim 4, the above combination discloses the system as claimed in claim 1, wherein the electro-optical unit is a stereo camera and the distance image is a disparity image. (See rejection of claim 1 regarding Liu’s ZED 2 stereo camera, which determines depth via the stereo disparity image. Also see Bonefas ¶ 0034.)
Claims 5 is the system claims corresponding to claim 4 but dependent on claims 2 and 3 respectively. Remaining limitations are rejected similarly. See detailed analysis above.
Regarding claim 7, the above combination discloses the system as claimed in claim 1, wherein the evaluation unit is configured to supply the position signal to a control unit, which is configured to use the position signal to generate a control signal for an actuator for the automatic supervision of a transfer process of material into the cargo container. (See Liu’s hopper positioning signal at pg. 11, right column, last paragraph as well as Bonefas’ automated control based on the container position in the image at ¶ 0023 and 0040.)
Claims 8 and 10 are the system claims corresponding to claim 7 but dependent on claims 2 and 4 respectively. Remaining limitations are rejected similarly. See detailed analysis above.
Regarding claim 11, the above combination discloses a vehicle, in particular a harvesting machine, having means for picking up and/or storing and for transferring material into the cargo container, comprising a system as claimed in claim 1. (See Liu Abstract and Fig 2 and Bonefas’ Fig. 5.)
Claims 12-17 are the method claims corresponding to the system claims 1-6. The system necessitates method steps. Remaining limitations are rejected similarly. See detailed analysis above.
Regarding claim 18, the above combination discloses the method as claimed in claim 13, wherein the plurality of training images comprises images of different cargo containers. (Liu, pg. 2, right column, ¶ 3.)
Regarding claim 19, the above combination discloses the method as claimed in claim 12, wherein the evaluation unit implements multitask learning and is trained to simultaneously generate a disparity image, a semantic segmentation, and determine the position of the corners. (See rejection of claim 1 in particular the combination with Sun’s multitask learning trained to simultaneously generate the disparity image, the semantic segmentation, and determine position.)
Regarding claim 20, the above combination discloses the system as claimed in claim 1, wherein the plurality of training images comprises images of different cargo containers. (See rejection of claim 18.)
Regarding claim 21, the above combination discloses the system as claimed in claim 20, wherein the distance image comprises a disparity image and the evaluation unit implements multitask learning and is trained to simultaneously generate the disparity image, the semantic segmentation, and determine the position of the corners based on the plurality of training images. (See rejection of claim 1 in particular the combination with Sun’s multitask learning trained to simultaneously generate the disparity image, the semantic segmentation, and determine position.)
Conclusion
Based on these facts, 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 extension fee 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.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Raphael Schwartz whose telephone number is (571)270-3822. The examiner can normally be reached Monday to Friday 9am-5pm CT.
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/RAPHAEL SCHWARTZ/ Examiner, Art Unit 2671
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