CTFR 18/394,853 CTFR 90214 DETAILED ACTION Applicant has amended claims 1, 3-6, 12, and 15-17. Claims 4, 13, and 18 have been canceled. Claims 21-23 are new. Claims 1-3, 5-12, 14-17, and 19-23 are pending. Response to Arguments Applicant’s arguments filed on March 10, 2026 have been with respect to pending have been considered but are moot in view of the new ground(s) of rejection. The amended claims resulted in changes to the scope and contents; therefore, the grounds of rejection are modified accordingly. Specification 07-44 AIA The specification is objected to as failing to provide proper antecedent basis for the claimed subject matter. See 37 CFR 1.75(d)(1) and MPEP § 608.01(o). Correction of the following is required: Claim 12 now recites the limitation “ determine whether the cargo comprises diamond-shaped sections within the foreground section of the images ; and determine a type of the cargo as a first type when the images include the diamond-shaped sections and determine that the cargo is a second type when the images do not include the diamond-shaped section ” in lines 10-14 of the claim. The aforementioned claimed subject matter has no antecedent basis the specification. New claim 21 recites the limitation “ convert the images to grayscale prior to performing the fine detection ” in line 2 of the claim. The aforementioned claimed subject matter has no antecedent basis the specification. New claim 22 recites the limitation “ converting the images to grayscale prior to determining whether the cargo includes diamond-shaped sections ” in lines 1-2 of the claim. The aforementioned claimed subject matter has no antecedent basis the specification. New claim 23 recites the limitation “ converting the images to grayscale prior to performing the fine detection ” in lines 1-2 of the claim. The aforementioned claimed subject matter has no antecedent basis the specification . Claim Rejections - 35 USC § 112 07-31-01 Claims 12 and 21-23 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claim 12 now recites the limitation “ determine whether the cargo comprises diamond-shaped sections within the foreground section of the images ; and determine a type of the cargo as a first type when the images include the diamond-shaped sections and determine that the cargo is a second type when the images do not include the diamond-shaped section ” in lines 10-14 of the claim. Par. [0038] of the specification of this application indicates “The mesh 205 can be formed from various materials including nylon or polyester and forms a netting with diamond-shaped openings 206”. Par. [0053] of the specification further indicates “The mesh 205 is identified by one or more diamond-shaped sections of the mesh within the image. In some examples, a single diamond-shaped section provides for identifying the mesh 205 and classifying the cargo 200”. However, the examiner was not able to clearly ascertain where support was found in the original disclosure for the claimed “ determine whether the cargo comprises diamond-shaped sections within the foreground section of the images ; and determine a type of the cargo as a first type when the images include the diamond-shaped sections and determine that the cargo is a second type when the images do not include the diamond-shaped section ” limitation recited in lines 10-14 of the claim. New claim 21 recites the limitation “ convert the images to grayscale prior to performing the fine detection ” in line 2 of the claim. Par. [0051] of the specification of this application indicates “In some examples, the image processing converts the images to grayscale . This conversion facilitates identifying edges of the cargo 200 and/or features 201. The conversion to grayscale also reduces the amount of data which simplifies the image processing algorithms and reduces the computational requirements which can be done with less processing power. After the conversion, a Canny edge detection algorithm is used to produce an edge image”. However, the examiner was not able to clearly ascertain where support was found in the original disclosure for the claimed “ convert the images to grayscale prior to performing the fine detection ” limitation recited in line 2 of the claim. New claim 22 recites the limitation “ converting the images to grayscale prior to determining whether the cargo includes diamond-shaped sections ” in lines 1-2 of the claim. The aforementioned claimed subject matter has no antecedent basis the specification. Par. [0051] of the specification of this application indicates “In some examples, the image processing converts the images to grayscale . This conversion facilitates identifying edges of the cargo 200 and/or features 201. The conversion to grayscale also reduces the amount of data which simplifies the image processing algorithms and reduces the computational requirements which can be done with less processing power. After the conversion, a Canny edge detection algorithm is used to produce an edge image”. However, the examiner was not able to clearly ascertain where support was found in the original disclosure for the claimed “ converting the images to grayscale prior to determining whether the cargo includes diamond-shaped sections ” limitation recited in lines 1-2 of the claim. New claim 23 recites the limitation “ converting the images to grayscale prior to performing the fine detection ” in lines 1-2 of the claim. The aforementioned claimed subject matter has no antecedent basis the specification. Par. [0051] of the specification of this application indicates “In some examples, the image processing converts the images to grayscale . This conversion facilitates identifying edges of the cargo 200 and/or features 201. The conversion to grayscale also reduces the amount of data which simplifies the image processing algorithms and reduces the computational requirements which can be done with less processing power. After the conversion, a Canny edge detection algorithm is used to produce an edge image”. However, the examiner was not able to clearly ascertain where support was found in the original disclosure for the claimed “ converting the images to grayscale prior to performing the fine detection ” limitation recited in lines 1-2 of the claim. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-21-aia AIA Claim s 1-2, 5-7, 9-11, 16 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Reed et al. (US PG Publication No. 2010/0100225 A1), hereafter referred to as Reed, Applicant cited prior art as US Patent No. 8515656 B2, in view of Souder et al. (US PG Publication No. 2019/0122173 A1), hereafter referred to as Souder, in further view of Wicks et al. (US PG Publication No. US 2015/0352721 A1), hereafter referred to as Wicks, and in further view of view of Barburescu et al. (US PG Publication No. US 2023/0245460 A1), hereafter referred to as Barburescu . Regarding claim 1 , Reed discloses a monitoring system to monitor cargo on a vehicle (Par. [0002]: an integrated system for monitoring and managing aircraft cargo loading and unloading activities, and for remotely monitoring an aircraft's cargo compartments and related cargo system components and systems ; Par. [0019-21]: invention includes a cargo loading and monitoring system for an aircraft having a plurality of separate cargo compartments… invention also includes a method of remotely monitoring a process of loading a plurality of cargo containers into a first cargo compartment of an aircraft having a plurality of power drive units located in the first cargo compartment. The method includes determining the locations of the cargo containers within the first cargo compartment, and determining the status of each of the power drive units in the first cargo compartment ), the monitoring system comprising: cameras configured to capture images of the cargo while on the vehicle, the cameras aligned to capture images of the cargo from different views; a computing device comprising processing circuitry configured to process the images received from the cameras (Par. [0002-6]: an integrated system for monitoring and managing aircraft cargo loading and unloading activities, and for remotely monitoring an aircraft's cargo compartments and related cargo system components and systems… Typically, items being shipped by air are first loaded onto specially configured pallets or into specially configured containers. In the airfreight industry, these various pallets and containers are commonly referred to as Unit Load Devices ("ULDs")… Once a ULD is loaded with cargo items, the ULD is weighed, transferred to the aircraft, and is loaded onto an aircraft through a doorway or hatch using a conveyor ramp, scissor lift, or the like. Once inside the aircraft, a ULD is moved about the cargo compartment until it reaches a final stowage position. Multiple ULDs are brought onboard the aircraft, and each is placed in its respective stowed position… Various types of aircraft that are used to exclusively transport cargo have variously arranged cargo compartments for receiving and stowing ULDs ); Par. [0018]: a system and method for surveying, monitoring, and recording activities and events that occur within an aircraft's cargo compartments, especially during loading and unloading activities. Preferably, such a system and method will assist air cargo carriers in determining the causes and/or sources of cargo tampering or damage, and will establish an evidentiary record of such activities and events. In addition, such a system and method preferably will be compatible with other onboard cargo loading and logistics systems, and even more preferably, will be integrated with such other onboard cargo systems ; Par. [0049-70]: a system and method according to the invention includes one or more cameras 100 strategically positioned within an aircraft cargo compartment… a plurality of cameras 100 are positioned within each of the cargo compartments 12a, 12b, 14 such that the combined fields of view of the plurality of cameras 100 at least include a substantial portion of each one of the cargo compartments 12a, 12b, and 14… FIG. 6 shows one arrangement of six cameras 100a-100f positioned at various locations within a main deck cargo compartment 14 of an aircraft 10… various numbers, positions, and angles of cameras 100 can be provided for viewing various regions of a main deck cargo compartment 14… A system and method according to the invention may include cameras 100 that provide periodic still images of associated cargo compartments 12a, 12b, 14. In a preferred embodiment, however, the cameras 100 are video cameras capable of providing continuous live video images of their associated cargo compartments 12a, 12b, 14… FIG. 18 shows one arrangement of mounting a camera 100 within a Cargo Maintenance Display Unit ("CMDU")… FIG. 18 shows one embodiment of an integrated cargo loading and cargo video monitoring system 200 according to the invention… the system 200 includes a main deck cargo control subsystem 202, a forward lower lobe cargo control subsystem 204, an aft lower lobe cargo control subsystem 206, and a cargo video monitoring/recording subsystem 300. In this embodiment, the cargo video monitoring/recording subsystem 300 includes eight cameras 100a-100h distributed about a main deck cargo compartment 14, a forward lower lobe cargo compartment 12a, and an aft lower lobe cargo compartment 12b like the camera placements shown in FIGS. 6, 9 and 10, for example. The cargo video monitoring/recording subsystem 300 also can include more or fewer cargo compartment cameras 100… FIG. 18 also shows a cargo video monitoring and recording subsystem 300 integrated with the cargo control subsystems 202, 204, 206 described above. As shown in FIG. 18, the video subsystem 300 includes a cargo video server ("CVS") 310 coupled to the main deck CMDU 230… the CVS 310 can be activated by other types of automated sensors for detecting activity within a cargo compartment, such as by motion detectors, aircraft wheel weight sensors, or the like. The CVS 310 can include an Ethernet connection 332 for connecting the CVS 310 to a portable computer or electronic flight bag ("EFB") 335, or to another electronic device capable of receiving video outputs from the CVS 310. In addition, the CVS 310 preferably is capable of recording video information on removable storage media 330 so that video image files can be saved and played later on a remote video-playing device, such as a PC 340… video signals received by the CVS 310 from any one of the main deck or lower lobe cargo compartment cameras 100a-100h can be selectively viewed on any of the cargo compartment CMDUs 230, 260, 294 ; a monitoring system to monitor cargo on a vehicle (e.g. integrated system and method for monitoring and managing aircraft (i.e. a vehicle) cargo loading and unloading activities, and for remotely monitoring aircraft's cargo compartments and related cargo system components and systems, as indicated above, for example), the monitoring system comprising: cameras configured to capture images of the cargo while on the vehicle, the cameras aligned to capture images of the cargo from different views (e.g. integrated system and method for monitoring and managing aircraft cargo includes one or more cameras strategically positioned within an aircraft cargo compartment such that the combined fields of view of the plurality of cameras include a substantial portion of each one of the cargo compartments, including the cargo at each one of the cargo compartments, for example, including various numbers, positions, and angles of cameras provided for viewing various regions of each cargo compartment, as indicated above, for example); a computing device comprising processing circuitry configured to process the images received from the cameras (e.g. integrated system and method for monitoring and managing aircraft cargo includes a portable computer (i.e. a computing device comprising processing circuitry) or another electronic device capable of receiving images or video outputs from any one of the one or more cameras strategically positioned within an aircraft cargo compartment, as indicated above), for example), but fails to teach the following as further recited in claim 1. However, Souder teaches the computing device configured to: perform coarse detection and identify a cargo section of the images that includes the cargo; and determine a type of cargo based on the feature (Par. [0003-30]: methods, including computer-implemented methods, devices, and computer-program products applying systems and methods for tracking pallets… a computer-implemented method for tracking goods carriers from a particular source is provided… the method includes receiving a first image file generated by a first imaging device, wherein the first image file includes a first image that depicts a first plurality of goods carriers from the particular source… using the trained artificial neural network to determine a first number of goods carriers from the particular source in the first image… receiving a second image file generated by a second imaging device, wherein the second image file includes a second image that depicts a second plurality of goods carriers from the particular source… using the trained artificial neural network to determine a second number of goods carriers from the particular source in the second image… using the artificial neural network to identify an object within both the first image and the second image. The method may also include matching at least a subset of the first plurality of goods carriers from the particular source in the first image to a first type of goods carriers from the particular source, and matching at least a subset of the second plurality of goods carriers from the particular source in the second image to the first type of goods carriers from the particular source… A goods carrier may be a structure that supports physical assets for storage, presentation, handling, and/or transportation. As used herein, the term “goods carrier” may be used to describe any load carrier or product conveyance platform… FIG. 1 is a bottom perspective view of a goods carrier 100… The goods carrier 100 shown in FIG. 1 is an example of a pallet ; Par. [0039-69]: track a plurality of goods carriers from a particular source as the goods carriers move through a transportation path having a plurality of nodes. For example, the particular source may be a manufacturer or distributor of the goods carriers… the goods carriers may be tracked through photographs without attaching physical tags or labels to the goods carriers. In particular, a plurality of goods carriers of a first type may be counted at various times and locations along the transportation path… server computer 420 may include a machine learning system, such as an artificial neural network, and may process an image within the image file to filter out goods carriers from third parties not associated with the particular source from the plurality of goods carriers; match at least a subset of the plurality of goods carriers to a first type of goods carriers from the particular source; and determine a number of goods carriers of the first type in the image. The server computer 420 may access a database 425 storing training image files including a respective plurality of training images in association with a known number of training goods carriers that are depicted in the respective training image. The server computer 420 may use the training image files to train the machine learning system to count the number of training goods carriers of the first type in subsequently acquired images… Processor 601 may include one or more microprocessors to execute program components for performing the goods carrier tracking functions of the server computer 420… Computer readable medium 606 may store code executable by the processor 601 for implementing some or all of the image analysis functions of server computer 420. For example, computer readable medium 606 may include code implementing a perspective correction engine 607, a scaling and rotation engine 608… the scaling and rotation engine 608 may be configured to crop the image of the goods carriers such that only the goods carriers are shown (i.e., eliminating background images)… matching engine 610 may be configured to analyze the image to match at least a subset of the remaining goods carriers to a first type of goods carriers from the particular source. Similar to the filtering, the matching may be based on visual features of any unique characteristics or combination of characteristics that are together unique to the first type of goods carriers… the machine learning system may match at least a subset of the remaining goods carriers to a first type of goods carriers from the particular source. The matching may be performed by a machine learning system, such as an artificial neural network. For example, the first type of goods carriers may be wooden pallets, plastic pallets, display pallets, automotive pallets, or aviation pallets ; Par. [0074-82]: the machine learning system may match at least a subset of the remaining goods carriers to the first type of goods carriers from the particular source. The matching may be performed by the machine learning system. For example, the first type of goods carriers may be wooden pallets, plastic pallets, display pallets, automotive pallets, or aviation pallets… FIG. 9 is a flow chart illustrating another exemplary method for goods carrier tracking, in accordance with some embodiments. As shown in FIG. 9, the results of the analysis of the first image file 705 and the second image file 805 may be used in conjunction to track the goods carriers as they move along the transportation path… various image processing techniques may be used in conjunction with the methods described above. For example, a specific stack of goods carriers may be isolated from an image that includes a plurality of stacks of goods carriers. This may be accomplished by any suitable method, such as applying a foreground/background segmentation to the image ; perform coarse detection and identify a cargo section of the images that includes the cargo; and determine a type of cargo based on the feature (e.g. systems and methods for tracking (i.e. monitoring) good carriers (i.e. cargo), including structures that support physical assets for storage, presentation, handling, and/or transportation, such as pallets, for example, and detect good carriers in foreground sections of images (i.e. detect the cargo within the images) by isolating goods carriers from an image that includes a plurality of stacks of goods carriers (i.e. perform coarse detection and identify a cargo section of the images that includes the cargo), for example, by applying a foreground/background segmentation to each image to crop the image of the goods carriers such that only the goods carriers are shown, thereby eliminating (i.e. removing, subtracting, etc.) background images (i.e. perform coarse detection), for example, and analyze each image to match (i.e. determine, identify, etc.) at least a subset of the remaining goods carriers to a first type of goods carriers (i.e. a type of the cargo), in which the first type of goods carriers include wooden pallets, plastic pallets, display pallets, automotive pallets, or aviation pallets, for example, based on visual features of any unique characteristics or combination of characteristics that are together unique to the first type of goods carriers (i.e. and determine a type of cargo based on the feature), as indicated above), for example). Reed and Souder are considered to be analogous art because they pertain to image processing applications related to cargo monitoring and tracking. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify the method and apparatus for monitoring and managing aircraft cargo loading and unloading activities, and for remotely monitoring an aircraft's cargo compartments and related cargo system components and systems (as disclosed by Reed) with perform coarse detection and identify a cargo section of the images that includes the cargo; and determine a type of cargo based on the feature (as taught by Souder, Abstract, Par. [0003-30, 39-69, 74-82]) to determine how many pallets are in each image, to determine whether any pallets have been added or removed between nodes of the transportation path, to determine a number of goods carriers from a particular source in a number of images, to determine whether the goods carriers are at a correct facility, to determine where the goods carriers are in a transportation path, to determine a cycle time of the goods carriers, to predict timing of the goods carriers at a particular location, to determine whether all of the goods carriers are still in a same shipment, etc. (Souder, Abstract, Par. [0002-3, 44]). The combination of Reed and Souder, as a whole, teaches the method and apparatus, as indicated above, but fails to teach the following as further recited in claim 1. However, Wicks teaches perform fine detection on the cargo section of the images and identify a feature of the cargo (Par. [0002-3]: trucks and trailers loaded with cargo and products move across the country to deliver products to commercial loading and unloading docks at stores, warehouses and distribution centers … Trucks are typically unloaded with forklifts if the loads are palletized and with manual labor if the products are stacked within the trucks… methods, devices, systems, and non-transitory process-readable storage media for a computing device of a robotic carton unloader to identify items to be unloaded from an unloading area within imagery from a vision system ; Par. [0031-34]: methods, devices, systems, and non-transitory process-readable storage media for a robotic carton unloader having a vision system including a computing device that utilizes imagery from a plurality of sensor devices to identify items to be unloaded from an unloading area… computing device may be configured to locate attributes of all identifiable items (e.g., cartons, boxes, etc.) within the imagery. For example, the computing device may perform image processing operations (e.g., apply a Hough transform) to identify the centroids (e.g., within +/−5 cm (x, y, z)), edges (e.g., the pickup edge of a box with respect to +/−1 cm of the centroid of the box, etc.), surfaces, and other geometry of boxes in a wall of boxes depicted in the imagery ; Par. [0082-142]: generalized Hough transform techniques may be used to identify various types of shapes within imagery… FIG. 14 shows an embodiment robotic carton unloader 1400 for quickly and efficiently moving items (e.g., cartons, boxes, etc.) from unloading areas, such as a truck or a semi-trailer, a store, a warehouse, a distribution center, an unloading bay, between product aisles, a rack, a pallet… FIG. 15 shows an embodiment robotic carton unloader 1500 for quickly and efficiently moving items (e.g., cartons, boxes, etc.) from unloading areas, such as a truck or a semi-trailer, a store, a warehouse, a distribution center, an unloading bay, between product aisles, a rack, a pallet… the processor may mask the edge map with the computed working area. For example, the processor may overlay a representation of the working area over the edge map. In block 1716 the processor may perform object detection in the masked edge map via multi-threshold object detection to identify bounded objects in the working area… the processor may be configured to locate attributes of all identifiable bounded objects (e.g., boxes, labels, holes, etc.) within the masked edge map via multi-thresholding object detection operations, such as applying Hough transforms ; perform fine detection on the cargo section of the images and identify a feature of the cargo (e.g. methods, devices, systems, and non-transitory process-readable storage media for a computing device of a robotic carton unloader to identify items to be unloaded from an unloading area, such as a pallet (i.e. cargo), for example, within imagery from a vision system, include generalized Hough transform techniques (i.e. perform fine detection on the cargo section of the images) which are used to identify various types of shapes within imagery (i.e. and identify a feature of the cargo), as indicated above), for example). Reed, Souder, and Wicks are considered to be analogous art because they pertain to image processing applications related to cargo monitoring and tracking. Therefore, the combined teachings of Reed, Souder, and Wicks, as a whole, would have rendered obvious the invention recited in claim 1 with a reasonable expectation of success in order to modify the method and apparatus for monitoring and managing aircraft cargo loading and unloading activities, and for remotely monitoring an aircraft's cargo compartments and related cargo system components and systems (as disclosed by Reed) with perform fine detection on the cargo section of the images and identify a feature of the cargo (as taught by Wicks, Abstract, Par. [0002-3, 31-34, 82-142]) to identify items to be unloaded from an unloading area within imagery from a vision system, to locate attributes of all identifiable items within the imagery, to identify various types of shapes within the imagery, to identify fallen objects and cause an alarm to be emitted, to identify edges in the images, and to identify bounded objects in a working area (Wicks, Abstract, Par. [0002-3, 31, 34, 82, 84, 92, 99, 141-142]). The combination of Reed, Souder, and Wicks, teaches the method and apparatus, as indicated above, but fails to teach the following as further recited in claim 1. However, Barburescu teaches with the feature comprising a base of the cargo and a limited number of vertical rows of pixels upward from the base (Par. [0208-215]: detection of the pallet is performed by the pallet detector module… Cropping 1002 from a received video frame a region in which the presence of a pallet is detected by the pallet detector module. The cropped region corresponds with a region of the video frame occupied by a bounding box surrounding the pallet combined with a further 20 pixels added on each side of the bounding box, to make sure that the whole pallet is contained in the cropped region. For brevity, this cropped region will be referred to henceforth as a “Cropped Pallet Region”. In practice, a Cropped Pallet Region comprises, for example, 128×128 pixels, with the top-left corner of the Cropped Pallet Region located at coordinates (x1, y1) in the received video frame… preferred embodiment assumes a flat and horizontal floor… and uses a homography to compute the real-world coordinates of floor points from camera coordinates… Using the correspondence of projections on the floor for corner points belonging to the same vertical line (e.g., top-left corner T′ of the front side of a rectangular cuboid corresponds to the bottom-left corner B′ of the same side as depicted in FIG. 12(a)) and basic geometry, the elevation of parallelepiped corners from the ground are calculated ; Par. [0219-220]: a pallet may be represented by a rectangular cuboid object. The volume of a rectangular cuboid object can be computed using the sizes (in pixels) of its edges. This is computed from the key points representing the corners of the pallet. In particular, the width and the length of the pallet are the sizes of two abutting edges sitting on the floor… The height of a rectangular cuboid object may be computed as the distance between one corner sitting on the floor, and an opposing corner located directly above it ; with the feature comprising a base of the cargo and a limited number of vertical rows of pixels upward from the base (e.g. Pallet Volumetric Estimation algorithm of the Pallet Volume Estimator computes the volume of objects by estimating their size (i.e. dimensions) in pixels from a 2D image, including a pallet represented by a rectangular cuboid object, in which the volume of a rectangular cuboid object is computed using the sizes (in pixels) of its edges from the key points representing corners of the pallet, for example, by cropping from a received video frame a region in which the presence of a pallet is detected, and the cropped region corresponds with a region of the video frame occupied by a bounding box surrounding the pallet combined with a further 20 pixels added on each side of the bounding box (i.e. a limited number of vertical rows of pixels), to make sure that the whole pallet is contained in the cropped region, for example, in which the width and the length of the pallet are the sizes of two abutting edges sitting on the floor (i.e. the feature comprising a base of the cargo), for example, and the height of a rectangular cuboid object is computed as the distance between one corner sitting on the floor, and an opposing corner located directly above it (i.e. with the feature comprising a base of the cargo and a limited number of vertical rows of pixels upward from the base), as indicated above), for example). Reed, Souder, Wicks, and Barburescu are considered to be analogous art because they pertain to image processing applications related to cargo monitoring and tracking. Therefore, the combined teachings of Reed, Souder, Wicks, and Barburescu, as a whole, would have rendered obvious the invention recited in claim 9 with a reasonable expectation of success in order to modify the method and apparatus for monitoring and managing aircraft cargo loading and unloading activities, and for remotely monitoring an aircraft's cargo compartments and related cargo system components and systems (as disclosed by Reed) with the feature comprising a base of the cargo and a limited number of vertical rows of pixels upward from the base (as taught by Barburescu, Abstract, Par. [0059-60, 208-215, 219-220]) to estimate the quantity of merchandise on a pallet, to detect inconsistencies between a planned inventory record of an incoming/outgoing order and the actual content of the corresponding received/pre-dispatch order, to improve both the accuracy of the delivery system and inventory management processes at both the order fulfillment facility and order receiving facility, to detect a pallet, to track the detected pallet, and to classify the merchandise on the detected pallet, and to detect products on a pallet by using Bounding box-based detection and mask-based detection synergistically to achieve lower errors because products on a pallet may be stacked erratically and a pixel level mask will increase the accuracy of detecting the products (Barburescu, Abstract, Par. [0003-10, 59-60, 174, 208-215, 219-220]). Regarding claim 2 , claim 1 is incorporated and the combination of Reed, Souder, Wicks and Barburescu, as a whole, teaches the system (Reed, Par. [0002]), wherein: the coarse detection comprises identifying a foreground section of the images and removing a background section of the images (Souder, Par. [0003-30]: methods, including computer-implemented methods, devices, and computer-program products applying systems and methods for tracking pallets… a computer-implemented method for tracking goods carriers from a particular source is provided… the method includes receiving a first image file generated by a first imaging device, wherein the first image file includes a first image that depicts a first plurality of goods carriers from the particular source… using the trained artificial neural network to determine a first number of goods carriers from the particular source in the first image… receiving a second image file generated by a second imaging device, wherein the second image file includes a second image that depicts a second plurality of goods carriers from the particular source… using the trained artificial neural network to determine a second number of goods carriers from the particular source in the second image… using the artificial neural network to identify an object within both the first image and the second image. The method may also include matching at least a subset of the first plurality of goods carriers from the particular source in the first image to a first type of goods carriers from the particular source, and matching at least a subset of the second plurality of goods carriers from the particular source in the second image to the first type of goods carriers from the particular source… A goods carrier may be a structure that supports physical assets for storage, presentation, handling, and/or transportation. As used herein, the term “goods carrier” may be used to describe any load carrier or product conveyance platform… FIG. 1 is a bottom perspective view of a goods carrier 100… The goods carrier 100 shown in FIG. 1 is an example of a pallet ; Par. [0039-69]: track a plurality of goods carriers from a particular source as the goods carriers move through a transportation path having a plurality of nodes. For example, the particular source may be a manufacturer or distributor of the goods carriers… the goods carriers may be tracked through photographs without attaching physical tags or labels to the goods carriers. In particular, a plurality of goods carriers of a first type may be counted at various times and locations along the transportation path… server computer 420 may include a machine learning system, such as an artificial neural network, and may process an image within the image file to filter out goods carriers from third parties not associated with the particular source from the plurality of goods carriers; match at least a subset of the plurality of goods carriers to a first type of goods carriers from the particular source; and determine a number of goods carriers of the first type in the image. The server computer 420 may access a database 425 storing training image files including a respective plurality of training images in association with a known number of training goods carriers that are depicted in the respective training image. The server computer 420 may use the training image files to train the machine learning system to count the number of training goods carriers of the first type in subsequently acquired images… Processor 601 may include one or more microprocessors to execute program components for performing the goods carrier tracking functions of the server computer 420… Computer readable medium 606 may store code executable by the processor 601 for implementing some or all of the image analysis functions of server computer 420. For example, computer readable medium 606 may include code implementing a perspective correction engine 607, a scaling and rotation engine 608… the scaling and rotation engine 608 may be configured to crop the image of the goods carriers such that only the goods carriers are shown (i.e., eliminating background images)… matching engine 610 may be configured to analyze the image to match at least a subset of the remaining goods carriers to a first type of goods carriers from the particular source. Similar to the filtering, the matching may be based on visual features of any unique characteristics or combination of characteristics that are together unique to the first type of goods carriers… the machine learning system may match at least a subset of the remaining goods carriers to a first type of goods carriers from the particular source. The matching may be performed by a machine learning system, such as an artificial neural network. For example, the first type of goods carriers may be wooden pallets, plastic pallets, display pallets, automotive pallets, or aviation pallets ; Par. [0074-82]: the machine learning system may match at least a subset of the remaining goods carriers to the first type of goods carriers from the particular source. The matching may be performed by the machine learning system. For example, the first type of goods carriers may be wooden pallets, plastic pallets, display pallets, automotive pallets, or aviation pallets… FIG. 9 is a flow chart illustrating another exemplary method for goods carrier tracking, in accordance with some embodiments. As shown in FIG. 9, the results of the analysis of the first image file 705 and the second image file 805 may be used in conjunction to track the goods carriers as they move along the transportation path… various image processing techniques may be used in conjunction with the methods described above. For example, a specific stack of goods carriers may be isolated from an image that includes a plurality of stacks of goods carriers. This may be accomplished by any suitable method, such as applying a foreground/background segmentation to the image ; the coarse detection comprises identifying a foreground section of the images and removing a background section of the images (e.g. systems and methods for tracking (i.e. monitoring) good carriers (i.e. cargo), including structures that support physical assets for storage, presentation, handling, and/or transportation, such as pallets, for example, and detect good carriers in foreground sections of images (i.e. detect the cargo within the images) by isolating goods carriers from an image that includes a plurality of stacks of goods carriers by applying a foreground/background segmentation to each image to crop the image of the goods carriers such that only the goods carriers are shown, thereby eliminating (i.e. removing, subtracting, etc.) background images (i.e. the coarse detection comprises identifying a foreground section of the images and removing a background section of the images), as indicated above), for example); and the fine detection comprises detecting the feature based on generalized Hough transforms (Wicks, Par. [0002-3]: trucks and trailers loaded with cargo and products move across the country to deliver products to commercial loading and unloading docks at stores, warehouses and distribution centers … Trucks are typically unloaded with forklifts if the loads are palletized and with manual labor if the products are stacked within the trucks… methods, devices, systems, and non-transitory process-readable storage media for a computing device of a robotic carton unloader to identify items to be unloaded from an unloading area within imagery from a vision system ; Par. [0031-34]: methods, devices, systems, and non-transitory process-readable storage media for a robotic carton unloader having a vision system including a computing device that utilizes imagery from a plurality of sensor devices to identify items to be unloaded from an unloading area… computing device may be configured to locate attributes of all identifiable items (e.g., cartons, boxes, etc.) within the imagery. For example, the computing device may perform image processing operations (e.g., apply a Hough transform) to identify the centroids (e.g., within +/−5 cm (x, y, z)), edges (e.g., the pickup edge of a box with respect to +/−1 cm of the centroid of the box, etc.), surfaces, and other geometry of boxes in a wall of boxes depicted in the imagery ; Par. [0082-142]: generalized Hough transform techniques may be used to identify various types of shapes within imagery… FIG. 14 shows an embodiment robotic carton unloader 1400 for quickly and efficiently moving items (e.g., cartons, boxes, etc.) from unloading areas, such as a truck or a semi-trailer, a store, a warehouse, a distribution center, an unloading bay, between product aisles, a rack, a pallet… FIG. 15 shows an embodiment robotic carton unloader 1500 for quickly and efficiently moving items (e.g., cartons, boxes, etc.) from unloading areas, such as a truck or a semi-trailer, a store, a warehouse, a distribution center, an unloading bay, between product aisles, a rack, a pallet… the processor may mask the edge map with the computed working area. For example, the processor may overlay a representation of the working area over the edge map. In block 1716 the processor may perform object detection in the masked edge map via multi-threshold object detection to identify bounded objects in the working area… the processor may be configured to locate attributes of all identifiable bounded objects (e.g., boxes, labels, holes, etc.) within the masked edge map via multi-thresholding object detection operations, such as applying Hough transforms ; the fine detection comprises detecting the feature based on generalized Hough transforms (e.g. methods, devices, systems, and non-transitory process-readable storage media for a computing device of a robotic carton unloader to identify items to be unloaded from an unloading area, such as a pallet (i.e. cargo), for example, within imagery from a vision system, include generalized Hough transform techniques (i.e. perform fine detection on the images) which are used to identify various types of shapes (i.e. features, characteristics, etc.) within imagery (i.e. the fine detection comprises detecting the feature based on generalized Hough transforms), as indicated above), for example). The same motivation to combine above-mentioned teachings applies, as previously indicated in claim 1. Regarding claim 5 , claim 1 is incorporated and the combination of Reed, Souder, Wicks and Barburescu, as a whole, teaches the system (Reed, Par. [0002]), wherein the computing device is further configured to determine a confidence value of the images based on a match between the cargo captured in the images and image templates (Souder, Par. [0003-30]: methods, including computer-implemented methods, devices, and computer-program products applying systems and methods for tracking pallets… a computer-implemented method for tracking goods carriers from a particular source is provided… the method includes receiving a first image file generated by a first imaging device, wherein the first image file includes a first image that depicts a first plurality of goods carriers from the particular source… using the trained artificial neural network to determine a first number of goods carriers from the particular source in the first image… receiving a second image file generated by a second imaging device, wherein the second image file includes a second image that depicts a second plurality of goods carriers from the particular source… using the trained artificial neural network to determine a second number of goods carriers from the particular source in the second image… using the artificial neural network to identify an object within both the first image and the second image. The method may also include matching at least a subset of the first plurality of goods carriers from the particular source in the first image to a first type of goods carriers from the particular source, and matching at least a subset of the second plurality of goods carriers from the particular source in the second image to the first type of goods carriers from the particular source… A goods carrier may be a structure that supports physical assets for storage, presentation, handling, and/or transportation. As used herein, the term “goods carrier” may be used to describe any load carrier or product conveyance platform… FIG. 1 is a bottom perspective view of a goods carrier 100… The goods carrier 100 shown in FIG. 1 is an example of a pallet ; Par. [0039-69]: track a plurality of goods carriers from a particular source as the goods carriers move through a transportation path having a plurality of nodes. For example, the particular source may be a manufacturer or distributor of the goods carriers… the goods carriers may be tracked through photographs without attaching physical tags or labels to the goods carriers. In particular, a plurality of goods carriers of a first type may be counted at various times and locations along the transportation path… server computer 420 may include a machine learning system, such as an artificial neural network, and may process an image within the image file to filter out goods carriers from third parties not associated with the particular source from the plurality of goods carriers; match at least a subset of the plurality of goods carriers to a first type of goods carriers from the particular source; and determine a number of goods carriers of the first type in the image. The server computer 420 may access a database 425 storing training image files including a respective plurality of training images in association with a known number of training goods carriers that are depicted in the respective training image. The server computer 420 may use the training image files to train the machine learning system to count the number of training goods carriers of the first type in subsequently acquired images… Processor 601 may include one or more microprocessors to execute program components for performing the goods carrier tracking functions of the server computer 420… Computer readable medium 606 may store code executable by the processor 601 for implementing some or all of the image analysis functions of server computer 420. For example, computer readable medium 606 may include code implementing a perspective correction engine 607, a scaling and rotation engine 608… the scaling and rotation engine 608 may be configured to crop the image of the goods carriers such that only the goods carriers are shown (i.e., eliminating background images)… matching engine 610 may be configured to analyze the image to match at least a subset of the remaining goods carriers to a first type of goods carriers from the particular source. Similar to the filtering, the matching may be based on visual features of any unique characteristics or combination of characteristics that are together unique to the first type of goods carriers… the machine learning system may match at least a subset of the remaining goods carriers to a first type of goods carriers from the particular source. The matching may be performed by a machine learning system, such as an artificial neural network. For example, the first type of goods carriers may be wooden pallets, plastic pallets, display pallets, automotive pallets, or aviation pallets ; Par. [0074-82]: the machine learning system may match at least a subset of the remaining goods carriers to the first type of goods carriers from the particular source. The matching may be performed by the machine learning system. For example, the first type of goods carriers may be wooden pallets, plastic pallets, display pallets, automotive pallets, or aviation pallets… FIG. 9 is a flow chart illustrating another exemplary method for goods carrier tracking, in accordance with some embodiments. As shown in FIG. 9, the results of the analysis of the first image file 705 and the second image file 805 may be used in conjunction to track the goods carriers as they move along the transportation path… various image processing techniques may be used in conjunction with the methods described above. For example, a specific stack of goods carriers may be isolated from an image that includes a plurality of stacks of goods carriers. This may be accomplished by any suitable method, such as applying a foreground/background segmentation to the image ; wherein the computing device is further configured to determine a confidence value of the images based on a match between the cargo captured in the images and image templates (e.g. systems and methods for tracking (i.e. monitoring) good carriers (i.e. cargo), including structures that support physical assets for storage, presentation, handling, and/or transportation, such as pallets, for example, and detect good carriers in foreground sections of images by isolating goods carriers from an image that includes a plurality of stacks of goods carriers by applying a foreground/background segmentation to each image to crop the image of the goods carriers such that only the goods carriers are shown, thereby eliminating (i.e. removing, subtracting, etc.) background images, for example, and analyze each image to match (i.e. determine, identify, etc.) at least a subset of the remaining goods carriers to a first type of goods carriers (i.e. determine a confidence value of the images based on a match between the cargo captured in the images and image templates), in which the first type of goods carriers include wooden pallets, plastic pallets, display pallets, automotive pallets, or aviation pallets, for example, based on visual features of any unique characteristics or combination of characteristics that are together unique to the first type of goods carriers, as indicated above), for example). The same motivation to combine above-mentioned teachings applies, as previously indicated in claim 1. Regarding claim 6 , claim 5 is incorporated and the combination of Reed, Souder, Wicks and Barburescu, as a whole, teaches the system (Reed, Par. [0002]), wherein the computing device is configured to determine that the images capture the cargo when the confidence value is above a predetermined threshold (Wicks, Par. [0140]: the processor may perform image segmentation. In the various embodiments image segmentation may be performed by various methods, such as histogram based methods, graph based methods, clustering based methods, etc. As a specific example, the processor may perform image segmentation by the efficient graph based image segmentation process. During image segmentation the lines in the image, such as box edges, labels, holes, creases, etc. may be detected along with some value of noise in the image. In determination block 1708 the processor may determine whether sufficient frames have been received for further processing. As an example, a minimum threshold of frames of RGB-D data may be required to enable box detection, and the processor may compare the number of received frames to the minimum threshold of frames to determine whether sufficient frames have been received for further processing. In response to determining sufficient frames have not been received (i.e., determination block 1708=“No”), the processor may continue to acquire RGB-D data ; wherein the computing device is configured to determine that the images capture the cargo when the confidence value is above a predetermined threshold (e.g. methods, devices, systems, and non-transitory process-readable storage media for a computing device of a robotic carton unloader to identify items to be unloaded from an unloading area, such as a pallet, for example, within imagery from a vision system, include a minimum threshold of frames of RGB-D data required to enable box detection (i.e. determine that the images capture the cargo when the confidence value is above a predetermined threshold), and the processor compares a number of received frames to a minimum threshold of frames to determine whether sufficient frames have been received for further processing, for example, and in response to determining sufficient frames have not been received, the processor continues to acquire RGB-D data, as indicated above), for example). The same motivation to combine above-mentioned teachings applies, as previously indicated in claim 1. Regarding claim 7 , claim 6 is incorporated and the combination of Reed, Souder, Wicks and Barburescu, as a whole, teaches the system (Reed, Par. [0002]), wherein the computing device is configured to determine a direction of movement of the cargo within the vehicle based on changes in the confidence values of the images (Reed, Par. [0004-5]: items being shipped by air are first loaded onto specially configured pallets or into specially configured containers. In the airfreight industry, these various pallets and containers are commonly referred to as Unit Load Devices ("ULDs"). ULDs are available in various sizes, shapes and capacities, and typically bear external markings that identify their type, maximum gross weight, tare weight, and other pertinent information… A ULD typically is loaded with cargo at a location that is distant from the immediate vicinity of an aircraft ; Par. [0075-96]: display may include graphic representations of one or more ULDs 620 that have been fully loaded in a particular cargo compartment, and may include graphic representations of the locations and directions of one or more ULDs 630 that presently are being moved to or from a stowage location within a particular cargo compartment. The cargo control portions 202, 204, 206 of the integrated system 200 can include one or more ULD-sensing PDUs to sense and track the current location of a particular ULD within an aircraft cargo compartment 12a, 12b, 14… CMDU display 1600 may include graphic representations of one or more ULDs 1620 that have been fully loaded in a particular cargo compartment, and may include graphic representations of the present locations and travel directions of one or more ULDs 1630 that presently are being moved to or from a stowage location within a particular cargo compartment. The cargo control portions 902, 904, 906 of the integrated system 900 shown in FIG. 25 can include one or more ULD-sensing PDUs to sense and track the current location of a particular ULD within an aircraft's cargo compartments ; wherein the computing device is configured to determine a direction of movement of the cargo within the vehicle based on changes in the confidence values of the images (e.g. integrated system and method for monitoring and managing aircraft cargo includes one or more cameras strategically positioned within an aircraft cargo compartment such that the combined fields of view of the plurality of cameras include a substantial portion of each one of the cargo compartments, including the cargo at each one of the cargo compartments, for example, including graphic representations of one or more unit load devices (ULDs), pallets and containers, that have been fully loaded in a particular cargo compartment, and include graphic representations of present locations and travel directions of one or more ULDs that presently are being moved to or from a stowage location within a particular cargo compartment (i.e. determine a direction of movement of the cargo within the vehicle based on changes in the confidence values of the images), as indicated above), for example). Regarding claim 9 , claim 1 is incorporated and the combination of Reed, Souder, Wicks and Barburescu, as a whole, teaches the system (Reed, Par. [0002]), wherein the computing device is configured to: determine dimensions of the cargo based on the images; determine a scale of the images; and determine a volume of the cargo based on the dimensions and the scale (Barburescu, Par. [0059-60]: Pallet Monitor Module 404 is dedicated to checking the contents of merchandise to be delivered from the premises or to be received into the premises i.e., the order fulfillment facility/order receiving facility. For this purpose, the first step is to detect a pallet using a Pallet Detector Module 404a, then to track the detected pallet using a Pallet Tracker Module 404b, and finally to classify the merchandise on the detected pallet using a Merchandise Classification per Pallet Module 404c. The Pallet Monitor Module 404 also comprises a Pallet Volume Estimator 404d for the purpose of estimating the quantity of merchandise on a pallet. The final component of the Pallet Monitor Module 404 is an IN/OUT counter 404e which is used to extract information about the total number of pallets passing through the receipt/dispatch portal… The Event (or Alert) Management Module 406 comprises an Alert Manager 406a and an Event Recorder 406b. The Alert Manager 406a is configured to issue alerts concerning the detection of an authorized entrant and information about the merchandise that is being supplied/delivered e.g. merchandise class, volume of the pallet, amount of pallets received during the supply/delivery episode in question ; Par. [0125]: the Pallet Detector module 404a, receives as input a video frame from the video footage captured by the video sensors 102 of the order checking system 110. In response, the Yolo_v5 network outputs three vectors, as follows: [0126] (a) the coordinates of the centre of a bounding box encompassing a pallet detected in the received image, together with the width and height of the bounding box, wherein the width and height are each normalized by scaling relative to the width and height respectively of the received video frame… an objectness score which denotes the confidence, valued between 0 and 1, of the neural network that a pallet center exists at a given location in a received video frame ; Par. [0212-224]: Scaling 1008 the points to the dimensions of the Cropped Pallet Region. Thus, if, for example, the Cropped Pallet Region is of dimension 128×128 and a point is defined by (x, y)=(0.46, 0.76), then the point is located at approximately (x′, y′)=(59, 97) in the coordinate system of the Cropped Pallet Region whose top left corner is denoted by (0, 0)… Pallet Volumetric Estimation algorithm of the Pallet Volume Estimator 404d computes the volume of objects by estimating their size from a 2D image… a pallet may be represented by a rectangular cuboid object. The volume of a rectangular cuboid object can be computed using the sizes (in pixels) of its edges. This is computed from the key points representing the corners of the pallet. In particular, the width and the length of the pallet are the sizes of two abutting edges sitting on the floor. Thus, defining the bottom edge of a parallelepiped as the edge thereof which sits on the floor, the length of the bottom edge may be computed from the corners of the pallet corresponding to the parallelepiped. To this end, the location of the corners of the pallet are estimated by reference to the points from the ground pattern of FIG. 11(a) observed to be closest to the corners. From this, the length of the bottom edge of the corresponding parallelepiped is computed… the Pallet Volumetric Estimation algorithm (not shown) of the Pallet Volume Estimator 404d is configured to use the above approaches to compute the volumes of the pallets in each of the video frames, if enough key points (pallet corners) are visible and detected, regardless of the angle of the pallet relative to the camera observing it ; wherein the computing device is configured to: determine dimensions of the cargo based on the images; determine a scale of the images; and determine a volume of the cargo based on the dimensions and the scale (e.g. Pallet Volumetric Estimation algorithm of the Pallet Volume Estimator computes the volume of objects by estimating their size (i.e. dimensions) from a 2D image, including a pallet represented by a rectangular cuboid object, in which the volume of a rectangular cuboid object is computed using the sizes (in pixels) of its edges from the key points representing corners of the pallet (i.e. determine dimensions of the cargo based on the images), including coordinates of a centre of a bounding box encompassing a pallet detected in a received image, together with the width and height of the bounding box, wherein the width and height are each normalized by scaling relative to the width and height respectively of the received video frame (i.e. determine a scale of the images), and compute the volumes of the pallets in each of the video frames (i.e. and determine a volume of the cargo based on the dimensions and the scale) to estimate the quantity of merchandise on a pallet, as indicated above), for example). The same motivation to combine above-mentioned teachings applies, as previously indicated in claim 1. Regarding claim 10 , claim 1 is incorporated and the combination of Reed, Souder and Wicks, as a whole, teaches the system (Reed, Par. [0002]), but fails to teach the following as further recited in claim 10. However, Barburescu teaches wherein the computing device determines a volume of the cargo just when the type of the cargo is a pallet (Par. [0059-60]: Pallet Monitor Module 404 is dedicated to checking the contents of merchandise to be delivered from the premises or to be received into the premises i.e., the order fulfillment facility/order receiving facility. For this purpose, the first step is to detect a pallet using a Pallet Detector Module 404a, then to track the detected pallet using a Pallet Tracker Module 404b, and finally to classify the merchandise on the detected pallet using a Merchandise Classification per Pallet Module 404c. The Pallet Monitor Module 404 also comprises a Pallet Volume Estimator 404d for the purpose of estimating the quantity of merchandise on a pallet. The final component of the Pallet Monitor Module 404 is an IN/OUT counter 404e which is used to extract information about the total number of pallets passing through the receipt/dispatch portal… The Event (or Alert) Management Module 406 comprises an Alert Manager 406a and an Event Recorder 406b. The Alert Manager 406a is configured to issue alerts concerning the detection of an authorized entrant and information about the merchandise that is being supplied/delivered e.g. merchandise class, volume of the pallet, amount of pallets received during the supply/delivery episode in question ; Par. [0125]: the Pallet Detector module 404a, receives as input a video frame from the video footage captured by the video sensors 102 of the order checking system 110. In response, the Yolo_v5 network outputs three vectors, as follows: [0126] (a) the coordinates of the centre of a bounding box encompassing a pallet detected in the received image, together with the width and height of the bounding box, wherein the width and height are each normalized by scaling relative to the width and height respectively of the received video frame… an objectness score which denotes the confidence, valued between 0 and 1, of the neural network that a pallet center exists at a given location in a received video frame ; Par. [0212-224]: Scaling 1008 the points to the dimensions of the Cropped Pallet Region. Thus, if, for example, the Cropped Pallet Region is of dimension 128×128 and a point is defined by (x, y)=(0.46, 0.76), then the point is located at approximately (x′, y′)=(59, 97) in the coordinate system of the Cropped Pallet Region whose top left corner is denoted by (0, 0)… Pallet Volumetric Estimation algorithm of the Pallet Volume Estimator 404d computes the volume of objects by estimating their size from a 2D image… a pallet may be represented by a rectangular cuboid object. The volume of a rectangular cuboid object can be computed using the sizes (in pixels) of its edges. This is computed from the key points representing the corners of the pallet. In particular, the width and the length of the pallet are the sizes of two abutting edges sitting on the floor. Thus, defining the bottom edge of a parallelepiped as the edge thereof which sits on the floor, the length of the bottom edge may be computed from the corners of the pallet corresponding to the parallelepiped. To this end, the location of the corners of the pallet are estimated by reference to the points from the ground pattern of FIG. 11(a) observed to be closest to the corners. From this, the length of the bottom edge of the corresponding parallelepiped is computed… the Pallet Volumetric Estimation algorithm (not shown) of the Pallet Volume Estimator 404d is configured to use the above approaches to compute the volumes of the pallets in each of the video frames, if enough key points (pallet corners) are visible and detected, regardless of the angle of the pallet relative to the camera observing it ; wherein the computing device determines a volume of the cargo just when the type of the cargo is a pallet (e.g. Pallet Volumetric Estimation algorithm of the Pallet Volume Estimator computes the volume of objects by estimating their size (i.e. dimensions) from a 2D image, including a pallet represented by a rectangular cuboid object, in which the volume of a rectangular cuboid object is computed using the sizes (in pixels) of its edges from the key points representing corners of the pallet (i.e. determines a volume of the cargo just when the type of the cargo is a pallet), including coordinates of a centre of a bounding box encompassing a pallet detected in a received image, together with the width and height of the bounding box, wherein the width and height are each normalized by scaling relative to the width and height respectively of the received video frame, and compute the volumes of the pallets in each of the video frames to estimate the quantity of merchandise on a pallet, as indicated above), for example). The same motivation to combine above-mentioned teachings applies, as previously indicated in claim 1. Regarding claim 11 , claim 1 is incorporated and the combination of Reed, Souder, Wicks and Barburescu, as a whole, teaches the system (Reed, Par. [0002]), wherein the computing device is further configured to: identify a reference point on the cargo; and track a location of the cargo within the vehicle based on a position of the reference point within the images (Wicks, Par. [0039-57]: it may be desirable to track a plurality of goods carriers from a particular source as the goods carriers move through a transportation path having a plurality of nodes. For example, the particular source may be a manufacturer or distributor of the goods carriers. The nodes may include facilities where the goods carriers are stored at least temporarily, such as warehouses, yards, docks, stores, and recyclers. According to some embodiments of the invention, the goods carriers may be tracked through photographs… application 512 may be an application that captures, stores, and/or transmits images of goods carriers for visual identification and tracking in a cloud environment… filtering engine 609 may be configured to indicate one or more of the visual features in the image and/or the image file. For example, the filtering engine 609 may be configured to indicate the visual features in the image (e.g., by outlining each of the identified visual features, by adding arrows pointing to each of the visual features, etc.). In another example, the feature identification engine 609 may be configured to indicate the visual features in a file separate from or combined with the image file (e.g., by listing pixel coordinates and/or areas in which the identified visual features are located). It is contemplated that the filtering engine 609 may be implemented using computer vision analysis and/or artificial neural networks, such as deep neural networks ; wherein the computing device is further configured to: identify a reference point on the cargo; and track a location of the cargo within the vehicle based on a position of the reference point within the images (e.g. methods, devices, systems, and non-transitory process-readable storage media for a computing device of a robotic carton unloader to identify items to be unloaded from an unloading area, such as a pallet (i.e. cargo), for example, within imagery from a vision system, include indicating visual features in an image by outlining each of the identified visual features (i.e. identify a reference point on the cargo) and tracking goods carriers through photographs as the goods carriers move through a transportation path (i.e. and track a location of the cargo within the vehicle based on a position of the reference point within the images), as indicated above), for example). The same motivation to combine above-mentioned teachings applies, as previously indicated in claim 1. Regarding claim 16 , Reed discloses a method of monitoring cargo within a vehicle Par. [0002]: an integrated system for monitoring and managing aircraft cargo loading and unloading activities, and for remotely monitoring an aircraft's cargo compartments and related cargo system components and systems ; Par. [0019-21]: invention includes a cargo loading and monitoring system for an aircraft having a plurality of separate cargo compartments… invention also includes a method of remotely monitoring a process of loading a plurality of cargo containers into a first cargo compartment of an aircraft having a plurality of power drive units located in the first cargo compartment. The method includes determining the locations of the cargo containers within the first cargo compartment, and determining the status of each of the power drive units in the first cargo compartment ), the method comprising: receiving images of the cargo while the cargo is positioned within the vehicle (Par. [0002-6]: an integrated system for monitoring and managing aircraft cargo loading and unloading activities, and for remotely monitoring an aircraft's cargo compartments and related cargo system components and systems… Typically, items being shipped by air are first loaded onto specially configured pallets or into specially configured containers. In the airfreight industry, these various pallets and containers are commonly referred to as Unit Load Devices ("ULDs")… Once a ULD is loaded with cargo items, the ULD is weighed, transferred to the aircraft, and is loaded onto an aircraft through a doorway or hatch using a conveyor ramp, scissor lift, or the like. Once inside the aircraft, a ULD is moved about the cargo compartment until it reaches a final stowage position. Multiple ULDs are brought onboard the aircraft, and each is placed in its respective stowed position… Various types of aircraft that are used to exclusively transport cargo have variously arranged cargo compartments for receiving and stowing ULDs ); Par. [0018]: a system and method for surveying, monitoring, and recording activities and events that occur within an aircraft's cargo compartments, especially during loading and unloading activities. Preferably, such a system and method will assist air cargo carriers in determining the causes and/or sources of cargo tampering or damage, and will establish an evidentiary record of such activities and events. In addition, such a system and method preferably will be compatible with other onboard cargo loading and logistics systems, and even more preferably, will be integrated with such other onboard cargo systems ; Par. [0049-70]: a system and method according to the invention includes one or more cameras 100 strategically positioned within an aircraft cargo compartment… a plurality of cameras 100 are positioned within each of the cargo compartments 12a, 12b, 14 such that the combined fields of view of the plurality of cameras 100 at least include a substantial portion of each one of the cargo compartments 12a, 12b, and 14… FIG. 6 shows one arrangement of six cameras 100a-100f positioned at various locations within a main deck cargo compartment 14 of an aircraft 10… various numbers, positions, and angles of cameras 100 can be provided for viewing various regions of a main deck cargo compartment 14… A system and method according to the invention may include cameras 100 that provide periodic still images of associated cargo compartments 12a, 12b, 14. In a preferred embodiment, however, the cameras 100 are video cameras capable of providing continuous live video images of their associated cargo compartments 12a, 12b, 14… FIG. 18 shows one arrangement of mounting a camera 100 within a Cargo Maintenance Display Unit ("CMDU")… FIG. 18 shows one embodiment of an integrated cargo loading and cargo video monitoring system 200 according to the invention… the system 200 includes a main deck cargo control subsystem 202, a forward lower lobe cargo control subsystem 204, an aft lower lobe cargo control subsystem 206, and a cargo video monitoring/recording subsystem 300. In this embodiment, the cargo video monitoring/recording subsystem 300 includes eight cameras 100a-100h distributed about a main deck cargo compartment 14, a forward lower lobe cargo compartment 12a, and an aft lower lobe cargo compartment 12b like the camera placements shown in FIGS. 6, 9 and 10, for example. The cargo video monitoring/recording subsystem 300 also can include more or fewer cargo compartment cameras 100… FIG. 18 also shows a cargo video monitoring and recording subsystem 300 integrated with the cargo control subsystems 202, 204, 206 described above. As shown in FIG. 18, the video subsystem 300 includes a cargo video server ("CVS") 310 coupled to the main deck CMDU 230… the CVS 310 can be activated by other types of automated sensors for detecting activity within a cargo compartment, such as by motion detectors, aircraft wheel weight sensors, or the like. The CVS 310 can include an Ethernet connection 332 for connecting the CVS 310 to a portable computer or electronic flight bag ("EFB") 335, or to another electronic device capable of receiving video outputs from the CVS 310. In addition, the CVS 310 preferably is capable of recording video information on removable storage media 330 so that video image files can be saved and played later on a remote video-playing device, such as a PC 340… video signals received by the CVS 310 from any one of the main deck or lower lobe cargo compartment cameras 100a-100h can be selectively viewed on any of the cargo compartment CMDUs 230, 260, 294 ; the method comprising: receiving images of the cargo while the cargo is positioned within the vehicle (e.g. integrated system and method for monitoring and managing aircraft (i.e. a vehicle) cargo loading and unloading activities, and for remotely monitoring aircraft's cargo compartments and related cargo system components and systems, as indicated above, for example includes one or more cameras strategically positioned within an aircraft cargo compartment such that the combined fields of view of the plurality of cameras include a substantial portion of each one of the cargo compartments, including the cargo at each one of the cargo compartments, for example, including various numbers, positions, and angles of cameras provided for viewing various regions of each cargo compartment, as indicated above, for example, including a portable computer or another electronic device capable of receiving images or video outputs from any one of the one or more cameras strategically positioned within an aircraft cargo compartment (i.e. receiving images of the cargo while the cargo is positioned within the vehicle), as indicated above), for example), but fails to teach the following as further recited in claim 16. However, Souder teaches performing coarse detection and identify a limited section of the images that includes the cargo; and determine a type of cargo based on the feature (Par. [0003-30]: methods, including computer-implemented methods, devices, and computer-program products applying systems and methods for tracking pallets… a computer-implemented method for tracking goods carriers from a particular source is provided… the method includes receiving a first image file generated by a first imaging device, wherein the first image file includes a first image that depicts a first plurality of goods carriers from the particular source… using the trained artificial neural network to determine a first number of goods carriers from the particular source in the first image… receiving a second image file generated by a second imaging device, wherein the second image file includes a second image that depicts a second plurality of goods carriers from the particular source… using the trained artificial neural network to determine a second number of goods carriers from the particular source in the second image… using the artificial neural network to identify an object within both the first image and the second image. The method may also include matching at least a subset of the first plurality of goods carriers from the particular source in the first image to a first type of goods carriers from the particular source, and matching at least a subset of the second plurality of goods carriers from the particular source in the second image to the first type of goods carriers from the particular source… A goods carrier may be a structure that supports physical assets for storage, presentation, handling, and/or transportation. As used herein, the term “goods carrier” may be used to describe any load carrier or product conveyance platform… FIG. 1 is a bottom perspective view of a goods carrier 100… The goods carrier 100 shown in FIG. 1 is an example of a pallet ; Par. [0039-69]: track a plurality of goods carriers from a particular source as the goods carriers move through a transportation path having a plurality of nodes. For example, the particular source may be a manufacturer or distributor of the goods carriers… the goods carriers may be tracked through photographs without attaching physical tags or labels to the goods carriers. In particular, a plurality of goods carriers of a first type may be counted at various times and locations along the transportation path… server computer 420 may include a machine learning system, such as an artificial neural network, and may process an image within the image file to filter out goods carriers from third parties not associated with the particular source from the plurality of goods carriers; match at least a subset of the plurality of goods carriers to a first type of goods carriers from the particular source; and determine a number of goods carriers of the first type in the image. The server computer 420 may access a database 425 storing training image files including a respective plurality of training images in association with a known number of training goods carriers that are depicted in the respective training image. The server computer 420 may use the training image files to train the machine learning system to count the number of training goods carriers of the first type in subsequently acquired images… Processor 601 may include one or more microprocessors to execute program components for performing the goods carrier tracking functions of the server computer 420… Computer readable medium 606 may store code executable by the processor 601 for implementing some or all of the image analysis functions of server computer 420. For example, computer readable medium 606 may include code implementing a perspective correction engine 607, a scaling and rotation engine 608… the scaling and rotation engine 608 may be configured to crop the image of the goods carriers such that only the goods carriers are shown (i.e., eliminating background images)… matching engine 610 may be configured to analyze the image to match at least a subset of the remaining goods carriers to a first type of goods carriers from the particular source. Similar to the filtering, the matching may be based on visual features of any unique characteristics or combination of characteristics that are together unique to the first type of goods carriers… the machine learning system may match at least a subset of the remaining goods carriers to a first type of goods carriers from the particular source. The matching may be performed by a machine learning system, such as an artificial neural network. For example, the first type of goods carriers may be wooden pallets, plastic pallets, display pallets, automotive pallets, or aviation pallets ; Par. [0074-82]: the machine learning system may match at least a subset of the remaining goods carriers to the first type of goods carriers from the particular source. The matching may be performed by the machine learning system. For example, the first type of goods carriers may be wooden pallets, plastic pallets, display pallets, automotive pallets, or aviation pallets… FIG. 9 is a flow chart illustrating another exemplary method for goods carrier tracking, in accordance with some embodiments. As shown in FIG. 9, the results of the analysis of the first image file 705 and the second image file 805 may be used in conjunction to track the goods carriers as they move along the transportation path… various image processing techniques may be used in conjunction with the methods described above. For example, a specific stack of goods carriers may be isolated from an image that includes a plurality of stacks of goods carriers. This may be accomplished by any suitable method, such as applying a foreground/background segmentation to the image ; performing coarse detection and identify a limited section of the images that includes the cargo; and determine a type of cargo based on the feature (e.g. systems and methods for tracking (i.e. monitoring) good carriers (i.e. cargo), including structures that support physical assets for storage, presentation, handling, and/or transportation, such as pallets, for example, and detect good carriers in foreground sections of images (i.e. detect the cargo within the images) by isolating goods carriers from an image that includes a plurality of stacks of goods carriers (i.e. perform coarse detection and identify a limited section of the images that includes the cargo), for example, by applying a foreground/background segmentation to each image to crop the image of the goods carriers such that only the goods carriers are shown, thereby eliminating (i.e. removing, subtracting, etc.) background images (i.e. perform coarse detection), for example, and analyze each image to match (i.e. determine, identify, etc.) at least a subset of the remaining goods carriers to a first type of goods carriers (i.e. a type of the cargo), in which the first type of goods carriers include wooden pallets, plastic pallets, display pallets, automotive pallets, or aviation pallets, for example, based on visual features of any unique characteristics or combination of characteristics that are together unique to the first type of goods carriers (i.e. and determine a type of cargo based on the feature), as indicated above), for example). The same motivation to combine above-mentioned teachings applies, as previously indicated in claim 1. The steps further recited in method claim 16 correspond to claim 1 when executed and are rejected as applied to apparatus claim 1 above. Regarding claim 19 , claim 16 is incorporated and the combination of Reed, Souder, Wicks and Barburescu, as a whole, teaches the method (Reed, Par. [0002, 19-21]), further comprising performing the fine detection and identifying edges of the cargo (Wicks, Par. [0033-34]: the computing device may then perform 2D detection operations to identify (or predict the existence of) boxes within the processed image data. Such detection operations may include edge detection operations (e.g., using a Canny technique) as well as matching predefined box template shapes to visual aspects within imagery (e.g., edges)… the computing device may be configured to locate attributes of all identifiable items (e.g., cartons, boxes, etc.) within the imagery. For example, the computing device may perform image processing operations (e.g., apply a Hough transform) to identify the centroids (e.g., within +/−5 cm (x, y, z)), edges (e.g., the pickup edge of a box with respect to +/−1 cm of the centroid of the box, etc.), surfaces, and other geometry of boxes in a wall of boxes depicted in the imagery ; Par. [0141-143]: the processor may compute an average edge image. Computing an average edge image may include performing various edge detection operations (e.g., using a Canny technique, efficient graph based image segmentation, or any other type edge detection technique) to identify edges in the image formed from the segmented RGB-D data. The result of computing the average edge image may be an edge map including the detected edges, lines, etc. In block 1710 the processor may compute the working area. For example, the processor may analyze the image including the detected edges to determine the area or interest… The processor may then fix a predefined working area plane to the preliminary area of interest to create the working area, and the working area may be used to define boxes of interest. Specifically, bounded objects or detected boxes that are aligned on the predefined working area plane or within a tolerance of the predefined working area plane may be identified as valid, and bounded objects or detected boxes that are not within a tolerance of the predefined working area plane may be rejected… the processor may perform object detection in the masked edge map via multi-threshold object detection to identify bounded objects in the working area. In some embodiments, the processor may be configured to locate attributes of all identifiable bounded objects (e.g., boxes, labels, holes, etc.) within the masked edge map via multi-thresholding object detection operations, such as applying Hough transforms… the bounded objects detected in the masked edge map may be seeded with boxes. To seed boxes the processor may fit a plane to a center of the front face of the bounded object and may increase the size of the plane until the plane is the size of the bounded object. Seeded or identified boxes may be stored in a memory available to the processor, for example in an array. Each box may have a set of four corners, each with an x, y, and z value, and the processor may track each box as a set of corner values ; further comprising performing the fine detection and identifying edges of the cargo (e.g. methods, devices, systems, and non-transitory process-readable storage media for a computing device of a robotic carton unloader to identify items to be unloaded from an unloading area, such as a pallet (i.e. cargo), for example, within imagery from a vision system, include generalized Hough transform techniques (i.e. performing the fine detection) which are used to identify various types of shapes within imagery, including to identify the centroids, edges (i.e. identifying edges of the cargo), surfaces, and other geometry of boxes in a wall of boxes depicted in the imagery, as indicated above), for example). The same motivation to combine above-mentioned teachings applies, as previously indicated in claim 1. Regarding claim 20 , claim 16 is incorporated and the combination of Reed, Souder, Wicks and Barburescu, as a whole, teaches the method (Reed, Par. [0002, 19-21]), further comprising: determining one of the images in which the cargo fills a bounding box; and determining a center of the cargo based on the one image (Wicks, Par. [0033-34]: the computing device may then perform 2D detection operations to identify (or predict the existence of) boxes within the processed image data. Such detection operations may include edge detection operations (e.g., using a Canny technique) as well as matching predefined box template shapes to visual aspects within imagery (e.g., edges)… the computing device may be configured to locate attributes of all identifiable items (e.g., cartons, boxes, etc.) within the imagery. For example, the computing device may perform image processing operations (e.g., apply a Hough transform) to identify the centroids (e.g., within +/−5 cm (x, y, z)), edges (e.g., the pickup edge of a box with respect to +/−1 cm of the centroid of the box, etc.), surfaces, and other geometry of boxes in a wall of boxes depicted in the imagery ; Par. [0141-143]: the processor may compute an average edge image. Computing an average edge image may include performing various edge detection operations (e.g., using a Canny technique, efficient graph based image segmentation, or any other type edge detection technique) to identify edges in the image formed from the segmented RGB-D data. The result of computing the average edge image may be an edge map including the detected edges, lines, etc. In block 1710 the processor may compute the working area. For example, the processor may analyze the image including the detected edges to determine the area or interest… The processor may then fix a predefined working area plane to the preliminary area of interest to create the working area, and the working area may be used to define boxes of interest. Specifically, bounded objects or detected boxes that are aligned on the predefined working area plane or within a tolerance of the predefined working area plane may be identified as valid, and bounded objects or detected boxes that are not within a tolerance of the predefined working area plane may be rejected… the processor may perform object detection in the masked edge map via multi-threshold object detection to identify bounded objects in the working area… the processor may be configured to locate attributes of all identifiable bounded objects (e.g., boxes, labels, holes, etc.) within the masked edge map via multi-thresholding object detection operations, such as applying Hough transforms… the bounded objects detected in the masked edge map may be seeded with boxes. To seed boxes the processor may fit a plane to a center of the front face of the bounded object and may increase the size of the plane until the plane is the size of the bounded object. Seeded or identified boxes may be stored in a memory available to the processor, for example in an array. Each box may have a set of four corners, each with an x, y, and z value, and the processor may track each box as a set of corner values ; further comprising: determining one of the images in which the cargo fills a bounding box; and determining a center of the cargo based on the one image (e.g. methods, devices, systems, and non-transitory process-readable storage media for a computing device of a robotic carton unloader to identify items to be unloaded from an unloading area, such as a pallet (i.e. cargo), for example, within imagery from a vision system, including a processor configured to locate attributes of all identifiable bounded objects (i.e. determining one of the images in which the cargo fills a bounding box), for example, and including generalized Hough transform techniques (i.e. performing the fine detection) which are used to identify various types of shapes within imagery, including to identify the centroids (i.e. determining a center of the cargo based on the one image), edges (i.e. identifying edges of the cargo), surfaces, and other geometry of boxes in a wall of boxes depicted in the imagery, as indicated above), for example). The same motivation to combine above-mentioned teachings applies, as previously indicated in claim 1 . 07-21-aia AIA Claim s 3 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Reed, in view of Souder, in further view of Wicks, n further view of Barburescu, as applied to claim 1 above, in further view of Allen et al. (US PG Publication No. US 2023/0106572 A1), hereafter referred to as Allen, and in further view of Wienke et al. (US PG Publication No. US 2010/0040428 A1), hereafter referred to as Wienke . Regarding claim 3 , claim 1 is incorporated and the combination of Reed, Souder, Wicks, and Barburescu, as a whole, teaches the system (Reed, Par. [0002]), wherein the computing device is further configured to identify a mesh and to determine that the cargo is a pallet (Barburescu, Par. [0208-215]: detection of the pallet is performed by the pallet detector module… Cropping 1002 from a received video frame a region in which the presence of a pallet is detected by the pallet detector module. The cropped region corresponds with a region of the video frame occupied by a bounding box surrounding the pallet combined with a further 20 pixels added on each side of the bounding box, to make sure that the whole pallet is contained in the cropped region. For brevity, this cropped region will be referred to henceforth as a “Cropped Pallet Region”. In practice, a Cropped Pallet Region comprises, for example, 128×128 pixels, with the top-left corner of the Cropped Pallet Region located at coordinates (x1, y1) in the received video frame ; Par. [0219-220]: a pallet may be represented by a rectangular cuboid object. The volume of a rectangular cuboid object can be computed using the sizes (in pixels) of its edges. This is computed from the key points representing the corners of the pallet ; wherein the computing device is further configured to identify a mesh and to determine that the cargo is a pallet (e.g. Pallet Volumetric Estimation algorithm of the Pallet Volume Estimator computes the volume of objects by estimating their size (i.e. dimensions) in pixels from a 2D image, including a pallet represented by a rectangular cuboid object, in which the volume of a rectangular cuboid object is computed using the sizes (in pixels) of its edges from the key points representing corners of the pallet, for example, by cropping from a received video frame a region in which the presence of a pallet is detected (i.e. wherein the computing device is further configured to determine that the cargo is a pallet), as indicated above), for example). The same motivation to combine above-mentioned teachings applies, as previously indicated in claim 1. The combination of Reed, Souder, Wicks, and Barburescu, as a whole, teaches the system, as indicated above, but fails to trach the following as further recited in claim 3. However, Allen teaches wherein the computing device is further configured to identify the feature within the images as a mesh (Par. [0071-72]: retention detection system may also be employed to determine whether a retention system is present within a trailer (e.g., such as a restraining net, wall, or set of objects that are wrapped together, for example and provided on a pallet)… The system then records instances of net lines extending vertically across multiple retained objects (step 4008), and then records any image of any portion of a pallet near the floor of the trailer (step 4010). The system them sets a net detection signal responsive to any instances of net lines in connection with a plurality of retained objects (step 4012), and then sets a pallet detection signal responsive to any image of any portion of a pallet near the floor of the trailer in connection with a plurality of retained objects… retaining detection routine may be run on the one or more computer processing systems 100 with perception data from the perception unit 28, and may analyze image data in combination with object grasp attempt data to identify whether any retention system is inhibiting the removal of objects from the trailer. If a retaining feature is present, the system will run for each object that is found to be not movable. A combination of the results of the multiple executions of the routine provides duplicative results that should confirm the type of retaining feature present. For example, FIG. 30 shows a netting 56 that spans the width and height of the trailer and are attached to mounts 55 ; wherein the computing device is further configured to identify the feature within the images as a mesh (e.g. retention detection system which is run on one or more computer processing systems and analyzes image data in combination to identify features (i.e. the computing device is further configured to identify the feature) in order to determine whether a retention system is present within a trailer, or not, for example, such as a restraining net (i.e. identify the feature within the images as a mesh), or set of objects that are wrapped together, and provided on a pallet, as indicated above), for example). Reed, Souder, Wicks, Barburescu, and Allen are considered to be analogous art because they pertain to image processing applications related to cargo monitoring and tracking. Therefore, the combined teachings of Reed, Souder, Wicks, Barburescu, and Allen, as a whole, would have rendered obvious the invention recited in claim 3 with a reasonable expectation of success in order to modify the method and apparatus for monitoring and managing aircraft cargo loading and unloading activities, and for remotely monitoring an aircraft's cargo compartments and related cargo system components and systems (as disclosed by Reed) with wherein the computing device is further configured to identify the feature as a mesh and determine that the cargo is a pallet (as taught by Allen, Abstract, Par. [0071]) to confirm a type of retaining feature present, or absent, within shipping container vehicles, in order to unload vehicles and efficiently and to effectively provide an ordered stream of objects (Allen, Abstract, Par. [0002-8, 71]). The combination of Reed, Souder, Wicks, Barburescu, and Allen, as a whole, teaches the system, as indicated above, but fails to teach the following as further recited in claim 3. However, Wienke teaches a diamond-shaped section as a mesh (Par. [0001-3]: invention relates to a cargo net comprising a plurality of interconnected straps arranged in a lattice structure… a cargo net, having a plurality of intersecting straps forming a lattice structure which is adapted to maintain the load on a support or a pallet… the cargo nets comprising a plurality of interconnecting straps arranged in a lattice structure are preferentially used in securing cargo for air transportation ; Par. [0010]: cargo net is observable with the thermal imaging devices ; Par. [0024]: structure of the cargo net according to the invention may be of any structure known in the art. The most preferred structures are a diamond- or square-patterned net ; a diamond-shaped section as a mesh (e.g. invention relates to a cargo net (i.e. a mesh) comprising a plurality of interconnected straps arranged in a lattice structure, for example, including a diamond-patterned net (i.e. a diamond-shaped section as a mesh), as indicated above), for example). Reed, Souder, Wicks, Barburescu, Allen, and Wienke are considered to be analogous art because they pertain to image processing applications related to cargo monitoring and tracking. Therefore, the combined teachings of Reed, Souder, Wicks, Barburescu, Allen, and Wienke, as a whole, would have rendered obvious the invention recited in claim 3 with a reasonable expectation of success in order to modify the method and apparatus for monitoring and managing aircraft cargo loading and unloading activities, and for remotely monitoring an aircraft's cargo compartments and related cargo system components and systems (as disclosed by Reed) with a diamond-shaped section as a mesh (as taught by Wienke, Abstract, Par. [0001-3, 10, 24]) to maintain a load on a support or a pallet and to secure cargo for air transportation (Wienke, Abstract, Par. [0001-3, 10, 24]). Regarding claim 17 , claim 16 is incorporated and the combination of Reed, Souder and Wicks, as a whole, teaches the method (Reed, Par. [0002, 19-21]), wherein identifying the feature further comprises identifying a mesh that extends over packages of the cargo (Par. [0071-72]: retention detection system may also be employed to determine whether a retention system is present within a trailer (e.g., such as a restraining net, wall, or set of objects that are wrapped together, for example and provided on a pallet)… The system then records instances of net lines extending vertically across multiple retained objects (step 4008), and then records any image of any portion of a pallet near the floor of the trailer (step 4010). The system them sets a net detection signal responsive to any instances of net lines in connection with a plurality of retained objects (step 4012), and then sets a pallet detection signal responsive to any image of any portion of a pallet near the floor of the trailer in connection with a plurality of retained objects… retaining detection routine may be run on the one or more computer processing systems 100 with perception data from the perception unit 28, and may analyze image data in combination with object grasp attempt data to identify whether any retention system is inhibiting the removal of objects from the trailer. If a retaining feature is present, the system will run for each object that is found to be not movable. A combination of the results of the multiple executions of the routine provides duplicative results that should confirm the type of retaining feature present. For example, FIG. 30 shows a netting 56 that spans the width and height of the trailer and are attached to mounts 55 ; wherein identifying the feature comprises identifying a mesh that extends over packages of the cargo (e.g. retention detection system which is run on one or more computer processing systems and analyzes image data in combination to identify features in order to determine whether a retention system is present within a trailer, or not, for example, such as a restraining net (i.e. a mesh), or set of objects that are wrapped together (i.e. wherein identifying the feature comprises identifying a mesh that extends over packages of the cargo), and provided on a pallet, as indicated above), for example). The same motivation to combine above-mentioned teachings applies, as previously indicated in claim 3. The combination of Reed, Souder, Wicks, Barburescu, and Allen, as a whole, teaches the method, as indicated above, but fails to teach the following as further recited in claim 17. However, Wienke teaches diamond-shaped openings of a mesh (Par. [0001-3]: invention relates to a cargo net comprising a plurality of interconnected straps arranged in a lattice structure… a cargo net, having a plurality of intersecting straps forming a lattice structure which is adapted to maintain the load on a support or a pallet… the cargo nets comprising a plurality of interconnecting straps arranged in a lattice structure are preferentially used in securing cargo for air transportation ; Par. [0010]: cargo net is observable with the thermal imaging devices ; Par. [0024]: structure of the cargo net according to the invention may be of any structure known in the art. The most preferred structures are a diamond- or square-patterned net ; diamond-shaped openings of a mesh (e.g. invention relates to a cargo net (i.e. a mesh) comprising a plurality of interconnected straps arranged in a lattice structure, for example, including a diamond-patterned net (i.e. diamond-shaped openings of a mesh), as indicated above), for example). The same motivation to combine above-mentioned teachings applies, as previously indicated in claim 3. Conclusion 07-40 AIA Applicant’s amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL . See MPEP § 706.07(a). 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 Any inquiry concerning this communication or earlier communications from the examiner should be directed to GUILLERMO RIVERA-MARTINEZ whose telephone number is 571-272-4979. The examiner can normally be reached on Monday-Friday (8am - 5pm Eastern Time). If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Bee can be reached on 571-270-5183. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /GUILLERMO M RIVERA-MARTINEZ/ Primary Examiner, Art Unit 2677 Application/Control Number: 18/394,853 Page 2 Art Unit: 2677 Application/Control Number: 18/394,853 Page 3 Art Unit: 2677 Application/Control Number: 18/394,853 Page 4 Art Unit: 2677 Application/Control Number: 18/394,853 Page 5 Art Unit: 2677 Application/Control Number: 18/394,853 Page 6 Art Unit: 2677 Application/Control Number: 18/394,853 Page 7 Art Unit: 2677 Application/Control Number: 18/394,853 Page 8 Art Unit: 2677 Application/Control Number: 18/394,853 Page 9 Art Unit: 2677 Application/Control Number: 18/394,853 Page 10 Art Unit: 2677 Application/Control Number: 18/394,853 Page 11 Art Unit: 2677 Application/Control Number: 18/394,853 Page 12 Art Unit: 2677 Application/Control Number: 18/394,853 Page 13 Art Unit: 2677 Application/Control Number: 18/394,853 Page 14 Art Unit: 2677 Application/Control Number: 18/394,853 Page 15 Art Unit: 2677 Application/Control Number: 18/394,853 Page 16 Art Unit: 2677 Application/Control Number: 18/394,853 Page 17 Art Unit: 2677 Application/Control Number: 18/394,853 Page 18 Art Unit: 2677 Application/Control Number: 18/394,853 Page 19 Art Unit: 2677 Application/Control Number: 18/394,853 Page 20 Art Unit: 2677 Application/Control Number: 18/394,853 Page 21 Art Unit: 2677 Application/Control Number: 18/394,853 Page 22 Art Unit: 2677 Application/Control Number: 18/394,853 Page 23 Art Unit: 2677 Application/Control Number: 18/394,853 Page 24 Art Unit: 2677 Application/Control Number: 18/394,853 Page 25 Art Unit: 2677 Application/Control Number: 18/394,853 Page 26 Art Unit: 2677 Application/Control Number: 18/394,853 Page 27 Art Unit: 2677 Application/Control Number: 18/394,853 Page 28 Art Unit: 2677 Application/Control Number: 18/394,853 Page 29 Art Unit: 2677 Application/Control Number: 18/394,853 Page 30 Art Unit: 2677 Application/Control Number: 18/394,853 Page 31 Art Unit: 2677 Application/Control Number: 18/394,853 Page 32 Art Unit: 2677 Application/Control Number: 18/394,853 Page 33 Art Unit: 2677 Application/Control Number: 18/394,853 Page 34 Art Unit: 2677 Application/Control Number: 18/394,853 Page 35 Art Unit: 2677 Application/Control Number: 18/394,853 Page 36 Art Unit: 2677 Application/Control Number: 18/394,853 Page 37 Art Unit: 2677 Application/Control Number: 18/394,853 Page 38 Art Unit: 2677 Application/Control Number: 18/394,853 Page 39 Art Unit: 2677 Application/Control Number: 18/394,853 Page 40 Art Unit: 2677 Application/Control Number: 18/394,853 Page 41 Art Unit: 2677 Application/Control Number: 18/394,853 Page 42 Art Unit: 2677 Application/Control Number: 18/394,853 Page 43 Art Unit: 2677 Application/Control Number: 18/394,853 Page 44 Art Unit: 2677 Application/Control Number: 18/394,853 Page 45 Art Unit: 2677 Application/Control Number: 18/394,853 Page 46 Art Unit: 2677 Application/Control Number: 18/394,853 Page 47 Art Unit: 2677 Application/Control Number: 18/394,853 Page 48 Art Unit: 2677 Application/Control Number: 18/394,853 Page 49 Art Unit: 2677 Application/Control Number: 18/394,853 Page 50 Art Unit: 2677 Application/Control Number: 18/394,853 Page 51 Art Unit: 2677 Application/Control Number: 18/394,853 Page 52 Art Unit: 2677 Application/Control Number: 18/394,853 Page 53 Art Unit: 2677 Application/Control Number: 18/394,853 Page 54 Art Unit: 2677 Application/Control Number: 18/394,853 Page 55 Art Unit: 2677 Application/Control Number: 18/394,853 Page 56 Art Unit: 2677 Application/Control Number: 18/394,853 Page 57 Art Unit: 2677 Application/Control Number: 18/394,853 Page 58 Art Unit: 2677 Application/Control Number: 18/394,853 Page 59 Art Unit: 2677 Application/Control Number: 18/394,853 Page 60 Art Unit: 2677 Application/Control Number: 18/394,853 Page 61 Art Unit: 2677 Application/Control Number: 18/394,853 Page 62 Art Unit: 2677 Application/Control Number: 18/394,853 Page 63 Art Unit: 2677
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