Patent Publication Number: US-2023147974-A1

Title: Pick assist system

Description:
BACKGROUND 
     The delivery of products to stores from distribution centers has many steps that have the potential for errors and inefficiencies. When the order from the store is received, at least one pallet is loaded with the specified products according to a “pick list” indicating a quantity of each product to be delivered to the store. 
     For example, the products may be cases of beverage containers (e.g. cartons of cans, beverage crates containing bottles or cans, cardboard trays with plastic overwrap containing cans or bottles, etc). There are numerous permutations of flavors, sizes, and types of beverage containers delivered to each store. When building pallets, missing or mis-picked product can account for significant additional operating costs. 
     SUMMARY 
     A pick assist system provides several novel features, each of which could be practiced independently of the others, but some of which achieve additional benefit when practiced together. 
     One of the features provided in the pick assist system is a pallet destacker (or pallet dispenser). The pallet destacker includes a vertical body configured to store a front column of pallets and a back column of pallet. At least one rfid reader is configured to read an rfid tag on a pallet in or below at least one of the front column of pallets or the back column of pallets. 
     The at least one rfid reader may include a front rfid reader positioned to read the rfid tag of a pallet in or below the front column of pallets and a back rfid reader positioned to read the rfid tag of a pallet in or below the back column of pallets. 
     The pallet destacker may be used in combination with a validation system including at least one camera for imaging a plurality of items stacked on a pallet. At least one processor may be programmed to identify skus of the plurality of items stacked on the pallet based upon images from the at least one camera. The at least one processor may be programmed to compare the identified skus to a list of desired skus based upon a pallet id of the pallet. The at least one processor may be programmed to identify the pallet id of the pallet based upon the rfid tag on the pallet read by the at least one rfid reader in the destacker. 
     Another feature disclosed herein relates to a method for dispensing pallets. A plurality of pallets including a bottom pallet are stored in a stack. The plurality of pallets other than the bottom pallet are lifted off the bottom pallet. An identifier on the bottom pallet is read. The bottom pallet is moved laterally away from the stack. 
     The bottom pallet may be read before or during moving the bottom pallet away from the stack. 
     Optionally, the stack may be a first stack and the steps of dispensing and reading may be performed for a second stack of pallets while they are performed for the first stack. 
     Reading the identifier may include reading an rfid tag. 
     As another optional feature, the bottom pallets of the first stack and the second stack may be lifted on tines of a pallet sled, such that the bottom pallet of the first stack is a front pallet and the bottom pallet of the second stack is a back pallet on the tines of the pallet sled. 
     The identifiers may be communicated to at least one processor on the pallet sled. The identifier of the front pallet may be associated to the front pallet and the identifier of the back pallet may be associated to the back pallet. 
     In another independent feature disclosed herein, a display on a pallet sled displays a product to be retrieved. It is determined that the product has been placed in a center of a pallet on the pallet sled (i.e. such that it is or will be or might be in an interior of a stack and not visible from the exterior sides). 
     There are several ways of determining that the product has been placed in a center of a pallet on the pallet sled. In one technique, a confirmation is received from a user that the product has been placed in a center of the pallet. In other technique, a user is instructed to place the product in the center of the pallet. 
     The method may further include placing a plurality of products including the first product in a stack on the pallet such that the first product is not visible from an exterior of the stack. The plurality of products may include a plurality of exterior products that are visible from the exterior of the stack. A plurality of images of the stack is received. Skus of each of the plurality of exterior products in the stack are identified. A sku of the first product is then determined based upon the skus of the exterior products. The skus of the plurality of exterior products and the sku of the first product are compared to a list of desired skus. 
     The method may further include determining that the product was in a layer pick. The determination that the product was in the center of the pallet (interior of the stack) may be based upon the determination that the product was in a layer pick. 
     Another method disclosed herein relates to loading and verifying a pallet. It is indicated on a display on a pallet sled a desired number of a product to be retrieved. A user is asked for a count of how many of the product was retrieved. The count is compared to the desired number of the product. Based upon the comparison, the user is asked why the count is less than the desired number. 
     The user may be asked why the count is low using the display. 
     A menu of a plurality of reasons why the count might be low is presented to the user. 
     Another method described herein relates verifying a pallet. A plurality of images of a plurality of products in a stack are received. The plurality of products includes a plurality of exterior products that are visible from the exterior of the stack. At least one processor identifies skus of each of the plurality of exterior products in the stack, including a plurality of exterior products in a layer. The skus of each of the plurality of exterior products in the layer are determined to be the same. d) based upon step c), determining that at least one interior product not visible in the plurality of images has the same sku as the plurality of exterior products. 
     The plurality of exterior products in the layer may be all of the exterior products in the layer. 
     The skus of the plurality of exterior products may be compared to the sku of the at least one interior product to a list of desired skus. 
     At least one processor may infer the skus of each of the plurality of exterior products using at least one machine learning model. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is a schematic view of a delivery system. 
         FIG.  2    is a flowchart of one version of a method for delivering items. 
         FIG.  3    shows an example loading station of the delivery system of  FIG.  1   . 
         FIG.  4    shows an example validation station of the delivery system of  FIG.  1   . 
         FIG.  5    is another view of the example validation system of  FIG.  4    with a loaded pallet thereon. 
         FIG.  6    shows yet another example validation system of the delivery system of  FIG.  1   . 
         FIG.  7    shows portions of a plurality of machine learning models. 
         FIG.  8    is a flowchart showing a method for creating the machine learning models of  FIG.  7   . 
         FIG.  9    shows sample text descriptions of a plurality of sample SKUs, including how SKUs are identified by both package type and brand. 
         FIG.  10    is a flowchart of a sku identification method. 
         FIG.  11    illustrates the step of detecting the package faces on each side of the stack of items. 
         FIG.  12    illustrates four pallet faces of a loaded pallet. 
         FIG.  12 A  shows stitching all package faces together for one of the packages from the pallet faces in  FIG.  12   . 
         FIG.  12 B  shows stitching all package faces together for another one of the packages from the pallet faces in  FIG.  12   . 
         FIG.  12 C  shows stitching all package faces together for another one of the packages from the pallet faces in  FIG.  12   . 
         FIG.  12 D  shows stitching all package faces together for another one of the packages from the pallet faces in  FIG.  12   . 
         FIGS.  13  and  14    illustrate the step of selecting the best package type from the stitched package faces. 
         FIG.  15    shows an example of a plurality stitched images and selecting the best brand from among the plurality of stitched images. 
         FIG.  16    shows a flowchart for a SKU set heuristic. 
         FIG.  17    shows one possible architecture of the training feature of the system of  FIG.  1   . 
         FIG.  18    is a flowchart of one version of a method for training a machine learning model. 
         FIG.  19    shows an example screen indicating a validated loaded pallet at the distribution center. 
         FIG.  20    shows an example screen indicating a mis-picked loaded pallet at the distribution center. 
         FIG.  21    shows one possible implementation of a pick system. 
         FIG.  22    is a front perspective view of the pick system of  FIG.  21   . 
         FIG.  23    shows one screen on the mobile device  4  of the pick system of  FIG.  21   . 
         FIG.  24    shows a screen on the mobile device in which the mobile device takes a picture of the picker. 
         FIG.  24 A  an operator level screen on the mobile device where the picker can choose a skill level. 
         FIG.  24 B  shows a metrics screen on the mobile device. 
         FIG.  25    shows the pick system and a plurality of products arranged on shelves throughout a distribution center. 
         FIG.  26    shows a next product screen displayed on the mobile device. 
         FIG.  27    shows a camera of the mobile device taking images of each product retrieved by the user as the user approaches the pallet sled. 
         FIG.  28    shows the pallet sled with the mobile device indicating the location to place the next product. 
         FIG.  29    shows the mobile device of  FIG.  28    showing a 3D representation of the partially-loaded pallets and an indication of the location to place the next product. 
         FIG.  30    shows the pallet sled with the mobile device indicating that the product has been placed in the correct location on the pallets and on the stack of products. 
         FIG.  31    shows the pallet sled with the mobile device indicating that the product has been placed in an incorrect location on the pallets and on the stack of products. 
         FIG.  31 A  shows the mobile device displaying a request for a number of items picked by the user. 
         FIG.  31 B  shows a screen of the mobile device requesting a reason from the user why the count was short. 
         FIG.  31 C  shows a screen of the mobile device instructing the user how to get to the next pick item. 
         FIG.  31 D  shows an error screen of the mobile device. 
         FIG.  31 E  shows a screen of the mobile device instructing the user to take the pallet to a particular quality control location. 
         FIG.  31 F  shows a screen of the mobile device instructing the user to take the pallet to a particular loading bay or truck door. 
         FIG.  31 G  shows a pallet complete screen of the mobile device. 
         FIG.  31 H  shows a performance screen on the mobile device indicating the user&#39;s statistics and ranking for the day. 
         FIG.  32    shows a screen of the mobile device instructing the picker which validation station to take the pallets. 
         FIG.  33    shows another example pallet sled incorporated as an automated guided vehicle that could be used in the pick system of  FIG.  21   . 
         FIG.  34    shows two of the pallet sleds of  FIG.  33   . 
         FIG.  35    shows the pallet sled of  FIG.  33    approaching a pallet destacker. 
         FIG.  36    shows the pallet sled and pallet destacker of  FIG.  35   , with the pallet sled retrieving two empty pallets from the pallet destacker. 
         FIG.  36 A  is a side view of the pallet destacker, broken away with some components shown schematically. 
         FIGS.  37  and  38    illustrate a particular method that can be used with the automated guided vehicle pallet sleds. 
         FIG.  39    shows the pallet sled of  FIG.  33    bringing two loaded pallets to a validation station. 
         FIG.  40    shows a pallet on a turntable of a validation station 
         FIG.  41    illustrates a variation of the pallet sled including smart glasses. 
         FIG.  42    shows the glasses of  FIG.  41    confirming the selection of the next product and indicating a location to place the next product. 
         FIG.  43    is another view of the user wearing the glasses of  FIG.  22    and placing the next product onto the pallets. 
         FIG.  44    shows a pallet sled with a full-size pallet thereon. 
         FIG.  45    shows two optional center confirmation screens that can be displayed on the mobile device of the pallet sled of  FIG.  44   . 
         FIG.  45 A  shows an interrogation screen in which the mobile device asks the user how many items were placed in the center of the pallet. 
         FIG.  46    is a flowchart using confirmation of center placement to validate a pallet. 
         FIG.  47    is a flowchart of another method for center placement validation. 
         FIG.  48    shows a first screen for the user to create the map. 
         FIG.  49    shows a screen enabling a user to choose from among several items to place on the map. 
         FIG.  50    shows a Pick Item screen. 
         FIG.  51    shows a screen in which the user has added all of the Pick Items to the map. 
         FIG.  52    shows a screen in which the user has selected “Walking Paths” and has added the walking paths (between the pick items) to the map. 
         FIG.  53    shows a “walls” user screen in which the user can add the walls to the map. 
         FIG.  54    shows a “loading bay” screen for the user to add loading bays to the map. 
         FIG.  55    shows a “QC Station” screen in which the user identifies the locations of several QC stations. 
         FIG.  56    shows a “wrapper” screen in which the user can identify the locations of wrappers. 
     
    
    
     DETAILED DESCRIPTION 
       FIG.  1    is a high-level view of a delivery system  10  including one or more distribution centers  12 , a central server  14  (e.g. cloud computer), and a plurality of stores  16 . A plurality of trucks  18  or other delivery vehicles each transport the products  20  on pallets  22  from one of the distribution centers  12  to a plurality of stores  16 . Each truck  18  carries a plurality of pallets  22  which may be half pallets (or full-size pallets), each loaded with a plurality of goods  20  for delivery to one of the stores  16 . A wheeled sled  24  is on each truck  18  to facilitate delivery of one of more pallets  22  of goods  20  to each store  16 . Generally, the goods  20  could be loaded on the half pallets, full-size pallets, carts, or hand carts, or dollies—all considered “platforms” herein. 
     Each distribution center  12  includes one or more pick stations  30 , a plurality of validation stations  32 , and a plurality of loading stations  34 . Each loading station  34  may be a loading dock for loading the trucks  18 . 
     Each distribution center  12  may include a DC computer  26 . The DC computer  26  receives orders  60  from the stores  16  and communicates with a central server  14 . Each DC computer  26  receives orders and generates pick sheets  64 , each of which stores SKUs and associates them with pallet ids. Alternatively, the orders  60  can be sent from the DC computer  26  to the central server  14  for generation of the pick sheets  64 , which are synced back to the DC computer  26 . 
     Some or all of the distribution centers  12  may include a training station  28  for generating image information and other information about new products  20  which can be transmitted to the central server  14  for analysis and future use. 
     The central server  14  may include a plurality of distribution center accounts  40 , including DC 1 -DCn, each associated with a distribution center  12 . Each DC account  40  includes a plurality of store accounts  42 , including store  1 -store n. The orders  60  and pick sheets  64  for each store are associated the associated store account  42 . The central server  14  further includes a plurality of machine learning models  44  trained as will be described herein based upon SKUs. The models  44  may be periodically synced to the DC computers  26  or may be operated on the server  14 . 
     The machine learning models  44  are used to identify SKUs. A “SKU” may be a single variation of a product that is available from the distribution center  12  and can be delivered to one of the stores  16 . For example, each SKU may be associated with a particular package type, e.g. the number of containers (e.g. 12 pack) in a particular form (e.g. can v bottle) and of a particular size (e.g. 24 ounces) optionally with a particular secondary container (cardboard vs reusable plastic crate, cardboard tray with plastic overwrap, etc). In other words, the package type may include both primary packaging (can, bottle, etc, in direct contact with the beverage or other product) and any secondary packaging (crate, tray, cardboard box, etc, containing a plurality of primary packaging containers). 
     Each SKU may also be associated with a particular “brand” (e.g. the manufacturer and the specific variation, e.g. flavor). The “brand” may also be considered the specific content of the primary package and secondary package (if any) for which there is a package type. This information is stored by the server  14  and associated with the SKU along with the name of the product, a description of the product, dimensions of the product, and optionally the weight of the product. This SKU information is associated with image information for that SKU in the machine learning models  44 . 
     It is also possible that more than one variation of a product may share a single SKU, such as where only the packaging, aesthetics, and outward appearance of the product varies, but the content and quantity/size is the same. For example, sometimes promotional packaging may be utilized, which would have different image information for a particular SKU, but it is the same beverage in the same primary packaging with secondary packaging having different colors, text, and/or images. Alternatively, the primary packaging may also be different (but may not be visible, depending on the secondary packaging). In general, all the machine learning models  44  may be generated based upon image information generated through the training module  28 . 
     Referring to  FIG.  1    and also to the flowchart in  FIG.  2   , an order  60  may be received from a store  16  in step  150 . As an example, an order  60  may be placed by a store employee using an app or mobile device  52 . The order  60  is sent to the distribution center computer  26  (or alternatively to the server  14 , and then relayed to the proper (e.g. closest) distribution center computer  26 ). The distribution center computer  26  analyzes the order  60  and creates a pick sheet  64  associated with that order  60  in step  152 . The pick sheet  64  assigns each of the SKUs (including the quantity of each SKU) from the order. The pick sheet  64  specifies how many pallets  22  will be necessary for that order (as determined by the DC computer  26 ). The DC computer  26  may also determine which SKUs should be loaded near one another on the same pallet  22 , or if more than one pallet  22  will be required, which SKUs should be loaded together on the same pallet  22 . For example, SKUs that go in the cooler may be together on the same pallet (or near one another on the same pallet), while SKUs that go on the shelf may be on another part of the pallet (or on another pallet, if there is more than one). If the pick sheet  64  is created on the DC computer  26 , it is copied to the server  14 . If it is created on the server  14 , it is copied to the DC computer  26 . 
       FIG.  3    shows one example of the pick station  30  of  FIG.  1   . Referring to  FIGS.  1  and  3   , workers at the distribution center read the palled id (e.g. via rfid, barcode, etc) on the pallet(s)  22  on a pallet jack  24   a,  such as with a mobile device or a reader on the pallet jack  24   a.  In  FIG.  3   , two pallets  22  are on a single pallet jack  24   a.  Shelves may contain a variety of items  20  for each SKU, such as first product  20   a  of a first SKU and a second product  20   b  of a second SKU (collectively “products  20 ”). A worker reading a computer screen or mobile device screen displaying from the pick sheet  64  retrieves each product  20  and places that product  20  on the pallet  22 . Alternatively, the pallet  22  may be loaded by automated handling equipment. 
     Workers place items  20  on the pallets  22  according to the pick sheets  64 , and report the palled ids to the DC computer  26  in step  154  ( FIG.  2   ). The DC computer  26  dictates merchandizing groups and sub groups for loading items  20   a, b  on the pallets  22  in order to make unloading easier at the store. In the example shown, the pick sheets  64  dictate that products  20   a  are on one pallet  22  while products  20   b  are on another pallet  22 . For example, cooler items should be grouped, and dry items should be grouped. Splitting of package groups is also minimized to make unloading easer. This makes pallets  22  more stable too. 
     The DC computer  26  records the pallet ids of the pallet(s)  22  that have been loaded with particular SKUs for each pick sheet  64 . The pick sheet  64  may associate each pallet id with each SKU. 
     After being loaded, each loaded pallet  22  is validated at the validation station  32 , which may be adjacent to or part of the pick station  30 . As will be described in more detail below, at least one still image, and preferably several still images or video, of the products  20  on the pallet  22  is taken at the validation station  32  in step  156  ( FIG.  2   ). The pallet id of the pallet  22  is also read. The images are analyzed to determine the SKUS of the products  20  that are currently on the identified pallet  22  in step  158 . The SKUs of the products  20  on the pallet  22  are compared to the pick sheet  64  by the DC computer  26  in step  160 , to ensure that all the SKUs associated with the pallet id of the pallet  22  on the pick sheet  64  are present on the correct pallet  22 , and that no additional SKUs are present. Several ways are of performing the aforementioned steps are disclosed below. 
     First, referring to  FIGS.  4  and  5   , the validation station may include a CV/RFID semi-automated wrapper  66   a  with turntable  67  that is fitted with a camera  68  and rfid reader  70  (and/or barcode reader). The wrapper  66   a  holds a roll of translucent, flexible, plastic wrap or stretch wrap  72 . As is known, a loaded pallet  22  can be placed on the turntable  67 , which rotates the loaded pallet  22  as stretch wrap  72  is applied. The camera  68  may be a depth camera. In this wrapper  66   a,  the camera  68  takes at least one image of the loaded pallet  22  while the turntable  67  is rotating the loaded pallet  22 , prior to or while wrapping the stretch wrap  72  around the loaded pallet  22 . Images/video of the loaded pallet  22  after wrapping may also be generated. As used herein, “image” or “images” refers broadly to any combination of still images and/or video, and “imaging” means capturing any combination of still images and/or video. Again, preferably  2  to  4  still images, or video, are taken. Most preferably, one still image of each of the four sides of a loaded pallet  22  is taken. 
     In one implementation, the camera  68  may be continuously determining depth while the turntable  67  is rotating. When the camera  68  detects that the two outer ends of the pallet  22  are equidistant (or otherwise that the side of the pallet  22  facing the camera  68  is perpendicular to the camera  68  view), the camera  68  records a still image. The camera  68  can record four still images in this manner, one of each side of the pallet  22 . 
     The rfid reader  70  (or barcode reader, or the like) reads the pallet id (a unique serial number) from the pallet  22 . The wrapper  66   a  includes a local computer  74  in communication with the camera  68  and rfid reader  70 . The computer  74  can communicate with the DC computer  26  (and/or server  14 ) via a wireless network card  76 . The image(s) and the pallet id are sent to the server  14  via the network card  76  and associated with the pick list  64  ( FIG.  1   ). Optionally, a weight sensor can be added to the turntable  67  and the known total weight of the products  20  and pallet  22  can be compared to the measured weight on the turntable  67  for confirmation. An alert is generated if the total weight on the turntable  67  does not match the expected weight (i.e. the total weight of the pallet plus the known weights for the SKUs for that pallet id on the pick sheet). Other examples using the weight sensor are provided below. 
     As an alternative, the turntable  67 , camera  68 , rfid reader  70 , and computer  74  of  FIGS.  3  and  4    can be used without the wrapper. The loaded pallet  22  can be placed on the turntable  67  for validation only and can be subsequently wrapped either manually or at another station. 
     Alternatively, the validation station can include the camera  68  and rfid reader  70  (or barcode reader, or the like) mounted to a robo wrapper (not shown). As is known, instead of holding the stretch wrap  72  stationary and rotating the pallet  22 , the robo wrapper travels around the loaded pallet  22  with the stretch wrap  72  to wrap the loaded pallet  22 . The robo wrapper carries the camera,  68 , rfid reader  70 , computer  74  and wireless network card  76 . 
     Alternatively, referring to  FIG.  6   , the validation station can include a worker with a networked camera, such as on a mobile device  78  (e.g. smartphone or tablet) for taking one or more images  62  of the loaded pallet  22 , prior to wrapping the loaded pallet  22 . Again, preferably, one image of each face of the loaded pallet  22  is taken. Note that  FIG.  6    shows a full-size pallet (e.g. 40×48 inches). Any imaging method can be used with any pallet size, but a full-size pallet is shown in  FIG.  6    to emphasize that the inventions herein (including the turntables and/or wrappers of  FIGS.  3  and  4   ) can also be used with full-size pallets, although with some modifications. 
     Other ways can be used to gather images of the loaded pallet. In any of the methods, the image analysis and/or comparison to the pick list is performed on the DC computer  26 , which has a copy of the machine learning models. Alternatively, the analysis and comparison can be done on the server  14 , locally on a computer  74 , or on the mobile device  78 , or on another locally networked computer. 
     As mentioned above, the camera  68  (or the camera on the mobile device  78 ) can be a depth camera, i.e. it also provides distance information correlated to the image (e.g. pixel-by-pixel distance information or distance information for regions of pixels). Depth cameras are known and utilize various technologies such as stereo vision (i.e. two cameras) or more than two cameras, time-of-flight, or lasers, etc. If a depth camera is used, then the edges of the products stacked on the pallet  22  are easily detected (i.e. the edges of the entire stack and possibly edges of individual adjacent products either by detecting a slight gap or difference in adjacent angled surfaces). Also, the depth camera  68  can more easily detect when the loaded pallet  22  is presenting a perpendicular face to the view of the camera  68  for a still image to be taken. 
     However the image(s) of the loaded pallet  22  are collected, the image(s) are then analyzed to determine the sku of every item  20  on the pallet  22  in step  158  ( FIG.  2   ). Image information, weight and dimensions of all sides of every possible product, including multiple versions of each SKU, if applicable, are stored in the server  14 . 
       FIG.  7    shows a portion of a brand model map  230  containing the machine learning models for the brand identification, in this example brand models  231   a,    231   b,    231   c.  In  FIG.  7   , each white node is a brand node  232  that represents a particular brand and each black node is a package node  234  that represents a package type. Each edge or link  236  connects a brand node  232  to a package node  234 , such that each link  236  represents a SKU. Each brand node  232  may be connected to one or more package nodes  234  and each package node  234  may connect to one or more brand nodes  232 . 
     In practice, there may be hundreds or thousands of such SKUs and there would likely be two to five models  231 . If there are even more SKUs, there could be more models  231 .  FIG.  7    is a simplified representation showing only a portion of each brand model  231   a,    231   b,    231   c.  Each model may have dozens or even hundreds of SKUs. 
     Within each of models  231   a  and  231   b,  all of the brand nodes  232  and package nodes  234  are connected in the graph, but this is not required. In fact, there may be one or more (four are shown) SKUs that are in both models  231   a  and  231   b.  There is a cut-line  238   a  separating the two models  231   a  and  231   b.  The cut-line  238   a  is positioned so that it cuts through as few SKUs as possible but also with an aim toward having a generally equal or similar number of SKUs in each model  231 . Each brand node  232  and each package node  234  of the SKUs along the cut-line  238   a  are duplicated in both adjacent models  231   a  and  231   b.  For the separation of model  231   c  from models  231   a  and  231   b,  it was not necessary for the cut line  238   b  to pass through (or duplicate) any of the SKUs or nodes  232 ,  234 . 
     In this manner, the models  231   a  and  231   b  both learn from the SKUs along the cut  238   b.  The model  231   b  learns more about the brand nodes  232  in the overlapping region because it also learns from those SKUs. The model  231   a  learns more about the package types  234  in the overlapping region because it also learns from those SKUs. If those SKUs were only placed in one of the models  231   a,    231   b,  then the other model would not have as many samples from which to learn. 
     In brand model  231   c,  for example, as shown, there are a plurality of groupings of SKUs that do not connect to other SKUs, i.e. they do not share either a brand or a package type. The model  231   c  may have many (dozens or more) of such non-interconnected groupings of SKUs. The model  231   a  and the model  231   b  may also have some non-interconnected groupings of SKUs (not shown). 
     Referring to  FIGS.  7  and  8   , the process for creating the models  231  is automated and performed in the central server  14  or the DC computer  26  ( FIG.  1   ). In particular, this is the process for creating the brand models. There would be one model for determining package type and then depending on how many brands there are, the SKUs are separated into multiple separate machine learning models for the brands. 
     This process is performed initially when creating the machine learning models and again when new SKUs are added. Initially, a target number of SKUs per model or a target number of models may be chosen to determine a target model size. Then the largest subgraph (i.e. a subset of SKUs that are all interconnected) is compared to the target model size. If the largest subgraph is within a threshold of the target model size, then no cuts need to be made. If the largest subgraph is more than a threshold larger than the target model size, then the largest subgraph will be cut according to the following method. In step  240 , the brand nodes  232 , package nodes  234 , and SKU links  236  are created. In steps  242  and  244 , the cut line  238  is determined as the fewest numbers of SKU links  236  to cut (cross), while placing a generally similar number of SKUs in each model  231 . The balance between these two factors may be adjusted by a user, depending on the total number of SKUs, for example. In step  246 , any SKU links  236  intersected by the “cut” are duplicated in each model  231 . In step  248 , the brand nodes  232  and package nodes  234  connected to any intersected SKU links  236  are also duplicated in each model  231 . In step  250 , the models  231  a, b, c are then trained according to one of the methods described herein, such as with actual photos of the SKUs and/or with the virtual pallets. 
     Referring to  FIG.  9   , each SKU  290  is also associated with a text description  292 , a package type  294  and a brand  296 . Each package type  294  corresponds to one of the package nodes  234  of  FIG.  7   , and each brand  296  corresponds to one of the brand nodes  232  of  FIG.  7   . Therefore, again, each package type  294  may be associated with more than one brand  296 , and each brand  296  may be available in more than one package type  294 . The package type  294  describes the packaging of the SKU  290 . For example  160 Z_CN_ 1 _ 24  is a package type  294  to describe sixteen ounce cans with twenty-four grouped together in one case. A case represents the sellable unit that a store can purchase from the manufacturer. The brand  296  is the flavor of the beverage and is marketed separately for each flavor. For example, Pepsi, Pepsi Wild Cherry and Mountain Dew are all “brands.” Each flavor of Gatorade is a different “brand.” 
       FIG.  10    shows an example of one method for identifying skus on the loaded pallet  22 . In step  300 , images of four sides of the loaded pallet  22  are captured according to any method, such as those described above. 
       FIG.  10    depicts optional step  302 , in which the pallet detector module is used to remove the background and to scale the images. The pallet detector uses a machine learning object detector model that detects all of the products on the pallet  22  as a single object. The model is trained using the same virtual pallets and real pallet images that also used for the package detector but labeled differently. The pallet detector is run against each of the four images of the pallet faces. The background is blacked out so that product not on the pallet  22  is hidden from the package detector inference run later. This prevents mistakenly including skus that are not on the pallet. The left and right pallet faces are closer to the camera than the front and back faces. This causes the packages on the left and right face to look bigger than the packages on the front and back faces. The pallet detector centers and scales the images so that the maximum amount of product is fed to the pallet detector model. Again this step of blacking out the background and scaling the images is optional. 
     Referring to  FIGS.  10  and  11   , in step  306 , a machine learning object detector detects all the package faces on the four pallet faces. The package type is independent from the brand. Package types are rectangular in shape. The long sides are called “SIDE” package faces and the short sides are called “END” package faces. In step  308 , all package faces are segmented into individual pictures as shown in  FIG.  11   , so that the brand can be classified separately from package type. This is repeated for all four pallet faces. 
     Referring to  FIGS.  10  and  12   , in step  310 , it is determined which package face images belong to the same package through stitching. In this sense, “stitching” means that the images of the same item are associated with one another and with a particular item location on the pallet. Some packages are only visible on one pallet face and only have one image. Packages may have zero to four package faces visible. Packages that are visible on all four pallet faces will have four package face images stitched together. In  FIG.  12   , the package faces that correspond to the same package are numbered the same. 
       FIG.  12 A  shows the three package faces for product  01  from  FIG.  12   .  FIG.  12 B  shows the three package faces for product  02  from  FIG.  12   .  FIG.  12 C  shows the three package faces for produce  03  from  FIG.  12   .  FIG.  12 D  shows the three package faces for product  04  from  FIG.  12   . 
     Referring to  FIGS.  10 ,  13 , and  14    in step  312 , the package type of each product is inferred for each of the (up to four) possible package faces, using a machine learning model for determining package type. The package type machine learning model infers at least one package type based upon each package face independently and generates an associated confidence level for that determined package type for that package face. The package type machine learning module may infer a plurality of package types (e.g. five to twenty) based upon each package face with a corresponding confidence level associated with each such inferred package type. In  FIGS.  13  and  14   , only the highest-confidence package type for each package face is shown. 
     For each item (i.e. the images stitched together), the package face(s) with lower confident package types are overridden with the highest confident package type out of the package face images for that item. The package type with the highest confidence out of all the package face images for that item is used to override any different package type of the rest of the package faces for that same item. 
     For the two examples shown in  FIGS.  13  and  14   , the package face end views may look the same for two SKUs so it is very hard to distinguish the package type from the end views; however, the package face side view is longer for the 32 pack than the 24 pack plus the respective 32 and 24 count is visible on the package and the machine learning module can easily distinguish the difference on the side view between the 24 and 32 pack from the long side view. For example in  FIG.  14   , the package end face view with a confidence of 62% was overridden by a higher confidence side view image of 98% to give a better package type accuracy. Other package types include reusable beverage crate with certain bottle sizes or can sizes, corrugated tray with translucent plastic wrap a certain bottle or can sizes, or fully enclosed cardboard or paperboard box. Again, “package type” may include a combination of the primary and secondary packaging. 
     In step  313  of  FIG.  10   , for each package face, a brand model (e.g. brand models  231   a, b,  or  c  of  FIG.  7   ) is loaded based upon the package type that was determined in step  312  (i.e. after the lower-confidence package types have been overridden). Some brands are only in their own package types. For example, Gatorade is sold in around a dozen package types but those package types are unique to Gatorade and other Pepsi products are not packaged that way. If it is determined that the package faces of a package have a Gatorade package type then those images are classified using the Gatorade brand model (for example, brand model  231   c  of  FIG.  7   ). Currently, the brand model for Gatorade contains over forty flavors that can be classified. It is much more accurate to classify a brand from forty brands than to classify a brand from many hundreds or more than a thousand of brands, which is why the possibilities are first limited by the inferred package type. 
     The machine learning model (e.g. models  231   a, b,  or  c  of  FIG.  7   ) that has been loaded based upon package type infers a brand independently for each package face of the item and associates a confidence level with that inferred brand for each package face. Initially, at least, higher-confidence inferred brands are used to override lower-confidence inferred brands of other package faces for the same item. 
     Referring to  FIG.  15   , one example was stitched to have the  160 Z_CN_ 1 _ 24  package type. The package was visible on three package faces. Based upon the package type model, the inference constantly agreed on this package type on all three faces. The best machine learning model  231   a, b  or  c  for brand was loaded based on the package type. If stitching would have overridden a package type for one or more package faces, then the same brand model  231   a, b  or  c  would still be used for all of the segmented images based upon the best package type out of all of the segmented images. 
     The example shown in  FIG.  15    shows that the machine learning algorithm first classified the front image to be RKSTR_ENRG with a low 35% confidence. Fortunately, the back image had a 97% confidence of the real brand of RKSTR_XD_SS_GRNAP and the brand on the front image was overridden. At least initially, and except as otherwise described below, the best brand (i.e. highest confidence brand) from all of the stitched package images is used to determine the brand for that item. Having determined all of the package types and then the brands for each item on the pallet, the SKU for each item is determined in step  314  ( FIG.  10   ). 
     It should be noted that some product is sold to stores in groups of loose packages. All of the packages are counted and divided by the number of packages sold in a case to get the inferred case quantity. The case quantity is the quantity that stores are used to dealing with on orders. 
     The pick list that has the expected results is then leveraged to the actual inferred results. There should be high confidence that there is an error before reporting the error so there are not too many false errors. The known results of the pick list can be leveraged to make corrections to the inferred results so that too many false errors are not reported 
     The number of false errors reported may be reduced by comparison to weight. The weight of the actual loaded pallet is particularly useful for removing false inferred counts like seeing the tops of the package as an extra count or detecting product beside the pallet in the background that is not part of the pallet. 
     If the actual weight is close to the expected weight then the pallet is likely to be picked correctly. If the inferred weight is then out of alignment with the expected weight while the actual weight from the scale is in alignment, then the inference likely has a false error. 
     In step  318  of  FIG.  10   , the system can learn from itself and improve over time unsupervised without human help through active learning. Often time, errors are automatically corrected through stitching. If the pallet inference generates the expected results as compared to the pick list SKUs and quantities then it is very likely that the correct product is on the pallet. The pallet face images can be labeled for machine learning training based on the object detector results and brand classification results and stitching algorithm corrections. 
     After individual items  20  are identified on each of the four sides of the loaded pallet  22 , based upon the known dimensions of the items  20  and pallet  22  duplicates are removed, i.e. it is determined which items are visible from more than one side and appear in more than one image. If some items are identified with less confidence from one side, but appear in another image where they are identified with more confidence, the identification with more confidence is used. 
     For example, if the pallet  22  is a half pallet, its dimensions would be approximately 40 to approximately 48 inches by approximately 20 to approximately 24 inches, including the metric 800 mm×600 mm Standard size beverage crates, beverage cartons, and wrapped corrugated trays would all be visible from at least one side, most would be visible from at least two sides, and some would be visible on three sides. 
     If the pallet  22  is a full-size pallet (e.g. approximately 48 inches by approximately 40 inches, or 800 mm by 1200 mm), most products would be visible from one or two sides, but there may be some products that are not visible from any of the sides. The dimensions and weight of the hidden products can be determined as a rough comparison against the pick list. Optionally, stored images (from the SKU files) of SKUs not matched with visible products can be displayed to the user, who could verify the presence of the hidden products manually. 
     The computer vision-generated sku count for that specific pallet  22  is compared against the pick list  64  to ensure the pallet  22  is built correctly in step  162  of  FIG.  2   . This may be done prior to the loaded pallet  22  being wrapped thus preventing unwrapping of the pallet  22  to audit and correct. If the built pallet  22  does not match the pick list  64  (step  162 ), the missing or wrong SKUs are indicated to the worker (step  164 ), e.g. via a display (e.g.  FIG.  19   ). Then the worker can correct the items  20  on the pallet  22  (step  166 ) and reinitiate the validation (i.e. initiate new images in step  156 ). 
     If the loaded pallet  22  is confirmed, positive feedback is given to the worker (e.g.  FIG.  20   ), who then continues wrapping the loaded pallet  22  (step  168 ). Additional images may be taken of the loaded pallet  22  after wrapping. For example, four image may be taken of the loaded pallet before wrapping, and four more images of the loaded pallet  22  may be taken after wrapping. All images are stored locally and sent to the server  14 . The worker then moves the validated loaded pallet  22  to the loading station  34  (step  170 ) 
     After the loaded pallet  22  has been validated, it is moved to a loading station  34  ( FIG.  1   ). At the loading station  34 , the distribution center computer  26  ensures that the loaded pallets  22 , as identified by each pallet id, are loaded onto the correct trucks  18  in the correct order. For example, pallets  22  that are to be delivered at the end of the route are loaded first. 
     Referring to  FIG.  1   , the loaded truck  18  carries a hand truck or pallet sled  24 , for moving the loaded pallets  22  off of the truck  18  and into the stores  16  ( FIG.  2   , step  172 ). The driver has a mobile device  50  which receives an optimized route from the distribution center computer  26  or central server  14 . The driver follows the route to each of the plurality of stores  16  for which the truck  18  contains loaded pallets  22 . 
     At each store  16  the driver&#39;s mobile device  50  indicates which of the loaded pallets  22  (based upon their pallet ids) are to be delivered to the store  16  (as verified by gps on the mobile device  50 ). The driver verifies the correct pallet(s) for that location with the mobile device  50  that checks the pallet id (rfid, barcode, etc). The driver moves the loaded pallet(s)  22  into the store  16  with the pallet sled  24 . 
     At each store, the driver may optionally image the loaded pallets with the mobile device  50  and send the images to the central server  14  to perform an additional verification. More preferably, the store worker has gained trust in the overall system  10  and simply confirms that the loaded pallet  22  has been delivered to the store  16 , without taking the time to go SKU by SKU and compare each to the list that he ordered and without any revalidation/imaging by the driver. In that way, the driver can immediately begin unloading the products  20  from the pallet  22  and placing them on shelves  54  or in coolers  56 , as appropriate. This greatly reduces the time of delivery for the driver. 
       FIG.  16    shows a sample training station  28  including a turntable  100  onto which a new product  20  (e.g. for a new SKU or new variation of an existing SKU) can be placed to create the machine learning models  44 . The turntable  100  may include an RFID reader  102  for reading an RFID tag  96  (if present) on the product  20  and a weight sensor  104  for determining the weight of the product  20 . A camera  106  takes a plurality of still images and/or video of the packaging of the product  20 , including any logos  108  or any other indicia on the packaging, as the product  20  is rotated on the turntable  100 . Preferably all sides of the packaging are imaged. The images, weight, RFID information are sent to the server  14  to be stored in the SKU file on the server  14 . Optionally, multiple images of the product  20  are taken at different angles and/or with different lighting. Alternatively, or additionally, the computer files with the artwork for the packaging for the product  20  (i.e. files from which the packaging is made) are sent directly to the server  14 . 
     In one possible implementation of training station  28 , shown in  FIG.  17   , cropped images of products  20  from the training station  28  are sent from the local computer  130  via a portal  132  to sku image storage  134 , which may be at the server  14 . Alternatively, or additionally, the computer files with the artwork for the packaging for the product  20  (i.e. files from which the packaging is made) are sent directly to the server  14 . Alternatively, or additionally, actual images of the skus are taken and segmented (i.e. removing the background, leaving only the sku). 
     Whichever method is used to obtain the images of the items, the images of the items are received in step  190  of  FIG.  18   . In step  192 , an API  136  takes the sku images and builds them into a plurality of virtual pallets, each of which shows how the products  20  would look on a pallet  22 . The virtual pallets may include four or five layers of the product  20  on the pallet  22 . Some of the virtual pallets may be made up solely of the single new product  20 , and some of the virtual pallets will have a mixture of images of different products  20  on the pallet  22 . The API  136  also automatically tags the locations and/or boundaries of the products  20  on the virtual pallet with the associated skus. The API creates multiple configurations of the virtual pallet to send to a machine learning model  138  in step  194  to update it with the new skus and pics. 
     The virtual pallets are built based upon a set of configurable rules, including, the dimensions of the pallet  22 , the dimensions of the products  20 , number of permitted layers (such as four, but it could be five or six), layer restrictions regarding which products can be on which layers (e.g. certain bottles can only be on the top layer), etc. The image of each virtual pallet is sized to be a constant size (or at least within a particular range) and placed on a virtual background, such as a warehouse scene. There may be a plurality of available virtual backgrounds from which to randomly select. 
     The API creates thousands of images of randomly-selected sku images on a virtual pallet. The API uses data augmentation to create even more unique images. Either a single loaded virtual pallet image can be augmented many different ways to create more unique images, or each randomly-loaded virtual pallet can have a random set of augmentations applied. For example, the API may add random blur (random amount of blur and/or random localization of blur) to a virtual pallet image. The API may additionally introduce random noise to the virtual pallet images, such as by adding randomly-located speckles of different colors over the images of the skus and virtual pallet. The API may additionally place the skus and virtual pallet in front of random backgrounds. The API may additionally place some of the skus at random (within reasonable limits) angles relative to one another both in the plane of the image and in perspective into the image. The API may additionally introduce random transparency (random amount of transparency and/or random localized transparency), such that the random background is partially visible through the virtual loaded pallet or portions thereof. Again, the augmentations of the loaded virtual pallets are used to generate even more virtual pallet images. 
     The thousands of virtual pallet images are sent to the machine learning model  138  along with the bounding boxes indicating the boundaries of each product on the image and the SKU associated with each product. The virtual pallet images along with the bounding boxes and associated SKUs constitute the training data for the machine learning models. 
     In step  196 , the machine learning model  138  is trained based upon the images of the virtual pallets and based upon the location, boundary, and sku tag information. The machine learning model is updated and stored in step  140 . The machine learning model is deployed in step  142  and used in conjunction with the validation stations  32  ( FIG.  1   ) and optionally with the delivery methods described above. The machine learning model  138  may also be trained based upon actual images taken in the distribution center or the stores after identification. Optionally, feedback from the workers can factor into whether the images are used, e.g. the identified images are not used until a user has had an opportunity to verify or contradict the identification. 
     It should be understood that each of the computers, servers or mobile devices described herein includes at least one processor and at least one non-transitory computer-readable media storing instructions that, when executed by the at least one processor, cause the computer, server, or mobile device to perform the operations described herein. The precise location where any of the operations described herein takes place is not important and some of the operations may be distributed across several different physical or virtual servers at the same or different locations. 
       FIG.  21    shows one possible implementation of a pick system  410  including a pallet sled  412  having a base  414  and pair of tines  416  that are selectively raised and lowered relative to the base  414 . Wheels  418  ( FIG.  22   ) support the base  414  and tines  416  and may propel the pallet sled  412 . A handle  420  is pivotably connected to the base  414  for controlling the pallet sled  412 . The pallet sled  412  may use a standard pallet jack mechanism for raising the tines  416  relative to the floor, or any type of electrical, hydraulic or mechanical lift system. 
     As is known, the tines  416  are selectively raised and lowered relative to the floor to lift pallets  450  and transport them with the pallet sled  412 . In the examples shown herein, two half-pallets  450  are carried on the tines  416 , but full-size pallets could also be used. For example, the pallet sleds may carry a single full-size pallet instead of two half-pallets  450 , but otherwise would operate the same. If two half-pallets  450  are carried by the pallet sled  412 , they are both picked at the same time. 
     A mobile device  424 , such as a tablet or smartphone (e.g. iPad or iPhone), is mounted to a frame  426  extending upward from the base  414 . The mobile device  424  may be a commercially-available tablet or smartphone having at least one processor, electronic storage (for storing data and instructions), a first touchscreen  427  facing the user, at least one rear-facing camera  544 , and multiple wireless communication modules (such as wi-fi, Bluetooth, cell data, NFC, etc). The mobile device  424  may also include circuitry (internally or as an external accessory) and programming for determining its location within the distribution center (e.g. relative to fiducials throughout the distribution center). 
     The pick system  410  includes a remote CPU  430 , such as a server, cloud computer, cluster of computers, etc. The remote CPU  430  could be multiple computers performing different functions at different locations. The remote CPU  430 , among other things, stores a plurality of images of each of a plurality of available SKUs. For example, the available SKUs in the example described herein are cases of beverage containers, such as cartons of cans, plastic beverage crates containing bottles or cans, cardboard trays with plastic overwrap containing bottles or cans, cardboard boxes of bottles or cans, etc. There are many different permutations of flavors, sizes, case types, and types of beverage containers that may each be a different SKU. 
     The remote CPU  430  is programmed to receive orders  434  from a plurality of stores  436 . Each order  434  is a list of SKUs and a quantity of each SKU. As will be explained in more detail below, the mobile device  424  and the remote CPU  430  are programmed to communicate, including (in broad terms) the mobile device  424  receiving pick sheets  438  from the remote CPU  430 . The pick sheets  438  each contain a list of SKUs that should be on the same pallet  450 . Additionally, the remote CPU  430  may also send pallet configuration  440  files containing information indicating the location on each pallet  450  where each SKU should be placed, as will be explained further below. The remote CPU  430  also sends the SKU images  432  (images of what each SKU should look like, including at least one side, but preferably two or three or all sides of the SKU) to the mobile device  424 . 
     The remote CPU  430  dictates merchandizing groups and sub groups for loading items  420  on the pallets  450  in order to make unloading easier at the store. For example, the pick sheets  438  may dictate that certain products  420  destined for one store are on one pallet  450  while other products  420  destined for the same store are on another pallet  450 . The pick sheets  438  and pallet configurations  440  also specify arrangements of SKUs on each pallet  450  that group products efficiently and for a stable load on the pallet  450 . For example, cooler items should be grouped, and dry items should be grouped. Splitting of package groups is also minimized to make unloading easer. This makes pallets  450  more stable too. The arrangement and location of the items  420  on the pallets  450  may be optimized by the remote CPU  430  to improve the stability of the loaded pallets  450 . Eventually, each pick sheet  438  is associated with a pallet id, such that each SKU is associated with a particular palled id (and a particular pallet  450 ). Products  420  destined for different stores would be on different pallets  450 , but more than one pallet  450  may be destined for one store. 
     As will be further explained, the mobile device  424  may send product images  442  (i.e. images of individual products being carried by a user) and pallet images  444  (images of loaded or partially loaded pallets) to the remote CPU  430 . Alternatively, these images  442 ,  444  are processed locally on the mobile device  424 . 
     Referring to  FIG.  22   , the mobile device  424  in this example also has a second touchscreen  428  (or an external, connected second touchscreen), facing the pallets  450 . A headset  547  worn by the picker may relay audible instructions from the mobile device  424  to the picker and may relay voice commands from the picker to the mobile device  424 , such as via Bluetooth. 
     Referring to  FIG.  23   , the pick sheet  438 , in this case for order number  1967 , is sent to the mobile device  424  from the remote CPU  430  ( FIG.  21   ). The remote CPU  430  also sends to the mobile device  424  SKU images  432  for every SKU on the pick sheet  438 . This can happen along with every pick sheet  438  or the mobile device  424  can store all the SKU images  432  and periodically receive updates. 
     The mobile device  424  generates a  3 D image  562  of what the final, loaded pallet  450  should look like, with all the products in the proper location according to the pallet configuration  440  from the remote CPU  430  and using the SKU images  432  from the remote CPU  430 . The user can rotate and otherwise manipulate (e.g. removing layers) the  3 D image  562  on the touchscreen  427  of the mobile device  424 . The user can at any time prompt the mobile device  424  to display either final pallet  450  carried by the pallet sled  412 . 
     As shown in  FIG.  24   , a back-facing camera  544  on the mobile device  424  takes a picture  549  of the picker for accountability management for every pallet  450 . 
     Referring to  FIG.  24 A , when the picker first engages the pallet sled  412  and the mobile device  424  (optionally, after logging into the mobile device  424 ), the picker can choose a skill level on operator level screen  460 . In the example shown the picker can choose from among three different levels. Alternatively, the user can choose from two levels or from more than three levels, or can choose a level on a slider. Alternatively, the picker&#39;s supervisor chooses the level based upon metrics collected by the mobile devices  424  on pallet sleds  412  and associated with each picker. The collected metrics can include one or more of number of pallets loaded, number of SKUs picked, rate of SKUs picked (e.g. SKUs per day or per hour), number of days worked, average speed in loading pallets, and accuracy in loading pallets. Alternatively, the level is determined automatically based upon the collected metrics. Based upon the user&#39;s level, the mobile device  424  may provide a different level of instruction and feedback, e.g. the mobile device  424  may provide reduced, more efficient instructions to the more experienced user, with less feedback, than to the novice picker. 
     Referring to  FIG.  24 B , the collected metrics can be reported to the picker at the beginning and end of the picker&#39;s shift and periodically throughout the day. The example gamification screen  462  in  FIG.  24 B  provides feedback to the picker based upon the metrics. As shown in  FIG.  24 B , the reported metrics may set goals for the picker, such as SKUs to pick for the day and number of SKUs per hour. The reported metrics also track accuracy and that feedback is provided to the picker as well. A bonus may be offered at a certain level of production and accuracy, as shown. The reported metrics may also indicate the picker&#39;s ranking against other pickers. For example, the pickers may compete based upon production and/or accuracy for a day or a week, etc. Other metrics and ways of gamifying the metrics could also be used. 
     Referring to  FIG.  25   , the different products  420  are arranged on shelves  532  throughout the distribution center. The pick sheet  438 , in this case for order number  679 , is sent to the mobile device  424 . The mobile device  424  displays the order number in an order number field  540 . The mobile device  424  identifies the next product in a next product field  542  and displays a map  538  of the distribution center indicating the current location  534  of the pallet sled  412  and the item location  546  of the next product  420  to be loaded onto one of the pallets  450 . The mobile device  424  may determine its position within the distribution center using known electronic and software methods. The mobile device  424  may indicate a route  543  from the current location  534  to the item location  546 , such as shown in  FIG.  25   . The route  543  would take into account that at least some of the paths in the warehouse only permit travel in one direction (if applicable). 
     Alternatively, the mobile device  424  assumes that the user has guided the pallet sled  412  to the locations as directed by the mobile device  424  according to the displayed maps  538  and sequentially displays maps of how to get from one location to the next. 
     The remote CPU  430  ( FIG.  21   ) has determined an exact desired arrangement of the products  420  on each pallet  450  and sends this information in the pallet configuration  440  file. The remote CPU  430  communicates the pick sheet  438  and pallet configuration  440  to the mobile device  424  along with the sequence of pick instructions. Alternatively, the mobile device  424  can determine the sequence of pick instructions based upon the pallet configuration  440  and optionally also based upon a stored map of the locations of the SKUs in the distribution center. As shown in  FIG.  25   , the mobile device  424  identifies the next item to be picked and the quantity in the next product field  542  and the location  546  of products  420  corresponding to that SKU on the map  538 . 
     As shown in  FIG.  26   , the when the mobile device  424  determines that it is at the location  536  of the next product  420  (or when the user tells the mobile device  424  that it is), the mobile device  424  then displays a full color image  552  of the next product  420  to be picked (based upon SKU images  432 ) and the associated quantity on the rear-facing screen. This is particularly helpful when the packaging for the product  420  has changed (for example), so the picker can find the right product  420  quickly. 
     Referring to  FIG.  27   , using camera  545 , the mobile device  424  may take images (stills or video) of each product  420  retrieved by the user as the user approaches the pallet sled  412 , i.e. while the product  420  is still in the user&#39;s hands. The image may be sent to the remote CPU  430  as product image  442  ( FIG.  21   ) or it may be processed locally by the mobile device  424 . The mobile device  424  (or remote CPU  430 ) identifies each product  420  by SKU (such as by using a machine learning model trained on the available SKUs). The mobile device  424  checks to ensure that the identified SKU matches the SKU that the mobile device had indicated was the next product to be retrieved. If it matches, a confirmation screen is displayed. If it does not match, a rejection screen  564  is displayed on the mobile device  424  as shown in  FIG.  27   . The user returns the incorrect product  420  to the shelves and retrieves the correct product  420  and the mobile device  424  repeats the verification. This step is repeated for each of the required quantity of product  420  associated with the current SKU. If there are not enough products  420  associated with the current SKU in stock on the shelves, the user can so indicate this on the mobile device  424 . This information is eventually passed on to the validation station. 
     Referring to  FIG.  28   , if the mobile device  424  confirms that the correct product  420  has been retrieved, the mobile device  424  instructs the user exactly where on the pallets  450  to place the next product  420 , including which pallet  450  and the location on that pallet  450 . As shown, the front-facing touchscreen displays a loading instruction screen  548 , which shows an image of the pallets  450  and tines and places an icon  550  at the location on the pallets  450  where the next product  420  should be placed. The user then places the product  420  on the pallets  450  according to the loading instruction screen  548 . If more than one product  420  with this SKU is required, the mobile device  424  indicates the location for each product  420  sequentially, or alternatively, indicates all of the locations at once. 
     Note that both pallets  450  are being picked at the same time and each is associated with a different pick sheet  438 . Therefore, the mobile device  424  may indicate that one or more products associated with a particular SKU should be placed on one pallet  450  and one or more products associated with the same SKU should be placed on the other pallet  450 . 
     After retrieving the required number of products  420  at the first location, the mobile device  424  indicates the next location where the next product(s)  420  can be retrieved (similar to  FIG.  25   ), and then the exact location(s) where the next product(s)  420  should be placed on the pallets  450  (similar to  FIG.  28   ). 
     The user can choose to have the mobile device  424  build and display an updated 3D image of the pallets  450  and products  420  that have already been loaded as the loading instruction screen  548 , as shown in  FIG.  29   . The mobile device  424  creates the 3D image from the stored SKU images  432  and the known locations of the already-loaded SKUs on the pallets  450 . The mobile device  424  indicates the exact location for the next product  420  in the 3D image of the partially loaded pallets  450 . Each of the previously-placed products  420  is displayed in full color on its proper location on the pallets  450 . The next product  420  is displayed in its desired location relative to the previously-loaded products  420 . The next product  420  is visually distinguished, such as by flashing, being outlined, being displayed translucently, being displayed in color while the loaded products  420  are displayed in greyscale (or at least reduced saturation), or other visual effect or some combination of such visual effects. 
     As shown in  FIGS.  30  and  31   , after the user places the next product  420 , the mobile device  424  takes an image (or images) with camera  545  to verify that the product  420  is placed in the correct location on the pallets  450  and on the stack of products  420 . This image may be sent to the remote CPU  430  as pallet image  444  it may be processed locally on the mobile device  424 . Again, either confirmation ( FIG.  30   ) or rejection (FIG.  31 ) is displayed. If a rejection is displayed, the mobile device  424  returns to a screen indicating the correct location (e.g.  FIG.  28    or  FIG.  29   ). 
     Optionally, if the mobile device  424  is not configured to verify that the correct product  420  was placed on the pallet  450 , or if the mobile device  424  was simply unable to do so (temporarily), the mobile device  424  may ask the user to confirm the quantity of the desired product  420  that was placed on the pallet  450 . Preferably the mobile device  424  asks the user over the headset  547  “How many pick items did you place on the pallet?” (or similar) and the user responds verbally with the count. Alternatively, the mobile device  424  can display the screen of  FIG.  31 A , which displays the text “How many pick items did you place on the pallet?” (or similar) and permits the user to enter number. 
     Whether through visual image verification, verbal interrogation of the user or text interrogation of the user, the mobile device  424  receives the count of the number of that product  420  that was placed on the pallet  450 . If that count is lower than that on the pick list, then the mobile device  424  asks the user “Why is the count short?” either verbally or via the display, such as in  FIG.  31 B . The user can then answer (again verbally or via the pull-down menu in  FIG.  31 B ), “out of stock items,” “damaged items,” or “other.” Other possible responses could also be configured. 
     The mobile device  424  then instructs the user via the display of  FIG.  31 C  how to get to the next pick item. The steps of  FIGS.  26  to  31    are repeated until both pallets  450  are loaded according to the pick sheets  438  and pallet configurations  440 . 
     The confirmations, any uncorrected errors or rejections, and any missing SKUs (or insufficient quantities) are recorded and sent to the remote CPU  430  and associated with the specific pallets  450 . Confirmations and uncorrected errors or rejections may be associated with specific SKUs at specific locations on the specific pallets  450 . Later, at a validation station, images of the loaded pallet  450  may be taken and analyzed, such as by using a machine learning model, to verify that the SKUs on the pallet  450  match the SKUs on the pick sheet  438 . Confirmations by the mobile device  424  on the pallet sled  24  can be used at validation as an input to validation, i.e. there is already a level of confidence that the correct SKUs are on the pallet  450  at the correct locations. Uncorrected problems are also passed along to the validation station so that they can be corrected there. Additionally, there may be a third state where the mobile device  424  was neither able to confirm nor reject with a high level of confidence. This is passed onto the validation station as well, along with the specific SKU(s) and location(s) on the pallets  450 . The validation state will then ensure that it can confirm or reject the SKUs at the locations on the pallets  450 , or flag it for manual confirmation. 
     In  FIG.  32   , the mobile device  424  then displays a screen  554  instructing the picker which validation station to take the pallets  450 . The validation station may be a wrapper or a dedicated validation station. The screen  554  may display a map of the distribution center with the location of the designated validation station. This ensures efficient use of the validation stations. The confirmation/rejection/unconfirmed status information discussed above is passed along to that validation station (but would be available to any validation station from remote CPU  430 ). 
     If an error is detected at the validation station (or wrapper), then the mobile device  424  may indicate the error on the screen as indicated in  FIG.  31 D  and then instruct the user to take the pallet(s)  450  to a specified quality control station as indicated on the display, such as is shown in  FIG.  31 E . 
     If no errors are detected at the validation station (or after the errors are corrected), the mobile device  424  may instruct the user to take the pallet(s) to a particular loading bay and truck door, such as indicated in  FIG.  31 F . 
     After the user delivers the pallet(s) at the specified loading bay and truck door, the mobile device  424  may indicate that the pallet(s) is complete, such as the display of  FIG.  31 G . The mobile device  424  then indicates the user&#39;s statistics and ranking for the day, such as the example screen shown in  FIG.  31 H . 
       FIGS.  33  and  34    illustrate an alternative pallet sled  412   a,  which is identical to the pallet sled  412  but is also an automated guided vehicle. The pallet sled  412   a  is used in the manner described above but in addition, the pallet sled  412   a  automatically retrieves pallets  450  and follows a route from product to product, so that the picker or pickers can place the right products on the right pallet  450  (again, according to displayed instructions by the mobile device  424   a ). The picker may ride on the pallet sled  412   a  or there may be a different picker at each location in the distribution center. 
     Referring to  FIGS.  35  and  36   , the pallet sled  412   a  retrieves two empty pallets  450  from a pallet destacker  560  (or “pallet dispenser”). The pallet destacker  560  includes a vertical body  570  for retaining a plurality of pallets  450 . In this example, the pallets  450  are retained in the vertical body  570  two columns: a front column of pallets, not visible in  FIGS.  35  and  36   , and aback column of pallets  450 , which is visible in  FIGS.  35  and  36   . The front column of pallets will be on the front ends of tines of the pallet sleds while the back column of pallets  450  will be toward the back of the tines of the pallet sleds. 
     When prompted, the pallet destacker  560 , releases or dispenses two pallets  450  from the bottom of the stacks onto the floor or directly onto the tines  416   a  of the pallet sled  412   a.    
     The pallet destacker  560  may include at least one processor  572  (together with electronic storage of data and instructions for causing the at least one processor  572  to perform the functions described herein). The pallet destacker  560  may also include a communication circuit  574 , such as wifi, Bluetooth, NFC, etc. for communicating with the mobile device  424   a  of the pallet sled  412   a  directly or via the remote CPU  430 . The pallet destacker  560  also includes a rfid reader  566  mounted on or near the pallet destacker  560  and connected to the at least one processor  572 . In this example an rfid tag  568  on the pallet sled  412   a  can be read by the rfid reader  566 . 
       FIG.  36 A  is a side view of the pallet destacker  560 , broken away with some components shown schematically. As shown, the pallets  450  are retained in the vertical body  570  in two columns a front column of pallets (on the left in  FIG.  36 A ) and a back column of pallets  450  (on the right in  FIG.  36 A ). The front column of pallets will be end up on the front ends of tines of the pallet sleds while the back column of pallets  450  will end up toward the back of the tines of the pallet sleds. 
     As is known, lift tines  578  (or at least one tine or rod or pin or the like) are inserted by the pallet destacker  560  below the pallets  450  in each stack. The lift tines  578  are configured to be raised and lowered by a motor  580  or hydraulic actuator, etc. In use, the motor  580  lowers the two stacks of pallets  450  to the floor, then retracts the lift tines  578  and places them under the second-to-bottom pallets  450  in each stack. The motor  580  then raises the two stacks of pallets  450  other than the bottom-most pallet  450  in each stack, which remains on the floor. 
     In the pallet destacker  560 , there are two rfid readers  576  aligned with the two dispensed pallets  450  on the floor below the two stacks. One rfid reader  576  reads the rfid tag  456  of the pallet  450  below one stack (on the left), which will be the next front pallet  450 , and the other rfid reader  576  reads the rfid tag  456  of the pallet  450  below the other stack (on the right), which will be the next back pallet  450 . This information is sent to the at least one processor  572 , which may be transmitted via the communication circuit  574  to the pallet sled  412   a.  Alternatively, the rfid readers  576  could be placed adjacent the bottom pallet  450  in each stack. Alternatively, if identifiers other than rfid tags are used (NFC, barcodes, QR codes, etc), the rfid readers would be replaced with complementary readers (NFC readers, barcode readers, QR code readers, etc). 
     The tines of the pallet sled  412   a,  then enter from the front of the pallet destacker  560  (to the right in  FIG.  36 A ), below the two pallets  450  on the floor. The pallet sled  412   a  then raises the tines to lift the two pallets  450  off the floor and then removes the pallets  450  from the destacker  560 . Alternatively, the pallets  450  are dispensed directly onto the tines. 
     In this manner, the pallet sled  412   a  has the palled ids (SSCC) of each of the pallets  450  and knows which one is the front pallet  450  and which one is the back pallet  450 . The at least one processor  570  also knows the pallet ids of the front pallet, the back pallet (and which is which) and the id of the pallet sled  412   a  now associated with those pallet ids. This information is transmitted to the server  14  and/or the DC computer  26  for use in the validation steps. 
     A pick list API downloads the customer&#39;s pallet details for all of their orders and includes a field for the picker name and the picker ID. Another API for Pick Assist receives the pick commands that are sent to the picker. The pick commands contain the SSCC number, Picker ID, along with the product and quantity of cases that they need to pick. In this way, the pallet SSCC numbers (pallet ids) are associated with the picker and/or the pallet sled  412   a,  and the specific pallet ids are associated with their respective pick lists (and again, it is known which pallet is front and which pallet is back). 
     There are a couple of ways to limit the possible pallets for a best match pick list algorithm once a pallet is placed on the wrapper for validation. The RFID tag from the pallet  450  will be read on the wrapper and then the validation station will have the SSCC value that identifies the pick list or have a limited number of possible SSCC pallets to pick from. For example, even if front/back could not be distinguished, then the validation station only needs to distinguish the two pallets that were picked at the same time. Or if only the Picker ID is known, then only the pallets that were picked by that picker need to be considered. The skus on the pallet are compared to the possible associated pick lists for a best match. The inferred skus on the pallet are then compared to that pick list as explained herein. 
     If the rfid reader  566  and/or rfid readers  576  are able to determine which pallet is on the front of the tines and which pallet is toward the rear of the tines, then the pallet  450  will be identified even before validation, but validation can also confirm which pallet  450  is at the validation station. 
     If it is not known which RFID tag belongs to the front pallet and which one belongs to the back pallet, then the validation station  32  can easily distinguish the two through comparison to the two associated pick lists. If there is more than one possible pallet for the pallet RFID value on the wrapper then a best match pick list algorithm looks at the list of possible pallets and selects the best matching pallet. The algorithm finds the best SSCC number that matches one in the list based on the inference results and the pick list for all of the pallets in the list. A score is given to each pallet and the pallet with the highest score is determined to be the most likely pallet. This SSCC number is then married to the pallet RFID value for Load Validation. The best matching pallet is also used for the display in SKU Verification for the results of the inference. 
     The number of possible pallets that could be on an individual wrapper is reduced in a few ways: 
     1) If the customer is able to provide the pick sequence of pallet SSCC numbers for each picker then the time that the three RFID tags were married together can be used to know that the pallet on the wrapper could either be a specific SSCC of a front pallet or the SSCC of the back pallet. If it can also be determined which RFID tag is from the front pallet and which one is from the back pallet at the rfid readers  566 ,  576  at the destacker, then the exact SSCC number for that pallet will be known. 
     2) Based upon the pick commands used to instruct the picker in loading the pallets, the number of possible pallets can be reduced to two (or if the customer is otherwise able to provide an echo of the pick commands). Again, if it can be determined which RFID tag is from the front pallet and which one is from the back pallet from the rfid readers  566 ,  576 , then the exact SSCC number for that pallet will be known. 
     3) If this destacker  560  and validation are used without the pick assist invention, then there are many more possible pallets. However, first, the list of possible pallets will be restricted by all of the pallets that the picker is assigned to for the day. For example, if a picker is assigned to twenty pallets for the day then it is known that the pallet on the wrapper would be one of the twenty pallets. The closest match would be found from the twenty. 
     Again, if the destacker  560  and validation inventions are used with pick assist, then the picker is known because the picker logs into the mobile device  424 ,  424   a  assigned to the pallet sled  412 ,  412   a.  There is a configuration set for that mobile device  424 ,  424   a  with the RFID tag of the pallet jack that it is mounted on. If the destacker and validation are used without the pick assist invention, then preferably a user interface in at the validation station  32  will link the picker to the pallet sled  412 ,  412   a.    
     Either way, the mobile device  424   a  knows which pallets  450  are on the pallet sled  412   a  and associates them with the pick lists  438 . At the same time, the mobile device  424   a  receives the pallet configuration  440  for each of the pallets  450  on the pallet sled  24   a.    
       FIGS.  37  and  38    illustrate a particular method that can be used with the automated guided vehicle pallet sleds  412   a.  Referring to  FIG.  37   , for high-volume products  420 , a picker can be stationed in the aisle near the high-volume products  420  and load each pallet sled  412   a  when it comes to the picker. As before, the picker would still view the mobile device  424   a  front facing screen to confirm the product  420  and to learn the quantity and where on the pallets  450  to place the product(s)  420 . 
     In low volume zones as shown in  FIG.  38   , a picker would travel with (on) each pallet sled  412   a  to pick the products  420  for the pallets  450  on the pallet sled  412   a  as described above. 
     If both high-volume and low-volume zones are necessary to load the pallets  450  on the pallet sled  412   a,  the pallet sled  412   a  preferably obtains the high-volume products  420  first as described above with respect to  FIG.  37    (without a picker riding or traveling with it), and then the pallet sled  412   a  picks up a picker who then travels with it to the low-volume zones to load the low-volume products  420 . 
     In  FIG.  39   , after the pallets  450  are loaded in any of the ways described above, the pallet sled  412   a  drops the loaded pallets  450  at the validation station  452 . As shown in  FIG.  40   , the pallet sled  412   a  may leave one loaded pallet  450  on a turntable  454  for validation, while placing the other loaded pallet  450  nearby. The pallet sled  412   a  may then go to retrieve two more empty pallets from the destacker  560  ( FIGS.  35  and  36   ). 
       FIG.  41    illustrates a variation of the pick stations disclosed above in which smart glasses  630  are used as the mobile device instead of (or in addition to) a tablet/smart phone form factor. As shown in  FIG.  41   , the smart glasses  630  have a camera  644  and can display an indication of the next product to retrieve and a map to the next product but the automated guided vehicle pallet sled  412   a  already can drive itself to the right locations. 
     As shown in  FIG.  42   , the glasses  630  will naturally have a good field of view of each product  420  carried by the user so that the glasses  630  (possibly in conjunction with the mobile device  424   a ) can display a confirmation (or rejection) that the correct product has been selected. Using augmented reality, the glasses  630  can overlay an indication of where to place the next product onto the user&#39;s real, live view of the products  420  stacked on the pallet sled  412   a.  The smart glasses  630  also verify the location of the product  420  placed on the pallets  450  based upon image(s) from the camera  644 .  FIG.  43    is another view of the user wearing the glasses  630  and placing the next product  420  onto the pallets  450 . 
       FIG.  44    shows a pallet sled  712  that may be identical to one of the pallet sleds  412 ,  412   a  described above. Alternatively, the pallet sled  712  does not include the camera that faces the tines. The mobile device  724  is identical to one of the mobile devices  424 ,  424   a,  with additional programming described here. As shown in  FIG.  44   , when loading a full-size pallet  722 , there will be a plurality of products  20  in the center of each layer of products  20  on the pallet that will be hidden by other products  20  around the periphery of the pallet  722 . These hidden products  20  will not be visible to cameras at the validation stations  32 ,  452  (any of the previously-described validation stations  32 ,  452  or variations thereon). Therefore, additional confirmation is performed during the picking process and this additional confirmation is passed onto the validation station. 
       FIG.  44    shows loading instruction screen  748  on mobile device  724  while the loading instruction screen  748  is instructing placement of two identical products  20  in the center area of the pallet  722 . The loading instruction screen  748  includes a confirmation button  749  activatable by the picker touching the touchscreen. The picker touches the confirmation button  749  to confirm that the products  20  were placed in the center area of the pallet  722 . The bottom layers is shown in  FIG.  44   , but this would be used for all layers. Alternatively, the picker can provide center confirmation in other ways, such as verbal feedback to the mobile device  724 . 
       FIG.  45    shows two optional center confirmation screens  750 ,  752  that can be displayed on the mobile device  724  after the user provides center confirmation ( FIG.  44   ) or instead of the center confirmation step of  FIG.  44   . In  FIG.  45   , the mobile device  724  shows the most-recently placed products  20  in the center area of the pallet  722  and asks the picker to confirm that the interior cases are correctly placed in the center area of the pallet  722  (which will be hidden from the cameras of the validation station) by touching the confirm button  754 . The picker can toggle between the screen  750  and the screen  752 . 
     Alternatively, if a tine-facing camera is provided (e.g.  FIGS.  30  and  31   ), the tine-facing camera can provide confirmation of the center-placed products  20 . 
     As another alternative, the mobile device  724  may interrogate the user audibly over the headset, or via the display shown in  FIG.  45 A , “How many pick items are in the middle?” and the use can either respond verbally over the headset or type in the number on the display. 
     However the confirmation of the center-placed products  20  is made, that confirmation is passed on to the validation station  32  ( FIG.  1   ) or validation station  452  (optionally via the DC computer  26  or the server  14 ). Referring to  FIG.  46   , in step  840  a set of SKUs on the pallet  722  is inferred at the validation station  32 ,  452  (according to the methods described above). In step  842 , the inferred SKUs are compared to the pick list. In step  844 , at least one but more likely a plurality of SKUs from the pick list are determined to be missing from the inferred SKU set. In step  846 , it is determined whether each the missing SKUs was confirmed to be in the interior of the pallet  722  (e.g. according to  FIG.  44  or  45   , or automatically by the tine-facing camera of  FIGS.  30  and  31   ). If so, then the SKUs that were missing but were confirmed in step  846  are added to the inferred set of SKUs in step  848 . Any missing SKUs that were not confirmed as being in the interior of the pallet  722  are flagged as errors in step  850 . 
     Another method for handling hidden products  20  is shown in  FIG.  47   . The method in  FIG.  47    determines whether missing SKUs are likely in a “layer pick” of products  20  on the pallet  722 . A layer pick is when the entire layer on the pallet  722  is the same product  20  (same SKU), e.g. all the same package type and all the same brand. In step  852 , a set of SKUs is inferred at the validation station (according to the methods described above). In step  854 , the inferred set of SKUs is compared to the pick list. In step  856 , missing SKUs (SKUs on the pick list but not inferred by the methods described above) are detected. In step  858 , it is determined whether there is a layer pick. A layer pick may be determined, at least in part, based upon a determination that all of the visible products  20  on one layer on the pallet  722  are the same SKU. If so, then in step  860  it is determined whether the missing SKUs match the SKUs on the layer picked layer (“match” in terms of both SKU and quantity of products). If so, then the missing SKUs are added to the inferred set (no error) in step  862 . If the SKUs do not match the SKUs in the layer-picked layer, then the missing SKUs may be flagged as an error in step  864 . Optionally, weight could be factored into the determination as to the presence of the missing SKUs in the interior of the pallet (as described above). 
     Further, the confirmation of SKUs in the interior of the pallet could be used in conjunction with the method of  FIG.  47   . 
     Alternatively, a determination that almost all of the visible products  20  on one layer on the pallet  722  are the same SKU (some threshold less than 100% of the visible products  20 ) could also be used to determine that there is a likelihood that the “missing SKUs” in the interior of the pallet  722  match the visible products  20  on the visible exterior of the pallet  722 . 
     Using one of the computers or a mobile device, a user can create a map of the warehouse using a map-creation tool. The created map of the warehouse would then be used to help the picker navigate to each product as shown above. 
     In  FIG.  48   , the user creates the map or imports an existing map (to modify an existing map or copy an existing map to modify it for a new map). As shown in  FIG.  49   , the user can choose from among several marks to place on the map: Pick Item (i.e. the location of SKUs), Walking Paths, Walls, Loading Bay, QC Station, and Wrapper. 
     In  FIG.  50   , the user has selected “Pick Item” and is adding a pick item (a SKU location) to the map. The user would then be able to assign a particular SKU to that location on the map. 
     In  FIG.  51   , the user has added all of the Pick Items to the map. In  FIG.  52   , the user has selected “Walking Paths” and has added the walking paths (between the pick items) to the map. The user is able to designate some or all of the walking paths as permitting travel in a single direction (one-way). 
     In  FIG.  53   , the user has selected “Walls” and is adding the walls to the map, leaving openings for the loading bays, for example. 
     In  FIG.  54   , the user has selected “Loading Bay” and has added six loading bays to the map. Each loading bay is identified in the map so that the picker can be routed to a specific loading bay as explained previously. 
     In  FIG.  55   , the user has selected “QC Station” and has identified the locations of several QC stations, each being labeled A-E, so that a picker can be routed to a specific QC station as explained previously. 
     In  FIG.  56   , the user has selected “Wrapper” and has identified the locations of three wrappers W 1 -W 3 , so that a picker can be routed to a specific wrapper as explained previously. 
     As shown, the user is also provided “Undo,” “Erase,” and “Save” buttons. 
     In accordance with the provisions of the patent statutes and jurisprudence, exemplary configurations described above are considered to represent preferred embodiments of the inventions. However, it should be noted that the inventions can be practiced otherwise than as specifically illustrated and described without departing from its spirit or scope. Alphanumeric identifiers on method steps are solely for ease in reference in dependent claims and such identifiers by themselves do not signify a required sequence of performance, unless otherwise explicitly specified.