Patent · US11087160B2 · B2 · US

Delivery system

(11) Publication number

US11087160B2

(21) Application number

17/204,088

(22) Filing date

2021-03-17

(30) Priority date

2019-02-25

(43) Publication date

2021-08-10

(45) Date of grant

2021-08-10

(51) IPC

B65B 11/04; B65G 1/137; B65G 57/03; B65G 57/20; B65G 57/24; G01C 21/34; G06K 7/10; G06K 7/14; G06N 20/00; G06Q 10/08; G06T 7/00; G06V 10/147

(52) CPC

G06Q Information and communication technology [ICT] specially adapted for administrative, commercial, financial, managerial or supervisory purposes; systems or methods specially adapted for administrative, commercial, financial, managerial or supervisory purposes, not otherwise provided for: 10/087, 10/08, 10/0833, 10/08743, 10/08744, 10/0875, 50/28
B65B Machines, apparatus or devices for, or methods of, packaging articles or materials; unpacking: 11/045
B65G Transport or storage devices, e.g. conveyors for loading or tipping, shop conveyor systems or pneumatic tube conveyors: 1/1378, 57/03, 57/20, 57/24
G01C Measuring distances, levels or bearings; surveying; navigation; gyroscopic instruments; photogrammetry or videogrammetry: 21/34, 21/343
G06K Graphical data reading; presentation of data; record carriers; handling record carriers: 7/10237, 7/1447, 9/3241, 9/46
G06N Computing arrangements based on specific computational models: 20/00
G06T Image data processing or generation, in general: 2207/30128, 7/0008, 7/001
G06V Image or video recognition or understanding: 10/147, 10/751, 20/56

(73) Assignee

Rehrig Pacific Co Inc

(72) Inventors

Robert Lee Martin, JR.; Kalpana Mahesh; Rachel Herstad; Georgey John; Hari Durga Tatineni; Rahul Agarwal; Jason Crawford Miller; Ravi Raghunathan; Joseph Melendez; Deanna Petrochilos; Charles Burden

(54) Title

Delivery system

(57) Abstract

A delivery system generates a pick sheet containing a plurality of SKUs based upon an order. A loaded pallet is imaged to identify the SKUs on the loaded pallet, which are compared to the order prior to the loaded pallet leaving the distribution center. The loaded pallet may be imaged while being wrapped with stretch wrap. At the point of delivery, the loaded pallet may be imaged again and analyzed to compare with the pick sheet.

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Claims (30)

A delivery system comprising: at least one computer programmed to perform the steps of: a) receiving a pick sheet for a plurality of SKUs, wherein each SKU has an associated package type and an associated brand, and wherein not all brands are available in all package types; b) receiving at least one image of a plurality of items stacked together; c) analyzing the at least one image to identify a package type of one of the plurality of items; d) based upon the identified package type from said step c) identifying a subset of possible brands of the one of the plurality of items; e) determining a brand of the one of the plurality of items based upon the identified subset of brands from said step d); f) identifying the SKU of the one of the plurality of items based upon said steps d) and e); g) repeating steps c) to f) for each of the plurality of items; h) comparing the SKUs identified in step f) to the SKUs on the pick sheet; and i) indicating whether the SKUs identified in step f) match the SKUs on the pick sheet based upon the comparison in step h).
The system of claim 1 wherein the at least one computer includes a machine learning model trained with images of cases of beverage containers.
The system of claim 2 wherein the machine learning model is trained to identify a plurality of available package types including reusable beverage crate, tray with translucent wrap, and fully enclosed box, wherein the at least one computer is programmed to perform said step c) using the machine learning model.
The system of claim 1 wherein in said step b), the plurality of items in the at least one image are stacked on a platform.
The system of claim 4 wherein the platform is a pallet.
The system of claim 5 wherein the at least one computer is further programmed to perform the step of indicating that a SKU from the pick sheet is missing on the pallet.
The system of claim 5 wherein the plurality of items are containers of beverage containers.
The system of claim 5 further including at least one camera for generating the at least one image.
The system of claim 8 wherein the at least one camera is a depth camera.
The system of claim 8 wherein the at least one camera is mounted to a wrapper configured to wrap the plurality of items stacked on the pallet.
A validation system comprising: a camera configured to generate at least one image of a plurality of items each having an associated SKU; and at least one computer programmed to: a) analyze the at least one image to identify a package type of one of the plurality of items; b) determine a brand of the one of the plurality of items; c) identify the associated SKU of the one of the plurality of items based upon said steps a) and b); d) perform steps a) to c) for each of the plurality of items; e) comparing the SKUs identified in step c) to SKUs on a pick sheet.
The system of claim 11 wherein the at least one computer is programmed to determine the brand of the one of the plurality of items in step b) by analyzing the at least one image.
The validation system of claim 12 wherein at least one computer is programmed to perform said step a) while the plurality of items are on a pallet and wherein the plurality of items are containers of beverage containers.
The validation system of claim 11 wherein said step a) is performed before said step b).
The validation system of claim 14 further including the step of: c) based upon the identified package type from said step a) identifying a subset of possible brands of the one of the plurality of items; and wherein said step b) includes determining the brand of the one of the plurality of items based upon the identified subset of brands from said step c).
The validation system of claim 15 further including a turntable for receiving and rotating the plurality of items while the camera generates the at least one image, wherein the at least one image is a plurality of images.
The validation system of claim 16 wherein the plurality of items are on a platform.
The validation system of claim 17 further including an RFID reader for reading an RFID tag on the platform when it is on the turntable.
The validation system of claim 11 wherein the at least one computer includes at least one machine learning model trained on images of cases of beverage containers of a plurality of available package types including the package types of the plurality of items and of a plurality of available brands including the brands of the plurality of items.
The validation system of claim 19 wherein the at least one computer is programmed to perform said steps a) and b) using the at least one machine learning model.
The validation system of claim 20 wherein the at least one machine learning model is trained on images of containers of beverage containers on a pallet.
A method for delivery validation including the steps of: a) bringing to a store a plurality of items on a pallet in response to an order, wherein the items are containers of beverage containers; b) imaging the plurality of items on the pallet after step a) to generate at least one store image; c) analyzing the at least one store image to determine SKUs of the plurality of items; d) comparing the SKUs determined in step c) to the order; and e) indicating whether the SKUs of the plurality of items match the order.
The method of claim 22 wherein step b) includes imaging multiple sides of the plurality of items and wherein the at least one store image is a plurality of store images; and wherein step c) includes determining the SKUs of the items visible in each of the plurality of store images and removing duplicate items that appear in more than one of the plurality of images.
The method of claim 23 wherein said step c) is performed by at least one computer including a machine learning model trained with images of cases of beverage containers.
The method of claim 23 wherein said step c) includes the step of determining layers of the plurality of items on the pallet in each of the plurality of store images.
The method of claim 25 further including the step of removing a wrap around the plurality of items prior to step b).
A delivery system comprising: at least one computer programmed to perform the steps of: a) receiving a pick sheet for a plurality of SKUs, wherein each SKU has an associated package type and an associated brand; b) receiving a plurality of images of a plurality of items stacked together, wherein the plurality of images are taken from a plurality of sides of a stack of the plurality of items; c) determining that a first item of the plurality of items is visible in a first image of the plurality of images and in a second image of the plurality of images; d) analyzing the first image to determine a first SKU of the first item at a first confidence level; e) analyzing the second image to determine a second SKU of the first item at a second confidence level; f) determining the SKU of the first item based upon a higher of the first confidence level or the second confidence level; and g) repeating steps c) to f) for each of a subset of the plurality of items.
The system of claim 27 wherein the at least one computer is further programed to perform the steps of: h) comparing the SKUs identified in step f) to the SKUs on the pick sheet; and i) indicating whether the SKUs identified in step f) match the SKUs on the pick sheet based upon the comparison in step h).
The system of claim 28 wherein the items are containers of beverage containers.
The system of claim 29 wherein the at least one computer includes a machine learning model trained with images of cases of beverage containers.

Description

The delivery of products to stores from distribution centers has many steps that are subject to errors and inefficiencies. When the order from the customer is received, at least one pallet is loaded with the specified products according to a “pick list.”

For example, the products may be cases of beverage containers (e.g. cartons of cans and beverage crates containing bottles or cans, etc.). There are many different 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.

The loaded pallet(s) are then loaded on a truck, along with pallets for other stores. Misloaded pallets cause significant time delays to the delivery route since the driver will have to rearrange the pallets during the delivery process with potentially limited space in the trailer to maneuver. Extra pallets on trucks can also cause additional loading times to find the errant pallet and re-load it on the correct trailer

At the store, the driver unloads the pallet(s) designated for that location. Drivers often spend a significant amount of time waiting in the store for a clerk to become available to check in the delivered product by physically counting it. During this process the clerk ensures that all product ordered is being delivered. The driver and clerk often break down the pallet and open each case to scan one UPC from every unique flavor and size. After the unique flavor and size is scanned, both the clerk and driver count the number of cases or bottles for that UPC.

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Record as JSON

{
  "publication_number": "US11087160B2",
  "country": "US",
  "kind": "B2",
  "title": "Delivery system",
  "abstract": "A delivery system generates a pick sheet containing a plurality of SKUs based upon an order. A loaded pallet is imaged to identify the SKUs on the loaded pallet, which are compared to the order prior to the loaded pallet leaving the distribution center. The loaded pallet may be imaged while being wrapped with stretch wrap. At the point of delivery, the loaded pallet may be imaged again and analyzed to compare with the pick sheet.",
  "claims": [
    "1. A delivery system comprising: at least one computer programmed to perform the steps of: a) receiving a pick sheet for a plurality of SKUs, wherein each SKU has an associated package type and an associated brand, and wherein not all brands are available in all package types; b) receiving at least one image of a plurality of items stacked together; c) analyzing the at least one image to identify a package type of one of the plurality of items; d) based upon the identified package type from said step c) identifying a subset of possible brands of the one of the plurality of items; e) determining a brand of the one of the plurality of items based upon the identified subset of brands from said step d); f) identifying the SKU of the one of the plurality of items based upon said steps d) and e); g) repeating steps c) to f) for each of the plurality of items; h) comparing the SKUs identified in step f) to the SKUs on the pick sheet; and i) indicating whether the SKUs identified in step f) match the SKUs on the pick sheet based upon the comparison in step h).",
    "2. The system of claim 1 wherein the at least one computer includes a machine learning model trained with images of cases of beverage containers.",
    "3. The system of claim 2 wherein the machine learning model is trained to identify a plurality of available package types including reusable beverage crate, tray with translucent wrap, and fully enclosed box, wherein the at least one computer is programmed to perform said step c) using the machine learning model.",
    "4. The system of claim 1 wherein in said step b), the plurality of items in the at least one image are stacked on a platform.",
    "5. The system of claim 4 wherein the platform is a pallet.",
    "6. The system of claim 5 wherein the at least one computer is further programmed to perform the step of indicating that a SKU from the pick sheet is missing on the pallet.",
    "7. The system of claim 5 wherein the plurality of items are containers of beverage containers.",
    "8. The system of claim 5 further including at least one camera for generating the at least one image.",
    "9. The system of claim 8 wherein the at least one camera is a depth camera.",
    "10. The system of claim 8 wherein the at least one camera is mounted to a wrapper configured to wrap the plurality of items stacked on the pallet.",
    "11. A validation system comprising: a camera configured to generate at least one image of a plurality of items each having an associated SKU; and at least one computer programmed to: a) analyze the at least one image to identify a package type of one of the plurality of items; b) determine a brand of the one of the plurality of items; c) identify the associated SKU of the one of the plurality of items based upon said steps a) and b); d) perform steps a) to c) for each of the plurality of items; e) comparing the SKUs identified in step c) to SKUs on a pick sheet.",
    "12. The system of claim 11 wherein the at least one computer is programmed to determine the brand of the one of the plurality of items in step b) by analyzing the at least one image.",
    "13. The validation system of claim 12 wherein at least one computer is programmed to perform said step a) while the plurality of items are on a pallet and wherein the plurality of items are containers of beverage containers.",
    "14. The validation system of claim 11 wherein said step a) is performed before said step b).",
    "15. The validation system of claim 14 further including the step of: c) based upon the identified package type from said step a) identifying a subset of possible brands of the one of the plurality of items; and wherein said step b) includes determining the brand of the one of the plurality of items based upon the identified subset of brands from said step c).",
    "16. The validation system of claim 15 further including a turntable for receiving and rotating the plurality of items while the camera generates the at least one image, wherein the at least one image is a plurality of images.",
    "17. The validation system of claim 16 wherein the plurality of items are on a platform.",
    "18. The validation system of claim 17 further including an RFID reader for reading an RFID tag on the platform when it is on the turntable.",
    "19. The validation system of claim 11 wherein the at least one computer includes at least one machine learning model trained on images of cases of beverage containers of a plurality of available package types including the package types of the plurality of items and of a plurality of available brands including the brands of the plurality of items.",
    "20. The validation system of claim 19 wherein the at least one computer is programmed to perform said steps a) and b) using the at least one machine learning model.",
    "21. The validation system of claim 20 wherein the at least one machine learning model is trained on images of containers of beverage containers on a pallet.",
    "22. A method for delivery validation including the steps of: a) bringing to a store a plurality of items on a pallet in response to an order, wherein the items are containers of beverage containers; b) imaging the plurality of items on the pallet after step a) to generate at least one store image; c) analyzing the at least one store image to determine SKUs of the plurality of items; d) comparing the SKUs determined in step c) to the order; and e) indicating whether the SKUs of the plurality of items match the order.",
    "23. The method of claim 22 wherein step b) includes imaging multiple sides of the plurality of items and wherein the at least one store image is a plurality of store images; and wherein step c) includes determining the SKUs of the items visible in each of the plurality of store images and removing duplicate items that appear in more than one of the plurality of images.",
    "24. The method of claim 23 wherein said step c) is performed by at least one computer including a machine learning model trained with images of cases of beverage containers.",
    "25. The method of claim 23 wherein said step c) includes the step of determining layers of the plurality of items on the pallet in each of the plurality of store images.",
    "26. The method of claim 25 further including the step of removing a wrap around the plurality of items prior to step b).",
    "27. A delivery system comprising: at least one computer programmed to perform the steps of: a) receiving a pick sheet for a plurality of SKUs, wherein each SKU has an associated package type and an associated brand; b) receiving a plurality of images of a plurality of items stacked together, wherein the plurality of images are taken from a plurality of sides of a stack of the plurality of items; c) determining that a first item of the plurality of items is visible in a first image of the plurality of images and in a second image of the plurality of images; d) analyzing the first image to determine a first SKU of the first item at a first confidence level; e) analyzing the second image to determine a second SKU of the first item at a second confidence level; f) determining the SKU of the first item based upon a higher of the first confidence level or the second confidence level; and g) repeating steps c) to f) for each of a subset of the plurality of items.",
    "28. The system of claim 27 wherein the at least one computer is further programed to perform the steps of: h) comparing the SKUs identified in step f) to the SKUs on the pick sheet; and i) indicating whether the SKUs identified in step f) match the SKUs on the pick sheet based upon the comparison in step h).",
    "29. The system of claim 28 wherein the items are containers of beverage containers.",
    "30. The system of claim 29 wherein the at least one computer includes a machine learning model trained with images of cases of beverage containers."
  ],
  "description_excerpt": "The delivery of products to stores from distribution centers has many steps that are subject to errors and inefficiencies. When the order from the customer is received, at least one pallet is loaded with the specified products according to a “pick list.”\n\nFor example, the products may be cases of beverage containers (e.g. cartons of cans and beverage crates containing bottles or cans, etc.). There are many different 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.\n\nThe loaded pallet(s) are then loaded on a truck, along with pallets for other stores. Misloaded pallets cause significant time delays to the delivery route since the driver will have to rearrange the pallets during the delivery process with potentially limited space in the trailer to maneuver. Extra pallets on trucks can also cause additional loading times to find the errant pallet and re-load it on the correct trailer\n\nAt the store, the driver unloads the pallet(s) designated for that location. Drivers often spend a significant amount of time waiting in the store for a clerk to become available to check in the delivered product by physically counting it. During this process the clerk ensures that all product ordered is being delivered. The driver and clerk often break down the pallet and open each case to scan one UPC from every unique flavor and size. After the unique flavor and size is scanned, both the clerk and driver count the number of cases or bottles for that UPC.",
  "cpc": [
    "G06Q 10/087",
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    "G06V 20/56"
  ],
  "ipc": [
    "B65B 11/04",
    "B65G 1/137",
    "B65G 57/03",
    "B65G 57/20",
    "B65G 57/24",
    "G01C 21/34",
    "G06K 7/10",
    "G06K 7/14",
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    "G06Q 10/08",
    "G06T 7/00",
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  ],
  "assignees": [
    "Rehrig Pacific Co Inc"
  ],
  "inventors": [
    "Robert Lee Martin, JR.",
    "Kalpana Mahesh",
    "Rachel Herstad",
    "Georgey John",
    "Hari Durga Tatineni",
    "Rahul Agarwal",
    "Jason Crawford Miller",
    "Ravi Raghunathan",
    "Joseph Melendez",
    "Deanna Petrochilos",
    "Charles Burden"
  ],
  "filing_date": "2021-03-17",
  "publication_date": "2021-08-10",
  "grant_date": "2021-08-10",
  "priority_date": "2019-02-25",
  "application_number": "US-202117204088-A",
  "family_id": "72142496",
  "cited_by_count": 5,
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}

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