IP Library Granted Patent US 11,313,820
Granted Patent B2
US 11,313,820 · App. 16/435,800 · Granted Apr 26, 2022

Methods and systems for determining an internal property of a food product

Inventors: Steven Lewis (Bentonville, AR); Matthew Biermann (Fayetteville, AR); Suman Pattnaik (Bentonville, AR)
Assignee: Walmart Apollo, LLC
G01N27/02G01N33/12G01R27/16
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Quick Facts
Patent No.
US 11,313,820
App. No.
16/435,800
Granted
Apr 26, 2022
Kind
B2
Abstract

Systems and methods are provided to determine an internal property of a food product. The system includes one or more analyzing devices, a camera and a central unit in communication with the camera and analyzing device. The analyzing device is configured to analyze an interior region of the food product. The camera is configured to analyze an external property of the food product. The central unit is configured to determine the internal property of the food product based on feedback provided by the analyzing device and the camera.

Claims (47)

1. A method comprising:

capturing an image of a food product;

analyzing an interior region of the food product via one or more analyzing devices;

based on the image and the analyzing of the interior region, generating, by a processor, a first dataset characterizing (i) at least one internal property of the food product, and (ii) an identity of the food product;

based at least on the first dataset, generating by the processor, a second dataset characterizing a predicted quality of the food product and a recommended optimal time of receipt and quality of the food product by applying a regression analysis on at least the first dataset; and

modifying, by the processor, a purchase order for additional quantities of the food product based on the second dataset.

2. The method of claim 1 , wherein the at least one internal property comprises an overall frequency response to ultra violet emission.

3. The method of claim 1 , wherein the regression analysis further uses, as inputs, a time of receipt of the food product and a purchase time from the time of receipt.

4. The method of claim 1 , wherein the one or more analyzing devices comprises an ultrasound device, and wherein the analyzing of the interior region via the ultrasound device generates an ultrasound.

5. The method of claim 1 , wherein the one or more analyzing devices comprises a probe, the probe having at least two electrodes; and

wherein the analyzing of the interior region comprises inserting the probe into the food product and measuring an impedance of the food product between the at least two electrodes.

6. The method of claim 1 , further comprising:

weighing the food product using a scale, resulting in a weight of the food product; and

identifying, via the processor, a food type of the food product based on the image and the weight, wherein the modifying of the purchase order is further based on the food type.

7. The method of claim 1 , wherein the at least one internal property comprises one or more of protein content, fat content, seed content, water content, and degree of ripeness.

8. A system, comprising:

a processor; and

a non-transitory computer-readable storage medium having instructions stored which, when executed by the processor, cause the processor to perform operations comprising:

capturing an image of a food product;

analyzing an interior region of the food product via one or more analyzing devices;

based on the image and the analyzing of the interior region, generating a first dataset characterizing (i) at least one internal property of the food product, and (ii) an identity of the food product;

based at least on the first dataset, generating a second dataset characterizing a predicted quality of the food product and a recommended optimal time of receipt and quality of the food product by applying a regression analysis on at least the first dataset;

and

modifying a purchase order for additional quantities of the food product based on the second dataset.

9. The system of claim 8 , wherein the at least one internal property comprises an overall frequency response to ultra violet emission.

10. The system of claim 8 , wherein the regression analysis further uses, as inputs, a time of receipt of the food product and a purchase time from the time of receipt.

11. The system of claim 8 , wherein the one or more analyzing devices comprises an ultrasound device, and wherein the analyzing of the interior region via the ultrasound device generates an ultrasound.

12. The system of claim 8 , wherein the one or more analyzing devices comprises a probe, the probe having at least two electrodes; and

wherein the analyzing of the interior region comprises inserting the probe into the food product and measuring an impedance of the food product between the at least two electrodes.

13. The system of claim 8 , having additional instructions stored which, when executed by the processor, cause the processor to perform operations comprising:

weighing the food product using a scale, resulting in a weight of the food product; and

identifying a food type of the food product based on the image and the weight, wherein the modifying of the purchase order is further based on the food type.

14. The system of claim 8 , wherein the at least one internal property comprises one or more of protein content, fat content, seed content, water content, and degree of ripeness.

15. A non-transitory computer-readable storage medium having instructions stored which, when executed by a processor, cause the processor to perform operations comprising:

capturing an image of a food product;

analyzing an interior region of the food product via one or more analyzing devices;

based on the image and the analyzing of the interior region, generating a first dataset characterizing (i) at least one internal property of the food product, and (ii) an identity of the food product;

based at least on the first dataset, generating a second dataset characterizing a predicted quality of the food product and a recommended optimal time of receipt and quality of the food product by applying a regression analysis on at least the first dataset; and

modifying a purchase order for additional quantities of the food product based on the second dataset.

16. The non-transitory computer-readable storage medium of claim 15 , wherein the at least one internal property comprises an overall frequency response to ultra violet emission.

17. The non-transitory computer-readable storage medium of claim 15 , wherein the regression analysis further uses, as inputs, a time of receipt of the food product and a purchase time from the time of receipt.

18. The non-transitory computer-readable storage medium of claim 15 , wherein the one or more analyzing devices comprises an ultrasound device, and wherein the analyzing of the interior region via the ultrasound device generates an ultrasound.

19. The non-transitory computer-readable storage medium of claim 15 , wherein the one or more analyzing devices comprises a probe, the probe having at least two electrodes; and

wherein the analyzing of the interior region comprises inserting the probe into the food product and measuring an impedance of the food product between the at least two electrodes.

20. The non-transitory computer-readable storage medium of claim 15 , having additional instructions stored which, when executed by the processor, cause the processor to perform operations comprising:

weighing the food product using a scale, resulting in a weight of the food product; and

identifying a food type of the food product based on the image and the weight, wherein the modifying of the purchase order is further based on the food type.

Assignments (2)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Mar 23, 2022
From: LEWIS, STEVEN; BIERMANN, MATTHEW; PATTNAIK, SUMAN
To: WAL-MART STORES, INC.
Reel/Frame 059373/0934 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Mar 23, 2022
From: WAL-MART STORES, INC.
To: WALMART APOLLO, LLC
Reel/Frame 059374/0044 →
Continuity (3)
Continuation 15918936 · Mar 12, 2018
Provisional Application 62470005 · Mar 10, 2017
Related Publication 20190293583A1 · Sep 26, 2019