IP Library › Granted Patent US 12,223,684
Granted Patent B2
US 12,223,684 · App. 18/472,994 · Granted Feb 11, 2025

Neural network based physical condition evaluation of electronic devices, and associated systems and methods

Inventors: John Silva (San Diego, CA); Babak Forutanpour (San Diego, CA)
Assignee: ecoATM, LLC
G06V10/141G02B27/30G06N3/08G06Q30/0283G06T7/0004G06V10/764G06V10/82H04N23/56G06T2207/20081G06T2207/20084G06T2207/30108
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Quick Facts
Patent No.
US 12,223,684
App. No.
18/472,994
Granted
Feb 11, 2025
Kind
B2
Abstract

Systems and methods for evaluating the physical and/or cosmetic condition of electronic devices using machine learning techniques are disclosed. In one example aspect, an example system includes a kiosk that comprises an inspection plate configured to hold an electronic device, one or more light sources arranged above the inspection plate configured to direct one or more light beams towards the electronic device, and one or more cameras configured to capture at least one image of a first side of the electronic device. The system also includes one or more processors in communication with the one or more cameras configured to extract a set of features of the electronic device and determine, via a first neural network, a condition of the electronic device based on the set of features.

Claims (45)

1. A system for evaluating a condition of an electronic device, the system comprising:

a kiosk that includes:

one or more cameras configured to capture at least one image of a first side of the electronic device; and

one or more processors in communication with the one or more cameras, the one or more processors configured to:

apply a first machine learning model to the at least one image of the electronic device,

wherein the first machine learning model is trained to output, based on analyzing the at least one image, a brand and/or model of the electronic device and a cosmetic rating of the electronic device specific to the brand and/or model;

determine the condition of the electronic device based on the cosmetic rating; and

determine, via a second machine learning model, an offer price for the electronic device based on the determined condition,

wherein the second machine learning model is different from the first machine learning model.

2. The system of claim 1 , wherein the one or more processors are configured to determine that the at least one image captured by the one or more cameras is defective.

3. The system of claim 1 , wherein the second machine learning model is configured to use a sub-model to predict a resale value of the electronic device to provide the offer price.

4. The system of claim 1 , wherein the kiosk includes:

a flipping mechanism configured to flip the electronic device to allow the one or more cameras to capture at least another image of the electronic device.

5. The system of claim 1 , wherein the second machine learning model is configured to use a sub-model to predict an incoming number of electronic devices.

6. The system of claim 1 , wherein the second machine learning model is configured to use a sub-model to predict processing costs associated with the electronic device.

7. The system of claim 1 , wherein the one or more cameras are configured to capture multiple images corresponding to multiple sides of the electronic device under different lighting conditions, and wherein the one or more processors are configured to process and combine the multiple images into a single input image.

8. The system of claim 1 , wherein the offer price is based on a location of the kiosk, a time at which the electronic device is examined, and/or an age of the electronic device.

9. The system of claim 1 , wherein the offer price is based on a predicted repair cost for the electronic device, a risk of counterfeit or fraud, and/or an anticipated demand for the electronic device.

10. The system of claim 1 , wherein the second machine learning model is configured to use sub-models distributed across different locations in a network in accordance with a cloud-based computing service.

11. A computer-implemented method for evaluating a condition of an electronic device, the method comprising:

capturing, by at least one camera of a kiosk, at least one image of a first side of the electronic device;

applying a first machine learning model to the at least one image of the electronic device,

wherein the first machine learning model is trained to output, based on analyzing the at least one image, a brand and/or model of the electronic device and a cosmetic rating of the electronic device specific to the brand and/or model;

determining the condition of the electronic device based on the cosmetic rating; and

determining, via a second machine learning model, an offer price for the electronic device based on the determined condition,

wherein the second machine learning model is different from the first machine learning model.

12. The method of claim 11 , comprising:

capturing, via the at least one camera, at least one image of a second side of the electronic device that is different from the first side.

13. The method of claim 12 , comprising:

prior to capturing the at least one image of the second side of the electronic device:

flipping the electronic device such that light beams of multiple light sources of the kiosk are directed towards the second side of the electronic device.

14. The method of claim 11 , comprising:

processing multiple images of multiple sides of the electronic device such that the multiple images have a uniform size; and

combining the multiple images into a single image to be provided to the first machine learning model.

15. The method of claim 11 , comprising:

determining whether the at least one image captured by the at least one camera is acceptable or defective,

wherein applying the first machine learning model to the at least one image is performed only when the at least one image captured by the at least one camera is determined to be acceptable.

16. The method of claim 11 , comprising:

using, by the second machine learning model, a sub-model to predict a resale value of the electronic device to provide the offer price.

17. The method of claim 11 , comprising:

receiving an input from a user indicating an acceptance or a rejection of the offer price; and

training the second machine learning model in part based on the at least one image and the input from the user.

18. The method of claim 11 , comprising using, by the second machine learning model, a sub-model to predict an incoming number of electronic devices to provide the offer price.

19. The method of claim 11 , comprising using, by the second machine learning model, a sub-model to predict processing costs associated with the electronic device to provide the offer price.

20. The method of claim 11 , wherein the offer price is based on a predicted repair cost for the electronic device, a risk of counterfeit or fraud, and/or an anticipated demand for the electronic device.

Assignments (3)
SECURITY AGREEMENT Recorded Mar 3, 2025
From: ECOATM, LLC
To: JPMORGAN CHASE BANK, N.A., AS COLLATERAL AGENT
Reel/Frame 070765/0868 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Dec 4, 2023
From: SILVA, JOHN
To: ECOATM, LLC
Reel/Frame 065755/0957 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Dec 4, 2023
From: FORUTANPOUR, BABAK
To: ECOATM, LLC
Reel/Frame 065755/0993 →
Continuity (3)
Continuation 16794009 · Feb 18, 2020
Provisional Application 62807165 · Feb 18, 2019
Related Publication 20240087276A1 · Mar 14, 2024
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