IP Library › Granted Patent US 12,574,638
Granted Patent B2
US 12,574,638 · App. 18/780,982 · Granted Mar 10, 2026

Laser-based adaptive imaging technique

Inventors: Aaron M. Kurneta (Chandler, AZ); Adam De-Nyangos (Mesa, AZ); Frank Martinez Triana (Chandler, AZ); Eric Gordon (Charlotte, NC); Holmberg Lopez (Bonsall, CA); Michael L. Mahar (Tempe, AZ)
Assignee: Optum, Inc.
H04N23/667B65G43/00G01F23/292G06K7/10297
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Quick Facts
Patent No.
US 12,574,638
App. No.
18/780,982
Granted
Mar 10, 2026
Kind
B2
Abstract

Various embodiments of the present disclosure provide an adaptive imaging process that includes receiving a measured fill level for a container based on a distance reading measurement, selecting a focus level from a plurality of calibrated focus levels based on the measured fill level, providing, to an imaging device, one or more imaging instructions to trigger a validation image at the focus level, receiving, from the imaging device, an imaging response to the one or more imaging instructions that comprises an image classification for the validation image, generating a verification event based on the image classification, and storing the verification event in association with the container.

Claims (56)

1 . A computer-implemented method comprising:

receiving, by one or more processors, a measured fill level for a container based on a distance reading measurement;

selecting, by the one or more processors, a focus level from a plurality of calibrated focus levels based on the measured fill level;

providing, by the one or more processors and to an imaging device, one or more imaging instructions to trigger a validation image at the focus level;

receiving, by the one or more processors and from the imaging device, an imaging response to the one or more imaging instructions that comprises an image classification for the validation image;

generating, by the one or more processors, a verification event based on the image classification; and

storing, by the one or more processors, the verification event in association with the container, wherein the verification event comprises the image classification and the measured fill level.

2 . The computer-implemented method of claim 1 , further comprising:

initiating a routing action for the container based on the verification event, wherein the routing action comprises one or more conveyance line movements configured to move the container to a location on a conveyance line based on the verification event.

3 . The computer-implemented method of claim 1 , further comprising:

receiving presence data that identifies a presence of the container at an imaging position relative to the imaging device;

receiving a container identifier for the container in response to the presence data;

determining that the container satisfies one or more qualification criteria based on the container identifier; and

responsive to the satisfaction of the one or more qualification criteria, initiating the distance reading measurement.

4 . The computer-implemented method of claim 3 , wherein the container identifier is associated with a radio frequency identifier (RFID), and receiving the container identifier comprises, in response to the presence data, providing, to a scanning device, one or more scanning instructions to trigger an RFID scan of the container.

5 . The computer-implemented method of claim 3 , wherein determining that the container satisfies the one or more qualification criteria based on the container identifier comprises:

identifying a container data object corresponding to the container within a container database; and

identifying an order corresponding to the container based on the container data object.

6 . The computer-implemented method of claim 5 , wherein the verification event is stored in association with the container data object.

7 . The computer-implemented method of claim 1 , wherein initiating the distance reading measurement comprises:

providing, to a height detection sensor, one or more sensor instructions to trigger the distance reading measurement, wherein the height detection sensor comprises a single point sensor that is calibrated based on a floor of a calibration container.

8 . The computer-implemented method of claim 1 , wherein the measured fill level for the container is determined based on a comparison between the distance reading measurement and a height of the container.

9 . The computer-implemented method of claim 8 , wherein the measured fill level is determined by:

determining a relative distance measurement based on a comparison between the distance reading measurement and the height of the container; and

identifying the measured fill level from a plurality of predefined fill level tiers based on the relative distance measurement.

10 . The computer-implemented method of claim 9 , wherein the plurality of calibrated focus levels respectively corresponds to the plurality of predefined fill level tiers.

11 . The computer-implemented method of claim 9 , wherein storing the verification event further comprises storing the validation image, the relative distance measurement, and the distance reading measurement.

12 . A system comprising:

one or more processors; and

at least one memory storing processor-executable instruction that, when executed by the one or more processors, cause the one or more processors to:

receive a measured fill level for a container based on a distance reading measurement;

select a focus level from a plurality of calibrated focus levels based on the measured fill level;

provide, to an imaging device, one or more imaging instructions to trigger a validation image at the focus level;

receive, from the imaging device, an imaging response to the one or more imaging instructions that comprises an image classification for the validation image;

generate a verification event based on the image classification; and

store the verification event in association with the container, wherein the verification event comprises the image classification and the measured fill level.

13 . The system of claim 12 , wherein the measured fill level for the container is determined based on a comparison between the distance reading measurement and a height of the container.

14 . The system of claim 13 , wherein the measured fill level is determined by:

determining a relative distance measurement based on a comparison between the distance reading measurement and the height of the container; and

identifying the measured fill level from a plurality of predefined fill level tiers based on the relative distance measurement.

15 . The system of claim 14 , wherein the plurality of calibrated focus levels respectively corresponds to the plurality of predefined fill level tiers.

16 . The system of claim 14 , wherein storing the verification event with the validation image further comprises storing the validation image, the relative distance measurement, and the distance reading measurement.

17 . One or more non-transitory computer-readable storage media including instructions that, when executed by one or more processors, cause the one or more processors to:

receive a measured fill level for a container based on a distance reading measurement;

select a focus level from a plurality of calibrated focus levels based on the measured fill level;

provide, to an imaging device, one or more imaging instructions to trigger a validation image at the focus level;

receive, from the imaging device, an imaging response to the one or more imaging instructions that comprises an image classification for the validation image;

generate a verification event based on the image classification; and

store the verification event in association with the container, wherein the verification event comprises the image classification and the measured fill level.

18 . The one or more non-transitory computer-readable storage media of claim 17 , wherein the instructions further cause the one or more processors to:

initiate a routing action for the container based on the verification event, wherein the routing action comprises one or more conveyance line movements configured to move the container to a location on a conveyance line based on the verification event.

19 . The one or more non-transitory computer-readable storage media of claim 17 , wherein the instructions further cause the one or more processors to:

receive presence data that identifies a presence of the container at an imaging position relative to the imaging device;

receive a container identifier for the container in response to the presence data; determining that the container satisfies one or more qualification criteria based on the container identifier; and

responsive to the satisfaction of the one or more qualification criteria, initiate the distance reading measurement.

20 . The one or more non-transitory computer-readable storage media of claim 19 , wherein the container identifier is associated with a radio frequency identifier (RFID), and receiving the container identifier comprises, in response to the presence data, providing, to a scanning device, one or more scanning instructions to trigger an RFID scan of the container.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jul 23, 2024
From: KURNETA, AARON M.; DE-NYANGOS, ADAM; MARTINEZ TRIANA, FRANK; GORDON, ERIC; LOPEZ, HOLMBERG; MAHAR, MICHAEL L.
To: OPTUM, INC.
Reel/Frame 068056/0751 →
Continuity (2)
Provisional Application 63637637 · Apr 23, 2024
Related Publication 20250330702A1 · Oct 23, 2025
References Cited (33)
US 4015645A · Chamberlin · 1977 [cited by examiner]
US 7076085B1 · Sah · 2006 [cited by applicant]
US 7983779B2 · Kotula · 2011 [cited by applicant]
US 8121392B2 · Popovich, Jr. et al. · 2012 [cited by applicant]
US 10121034B1 · Bathurst et al. · 2018 [cited by applicant]
US 10229487B2 · Goyal et al. · 2019 [cited by applicant]
US 10947708B2 · Chung · 2021 [cited by examiner]
US 12024367B1 · Day · 2024 [cited by examiner]
US 20070174071A1 · Hunscher · 2007 [cited by examiner]
US 20110075156A1 · Patel et al. · 2011 [cited by applicant]
US 20140025198A1 · Mattern · 2014 [cited by examiner]
US 20150113324A1 · Factor et al. · 2015 [cited by applicant]
US 20150186206A1 · Bhattacharya et al. · 2015 [cited by applicant]
US 20150206095A1 · De Boer · 2015 [cited by examiner]
US 20150341542A1 · Preston · 2015 [cited by applicant]
US 20160299114A1 · Guthrie et al. · 2016 [cited by applicant]
US 20180164143A1 · Gurumohan · 2018 [cited by examiner]
US 20180194573A1 · Iwai et al. · 2018 [cited by applicant]
US 20180286200A1 · Gordon et al. · 2018 [cited by applicant]
US 20200033179A1 · Gurumohan · 2020 [cited by examiner]
US 20200276996A1 · Gresset · 2020 [cited by applicant]
US 20230129436A1 · Park et al. · 2023 [cited by applicant]
US 20230132104A1 · Bossaer et al. · 2023 [cited by applicant]
US 20230229139A1 · Nicholls et al. · 2023 [cited by applicant]
CN 107454364A · 2017 [cited by applicant]
CN 110619617A · 2019 [cited by applicant]
WO 2021225876A1 · 2021 [cited by applicant]
WO WO2022263100A1 · 2022 [cited by examiner]
WO 2023129436A1 · 2023 [cited by applicant]
IQS Directory, “Machine Vision System: What is it?”, (38 pages), Retrieved online Nov. 18, 2024 at https://www.iqsdirectory.com/articles/machine-vision-system.html. [cited by applicant]
Trujillo, et al., “Container Monitoring with Infrared Catadioptric Imaging and Automatic Intruder Detection”, SN Applied Sciences, vol. 1, (25 pages), Nov. 25, 2019, https://doi.org/10.1007/s42452-019-1721-8. [cited by applicant]
Notice of Allowance and Fees Due (PTOL-85) Mailed on Sep. 4, 2025 for U.S. Appl. No. 18/781,005, 7 page(s). [cited by applicant]
Non-Final Rejection Mailed on Nov. 28, 2025 for U.S. Appl. No. 18/780,987, 16 page(s). [cited by applicant]