IP Library Granted Patent US 12,333,581
Granted Patent B2
US 12,333,581 · App. 17/676,701 · Granted Jun 17, 2025

Processing system having a machine learning engine for providing a surface dimension output

Inventors: Michael T. Cornelison (LaGrange Highlands, IL); Anurag Sharma (Buffalo Grove, IL); Daniel Brickman (Chicago, IL); David M. Zahn (Lake Villa, IL); Andrew Daniels (Columbus, OH); Pinal Patel (Libertyville, IL); David L Gilkison (Libertyville, IL); Steven Genc (Hainesville, IL)
Assignee: Allstate Insurance Company
G06Q30/0283G06N3/02G06N20/00G06Q10/20G06T7/13G06T2210/12G06V40/162
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,333,581
App. No.
17/676,701
Granted
Jun 17, 2025
Kind
B2
Abstract

Systems and apparatuses for generating surface dimension outputs are provided. The system may collect an image from a mobile device. The system may analyze the image to determine whether they comprise one or more standardized reference objects. Based on analysis of the image and the one or more standardized reference objects, the system may determine a surface dimension output. The system may determine one or more settlement outputs and one or more repair outputs for the driver based on the surface dimension output.

Claims (56)

1. A method comprising:

receiving, by an image analysis and device control system and from a mobile device, at least one image;

determining, by the image analysis and device control system and using edge detection, an indication of boundaries of a surface comprising included in the at least one image, comprising:

determining, using one or more machine learning algorithms, a plurality of bounding boxes corresponding to the at least one image, wherein determining the plurality of bounding boxes includes adjusting dimensions of the plurality of bounding boxes to match predetermined dimensions for a neural network, and

inputting, into the neural network, the plurality of bounding boxes for analysis by the one or more machine learning algorithms to determine whether the at least one image comprises a reference object;

determining, by the image analysis and device control system, pixel dimensions for the surface;

determining, by the image analysis and device control system and based at least on the pixel dimensions for the surface, an actual surface dimension comprising actual dimensions for the surface; and

transmitting, by the image analysis and device control system and to the mobile device, the actual surface dimension output.

2. The method of claim 1 , further comprising transmitting, by the image analysis and device control system and to the mobile device, an instruction to capture the at least one image.

3. The method of claim 2 , further comprising receiving, by the image analysis and device control system and from the mobile device, a damage indication output, wherein the transmitting the instruction to capture the at least one image is responsive to the receiving the damage indication output.

4. The method of claim 2 , wherein the instruction to capture the at least one image comprises a link to download a damage processing application.

5. The method of claim 1 , wherein the reference object comprises at least one of: a light switch, an outlet, an outlet plate, a light bulb, a can light, a phone outlet, a data jack, a base board, a nest, a smoke detector, a kitchen sink, a faucet, a stove, a dishwasher, a floor tile, hot and cold faucets, a heat vent, a key hole, a door handle, a door frame, a deadbolt, a door, a stair, a railing, a table, a chair, a bar stool, a toilet, and a cabinet.

6. The method of claim 1 , further comprising:

transmitting, by the image analysis and device control system and to the mobile device, an instruction to prompt for a room indication input comprising an indication of a type of room in which the at least one image was captured;

receiving, by the image analysis and device control system and from the mobile device, the room indication input;

determining, by the image analysis and device control system and based on the room indication input, a room indication output; and

determining, by the image analysis and device control system and based on the room indication output, a plurality of reference objects.

7. The method of claim 6 , wherein the at least one image comprises at least one of the plurality of reference objects.

8. An image analysis and device control system comprising:

a memory; and

a processor coupled to the memory and programmed with computer-executable instructions for performing operations comprising:

receiving, from a mobile device, at least one image;

determining, using edge detection, an indication of boundaries of a surface included in the at least one image, comprising:

determining, using one or more machine learning algorithms, a plurality of bounding boxes corresponding to the at least one image, wherein determining the plurality of bounding boxes includes adjusting dimensions of the plurality of bounding boxes to match predetermined dimensions for a neural network, and

inputting, into the neural network, the plurality of bounding boxes for analysis by the one or more machine learning algorithms to determine whether the at least one image comprises a reference object;

determining pixel dimensions for the surface;

determining, based at least on the pixel dimensions for the surface, an actual surface dimension comprising actual dimensions for the surface; and

transmitting, by the image analysis and device control system and to the mobile device, the actual surface dimension output.

9. The system of claim 8 , the operations further comprising transmitting, to the mobile device, an instruction to capture the at least one image.

10. The system of claim 9 , the operations further comprising receiving, from the mobile device, a damage indication output, wherein the transmitting the instruction to capture the at least one image is responsive to the receiving the damage indication output.

11. The system of claim 9 , wherein the instruction to capture the at least one image comprises a link to download a damage processing application.

12. The system of claim 8 , wherein the reference object comprises at least one of: a light switch, an outlet, an outlet plate, a light bulb, a can light, a phone outlet, a data jack, a base board, a nest, a smoke detector, a kitchen sink, a faucet, a stove, a dishwasher, a floor tile, hot and cold faucets, a heat vent, a key hole, a door handle, a door frame, a deadbolt, a door, a stair, a railing, a table, a chair, a bar stool, a toilet, and a cabinet.

13. The system of claim 8 , the operations further comprising:

transmitting, by the image analysis and device control system and to the mobile device, an instruction to prompt for a room indication input comprising an indication of a type of room in which the at least one image was captured;

receiving, by the image analysis and device control system and from the mobile device, the room indication input;

determining, by the image analysis and device control system and based on the room indication input, a room indication output; and

determining, by the image analysis and device control system and based on the room indication output, a plurality of reference objects.

14. The system of claim 13 , wherein the at least one image comprises at least one of the plurality of reference objects.

15. A non-transitory computer-readable medium storing computer executable instructions, which when executed by a processor, cause an image analysis and device control system to perform operations comprising:

receiving, from a mobile device, at least one image;

determining, using edge detection, an indication of boundaries of a surface included in the at least one image, comprising:

determining, using one or more machine learning algorithms, a plurality of bounding boxes corresponding to the at least one image, wherein determining the plurality of bounding boxes includes adjusting dimensions of the plurality of bounding boxes to match predetermined dimensions for a neural network, and

inputting, into the neural network, the plurality of bounding boxes for analysis by the one or more machine learning algorithms to determine whether the at least one image comprises a reference object;

determining pixel dimensions for the surface;

determining, based at least on the pixel dimensions for the surface, an actual surface dimension comprising actual dimensions for the surface; and

transmitting, by the image analysis and device control system and to the mobile device, the actual surface dimension output.

16. The media of claim 15 , the operations further comprising transmitting, to the mobile device, an instruction to capture the at least one image.

17. The media of claim 16 , the operations further comprising receiving, from the mobile device, a damage indication output, wherein the transmitting the instruction to capture the at least one image is responsive to the receiving the damage indication output.

18. The media of claim 16 , wherein the instruction to capture the at least one image comprises a link to download a damage processing application.

19. The media of claim 15 , wherein the reference object comprises at least one of: a light switch, an outlet, an outlet plate, a light bulb, a can light, a phone outlet, a data jack, a base board, a nest, a smoke detector, a kitchen sink, a faucet, a stove, a dishwasher, a floor tile, hot and cold faucets, a heat vent, a key hole, a door handle, a door frame, a deadbolt, a door, a stair, a railing, a table, a chair, a bar stool, a toilet, and a cabinet.

20. The media of claim 15 , the operations further comprising:

transmitting, by the image analysis and device control system and to the mobile device, an instruction to prompt for a room indication input comprising an indication of a type of room in which the at least one image was captured;

receiving, by the image analysis and device control system and from the mobile device, the room indication input;

determining, by the image analysis and device control system and based on the room indication input, a room indication output; and

determining, by the image analysis and device control system and based on the room indication output, a plurality of reference objects,

wherein the at least one image comprises at least one of the plurality of reference objects.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Feb 23, 2022
From: CORNELISON, MICHAEL T.; ZAHN, DAVID M.; DANIELS, ANDREW; PATEL, PINAL; GILKISON, DAVID L.; GENC, STEVEN; SHARMA, ANURAG; BRICKMAN, DANIEL
To: ALLSTATE INSURANCE COMPANY
Reel/Frame 059072/0704 →
Continuity (2)
Continuation 15971294 · May 4, 2018
Related Publication 20220180411A1 · Jun 9, 2022
References Cited (171)
US 5504674A · Chen et al. · 1996 [cited by applicant]
US 5655085A · Ryan et al. · 1997 [cited by applicant]
US 5697001A · Ring et al. · 1997 [cited by applicant]
US 6611807B1 · Bernheim et al. · 2003 [cited by applicant]
US 6678685B2 · Mcgill et al. · 2004 [cited by applicant]
US 6829584B2 · Loveland · 2004 [cited by applicant]
US 6851611B1 · Shaw-Sinclair · 2005 [cited by applicant]
US 6904410B1 · Weiss et al. · 2005 [cited by applicant]
US 7184975B2 · Ikeda · 2007 [cited by applicant]
US 7343310B1 · Stender · 2008 [cited by applicant]
US 7424473B2 · Orton et al. · 2008 [cited by applicant]
US 7551849B1 · Abad · 2009 [cited by applicant]
US 7568200B2 · Weiss · 2009 [cited by applicant]
US 7765118B1 · Bohanek · 2010 [cited by applicant]
US 7801750B1 · Wallach et al. · 2010 [cited by applicant]
US 7809587B2 · Dorai et al. · 2010 [cited by applicant]
US 7899823B1 · Trandal et al. · 2011 [cited by applicant]
US 7941330B1 · Buentello et al. · 2011 [cited by applicant]
US 7953615B2 · Aquila et al. · 2011 [cited by applicant]
US 8041636B1 · Hunter et al. · 2011 [cited by applicant]
US 8095391B2 · Obora et al. · 2012 [cited by applicant]
US 8140421B1 · Humphries et al. · 2012 [cited by applicant]
US 8185463B1 · Ball · 2012 [cited by applicant]
US 8219558B1 · Trandal et al. · 2012 [cited by applicant]
US 8266017B1 · Dearlove et al. · 2012 [cited by applicant]
US 8655683B2 · Grundel et al. · 2014 [cited by applicant]
US 8712893B1 · Brandmaier et al. · 2014 [cited by applicant]
US 8756085B1 · Plummer et al. · 2014 [cited by applicant]
US 8977033B1 · Maurer et al. · 2015 [cited by applicant]
US 9002719B2 · Tofte · 2015 [cited by applicant]
US 9082015B2 · Christopulos et al. · 2015 [cited by applicant]
US 9129276B1 · Fasoli et al. · 2015 [cited by applicant]
US 9317753B2 · Saptharishi et al. · 2016 [cited by applicant]
US 9519734B2 · Randolph · 2016 [cited by applicant]
US 9605704B1 · Humphries et al. · 2017 [cited by applicant]
US 9633146B2 · Plummer et al. · 2017 [cited by applicant]
US 9672567B2 · Thomas et al. · 2017 [cited by applicant]
US 9723251B2 · Slotky · 2017 [cited by applicant]
US 9824397B1 · Patel · 2017 [cited by examiner]
US 9841311B2 · Mccloskey et al. · 2017 [cited by applicant]
US 9846915B2 · Howe et al. · 2017 [cited by applicant]
US 10223750B1 · Loo et al. · 2019 [cited by applicant]
US 10706321B1 · Chen et al. · 2020 [cited by applicant]
US 10832332B1 · Spader et al. · 2020 [cited by applicant]
US 11436648B1 · Zahn · 2022 [cited by applicant]
US 11481925B1 · Li · 2022 [cited by applicant]
US 11546657B1 · O'Pray · 2023 [cited by applicant]
US 20020002475A1 · Freedman et al. · 2002 [cited by applicant]
US 20020035522A1 · Pilcher · 2002 [cited by applicant]
US 20020116163A1 · Loveland · 2002 [cited by applicant]
US 20020143564A1 · Webb et al. · 2002 [cited by applicant]
US 20020184107A1 · Tsuda et al. · 2002 [cited by applicant]
US 20030061104A1 · Thomson et al. · 2003 [cited by applicant]
US 20040153346A1 · Grundel et al. · 2004 [cited by applicant]
US 20040204966A1 · Duffey et al. · 2004 [cited by applicant]
US 20050027571A1 · Gamarnik et al. · 2005 [cited by applicant]
US 20050060236A1 · Iulo · 2005 [cited by applicant]
US 20050197907A1 · Weiss · 2005 [cited by applicant]
US 20050267900A1 · Ahmed et al. · 2005 [cited by applicant]
US 20060161462A1 · Sharma · 2006 [cited by applicant]
US 20060178902A1 · Vicars et al. · 2006 [cited by applicant]
US 20060253351A1 · Keaney · 2006 [cited by applicant]
US 20060259380A1 · Milstein et al. · 2006 [cited by applicant]
US 20060282304A1 · Bedard et al. · 2006 [cited by applicant]
US 20060282342A1 · Chapman · 2006 [cited by applicant]
US 20060293928A1 · Schumacher et al. · 2006 [cited by applicant]
US 20070011033A1 · Atkinson et al. · 2007 [cited by applicant]
US 20070143123A1 · Goldberg et al. · 2007 [cited by applicant]
US 20080065427A1 · Helitzer et al. · 2008 [cited by applicant]
US 20090024420A1 · Winkler · 2009 [cited by applicant]
US 20090138560A1 · Stahl · 2009 [cited by applicant]
US 20090164304A1 · Otto et al. · 2009 [cited by applicant]
US 20090171813A1 · Byrne et al. · 2009 [cited by applicant]
US 20090177499A1 · Westerberg et al. · 2009 [cited by applicant]
US 20090187468A1 · Krech · 2009 [cited by applicant]
US 20090265193A1 · Collins et al. · 2009 [cited by applicant]
US 20090307017A1 · Mahdessian · 2009 [cited by applicant]
US 20090319362A1 · Dashnaw · 2009 [cited by applicant]
US 20090326989A1 · Schmitt et al. · 2009 [cited by applicant]
US 20100076794A1 · Seippel · 2010 [cited by applicant]
US 20100103241A1 · Linaker · 2010 [cited by applicant]
US 20100131308A1 · Collopy et al. · 2010 [cited by applicant]
US 20100241463A1 · Corben et al. · 2010 [cited by applicant]
US 20100312584A1 · Bradshaw et al. · 2010 [cited by applicant]
US 20110057789A1 · Cai et al. · 2011 [cited by applicant]
US 20110161117A1 · Busque et al. · 2011 [cited by applicant]
US 20110218875A1 · Scruton et al. · 2011 [cited by applicant]
US 20110238451A1 · Bazzani et al. · 2011 [cited by applicant]
US 20110288891A1 · Zaid et al. · 2011 [cited by applicant]
US 20110314038A1 · Pacella · 2011 [cited by applicant]
US 20110320222A1 · Fini et al. · 2011 [cited by applicant]
US 20110320322A1 · Roslak et al. · 2011 [cited by applicant]
US 20120016695A1 · Bernard et al. · 2012 [cited by applicant]
US 20120036033A1 · Seergy et al. · 2012 [cited by applicant]
US 20120042253A1 · Priyadarshan et al. · 2012 [cited by applicant]
US 20120047082A1 · Bodrozic · 2012 [cited by applicant]
US 20120095783A1 · Buentello et al. · 2012 [cited by applicant]
US 20120310675A1 · Binder · 2012 [cited by applicant]
US 20130013344A1 · Ernstberger et al. · 2013 [cited by applicant]
US 20130185100A1 · Allu · 2013 [cited by applicant]
US 20130290033A1 · Reeser et al. · 2013 [cited by applicant]
US 20130317860A1 · Schumann, Jr. · 2013 [cited by applicant]
US 20140067430A1 · Dardick et al. · 2014 [cited by applicant]
US 20140100889A1 · Tofte · 2014 [cited by applicant]
US 20140180725A1 · Ton-That et al. · 2014 [cited by applicant]
US 20140188522A1 · Fini · 2014 [cited by applicant]
US 20150025915A1 · Lekas · 2015 [cited by applicant]
US 20150228028A1 · Friedman · 2015 [cited by applicant]
US 20150332407A1 · Wilson et al. · 2015 [cited by applicant]
US 20160171622A1 · Perkins et al. · 2016 [cited by applicant]
US 20160284084A1 · Gurcan et al. · 2016 [cited by applicant]
US 20160335727A1 · Jimenez · 2016 [cited by applicant]
US 20170148101A1 · Franke et al. · 2017 [cited by applicant]
US 20170221110A1 · Sullivan et al. · 2017 [cited by applicant]
US 20170270650A1 · Howe et al. · 2017 [cited by applicant]
US 20170323319A1 · Rattner et al. · 2017 [cited by applicant]
US 20170330207A1 · Labrie et al. · 2017 [cited by applicant]
US 20180260793A1 · Li et al. · 2018 [cited by applicant]
US 20180330018A1 · Wojczyk, Jr. · 2018 [cited by examiner]
US 20190108396A1 · Dal Mutto et al. · 2019 [cited by applicant]
US 20190340432A1 · Mousavian · 2019 [cited by examiner]
US 20220224833A1 · Cier · 2022 [cited by applicant]
US 20230098319A1 · Mohr · 2023 [cited by applicant]
CA 2303724 · 2001 [cited by applicant]
EP 1220131A2 · 2002 [cited by applicant]
EP 1220131 · 2002 [cited by applicant]
WO 2006076566 · 2006 [cited by applicant]
WO 2006122343 · 2006 [cited by applicant]
WO WO2006122343A1 · 2006 [cited by applicant]
WO 2013144772 · 2013 [cited by applicant]
WO 2017176304 · 2017 [cited by applicant]
WO 2018055340 · 2018 [cited by applicant]
Final Office Action for U.S. Appl. No. 16/131,320 dated Jul. 9, 2021, 21 pages. [cited by applicant]
Notice of Allowance for U.S. Appl. No. 15/971,294 dated Jun. 24, 2021, 22 pages. [cited by applicant]
Notice of Allowance for U.S. Appl. No. 15/971,294 dated Sep. 23, 2021, 22 pages. [cited by applicant]
Notice of Allowance on U.S. Appl. No. 15/971,294 dated Mar. 4, 2021, 22 pages. [cited by applicant]
Notice of Allowance on U.S. Appl. No. 16/131,320 dated Apr. 22, 2022, 21 pages. [cited by applicant]
Office Action for U.S. Appl. No. 16/131,320 dated Jan. 25, 2022, 22 pages. [cited by applicant]
Office Action on U.S. Appl. No. 15/971,294 dated Dec. 11, 2020, 16 pages. [cited by applicant]
Office Action on U.S. Appl. No. 16/131,320 dated Jan. 7, 2021, 28 pages. [cited by applicant]
Zheng et al., Spotlight: The Rise of the Smart Phone, IEEE Distributed Systems Online 7(3), 2006. [cited by applicant]
Accurence, “SettleAssist,” retrieved from https://www.accurence.com/settleassist, 6 pages (2017). [cited by applicant]
Allstate, “Allstate Digital Locker,” retrieved from https://www.allstate.com/mobile/digitallocker.aspx, 3 pages (2015). [cited by applicant]
Android Market, “MyHome Pro: Home Inventory by Access Lane, Inc.,” retrieved from https://market.android.com/details?id=com.accesslane.myhome, 2 pages (2012). [cited by applicant]
Boomerangit, “Welcome to BoomerangIt!,” retrieved from https://www.boomerangit.com, 1 page (2012). [cited by applicant]
Country Financial, “Intro to Home Insurance,” retrieved from https://www.countryfinancial.com/en/insurance/home-renters-personal/home.html, 10 pages (2018). [cited by applicant]
Gio General, “Classic Home & Contents Insurance Overview,” retrieved from www.gio.com.au/home-insurance/home-contents-insurance (2012). [cited by applicant]
Hemi Ventures, “An Intro to Computer Vision and It's Impact on Insurance Claims and Analytics,” Medium, retrieved from https://medium.com/hemi-events/an-intro-to-computer-vision-and-its-impacton-insurance-claims-and-ana… [cited by applicant]
HOVER, “HOVER Apps for Insurance Adjusters,” retrieved from https://hover.to/apps-insuranceadjusters/, 5 pages (2018). [cited by applicant]
Hunts & Cude, “Household and Personal Property Inventory Book,” University of Illinois Board of Trustees Circular 1346, 61 pages (1997). [cited by applicant]
Kaizen Software Solutions, “Home Inventory Software,” retrieved from https://www.kzsoftware.com/products/home-inventory-software/, 3 pages (2018). [cited by applicant]
Keir, “What role is image recognition technology poised to play for insurance?,” Hartford Insurtech Hub, retrieved from https://hartfordinsurtechhub.com/role-imagerecognition-technology-poised-play-insurance/, 4 pages (… [cited by applicant]
Know Your Stuff, “Home Inventory,” retrieved from https://www.knowyourstuff.org/iii/login.html, 1 page (2012). [cited by applicant]
Know Your Stuff, “Know Your Stuff Mobile Apps,” retrieved from https://www.knowyourstuff.org/iii/viewOnlyNoLogin.html?page=front_iphone_site, 2 pages (2015). [cited by applicant]
Kyle Switch Plates, “Switch Plate Size & Reference Information,” retrieved from https://www.kyleswitchplates.com/switch-plate-size-reference-information/, 13 pages (2018). [cited by applicant]
Mac App Store, “Home Inventory by Binary Formations, LLC,” retrieved from itunes.apple.com/us/app/home-inventory/id413564952?mt=12, 2 pages (2012). [cited by applicant]
Machost, Protect Yourself by Conducting a Home inventory, retrieved from wwww.garrett-insurance.com/pdfs/Protect%20Yourself%20by%20Conducting%20a%20Home%20Inventory.pdf, 1 page (2010). [cited by applicant]
moneysavingexpert.com, “Mobile Phone Insurance,” retrieved from https://www.moneysavingexpert.com/insurance/cheap-mobile-phone-insurance/, 7 pages (2008). [cited by applicant]
Moran, “Create a Home Inventory for Insurance,” retrieved from https://www.houselogic.com/finances-taxes/home-insurance/homeinventory- for-insurance/, 5 pages (2018). [cited by applicant]
Nia & Mori, “Building Damage Assessment Using Deep Learning and Ground-Level Image Data,” 14th Conference on Computer and Robot Vision (CRV), 8 pages (2017). [cited by applicant]
Panton Insurance, “Property & Casualty Insurance Claims and Underwriting Management,” retrieved from https://www.pantoninc.com/insurance.html, 5 pages (2017). [cited by applicant]
Record It, “Professional & Confidential Home Inventory Services,” retrieved from https://www.RecordItHBS.com, 2 pages (2011). [cited by applicant]
Rosebrock, “Measuring size of objects in an image with OpenCV,” retrieved from https://www.pyimagesearch.com/2016/03/28/measuring-size-of-objects-in-an-image-with-opencv/, 72 pages (2016). [cited by applicant]
Safeco Insurance, “Estimate Your Home Value to Match Coverage Limits,” retrieved from www.safeco.com/insurance-101/consumer-tips/your-home/insurance-to-value, 2 pages (2012). [cited by applicant]
Spex, “The Spex Field App—Features,” retrieved from https://spexreport.com/features/, 6 pages (2018). [cited by applicant]
State Farm, “Know Your Homeowners Insurance Coverage,” retrieved from https://www.statefarm.com/insurance/home-and-property/homeowners/, 4 pages (2018). [cited by applicant]
State Farm, “This Task Ensures That You Cover All of Your Assets,” retrieved from https://www.statefarm.com/simple-insights/residence/th is-task-ensures-that-you-cover-all-of-yourassets, 3 pages (2018). [cited by applicant]
Top Ten Reviews, “Home Inventory Software Review,” retrieved from home-inventory-softwarereview.toptenreviews.com, 4 pages (2012). [cited by applicant]
Vogel, “Will Social Media Change the Insurance Industry?,” Seeking Alpha, retrieved from https://seekingalpha.com/article/255962-will-social-media-change-the-insurance-industry, 2 pages (2011). [cited by applicant]
Wikipedia, “Grayscale,” retrieved from https://en.wikipedia.org/wiki/Grayscale, 6 pages (2018). [cited by applicant]
Zhao, “Deep-Learning-Based-Structural-Damage-Detection,” retrieved from https://github.com/QinganZhao/Deep-Learning-Based-Structural-Damage-Detection, 8 pages (2017). [cited by applicant]