Patent Issued for Processing system having a machine learning engine for providing a surface dimension output (USPTO 11436648): Allstate Insurance Company

2022 SEP 26 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- Allstate Insurance Company (Northbrook, Illinois, United States) has been issued patent number 11436648, according to news reporting originating out of Alexandria, Virginia, by NewsRx editors.

The patent’s inventors are Daniels, Andrew (Columbus, OH, US), Genc, Steven (Hainesville, IL, US), Gilkison, David L. (Libertyville, IL, US), Patel, Pinal (Libertyville, IL, US), Zahn, David M. (Lake Villa, IL, US).

This patent was filed on September 14, 2018 and was published online on September 6, 2022.

From the background information supplied by the inventors, news correspondents obtained the following quote: “Mobile devices comprise cameras, or other image capturing devices, that may be used to collect images associated with various objects. For instance, cameras or other image capturing devices may be used to capture images or objects, devices, homes, vehicles, or portions thereof that have been damaged. Once the images are collected, it may be difficult to determine the actual size of the damaged item, portion, or other objects in the images without placing a reference object (e.g., an object having a known size, shape, dimension, or the like) into the camera frame. Accordingly, it would be advantageous to instruct a mobile device to capture images including a standardized reference object, and to analyze the standardized reference object to generate object dimension outputs. In many instances, however, it may be difficult to determine all damaged objects using such analysis, and thus it may be advantageous to predict a list of damaged objects. This may improve repair cost estimation corresponding to particular damage.”

Supplementing the background information on this patent, NewsRx reporters also obtained the inventors’ summary information for this patent: “In light of the foregoing background, the following presents a simplified summary of the present disclosure in order to provide a basic understanding of some aspects of the disclosure. This summary is not an extensive overview of the disclosure. It is not intended to identify key or critical elements of the disclosure or to delineate the scope of the disclosure. The following summary merely presents some concepts of the disclosure in a simplified form as a prelude to the more detailed description provided below.

“Methods, systems, and non-transitory computer-readable media are described herein. In some embodiments a computing platform including a processor may send, to a user device, one or more commands to capture at least one image and, in response, may receive the at least one image. In addition, the computing platform may generate one or more commands directing an object prediction control platform to: determine source data corresponding to the at least one image and a user of the user device, and determine, using the source data, a predicted object output corresponding to objects predicted to be in a room shown in the at least one image. The computing platform may send, to the object prediction control platform, the one or more commands. In response to the one or more commands, the computing platform may receive the predicted object output. In some embodiments, the computing platform determine, based at least in part on the predicted object output, an estimated repair cost corresponding to damage shown in the at least one image. The computing platform may send the estimated repair cost and one or more commands directing the user device to cause display of the estimated repair cost.

“In some examples, the computing platform may determine a reference object in the at least one image. In addition, the computing platform may determine pixel dimensions of the reference object. Using predetermined actual dimensions of the reference object and the pixel dimensions of the reference object, the computing platform may determine an actual to pixel ratio for the at least one image.

“In some examples, the computing platform may determine an object boundary corresponding to an object in the at least one image. In addition, the computing platform may determine pixel dimensions corresponding to the object. The computing platform may determine, using the pixel dimensions corresponding to the object and the actual to pixel ratio for the at least one image, actual dimensions corresponding to the object.

“In some examples, the computing platform may determine, using the actual to pixel ratio for the at least one image, actual surface dimensions of a surface in the at least one image. In some examples, the computing platform may determine a material corresponding to the surface in the at least one image.”

The claims supplied by the inventors are:

“1. A computing platform, comprising: at least one processor; a communication interface commutatively coupled to the at least one processor; and memory storing computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: send, to a user device, one or more commands to capture at least one image; receive the at least one image; execute an image analysis operation, the image analysis operation comprising generating an object dimension output by at least: determining, by an image analysis and device control system, a plurality of bounding boxes comprising the at least one image, wherein at least some of the plurality of bounding boxes have dimensions that match predetermined dimensions for a neural network; reducing, by the image analysis and device control system, image quality of the plurality of bounding boxes; transposing, by the image analysis and device control system, the plurality of bounding boxes on top of a black image that comprises the predetermined dimensions for the neural network; and determining, by the image analysis and device control system, a pixel dimension for each bounding box of the plurality of bounding boxes; generate one or more commands directing an object prediction control platform, comprising a second processor, a second communication interface, and second memory, to: determine source data corresponding to the at least one image and a user of the user device, and determine a predicted object output corresponding to objects predicted to be in a room shown in the at least one image, wherein the object prediction control platform is configured to input the source data into one or more machine learning models to output the predicted object output, and wherein determining the predicted object output comprises: determining, based on a zip code and a room type corresponding to the at least one image, the objects predicted to be in the room, identifying a correlation between each determined objects and the source data, in response to determining that a particular correlation exceeds a predetermined threshold, adding the corresponding determined object to the predicted object output, and in response to determining that a particular correlation does not exceed the predetermined threshold, not adding the corresponding determined object to the predicted object output; send, to the object prediction control platform, the one or more commands directing the object prediction control platform; receive, in response to the one or more commands directing the object prediction control platform, the predicted object output; determine, based at least in part on the predicted object output, an estimated repair cost corresponding to damage shown in the at least one image; and send the estimated repair cost and one or more commands directing the user device to cause display of the estimated repair cost.

“2. The computing platform of claim 1, wherein the memory stores additional computer-readable instructions that, when executed by the at least one processor, further cause the computing platform to: determine a reference object in the at least one image; determine pixel dimensions of the reference object; and determine, using predetermined actual dimensions of the reference object and the pixel dimensions of the reference object, an actual to pixel ratio for the at least one image.

“3. The computing platform of claim 2, wherein the memory stores additional computer-readable instructions that, when executed by the at least one processor, further cause the computing platform to: determine an object boundary corresponding to an object in the at least one image; determine pixel dimensions corresponding to the object; and determine, using the pixel dimensions corresponding to the object and the actual to pixel ratio for the at least one image, actual dimensions corresponding to the object.

“4. The computing platform of claim 2, wherein the memory stores additional computer-readable instructions that, when executed by the at least one processor, further cause the computing platform to determine, using the actual to pixel ratio for the at least one image, actual surface dimensions of a surface in the at least one image.

“5. The computing platform of claim 4, wherein the memory stores additional computer-readable instructions that, when executed by the at least one processor, further cause the computing platform to determine a material corresponding to the surface in the at least one image.

“6. The computing platform of claim 4, wherein the memory stores additional computer-readable instructions that, when executed by the at least one processor, further cause the computing platform to determine a cause of damage to the surface in the at least one image.

“7. The computing platform of claim 1, wherein the source data corresponds to one or more of: a zip code, a credit score, a home cost, and a room type.

“8. The computing platform of claim 1, wherein the memory stores additional computer-readable instructions that, when executed by the at least one processor, further cause the computing platform to determine the estimated repair cost corresponding to damage shown in the at least one image by: generating one or more commands directing an object replacement and advisor platform to determine the estimated repair cost; sending, along with the one or more commands directing the object replacement and advisor platform to determine the estimated repair cost and to the object replacement and advisor platform, the predicted object output; and receiving, in response to the one or more commands directing the object replacement and advisor platform to determine the estimated repair cost, the estimated repair cost.

“9. The computing platform of claim 8, wherein the memory stores additional computer-readable instructions that, when executed by the at least one processor, further cause the computing platform to: generate one or more commands directing the object replacement and advisor platform to determine a claim advisor output; send, to the object replacement and advisor platform, the one or more commands directing the object replacement and advisor platform to determine the claim advisor output; and receive, in response to the one or more commands directing the object replacement and advisor platform to determine the claim advisor output, the claim advisor output.

“10. The computing platform of claim 8, wherein the one or more commands directing the object replacement and advisor platform to determine the estimated repair cost further direct the object replacement and advisor platform to cause objects included in the predicted object output to be added to an online shopping cart corresponding to a user of the user device.

“11. The computing platform of claim 1, wherein the memory stores additional computer-readable instructions that, when executed by the at least one processor, further cause the computing platform to generate, based on the at least one image, a room indication output comprising an indication of a room type.

“12. The computing platform of claim 1, wherein the one or more commands further comprises receiving third party source data, the third party source data comprising information that corresponds to at least one of a zip code and the room type.

“13. The computing platform of claim 12, wherein the one or more machine learning models are associated with one or more machine learning datasets, the one or more machine learning datasets comprising a plurality of images corresponding to at least one of (1) one or more damage types and (2) one or more material types, and a combination of circumstances indicated by the third party source data.

“14. The computing platform of claim 13, wherein determining the predicted object output further comprises refining the predicted object output based on the objects predicted to be in the room based on the one or more machine learning datasets.”

There are additional claims. Please visit full patent to read further.

For the URL and additional information on this patent, see: Daniels, Andrew. Processing system having a machine learning engine for providing a surface dimension output. U.S. Patent Number 11436648, filed September 14, 2018, and published online on September 6, 2022. Patent URL: http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PALL&p=1&u=%2Fnetahtml%2FPTO%2Fsrchnum.htm&r=1&f=G&l=50&s1=11436648.PN.&OS=PN/11436648RS=PN/11436648

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