Patent Application Titled “Processing System Having A Machine Learning Engine For Providing A Surface Dimension Output” Published Online (USPTO 20220405816): Allstate Insurance Company

2023 JAN 11 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- According to news reporting originating from Washington, D.C., by NewsRx journalists, a patent application by the inventors Daniels, Andrew (Columbus, OH, US); Genc, Steven (Hainesville, IL, US); Gilkson, David L. (Libertyville, IL, US); Patel, Pinal (Libertyville, IL, US); Zahn, David M. (Lake Villa, IL, US), filed on August 23, 2022, was made available online on December 22, 2022.

The assignee for this patent application is Allstate Insurance Company (Northbrook, Illinois, United States).

Reporters obtained the following quote from the background information supplied by the inventors: “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.”

In addition to obtaining background information on this patent application, NewsRx editors also obtained the inventors’ summary information for this patent application: “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.

“In some examples, the computing platform may determine a cause of damage to the surface in the at least one image. In some examples, the source data corresponds to one or more of: a zip code, a credit score, a home cost, and a room type.

“In some examples, the computing platform may 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.

“In some examples, the computing platform may generate one or more commands directing the object replacement and advisor platform to determine a claim advisor output. In addition, the computing platform may 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. In response to the one or more commands directing the object replacement and advisor platform to determine the claim advisor output, the computing platform may receive the claim advisor output.

“In some examples, the one or more commands directing the object replacement and advisor platform to determine the estimated repair cost may further direct the object replacement and advisor platform to cause objects included in the predicted object output to be added to a personalized queue corresponding to a user of the user device.

“The arrangements described may also include other additional elements, steps, computer-executable instructions, or computer-readable data structures. In this regard, other embodiments are disclosed and claimed herein as well. The details of these and other embodiments of the present disclosure are set forth in the accompanying drawings and the description below. Other features and advantages of the disclosure will be apparent from the description, drawings, and claims.”

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: receive at least one image; execute an image analysis operation causing an image analysis and device control system to generate an object dimension output by at least: determining 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 image quality of the plurality of bounding boxes; transposing the plurality of bounding boxes on top of a black image that comprises the predetermined dimensions for the neural network; and determining a pixel dimension for each bounding box of the plurality of bounding boxes; causing an object prediction control platform to: determine source data corresponding to the at least one image and a user, and determine a predicted object output by inputting 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 room type corresponding to the at least one image, objects predicted to be in a room, identifying a correlation between each of the objects predicted to be in the room and the source data, and in response to determining that a particular correlation exceeds a predetermined threshold, adding the corresponding objects predicted to be in the room to the predicted object output.

“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 memory stores additional computer-readable instructions that, when executed by the at least one processor, further cause the computing platform to determine an 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.

“8. The computing platform of claim 7, 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.

“9. 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 the objects included in the predicted object output to be added to an online shopping cart corresponding to the user.

“10. 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 the room type.

“11. 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 the room type.

“12. The computing platform of claim 11, 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.

“13. A method comprising: receiving at least one image; executing an image analysis operation, the image analysis operation comprising causing an image analysis and device control system to generate an object dimension output by at least: determining a plurality of bounding boxes comprising the at least one image, wherein at least some of the plurality of the bounding boxes have dimensions that match predetermined dimensions for a neural network; reducing image quality of the plurality of bounding boxes; transposing the plurality of bounding boxes on top of a black image that comprises the predetermined dimensions for the neural network; and determining a pixel dimension for each bounding box of the plurality of bounding boxes; causing an object prediction control platform to: determine source data corresponding to the at least one image and a user, and determine a predicted object output by inputting 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 room type corresponding to the at least one image, objects predicted to be in a room, identifying a correlation between each of the objects predicted to be in the room and the source data, in response to determining that a particular correlation exceeds a predetermined threshold, adding the corresponding objects predicted to be in the room to the predicted object output, and in response to determining that a particular correlation does not exceed the predetermined threshold, not adding the corresponding objects predicted to be in the room to the predicted object output.

“14. The method of claim 13, further comprising: determining a reference object in the at least one image; determining pixel dimensions of the reference object; and determining, 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.

“15. The method of claim 14, further comprising: determining an object boundary corresponding to an object in the at least one image; determining pixel dimensions corresponding to the object; and determining, 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.

“16. The method of claim 14, further comprising: determining, using the actual to pixel ratio for the at least one image, actual surface dimensions of a surface in the at least one image determining a material corresponding to the surface in the at least one image; and determining a cause of damage to the surface in the at least one image.

“17. The method of claim 13, further comprising determining an 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.”

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

For more information, see this patent application: Daniels, Andrew; Genc, Steven; Gilkson, David L.; Patel, Pinal; Zahn, David M. Processing System Having A Machine Learning Engine For Providing A Surface Dimension Output. U.S. Patent Application Number 20220405816, filed August 23, 2022 and posted December 22, 2022. Patent URL (for desktop use only): https://ppubs.uspto.gov/pubwebapp/external.html?q=(20220405816)&db=US-PGPUB&type=ids

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