Patent Issued for Inspection and assessment based on mobile edge-computing (USPTO 11012526)

2021 JUN 24 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- Allstate Insurance Company (Northbrook, Illinois, United States) has been issued patent number 11012526, according to news reporting originating out of Alexandria, Virginia, by NewsRx editors.

The patent’s inventors are Iynoolkhan, Younuskhan Mohamed (Rolling Meadows, IL, US), Kumar, Surender (Palatine, IL, US), Malpekar, Nilesh (Lincolnshire, IL, US), Peavie, Charles (Lake Villa, IL, US).

This patent was filed on September 19, 2019 and was published online on May 18, 2021.

From the background information supplied by the inventors, news correspondents obtained the following quote: “Home inspection, commercial property inspection, claims assessment, and underwriting are generally time consuming, may not be cost-effective, and may be inefficient. Data is generally collected manually on-site and sent to a central processing unit for analysis. In some instances, multiple data collection efforts may be required over time to gather relevant data. Also, in some instances, insurance estimates may be generated without an assessment of local costs of materials, labor, and availability of the same. Also at least part of the assessment workflow may be manual, expensive and time consuming.”

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 invention. This summary is not an extensive overview of the invention. It is not intended to identify key or critical elements of the invention or to delineate the scope of the invention. The following summary merely presents some concepts of the invention in a simplified form as a prelude to the more detailed description provided below.

“Aspects of the disclosure address one or more of the issues mentioned above by disclosing methods, computer readable storage media, software, systems, and apparatuses for decentralized and automated processing and data analysis on or near a location using edge-computing methodology and one or more unmanned autonomous vehicles (“UAVs”). As used herein, “unmanned autonomous vehicles” may include unmanned aerial vehicles, such as drones, flying vehicles, autonomous road vehicles, and so forth.

“In some aspects, an edge-computing system may include an edge-computing data processing system and an edge-computing data analysis system. The edge-computing system may include at least one processor and a memory unit storing computer-executable instructions. In some embodiments, the computer-executable instructions may be stored in one or more non-transitory computer-readable media. The edge-computing system may be configured to, in operation, receive, by a computing device at a field vehicle, such as an unmanned autonomous vehicle (“UAV”) carrier, field data from one or more UAVs, where the field data may be indicative of an item for assessment. The edge-computing system may be configured to, in operation, identify, in real-time based on a machine learning model, one or more characteristics of the assessment. The edge-computing system may be configured to, in operation, determine, in real-time based on the machine learning model, a projected cost for the assessment. The edge-computing system may be configured to, in operation, send, to a user associated with the item, an estimate for the assessment.

“Deployed UAVs may generally have a shorter range due to limitations of power capabilities. Since UAVs may need to recharge their power source within short durations of being deployed, in some instances, it may be advantageous to have a UAV carrier deployed in the field. The UAV carrier may then allow UAVs to dock and recharge. The UAV carrier may also be equipped with an edge-computing device to deploy and manage UAVs in the field, receive data from them, analyze such data, and so forth.

“In other aspects, the edge-computing system may also be configured to, in operation, analyze the field data to determine one or more of a type of material, an amount of material, an amount of labor, and an estimated time to complete a repair or a replacement.

“In some aspects, the edge-computing system may, in operation, train the machine learning model to identify the one or more characteristics based on an analysis of the item.

“In other aspects, the edge-computing system may also be configured to, in operation, determine, based on local data related to a geographical location of the item, a projected cost of material and a projected cost of labor.

“In other aspects, the edge-computing system may also be configured to, in operation, dynamically update the local data.

“In other aspects, the edge-computing system may also be configured to, in operation, cause, based on the one or more characteristics of the assessment, the one or more unmanned autonomous vehicles to collect additional field data.

“In other aspects, the edge-computing system may also be configured to, in operation, perform, in real-time at the field vehicle, an underwriting task associated with the assessment.

“In other aspects, the edge-computing system may also be configured to, in operation, train the machine learning model to perform the underwriting task.

“Methods and systems of the above-referenced embodiments 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 invention are set forth in the accompanying drawings and the description below. Other features and advantages of the invention will be apparent from the description, drawings, and claims.”

The claims supplied by the inventors are:

“1. A method comprising: receiving, by a computing device at a field vehicle, field data from one or more unmanned autonomous vehicles, wherein the field data is indicative of an item for assessment; identifying, by the computing device and in real-time based on a machine learning model, one or more characteristics of the assessment; determining, by the computing device at the field vehicle and in real-time based on the machine learning model, a projected cost for the assessment without communicating with a central server; and sending, to a user associated with the item, an estimate for the assessment.

“2. The method of claim 1, wherein the identifying the one or more characteristics of the assessment comprises: analyzing the field data to determine one or more of a type of material, an amount of material, an amount of labor, and an estimated time to complete a repair or a replacement.

“3. The method of claim 1, further comprising: training the machine learning model to identify the one or more characteristics based on an analysis of the item.

“4. The method of claim 1, wherein the determining the projected cost for the assessment comprises: determining, based on local data related to a geographical location of the item, a projected cost of material and a projected cost of labor.

“5. The method of claim 4, further comprising: dynamically updating, by the computing device, the local data.

“6. The method of claim 1, further comprising: causing, in real-time and based on the one or more characteristics of the assessment, the one or more unmanned autonomous vehicles to collect additional field data.

“7. The method of claim 1, further comprising: performing, in real-time and by the computing device at the field vehicle, an underwriting task associated with the assessment.

“8. The method of claim 7, further comprising: training the machine learning model to perform the underwriting task.

“9. An apparatus, comprising: a processor; a memory unit storing computer-executable instructions, which when executed by the processor, cause the apparatus to: receive, at a field vehicle, field data from one or more unmanned autonomous vehicles, wherein the field data is indicative of an item for assessment; identify, in real-time based on a machine learning model, one or more characteristics of the assessment; determine, at the field vehicle, in real-time based on the machine learning model, a projected cost for the assessment without communicating with a central server; and send, to a user associated with the item, an estimate for the assessment.

“10. The apparatus of claim 9, wherein the computer-executable instructions, when executed by the processor, further cause the apparatus to identify the one or more characteristics of the assessment by causing the apparatus to: analyze the field data to determine one or more of a type of material, an amount of material, an amount of labor, and an estimated time to complete a repair or a replacement.

“11. The apparatus of claim 9, wherein the computer-executable instructions, when executed by the processor, cause the apparatus to: train the machine learning model to identify the one or more characteristics based on an analysis of the item.

“12. The apparatus of claim 9, wherein the computer-executable instructions, when executed by the processor, further cause the apparatus to determine the projected cost for the assessment by causing the apparatus to: determine, based on local data related to a geographical location of the item, a projected cost of material and a projected cost of labor.

“13. The apparatus of claim 12, wherein the computer-executable instructions, when executed by the processor, cause the apparatus to: dynamically update the local data.

“14. The apparatus of claim 9, wherein the computer-executable instructions, when executed by the processor, cause the apparatus to: cause, in real-time and based on the one or more characteristics of the assessment, the one or more unmanned autonomous vehicles to collect additional field data.

“15. The apparatus of claim 9, wherein the computer-executable instructions, when executed by the processor, cause the apparatus to: perform, in real-time at the field vehicle, an underwriting task associated with the assessment.

“16. The apparatus of claim 15, wherein the computer-executable instructions, when executed by the processor, cause the apparatus to: train the machine learning model to perform the underwriting task.

“17. One or more non-transitory computer-readable media storing instructions that, when executed by a computing device, cause the computing device to: receive, at a field vehicle, field data from one or more unmanned autonomous vehicles, wherein the field data is indicative of an item for assessment; identify, in real-time based on a machine learning model, one or more characteristics of the assessment; determine, at the field vehicle, in real-time based on the machine learning model, a projected cost for the assessment without communicating with a central server; and send, to a user associated with the item, an estimate for the assessment.

“18. The one or more non-transitory computer-readable media of claim 17, storing further instructions that, when executed by the computing device, cause the computing device to: analyze the field data to determine one or more of a type of material, an amount of material, an amount of labor, and an estimated time to complete a repair or a replacement.

“19. The one or more non-transitory computer-readable media of claim 17, storing further instructions that, when executed by the computing device, cause the computing device to: determine, for the one or more characteristics and based on the machine learning model, a projected cost for the assessment; and wherein the estimate is based on the projected cost.

“20. The one or more non-transitory computer-readable media of claim 17, storing further instructions that, when executed by the computing device, cause the computing device to: perform, in real-time at the field vehicle, an underwriting task associated with the assessment.”

For the URL and additional information on this patent, see: Iynoolkhan, Younuskhan Mohamed. Inspection and assessment based on mobile edge-computing. U.S. Patent Number 11012526, filed September 19, 2019, and published online on May 18, 2021. 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=11012526.PN.&OS=PN/11012526RS=PN/11012526

(Our reports deliver fact-based news of research and discoveries from around the world.)