Patent Issued for Corroborative claim view interface (USPTO 11915320): Assured Insurance Technologies Inc.

2024 MAR 18 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- Assured Insurance Technologies Inc. (Stanford, California, United States) has been issued patent number 11915320, according to news reporting originating out of Alexandria, Virginia, by NewsRx editors.

The patent’s inventors are Lewis-Weber, Justin (Stanford, CA, US), Patt, Theo (Stanford, CA, US).

This patent was filed on October 13, 2021 and was published online on February 27, 2024.

From the background information supplied by the inventors, news correspondents obtained the following quote: “Catastrophic event preparedness is typically left to affected individuals within predicted or observed event areas. Generalities regarding the manner of preparedness continue to result in high damage costs, loss of life, and inadequate mitigation on a collective basis with little to no individualized preparedness guidance, and for certain catastrophic events, imprecise predictions regarding localized severity.

“Additionally, the insurance industry is inherently reactive with regard to processing claims, with insurance companies typically awaiting claim events and resultant claim filings prior to performing investigative processes. Accordingly, the insurance industry is plagued by rampant fraud that effectively increases premium costs for all policy holders. The investigative processes themselves are also typically manual and inefficient, with investigators and even law enforcement being tasked with identifying fraudulent behavior long after a claim event, enabling perpetrators of insurance fraud to plan carefully and then cover their tracks prior to making a fraudulent claim.”

Supplementing the background information on this patent, NewsRx reporters also obtained the inventors’ summary information for this patent: “A computing system can provide an integrated claims intelligence platform for policy holders and policy providers that leverages various combinations of technologies in machine learning, artificial intelligence, data augmentation, convolutional neural networks, and/or recursive modeling to provide highly predictive and individualized loss prevention and mitigation services, as well as highly detailed and accurate contextual information gathering, corroboration, and claim processing for both policy holders and policy providers. In various implementations, the system can integrate with various third-party data sources to increase contextual awareness for potential claim events, such as catastrophic phenomena (e.g., weather events, natural disasters, etc.), dangerous travel routes or locations (e.g., hazardous road intersections, highway segments, etc.), individual risk behaviors and habits, and the like.

“In further implementations, the system can provide an individually tailored loss or damage mitigation service prior to claim events, such as extreme weather events, by integrating with weather forecasting services, satellite services, policy provider computing systems, and various third-party databases to predict which users or policy holders will be affected by an event, predict damage severity for each affected user resulting from the event, and provide interactive and individualized loss prevention content to the users based on various factors, such as the predicted severity of the event, the locale of the user or user’s property, the unique attributes of the user’s property, and/or the policy information of the user.

“Prior to a predicted event, the system can determine the unique characteristics or attributes of a user’s property, such as the user’s home and/or personal property (e.g., vehicle(s) and other insured assets). In certain implementations, the system predicts a localized severity of the event for the user’s location, and generates individually tailored, loss mitigation content for the user, which can be comprised in an interactive user interface presented on a computing device of the user. The computing system can determine the unique characteristics of the user’s property as well as the user by linking with various data sources, such as real-estate information sources, tax records, census data sources, satellite data sources, construction data sources, social media sources, etc. As provided herein, the unique characteristics of the user’s property can include the square footage of the user’s home, number of stories, number of bedrooms and bathrooms, the size of the garage (if applicable), heating source, water source, power source(s) (e.g., natural gas, solar, wind, etc.), the type of climate control system, home elevation, accessibility, and the like.

“For each user predicted to be affected by an event (e.g., a catastrophic weather event), the system can generate loss mitigation content that can include a set of actions to be performed to mitigate or prevent loss or damage due to the upcoming event based on the unique characteristics of the user’s property, as described in detail below. As the user performs the mitigative actions, the user can indicate so via the application interface displaying the mitigative content. For an entire affected area, the system can interact individually with users via the content interface to provide mitigative content data and receive responses from the users, which the system can utilize to generate a data set for policy providers. For example, the data set can comprise reserve estimates, adjusted loss predictions, and/or a predicted exposure risk for a given area that will be affected by the event. Additionally, the data set provided to policy providers may further be based on historical event damage information from similar events to the predicted event. As such, the system can execute machine learning techniques using the historical event damage information to calculate and refine reserve estimates, adjusted loss predictions, and/or predicted exposure risks for any given area and for any given claim event.

“In various implementations, the system can dynamically update the loss mitigation content based on updates to the localized severity of the event at the location of the user or the user’s property. For example, if the predicted localized severity increases substantially with respect to the user’s location, the system can provide additional recommended actions to mitigate or prevent loss or damage, and can further include a recommendation or order to evacuate to a safer location. In further examples, the system can provide third-party resources, such as mapping, routing, and/or travel resources (e.g., hotel booking) when the user is recommended to evacuate.”

The claims supplied by the inventors are:

“1. A computing system comprising: a network communication interface; one or more processors; and a memory storing instructions that, when executed by the one or more processors, cause the computing device to: receive, over one or more networks, loss information from a computing device of a claimant, the loss information indicating damage or loss to property of the claimant from an event; connect with a plurality of data sources to receive, over the one or more networks, contextual information corresponding to the event; connect with one or more third party data sources to determine a set of unique attributes of the property of the claimant; based on (i) the unique attributes of the property of the claimant, (ii) the contextual information corresponding to the event, and (iii) the loss information from the claimant, execute a fraud detection engine to implement a corroborative process, the corroborative process comprising automatically obtaining multi-party data corresponding to the loss information provided by the claimant by: identifying a plurality of individuals having additional contextual information corresponding to the event; generating an interactive user interface for each of the plurality of individuals to provide the additional contextual information corresponding to the event; transmitting, over the one or more networks, content data to computing devices of the plurality of individuals to cause each of the computing devices to present the interactive user interface; executing an engagement monitor to receive, over the one or more networks, engagement data corresponding to user interactions by each of the plurality of individuals on the interactive user interface, and dynamically adapt a content flow of the interactive user interface based on the engagement data of each of the plurality of individuals to induce user engagement with the interactive user interface; generate a set of fraud scores for the loss information based at least in part on the multi-party data; and generate a graphical user interface for a policy provider of the claimant, the graphical user interface presenting corroborative data based on the multi-party data obtained in the corroborative process and providing the set of fraud scores for the loss information provided by the claimant.

“2. The computing system of claim 1, wherein the corroborative process further comprises: based on the user interactions by the plurality of individuals with the interactive user interface, receiving, over the one or more networks, the additional contextual information corresponding to the event from the plurality of individuals.

“3. The computing system of claim 2, wherein the executed instructions further cause the computing system to: based on the additional contextual information provided by the one or more individuals and the loss information provided by the claimant, identify one or more inconsistencies in the loss information; wherein the executed instructions cause the computing system to further base the set of fraud scores on the one or more inconsistencies identified in the loss information.

“4. The computing system of claim 3, wherein the graphical user interface provides the policy provider with a claim view indicating the one or more inconsistencies.

“5. The computing system of claim 1, wherein the plurality of individuals comprise one or more witnesses to the event, or one or more individuals affected by the event.

“6. The computing system of claim 1, wherein the event comprises a catastrophic event comprising at least one of a storm event, a flooding event, a power outage event, a wildfire event, a drought event, an earthquake event, or a disaster event.

“7. The computing system of claim 1, wherein the event comprises a vehicle incident.

“8. A non-transitory computer readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to: receive, over one or more networks, loss information from a computing device of a claimant, the loss information indicating damage or loss to property of the claimant from an event; connect with a plurality of data sources to receive, over the one or more networks, contextual information corresponding to the event; connect with one or more third party data sources to determine a set of unique attributes of the property of the claimant; based on (i) the unique attributes of the property of the claimant, (ii) the contextual information corresponding to the event, and (iii) the loss information from the claimant, execute a fraud detection engine to implement a corroborative process, the corroborative process comprising automatically obtaining multi-party data corresponding to the loss information provided by the claimant by: identifying a plurality of individuals having additional contextual information corresponding to the event; generating an interactive user interface for each of the plurality of individuals to provide the additional contextual information corresponding to the event; transmitting, over the one or more networks, content data to computing devices of the plurality of individuals to cause each of the computing devices to present the interactive user interface; executing an engagement monitor to receive, over the one or more networks, engagement data corresponding to user interactions by each of the plurality of individuals on the interactive user interface, and dynamically adapt a content flow of the interactive user interface based on the engagement data of each of the plurality of individuals to induce user engagement with the interactive user interface; generate a set of fraud scores for the loss information based at least in part on the multi-party data; and generate a graphical user interface for a policy provider of the claimant, the graphical user interface presenting corroborative data based on the multi-party data obtained in the corroborative process and providing the set of fraud scores for the loss information provided by the claimant.

“9. The non-transitory computer readable medium of claim 8, wherein the corroborative process further comprises: based on the user interactions by the plurality of individuals with the interactive user interface, receiving, over the one or more networks, the additional contextual information corresponding to the event from the plurality of individuals.

“10. The non-transitory computer readable medium of claim 9, wherein the executed instructions further cause the one or more processors to: based on the additional contextual information provided by the one or more individuals and the loss information provided by the claimant, identify one or more inconsistencies in the loss information; wherein the executed instructions cause the one or more processors to further base the set of fraud scores on the one or more inconsistencies identified in the loss information.

“11. The non-transitory computer readable medium of claim 10, wherein the graphical user interface provides the policy provider with a claim view indicating the one or more inconsistencies.

“12. The non-transitory computer readable medium of claim 8, wherein the plurality of individuals comprise one or more witnesses to the event, or one or more individuals affected by the event.

“13. The non-transitory computer readable medium of claim 8, wherein the event comprises a catastrophic event comprising at least one of a storm event, a flooding event, a power outage event, a wildfire event, a drought event, an earthquake event, or a disaster event.

“14. The non-transitory computer readable medium of claim 8, wherein the event comprises a vehicle incident.

“15. A computer-implemented method of facilitating loss mitigation claims, the method being performing by one or more processors and comprising: receiving, over one or more networks, loss information from a computing device of a claimant, the loss information indicating damage or loss to property of the claimant from an event; connecting with a plurality of data sources to receive, over the one or more networks, contextual information corresponding to the event; connecting with one or more third party data sources to determine a set of unique attributes of the property of the claimant; based on (i) the unique attributes of the property of the claimant, (ii) the contextual information corresponding to the event, and (iii) the loss information from the claimant, executing a fraud detection engine to implement a corroborative process, the corroborative process comprising automatically obtaining multi-party data corresponding to the loss information provided by the claimant by: identifying a plurality of individuals having additional contextual information corresponding to the event; generating an interactive user interface for each of the plurality of individuals to provide the additional contextual information corresponding to the event; transmitting, over the one or more networks, content data to computing devices of the plurality of individuals to cause each of the computing devices to present the interactive user interface; executing an engagement monitor to receive, over the one or more networks, engagement data corresponding to user interactions by each of the plurality of individuals on the interactive user interface, and dynamically adapt a content flow of the interactive user interface based on the engagement data of each of the plurality of individuals to induce user engagement with the interactive user interface; generating a set of fraud scores for the loss information based at least in part on the multi-party data; and generating a graphical user interface for a policy provider of the claimant, the graphical user interface presenting corroborative data based on the multi-party data obtained in the corroborative process and providing the set of fraud scores for the loss information provided by the claimant.”

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

For the URL and additional information on this patent, see: Lewis-Weber, Justin. Corroborative claim view interface. U.S. Patent Number 11915320, filed October 13, 2021, and published online on February 27, 2024. Patent URL (for desktop use only): https://ppubs.uspto.gov/pubwebapp/external.html?q=(11915320)&db=USPAT&type=ids

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