Patent Issued for Interactive preparedness content for predicted events (USPTO 11948201): Assured Insurance Technologies Inc.

2024 APR 24 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- According to news reporting originating from Alexandria, Virginia, by NewsRx journalists, a patent by the inventors Lewis-Weber, Justin (Stanford, CA, US), Patt, Theo (Stanford, CA, US), filed on October 13, 2021, was published online on April 2, 2024.

The assignee for this patent, patent number 11948201, is Assured Insurance Technologies Inc. (Stanford, California, United States).

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

In addition to obtaining background information on this patent, NewsRx editors 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 implementing a targeted event alert service, comprising: a network communication interface to communicate, over one or more networks, with (i) computing devices of users of the targeted event alert service, and (ii) computing systems of one or more policy providers; one or more processors; and a memory storing instructions that, when executed by the one or more processors, cause the computing system to: determine a subset of the users that are to be affected by a predicted event; for each respective user of the subset of users, perform a lookup that identifies unique property characteristics of the respective user; based on the unique property characteristics of the respective user, transmit, over the one or more networks, interactive content data to a computing device of the respective user, the interactive content data causing the computing device of the respective user to generate an individualized graphical user interface for the respective user, the individualized graphical user interface presenting interactive mitigation content comprising a customized set of tasks to perform, based on the unique property characteristics of the respective user, to mitigate and/or prevent loss resulting from the predicted event; during each of one or more interactive sessions in which the respective user engages with the interactive mitigation content via the individualized graphical user interface, initiate a live engagement monitor implementing machine learning to (i) receive, over the one or more networks, engagement data from the computing device of the respective user, the engagement data indicating interactions by the respective user with the interactive mitigation content displayed on the computing device of the respective user, and (ii) based on the engagement data from the computing device of the respective user, dynamically adapt the interactive mitigation content to induce or increase user engagement by the respective user with the interactive mitigation content and encourage performance of the customized set of tasks; receive, over the one or more networks, feedback data from the computing device of the respective user, the feedback data indicating whether the respective user has performed one or more of the customized set of tasks prior to the predicted event; access historical loss data corresponding to a set of similar events as compared to the predicted event; based on (i) the historical loss data, (ii) the predicted event that will occur in a given area, (iii) policy data for the users predicted to be affected in the given area, and (iv) the feedback data received from each respective user predicted to be affected by the predicted event, generate an exposure risk set for each respective policy provider of the one or more policy providers; and transmit, over the one or more network, the exposure risk set generated for the respective policy provider to a computing system of the respective policy provider.

“2. The computing system of claim 1, where the unique property characteristics comprise at least one of a number of bedrooms, a number of bathrooms, one or more climate control system types, a water heater type, a roof type, a square footage, one or more power system types, a landscape type, a build type, a home value, or internal assets of the home of the respective user.

“3. The computing system of claim 1, wherein the interactive mitigation content comprises an item checklist that includes the customized set of tasks for the respective user.

“4. The computing system of claim 1, wherein the predicted event comprises a catastrophic weather event.

“5. The computing system of claim 4, wherein the catastrophic weather event comprises 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.

“6. The computing system of claim 1, wherein the executed instructions cause the computing system to determine the unique property characteristics of the respective user by performing a lookup in one or more third-party databases.

“7. The computing system of claim 6, wherein the one or more third-party databases comprise an insurance provider database, and wherein the executed instructions cause the computing system to determine the unique property characteristics of the respective user by performing a lookup of one or more insurance coverage policies of the respective user.

“8. The computing system of claim 6, wherein the one or more third-party databases comprise at least one of a census database, a tax database, a real estate database, or a social media database.

“9. The computing system of claim 1, wherein the exposure risk set comprises a predicted cumulative loss resulting from the event for the respective policy provider.

“10. A non-transitory computer readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to: communicate, over one or more networks, with (i) computing devices of users of a targeted event alert service, and (ii) computing systems of one or more policy providers; determine a subset of the users that are to be affected by a predicted event; for each respective user of the subset of users, perform a lookup that identifies unique property characteristics of the respective user; based on the unique property characteristics of the respective user, transmit, over the one or more networks, interactive content data to a computing device of the respective user, the interactive content data causing the computing device of the respective user to generate an individualized graphical user interface for the respective user, the individualized graphical user interface presenting interactive mitigation content comprising a customized set of tasks to perform, based on the unique property characteristics of the respective user, to mitigate and/or prevent loss resulting from the predicted event; during each of one or more interactive sessions in which the respective user engages with the interactive mitigation content via the individualized graphical user interface, initiate a live engagement monitor implementing machine learning to (i) receive, over the one or more networks, engagement data from the computing device of the respective user, the engagement data indicating interactions by the respective user with the interactive mitigation content displayed on the computing device of the respective user, and (ii) based on the engagement data from the computing device of the respective user, dynamically adapt the interactive mitigation content to induce or increase user engagement by the respective user with the interactive mitigation content and encourage performance of the customized set of tasks; receive, over the one or more networks, feedback data from the computing device of the respective user, the feedback data indicating whether the respective user has performed one or more of the customized set of tasks prior to the predicted event; access historical loss data corresponding to a set of similar events as compared to the predicted event; based on (i) the historical loss data, (ii) the predicted event that will occur in a given area, (iii) policy data for the users predicted to be affected in the given area, and (iv) the feedback data received from each respective user predicted to be affected by the predicted event, generate an exposure risk set for each respective policy provider of the one or more policy providers; and transmit, over the one or more network, the exposure risk set generated for the respective policy provider to a computing system of the respective policy provider.

“11. The non-transitory computer readable medium of claim 10, where the unique property characteristics comprise at least one of a number of bedrooms, a number of bathrooms, one or more climate control system types, a water heater type, a roof type, a square footage, one or more power system types, a landscape type, a build type, a home value, or internal assets of the home of the respective user.

“12. The non-transitory computer readable medium of claim 10, wherein the interactive mitigation content comprises an item checklist that includes the customized set of tasks for the respective user.

“13. The non-transitory computer readable medium of claim 10, wherein the predicted event comprises a catastrophic weather event.

“14. The non-transitory computer readable medium of claim 13, wherein the catastrophic weather event comprises 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.

“15. The non-transitory computer readable medium of claim 10, wherein the executed instructions cause the one or more processors to determine the unique property characteristics of the respective user by performing a lookup in one or more third-party databases.

“16. The non-transitory computer readable medium of claim 15, wherein the one or more third-party databases comprise an insurance provider database, and wherein the executed instructions cause the one or more processors to determine the unique property characteristics of the respective user by performing a lookup of one or more insurance coverage policies of the respective user.”

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

For more information, see this patent: Lewis-Weber, Justin. Interactive preparedness content for predicted events. U.S. Patent Number 11948201, filed October 13, 2021, and published online on April 2, 2024. Patent URL (for desktop use only): https://ppubs.uspto.gov/pubwebapp/external.html?q=(11948201)&db=USPAT&type=ids

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