Researchers Submit Patent Application, “Systems And Methods For Generating And Updating An Inventory Of Personal Possessions Of A User For Insurance Purposes”, for Approval (USPTO 20240127356): Blueowl LLC

2024 MAY 06 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- From Washington, D.C., NewsRx journalists report that a patent application by the inventor Sanchez, Kenneth Jason (San Francisco, CA, US), filed on December 27, 2023, was made available online on April 18, 2024.

The patent’s assignee is Blueowl LLC (San Francisco, California, United States).

News editors obtained the following quote from the background information supplied by the inventors: “Some insurance policies (e.g., renter’s insurance, rental insurance, homeowners insurance, and/or property insurance) provide coverage for loss or damage to the personal possessions of a policyholder during a policy claim (e.g., a formal request by the policyholder to an insurance provider for reimbursement for one or more personal possessions covered under an insurance policy). Loss events may include residential fires, theft, vandalism and/or other events that cause partial or complete loss of the personal possessions of the policyholder. Policy coverage is associated with the amount of risk or liability that is covered by the insurance provider for the policyholder’s possessions during these loss events. Insurance providers set policy premiums based at least in part upon a number of factors including the amount of coverage that the policy provides (e.g., policy coverage or insurance coverage). In other words, the policy coverage is related to the amount of funds an insurance provider may have to pay a policyholder for damaged or lost possessions. As such, a policy coverage amount should aim to cover the amount it would cost to replace or repair each of the policyholder’s personal possessions.

“During a policy claim, the policyholder may submit an insurance claim request to the insurance provider, requesting reimbursement for lost or destroyed possessions. The insurance claim request may include a list of the personal possessions and values associated with the cost of replacing the personal possessions.

“In some cases, the policyholder may not have created an inventory list of their personal possessions prior to the loss event. Consequentially, the policyholder may be unable to remember or identify all personal possessions that were destroyed, lost, and/or damaged. It may be particularly challenging for a policyholder to recall personal possessions in the case of a total loss, when there may be limited evidence of the policyholder’s possessions (e.g. after a residential fire). As such, the policyholder may be unable to create a complete and/or accurate list of possessions for the policy claim. In other cases, a policyholder may have created an inventory list prior to the loss event, but failed to update or maintain the list such that the inventory list does not accurately reflect the most current personal possessions of the policyholder.

“Further, upon receiving the policy claim request, the insurance provider may subsequently request documentation or proof from the policyholder for one or more items in the list of possessions in order to confirm that the policyholder owned the item and/or to verify the cost or value associated with the item. Requested documentation may include images of the items, receipts, or authentication documentations such as titles, certifications of authenticity, or any other documentation that can be used to verify the value of the possessions. In some cases, the policyholder may be unable to provide documentation supporting the claimed lost items. For example, in some cases, the policyholder’s documents may have been lost or destroyed during the loss event. In other cases, the policyholder may not have kept or recorded documentation for every personal possession.

“Insurance premiums, coverage rates, and insurance claims may depend on the list of policyholder’s possessions owned by the policyholder. It would be advantageous for both the policyholder and the insurance provider to generate and update a complete and accurate list of personal possessions. The inventory of personal possessions should further include a cost or value assigned to each possession in the inventory of personal possessions, and documentation of the ownership and/or the value of the possessions. More specifically, the inventory of personal possessions may aid the insurance provider in determining policy rates and additionally aid the policyholder in determining the amount of coverage they will need. Further, during a policy claim, the inventory list may be used to determine reimbursement amounts for each possession.”

As a supplement to the background information on this patent application, NewsRx correspondents also obtained the inventor’s summary information for this patent application: “The present embodiment may relate to systems and methods systems and methods for generating and updating a list of personal possessions of a user based at least in part upon personal data associated with the user.

“In one aspect, a computer system for generating a list of items predicted to be associated with a candidate user is provided, and the computer system may include one processor in communication with at least one memory device. The at least one processor may be configured to: (i) generate a predictive possession model based at least in part upon a plurality of historical policyholder records associated with a plurality of policyholders, the plurality of historical policyholder records includes one or more historical insurance claims that includes one or more items owned by the plurality of policyholders and personal data associated with the plurality of policyholders, (ii) receive personal data associated with the candidate user, (iii) predict a first set of items owned by the candidate user based at least in part upon the received personal data associated with the candidate user and the generated predictive possession model, (iv) assign a value and a range of predesignated values for each item included in the first set of items, (v) cause the first set of items and their corresponding values to be displayed on a user device of the candidate user, and (vi) prompt the candidate user to input confirmation data including one of (a) a confirmation that the first set of items accurately describes a set of actual items possessed by the candidate user and (b) a confirmation that the values corresponding to the first set of items are satisfactory. The computer system may include additional, less, or alternate functionality, including that discussed elsewhere herein.

“In another aspect, a computer-implemented method for generating a list of items predicted to be associated with a candidate user using a computer system including one processor in communication with at least one memory device is provided. The method may include: (i) generating a predictive possession model based at least in part upon a plurality of historical policyholder records associated with a plurality of policyholders, the plurality of historical policyholder records includes one or more historical insurance claims that includes one or more items owned by the plurality of policyholders and personal data associated with the plurality of policyholders, (ii) receiving personal data associated with the candidate user, (iii) predicting a first set of items owned by the candidate user based at least in part upon the received personal data associated with the candidate user and the generated predictive possession model, (iv) assigning a value and a range of predesignated values for each item included in the first set of items, (v) causing the predictive set of items and corresponding values to be displayed on a user device of the candidate user, and (vi) prompting the candidate user to input confirmation data including one of (a) a confirmation that the first set of items accurately describes a set of actual items possessed by the candidate user and (b) a confirmation that the values corresponding to the first set of items are satisfactory. The method may include additional, less, or alternate actions, including those discussed elsewhere herein.

“In yet another aspect, at least one non-transitory computer-readable media having computer-executable instructions thereon is provided, wherein when executed by at least one processor of a computer system causes the at least one processor to: (i) generate a predictive possession model based at least in part upon a plurality of historical policyholder records associated with a plurality of policyholders, the plurality of historical policyholder records includes one or more historical insurance claims that includes one or more items owned by the plurality of policyholders and personal data associated with the plurality of policyholders, (ii) receive personal data from a candidate user, (iii) predict a first set of items owned by the candidate user based at least in part upon the received personal data associated with the candidate user and the generated predictive possession model, (iv) assign a value and a range of predesignated values for each item included in the first set of items, (v) cause the first set of items and their corresponding values to be displayed on a user device of the candidate user, (vi) prompt the candidate user to input confirmation data including one of (a) a confirmation that the first set of items accurately describes a set of actual items possessed by the candidate user and (b) a confirmation that the values corresponding to the first set of items are satisfactory. The instructions may direct additional, less, or alternate functionality, including that discussed elsewhere herein.

“In yet another aspect, a system comprising one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed by the one or more processors, cause the one or more processors to perform certain operations. The operations can include receiving personal data associated with a user. The operations also can include predicting, by a trained predictive possession model, a set of items owned by the user based at least in part upon the personal data associated with the user. The trained predictive possession model is configured to extract data associated with the set of items from the personal data. The operations additionally can include assigning, by the trained predictive possession model, a predicted value for each item of the set of items. The operations further can include causing information indicative of an item of the set of items and the predicted value for the item to be displayed on a user device of the user. The operations additionally can include receiving an adjusted value for the item in the set of items. The operations further can include causing the trained predictive possession model to be updated based on the adjusted value for the item.

“In yet another aspect, a computer-implemented method can include receiving personal data associated with a user. The method also can include predicting, by a trained predictive possession model, a set of items owned by the user based at least in part upon the personal data associated with the user. The trained predictive possession model is configured to extract data associated with the set of items from the personal data. The method additionally can include assigning, by the trained predictive possession model, a predicted value for each item of the set of items. The method further can include causing information indicative of an item of the set of items and the predicted value for the item to be displayed on a user device of the user. The method additionally can include receiving an adjusted value for the item in the set of items. The method further can include causing the trained predictive possession model to be updated based on the adjusted value for the item.

“In yet another aspect, one or more non-transitory computer-readable media storing computing instructions that, when executed by one or more processors, cause the one or more processors to perform certain operations. The operations can include receiving personal data associated with a user. The operations also can include predicting, by a trained predictive possession model, a set of items owned by the user based at least in part upon the personal data associated with the user. The trained predictive possession model is configured to extract data associated with the set of items from the personal data. The operations additionally can include assigning, by the trained predictive possession model, a predicted value for each item of the set of items. The operations further can include causing information indicative of an item of the set of items and the predicted value for the item to be displayed on a user device of the user. The operations additionally can include receiving an adjusted value for the item in the set of items. The operations further can include causing the trained predictive possession model to be updated based on the adjusted value for the item.

“Depending upon embodiment, one or more benefits may be achieved. These benefits and various additional objects, features and advantages of the present invention can be fully appreciated with reference to the detailed description and accompanying drawings that follow.

“The Figures depict preferred embodiments for purposes of illustration only. One skilled in the art will readily recognize from the following discussion that alternative embodiments of the systems and methods illustrated herein may be employed without departing from the principles of the disclosure.”

The claims supplied by the inventors are:

“1. A system comprising one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising: receiving personal data associated with a user; predicting, by a trained predictive possession model, a set of items owned by the user based at least in part upon the personal data associated with the user, wherein the trained predictive possession model is configured to extract data associated with the set of items from the personal data; assigning, by the trained predictive possession model, a predicted value for each item of the set of items; causing information indicative of an item of the set of items and the predicted value for the item to be displayed on a user device of the user; receiving an adjusted value for the item in the set of items; and causing the trained predictive possession model to be updated based on the adjusted value for the item.

“2. The system of claim 1, wherein: the trained predictive possession model comprises a machine learning model; the trained predictive possession model is trained using a plurality of historical policyholder records associated with a plurality of policyholders; and the plurality of historical policyholder records comprise one or more historical insurance claims that comprise one or more items owned by the plurality of policyholders and personal data associated with the plurality of policyholders.

“3. The system of claim 1, wherein causing the trained predictive possession model to be updated based on the adjusted value for the item comprises: retraining the trained predictive possession model based at least on the adjusted value for the item.

“4. The system of claim 1, wherein the operations further comprise: prompting the user to add or remove one or more items of the set of items.

“5. The system of claim 1, wherein the operations further comprise: determining whether the adjusted value for the item is within a range of predesignated values assigned to the item.

“6. The system of claim 1, wherein the operations further comprise: storing the adjusted value of the item as a new value for the item.

“7. The system of claim 1, wherein the personal data associated with the user comprises at least one of demographic data, age data, marital status, education, or employment data associated with the user.

“8. A computer-implemented method comprising: receiving personal data associated with a user; predicting, by a trained predictive possession model, a set of items owned by the user based at least in part upon the personal data associated with the user, wherein the trained predictive possession model is configured to extract data associated with the set of items from the personal data; assigning, by the trained predictive possession model, a predicted value for each item of the set of items; causing information indicative of an item of the set of items and the predicted value for the item to be displayed on a user device of the user; receiving an adjusted value for the item in the set of items; and causing the trained predictive possession model to be updated based on the adjusted value for the item.

“9. The computer-implemented method of claim 8, wherein: the trained predictive possession model comprises a machine learning model; the trained predictive possession model is trained using a plurality of historical policyholder records associated with a plurality of policyholders; and the plurality of historical policyholder records comprise one or more historical insurance claims that comprise one or more items owned by the plurality of policyholders and personal data associated with the plurality of policyholders.

“10. The computer-implemented method of claim 8, wherein causing the trained predictive possession model to be updated based on the adjusted value for the item comprises: retraining the trained predictive possession model based at least on the adjusted value for the item.

“11. The computer-implemented method of claim 8 further comprising: prompting the user to add or remove one or more items of the set of items.

“12. The computer-implemented method of claim 8 further comprising: determining whether the adjusted value for the item is within a range of predesignated values assigned to the item.

“13. The computer-implemented method of claim 8 further comprising: storing the adjusted value of the item as a new value for the item.

“14. The computer-implemented method of claim 8, wherein the personal data associated with the user comprises at least one of demographic data, age data, marital status, education, or employment data associated with the user.

“15. One or more non-transitory computer-readable media storing computing instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising: receiving personal data associated with a user; predicting, by a trained predictive possession model, a set of items owned by the user based at least in part upon the personal data associated with the user, wherein the trained predictive possession model is configured to extract data associated with the set of items from the personal data; assigning, by the trained predictive possession model, a predicted value for each item of the set of items; causing information indicative of an item of the set of items and the predicted value for the item to be displayed on a user device of the user; receiving an adjusted value for the item in the set of items; and causing the trained predictive possession model to be updated based on the adjusted value for the item.

“16. The one or more non-transitory computer-readable media of claim 15, wherein: the trained predictive possession model comprises a machine learning model; the trained predictive possession model is trained using a plurality of historical policyholder records associated with a plurality of policyholders; and the plurality of historical policyholder records comprise one or more historical insurance claims that comprise one or more items owned by the plurality of policyholders and personal data associated with the plurality of policyholders.

“17. The one or more non-transitory computer-readable media of claim 15, wherein causing the trained predictive possession model to be updated based on the adjusted value for the item comprises: retraining the trained predictive possession model based at least on the adjusted value for the item.

“18. The one or more non-transitory computer-readable media of claim 15, wherein the operations further comprise: prompting the user to add or remove one or more items of the set of items.

“19. The one or more non-transitory computer-readable media of claim 15, wherein the operations further comprise: determining whether the adjusted value for the item is within a range of predesignated values assigned to the item; and storing the adjusted value of the item as a new value for the item.

“20. The one or more non-transitory computer-readable media of claim 15, wherein the personal data associated with the user comprises at least one of demographic data, age data, marital status, education, or employment data associated with the user.

“21. A system comprising: means for receiving personal data associated with a user; means for predicting, by a trained predictive possession model, a set of items owned by the user based at least in part upon the personal data associated with the user, wherein the trained predictive possession model is configured to extract data associated with the set of items from the personal data; means for assigning, by the trained predictive possession model, a predicted value for each item of the set of items; means for causing information indicative of an item of the set of items and the predicted value for the item to be displayed on a user device of the user; means for receiving an adjusted value for the item in the set of items; and means for causing the trained predictive possession model to be updated based on the adjusted value for the item.”

For additional information on this patent application, see: Sanchez, Kenneth Jason. Systems And Methods For Generating And Updating An Inventory Of Personal Possessions Of A User For Insurance Purposes. U.S. Patent Application Number 20240127356, filed December 27, 2023 and posted April 18, 2024. Patent URL (for desktop use only): https://ppubs.uspto.gov/pubwebapp/external.html?q=(20240127356)&db=US-PGPUB&type=ids

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