Granted Patent B2

US 12,700,041 · App. 18/397,932 · Granted Aug 4, 2026

Inventor: Kenneth Jason Sanchez (San Francisco, CA)

Assignee: QUANATA, LLC

G06Q40/08G06F16/9535G06Q30/0278

Abstract

Claims (69)

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 comprises a machine learning model and is configured to extract data associated with the set of items from the personal data;

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;

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 retrained based on the adjusted value for the item.

2 . 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.

3 . 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.

4 . The system of claim 1 , wherein the operations further comprise:

storing the adjusted value of the item as a new value for the item.

5 . 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.

6 . 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 comprises a machine learning model and is configured to extract data associated with the set of items from the personal data;

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;

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 retrained based on the adjusted value for the item.

7 . The computer-implemented method of claim 6 further comprising:

prompting the user to add or remove one or more items of the set of items.

8 . The computer-implemented method of claim 6 further comprising:

determining whether the adjusted value for the item is within a range of predesignated values assigned to the item.

9 . The computer-implemented method of claim 6 further comprising:

storing the adjusted value of the item as a new value for the item.

10 . The computer-implemented method of claim 6 , 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.

11 . The computer-implemented method of claim 6 , wherein assigning the predicted value further comprises assigning a confidence range for the predicted value.

12 . The computer-implemented method of claim 11 , wherein the operations further comprise:

determining that the adjusted value is within the confidence range.

13 . 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 comprises a machine learning model and is configured to extract data associated with the set of items from the personal data;

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;

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 retrained based on the adjusted value for the item.

14 . The one or more non-transitory computer-readable media of claim 13 , wherein the operations further comprise:

prompting the user to add or remove one or more items of the set of items.

15 . The one or more non-transitory computer-readable media of claim 13 , 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.

16 . The one or more non-transitory computer-readable media of claim 13 , 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.

17 . The one or more non-transitory computer-readable media of claim 13 , wherein assigning the predicted value further comprises assigning a confidence range for the predicted value.

18 . The one or more non-transitory computer-readable media of claim 17 , wherein the operations further comprise:

determining that the adjusted value is within the confidence range.

19 . 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 comprises a machine learning model and is configured to extract data associated with the set of items from the personal data;

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;

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 retrained based on the adjusted value for the item.

20 . The system of claim 1 , wherein assigning the predicted value further comprises assigning a confidence range for the predicted value.

21 . The system of claim 20 , wherein the operations further comprise:

determining that the adjusted value is within the confidence range.

Assignments (2)

Continuity (2)

References Cited (400)