Granted Patent B2

US 12,554,610 · App. 18/665,103 · Granted Feb 17, 2026

Inventors: Alexander Cardona (Gilbert, AZ); Michael P. Baran (Bloomington, IL); Daniel Wilson (Glendale, AZ)

Assignee: State Farm Mutual Automobile Insurance Company

G06F11/3013G06F11/321G06K19/06037G06Q10/20

Abstract

Claims (45)

1 . A computing device for predicting a lifetime of one or more appliances, the computing device comprising at least one processor and at least one memory device, the at least one processor configured to:

receive historical appliance data including historical lifetimes of a plurality of appliances, at least some of the historical appliance data generated by sensors configured to monitor one or more parameters of at least one of the plurality of appliances;

train an artificial intelligence model using the historical appliance data including the historical lifetimes of the plurality of appliances;

receive appliance data relating to a first appliance;

compute, using the artificial intelligence model, a predicted remaining lifetime of the first appliance based upon the received appliance data; and

transmit content data to a user device that, when received by the user device, causes the user device to generate a user interface including at least the predicted remaining lifetime.

2 . The computing device claim 1 , wherein the at least one processor is further configured to:

receive image data including a quick response (QR) code or a bar code disposed on the first appliance; and

parse one or more databases to retrieve appliance data relating to the first appliance based upon the QR code or bar code.

3 . The computing device of claim 1 , wherein the at least one processor is further configured to receive at least some of the appliance data as a natural language input by a user via the user device.

4 . The computing device of claim 1 , wherein the at least one processor is further configured to:

retrieve additional appliance data relating to the appliance from one or more external data sources; and

compute the predicted remaining lifetime further based upon the retrieved additional appliance data.

5 . The computing device of claim 1 , wherein the at least one processor is further configured to:

generate, using the artificial intelligence model, a recommendation for increasing a lifetime of the first appliance; and

transmit recommendation data to the user device that, when received by the user device, causes the user device to generate the user interface to include the recommendation.

6 . The computing device of claim 5 , wherein the user interface indicates a change in the predicted remaining lifetime associated with performing the recommendation.

7 . The computing device of claim 5 , wherein the recommendation includes a list of recommended maintenance actions.

8 . The computing device of claim 7 , wherein the recommended maintenance actions are ordered within the list based upon a change in the predicted remaining lifetime associated with each of the recommended maintenance actions.

9 . The computing device of claim 7 , wherein the recommendation includes at least one of a timeline or a calendar for performing the recommended maintenance actions.

10 . The computing device of claim 7 , wherein the user interface includes at least one of a link to contact at least one service provider associated with at least one of the recommended maintenance actions.

11 . The computing device of claim 1 , wherein the processor is further configured to record a plurality of appliances associated with a user, wherein the plurality of appliances including the first appliance, and wherein the user interface incudes a respective predicted remaining lifetime corresponding to each of the plurality of appliances associated with the user.

12 . A computer-implemented method for predicting a lifetime of one or more appliances, the computer-implemented method performed by a computing device including at least one processor and at least one memory device, the computer-implemented method comprising:

receiving historical appliance data including historical lifetimes of a plurality of appliances, at least some of the historical appliance data generated by sensors configured to monitor one or more parameters of at least one of the plurality of appliances;

training an artificial intelligence model using the historical appliance data including the historical lifetimes of the plurality of appliances;

receiving appliance data relating to a first appliance;

computing, using the artificial intelligence model, a predicted remaining lifetime of the first appliance based upon the received appliance data; and

transmitting content data to a user device that, when received by the user device, causes the user device to generate a user interface including at least the predicted remaining lifetime.

13 . The computer-implemented method claim 12 , further comprising:

receiving image data including a quick response (QR) code or a bar code disposed on the first appliance; and

parsing one or more databases to retrieve appliance data relating to the first appliance based upon the QR code or bar code.

14 . The computer-implemented method of claim 12 , further comprising receiving at least some of the appliance data as a natural language input by a user via the user device.

15 . The computer-implemented method of claim 12 , further comprising:

retrieving additional appliance data relating to the appliance from one or more external data sources; and

computing the predicted remaining lifetime further based upon the retrieved additional appliance data.

16 . The computer-implemented method of claim 12 , further comprising:

generating, using the artificial intelligence model, a recommendation for increasing a lifetime of the first appliance; and

transmitting recommendation data to the user device that, when received by the user device, causes the user device to generate the user interface to include the recommendation.

17 . The computer-implemented method of claim 16 , wherein the user interface indicates a change in the predicted remaining lifetime associated with performing the recommendation.

18 . At least one non-transitory computer-readable storage media having computer-executable instructions embodied thereon, wherein when executed by a computing device including at least one processor and at least one memory device, the computer-executable instructions cause the at least one processor to:

receive historical appliance data including historical lifetimes of a plurality of appliances, at least some of the historical appliance data generated by sensors configured to monitor one or more parameters of at least one of the plurality of appliances;

train an artificial intelligence model using the historical appliance data including the historical lifetimes of the plurality of appliances;

receive appliance data relating to a first appliance;

compute, using the artificial intelligence model, a predicted remaining lifetime of the first appliance based upon the received appliance data; and

transmit content data to a user device that, when received by the user device, causes the user device to generate a user interface including at least the predicted remaining lifetime.

Assignments (1)

Continuity (2)

References Cited (65)