公开(公告)号：US20240318994A1

公开(公告)日：2024-09-26

申请号：US18372372

申请日：2023-09-25

Applicant: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY

Inventor： Kyle Malan ,  Sean Kingsbury

IPC: G01F23/22 ,  H04L51/02

CPC classification number: G01F23/22 ,  H04L51/02

Abstract: Systems and methods disclosed herein relate to determining an optimal placement location of one or more water sensors proximate a structure using a machine learning (ML) chatbot. The ML chatbot may detect a request to identify the optimal placement location of the water sensors. In response to the request, structure information is provided to a trained ML model to generate an indication of the optimal placement location of the water sensors. The ML chatbot may detect the indication of the optimal placement location of the water sensors. The indication of the optimal placement location of the water sensors is provided to a user device.

公开(公告)号：US20240320870A1

公开(公告)日：2024-09-26

申请号：US18372370

申请日：2023-09-25

Applicant: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY

Inventor： Kyle Malan ,  Sean Kingsbury

IPC: G06T11/00 ,  G06T7/70

CPC classification number: G06T11/00 ,  G06T7/70

Abstract: Systems and methods disclosed herein relate to using augmented reality (AR) to visualize an optimal placement location of one or more water sensors proximate a structure. Underlay layer data indicative of a field of view associated with an AR viewer device is correlated with overlay layer data that includes an indication of the water sensor optimal placement locations to create an AR display. The AR viewer displays the AR display which includes an instruction for the placing one or more water sensors proximate the structure.

公开(公告)号：US20240319683A1

公开(公告)日：2024-09-26

申请号：US18372366

申请日：2023-09-25

Applicant: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY

Inventor： Kyle Malan ,  Sean Kingsbury

IPC: G05B13/02

CPC classification number: G05B13/0265

Abstract: Systems and methods disclosed herein relate to determining optimal placement location of one or more water sensors proximate a structure using machine learning. The machine learning model may be provided structure information for a structure at which the water sensors are to be placed, and generate an indication of the optimal placement location of the one or more water sensors proximate the structure. The optimal placement location of the water sensors may correspond to potential sources of water damage. A user device may receive the indication of the optimal placement location of the one or more water sensors proximate the structure to a user device.