DETERMINING OPTIMAL WATER SENSOR PLACEMENT USING A MACHINE LEARNING CHATBOT
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.
1 . A computer-implemented method for determining an optimal placement location of one or more water sensors proximate a structure using a machine learning (ML) chatbot, the method comprising:
obtaining, by one or more processors, structure information for a structure at which the one or more water sensors are to be placed;
detecting, by the one or more processors via the ML chatbot, a request to identify the optimal placement location of the one or more water sensors proximate the structure;
in response to detecting the request, providing, by the one or more processors, the structure information to a trained machine learning model to generate an indication of the optimal placement location of the one or more water sensors proximate the structure, wherein:
the trained machine learning model is trained using historical water damage claims data,
the optimal placement location of the one or more water sensors corresponds to the potential sources of water damage, and
the ML chatbot is configured to input the structure information into the ML chatbot to provide the structure information to the trained machine learning model;
detecting, by the one or more processors via the ML chatbot, an output of the trained machine learning model that includes the indication of the optimal placement location of the one or more water sensors proximate the structure; and
providing, by the one or more processors, the indication of the optimal placement location of the one or more water sensors proximate the structure to a user device.
2 . The computer-implemented method of claim 1 , wherein obtaining the structure information comprises:
generating, by the one or more processors via the ML chatbot, one or more requests for supplemental information based upon the structure information;
providing, by the one or more processors via the ML chatbot, the one or more requests for the supplemental information to the user device; and
responsive to providing the one or more requests for supplemental information, receiving, by the one or more processors via the ML chatbot from the user device, the supplemental information, wherein the structure information includes the supplemental information.
3 . The computer-implemented method of claim 1 , wherein the structure information includes data associated with one or more of: (i) a floorplan of the structure, (ii) structural components of the structure, (iii) a property the structure is located upon, (iv) plumbing at the structure, and/or (v) appliances at the structure.
4 . The computer-implemented method of claim 1 , wherein the structure information includes a geographic location of the structure.
5 . The computer-implemented method of claim 1 , wherein:
the historical water damage claims data includes a geographic location of a structure associated with the corresponding historical water damage claims data, and
the trained machine learning model is configured to learn a relationship between building code requirements for the geographic locations and potential sources of water damage.
6 . The computer-implemented method of claim 1 , wherein providing the indication of the optimal water placement of the one or more water sensors to the user device comprises:
generating, by the one or more processors via the ML chatbot, a multimedia representation of the optimal placement location of the one or more water sensors proximate the structure based upon the indication of the optimal placement location of the one or more water sensors proximate the structure; and
providing, by the one or more processors, the multimedia representation of the optimal placement location of the one or more water sensors proximate the structure to the user device.
7 . The computer-implemented method of claim 6 , wherein the multimedia representation includes one or more of: (i) a text component, (ii) an audio component, (iii) an image component, (iv) a video component, (v) a slide component, (vi) a virtual reality component, (vii) an augmented reality component, (viii) a mixed reality component, (ix) a multimedia component, and/or (x) a metaverse component.
8 . The computer-implemented method of claim 1 , wherein the ML chatbot is trained using one or more of: (i) supervised learning, (ii) unsupervised learning, and/or (iii) reinforcement learning.
9 . The computer-implemented method of claim 1 , wherein providing the indication of the optimal placement location of the one or more water sensors to the user device comprises:
generating, by the one or more processors, guidance information to a location of the structure associated with the optimal placement location of the one or more water sensors proximate the structure; and
providing, by the one or more processors, the guidance information.
10 . The computer-implemented method of claim 1 , wherein providing the indication of the optimal placement location of the one or more water sensors to the user device comprises:
determining, by the one or more processors, the number of water sensors to place proximate the structure; and
providing, by the one or more processors, a ranked list of optimal placement locations which correspond to the number of water sensors to place proximate the structure.
11 . The computer-implemented method of claim 10 , further comprising:
removing, by the one or more processors, from the ranked list one or more optimal placement locations which do not exist at the structure.
12 . A computer system configured to determine an optimal placement location of one or more water sensors proximate a structure using a machine learning (ML) chatbot, the computer system comprising:
one or more processors; and
one or more non-transitory memories storing processor-executable instructions that, when executed by the one or more processors, cause the system to:
obtain structure information for a structure at which the one or more water sensors are to be placed;
detect a request to identify the optimal placement location of the one or more water sensors proximate the structure;
in response to detecting the request, provide the structure information to a trained machine learning model to generate an indication of the optimal placement location of the one or more water sensors proximate the structure, wherein:
the trained machine learning model is trained using historical water damage claims data,
the optimal placement location of the one or more water sensors corresponds to the potential sources of water damage, and
the ML chatbot is configured to input the structure information into the ML chatbot to provide the structure information to the trained machine learning model;
detect an output of the trained machine learning model that includes the indication of the optimal placement location of the one or more water sensors proximate the structure; and
provide the indication of the optimal placement location of the one or more water sensors proximate the structure to a user device.
13 . The computer system of claim 12 , wherein to obtain the structure information comprises instructions that, when executed by the one or more processors, cause the system to:
generate, via the ML chatbot, one or more requests for supplemental information based upon the structure information;
provide, via the ML chatbot, the one or more requests for the supplemental information to the user device; and
responsive to providing the one or more requests for supplemental information, receive, via the ML chatbot from the user device, the supplemental information, wherein the structure information includes the supplemental information.
14 . The computer system of claim 12 , wherein the structure information includes data associated with one or more of: (i) a floorplan of the structure, (ii) structural components of the structure, (iii) a property the structure is located upon, (iv) plumbing at the structure, and/or (v) appliances at the structure.
15 . The computer system of claim 12 , wherein:
the historical water damage claims data includes a geographic location of a structure associated with the corresponding historical water damage claims data, and
the trained machine learning model is configured to learn a relationship between building code requirements for the geographic locations and potential sources of water damage.
16 . The computer system of claim 12 , wherein providing the indication of the optimal water placement of the one or more water sensors to the user device comprises instructions that, when executed by the one or more processors, cause the system to:
generate, via the ML chatbot, a multimedia representation of the optimal placement location of the one or more water sensors proximate the structure based upon the indication of the optimal placement location of the one or more water sensors proximate the structure; and
provide the multimedia representation of the optimal placement location of the one or more water sensors proximate the structure to the user device.
17 . The computer system of claim 16 , wherein the multimedia representation includes one or more of: (i) a text component, (ii) an audio component, (iii) an image component, (iv) a video component, (v) a slide component, (vi) a virtual reality component, (vii) an augmented reality component, (viii) a mixed reality component, (ix) a multimedia component, and/or (x) a metaverse component.
19 . The computer system of claim 12 , wherein providing the indication of the optimal placement location of the one or more water sensors to the user device comprises instructions that, when executed by the one or more processors, cause the system to:
generate guidance information to a location of the structure associated with the optimal placement location of the one or more water sensors proximate the structure; and
provide the guidance information.
20 . A non-transitory computer-readable medium storing processor-executable instructions that, when executed by one or more processors, cause the one or more processors to:
obtain structure information for a structure at which the one or more water sensors are to be placed;
detect a request to identify the optimal placement location of the one or more water sensors proximate the structure;
in response to detecting the request, provide the structure information to a trained machine learning model to generate an indication of the optimal placement location of the one or more water sensors proximate the structure, wherein:
the trained machine learning model is trained using historical water damage claims data,
the optimal placement location of the one or more water sensors corresponds to the potential sources of water damage, and
the ML chatbot is configured to input the structure information into the ML chatbot to provide the structure information to the trained machine learning model;
detect an output of the trained machine learning model that includes the indication of the optimal placement location of the one or more water sensors proximate the structure; and
provide the indication of the optimal placement location of the one or more water sensors proximate the structure to a user device.