System and methods for utilizing predictions of future real-world events to generate actionable decisions

Patent metadata

Field	Value
Publication number	US-2023334366-A1
Application number	US-202318184830-A
Country	US
Kind code	A1
Filing date	Mar 16, 2023
Priority date	Apr 15, 2022
Publication date	Oct 19, 2023
Grant date	—

Abstract

Methods, systems, and apparatus for utilizing predictions of future real-world events to generate actionable decisions. In some implementations, a computer can obtain a plurality of prediction results in association with an event from a machine learning model. The computer can receive from semantic information from a user device. The computer can match one or more prediction results of the plurality of prediction results to the semantic information. The computer can generate one or more actionable outputs by processing the one or more prediction results and the semantic information for the user device. The computer can forward the one or more actionable outputs to the user device, wherein the one or more actionable outputs comprise information that allows a user of the user device to act on in advance of occurrence of the event.

First claim

What is claimed is:

1 . A computer-implemented method for utilizing predictions of future real-world events to make actionable decisions that prepare users for the real-world events, comprising:
obtaining, by a computer and from a machine learning model, a plurality of prediction results in association with an event; receiving, by the computer and from a user device, semantic information; matching, by the computer, one or more prediction results of the plurality of prediction results to the semantic information; generating, by the computer for the user device, one or more actionable outputs by processing the one or more prediction results and the semantic information; and forwarding, by the computer to the user device, the one or more actionable outputs, wherein the one or more actionable outputs comprise information that allows a user of the user device to act on in advance of occurrence of the event.

2 . The computer-implemented method of claim 1 , wherein each of the plurality of prediction results comprises one or more parameters of:
(1) the event; (2) a time window that the event occurs; (3) a geospatial area that the event occurs; (4) an intensity of the event; and (5) a probability of occurrence of the event corresponding to one or more of (2), (3), and (4).

3 . The computer-implemented method of claim 1 , wherein matching, by the computer, the one or more prediction results of the plurality of prediction results to the semantic information is based on a predetermined mapping relationship between the plurality of prediction results and the semantic information.

4 . The computer-implemented method of claim 1 , wherein generating, by the computer for the user device, one or more actionable outputs by processing the one or more prediction results and the semantic information, comprises:
providing the one or more prediction results and the semantic information to a trained machine learning model; and receiving an output from the trained machine learning model, including the one or more actionable outputs.

5 . The computer-implemented method of claim 1 , wherein the one or more actionable outputs is automatically executed by the user device.

6 . The computer-implemented method of claim 1 , wherein the one or more actionable outputs is displayed by the user device to a user using a graphic user interface (GUI).

7 . The computer-implemented method of claim 1 , wherein the event comprises a natural event or condition.

8 . The computer-implemented method of claim 7 , wherein the natural event or condition comprises a natural hazardous event.

9 . The computer-implemented method of claim 1 , wherein the semantic information comprises information corresponding to a product, a service, and/or a provider thereof.

10 . The computer-implemented method of claim 1 , wherein obtaining, by the computer and from the machine learning model, the plurality of prediction results in association with the event is based on the semantic information.

11 . The computer-implemented method of claim 1 , wherein generating the one or more actionable outputs by processing the one or more prediction results and the semantic information is in response to determining that at least one of the one or more parameters of the event satisfies a predetermined threshold.

12 . The computer-implemented method of claim 1 , wherein matching, by the computer, the one or more prediction results of the plurality of prediction results to the semantic information is by calculating a correlation between historical events and historical data in correspondence to the semantic information.

13 . The computer-implemented method of claim 1 , wherein matching, by the computer, the one or more prediction results of the plurality of prediction results to the semantic information is by using a machine learning model trained on historical events and historical data in correspondence to the semantic information.

14 . A system comprising:
one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations for utilizing predictions of future real-world events to make actionable decisions that prepare users for the real-world events, comprising:
obtaining, by a computer and from a machine learning model, a plurality of prediction results in association with an event;
receiving, by the computer and from a user device, semantic information;
matching, by the computer, one or more prediction results of the plurality of prediction results to the semantic information;
generating, by the computer for the user device, one or more actionable outputs by processing the one or more prediction results and the semantic information; and
forwarding, by the computer to the user device, the one or more actionable outputs, wherein the one or more actionable outputs comprise information that allows a user of the user device to act on in advance of occurrence of the event.

15 . The system of claim 14 , wherein each of the plurality of prediction results comprises one or more parameters of:
(1) the event; (2) a time window that the event occurs; (3) a geospatial area that the event occurs; (4) an intensity of the event; and (5) a probability of occurrence of the event corresponding to one or more of (2), (3), and (4).

16 . The system of claim 14 , wherein matching, by the computer, the one or more prediction results of the plurality of prediction results to the semantic information is based on a predetermined mapping relationship between the plurality of prediction results and the semantic information.

17 . The system of claim 14 , wherein generating, by the computer for the user device, one or more actionable outputs by processing the one or more prediction results and the semantic information, comprises:
providing the one or more prediction results and the semantic information to a trained machine learning model; and receiving an output from the trained machine learning model, including the one or more actionable outputs.

18 . The system of claim 14 , wherein the one or more actionable outputs is automatically executed by the user device.

19 . The system of claim 14 , wherein the one or more actionable outputs is displayed by the user device to a user using a graphic user interface (GUI).

20 . A non-transitory computer-readable medium storing software comprising instructions executable by one or more computers which, upon such execution, cause the one or more computers to perform operations for utilizing predictions of future real-world events to make actionable decisions that prepare users for the real-world events, comprising:
obtaining, by a computer and from a machine learning model, a plurality of prediction results in association with an event; receiving, by the computer and from a user device, semantic information; matching, by the computer, one or more prediction results of the plurality of prediction results to the semantic information; generating, by the computer for the user device, one or more actionable outputs by processing the one or more prediction results and the semantic information; and forwarding, by the computer to the user device, the one or more actionable outputs, wherein the one or more actionable outputs comprise information that allows a user of the user device to act on in advance of occurrence of the event.

Assignees

• X Dev Llc

Inventors

• Jeffery Joshua Paul
• Woolley Callum

Classifications

• G06N20/00 Primary

  Machine learning · CPC title

• G06N3/09 Primary

  Supervised learning · CPC title

• G06N3/045

  Combinations of networks · CPC title

• G06N20/20

  Ensemble learning · CPC title

• G06Q50/26

  Government or public services (business processes related to the transportation industry G06Q50/40) · CPC title