Application (pre-grant publication)
METHOD OF PREDICTING WATER ENVIRONMENTAL CONDITIONS
- Number
- 20260260110
- Published
- 2026-09-03
- Filed
- 2026-02-04
- Assignee
- DISNEY ENTERPRISES, INC.
- Inventors
- Hale; Gregory Brooks, Markowitz; Gary David, Gilmore; David Benson
- CPC
- G01W1/10; G06N3/08; G06N3/0464; G06N3/0475; G06N3/0499
- Verdict
- Set aside streaming, nlp/localization, business-ops
- In edition
- 2026-W36
- Source
- Google Patents · FreePatentsOnline
The keeper's note
A method of predicting a localized climate condition includes: receiving, by a processing element, regional environmental data; translating, by the processing element by utilizing a water environment machine learning mo…
Abstract
A method of predicting a localized climate condition includes: receiving, by a processing element, regional environmental data; translating, by the processing element by utilizing a water environment machine learning model, the regional environmental data into the localized climate condition; and generating, by the processing element, an alert based to the localized climate condition.
Background
BACKGROUND
Determining if the water and surf conditions at or adjacent to a body of water (e.g., a beach, lake, stream, river, sea, ocean, etc.) are safe for users such as beach goers and swimmers is a subjective process. Each body of water and shore of each body of water may have different characteristics such as the direction of surf orientation, tidal conditions, weather conditions, topography, and many other factors. There currently is no standard or approach for consistently forecasting or determining environmental water (e.g., surf or shore) conditions.
There are no systems available for capturing variations in water weather conditions on a local level nor in real time. Water safety personnel such as lifeguards manually assess, report, and respond to weather conditions and incidents. This process is not a prediction, but merely an observation of current conditions and may occur too late for users to make appropriate decisions about use of water areas. Furthermore, the condition observations may be generalized for large areas (e.g., an entire coastline, a city, a zip code, etc.) without taking into account the specific beach conditions and objective, measurable data that vary between beaches or even specific portions of the same beach (coastal areas). Where manual observations are used in forecasting, the results are often inaccurate due to the subjective nature of the manual observations, scarce, and involve too much of a time lag between observation and f