公开(公告)号：US12185679B2

公开(公告)日：2025-01-07

申请号：US17990275

申请日：2022-11-18

Applicant: TidalX AI Inc.

Inventor： Dominico P. Julian ,  Charles Nordstrom ,  Jeff Zerger ,  Joel Fraser Atwater ,  Benjamin Lundquist Saenz

IPC: A01G33/00 ,  A01D44/00 ,  G05B13/02 ,  G05D1/00

Abstract: Methods, systems, and computer-readable media that implement an autonomous or semi-autonomous growth platform used to control live cargo exposures to environmental parameters by changing depth in an offshore environment. For example, the growth platform can be lowered at night so that farmed seaweed can perform luxury uptake of nutrients and raised during the daytime so that the farmed seaweed can capture sunlight.

公开(公告)号：US12190592B2

公开(公告)日：2025-01-07

申请号：US18315408

申请日：2023-05-10

Applicant: TidalX AI Inc.

Inventor： Joel Fraser Atwater ,  Barnaby John James ,  Matthew William Messana

IPC: G06V20/52 ,  A01K61/10 ,  A01K61/95 ,  G06T7/20 ,  H04N23/667

Abstract: A fish monitoring system deployed in a particular area to obtain fish images is described. Neural networks and machine-learning techniques may be implemented to periodically train fish monitoring systems and generate monitoring modes to capture high quality images of fish based on the conditions in the determined area. The camera systems may be configured according to the settings, e.g., positions, viewing angles, specified by the monitoring modes when conditions matching the monitoring modes are detected. Each monitoring mode may be associated with one or more fish activities, such as sleeping, eating, swimming alone, and one or more parameters, such as time, location, and fish type.

公开(公告)号：US12223723B2

公开(公告)日：2025-02-11

申请号：US17391392

申请日：2021-08-02

Applicant: TidalX AI Inc

Inventor： Joel Fraser Atwater ,  Barnaby John James ,  Matthew Messana

IPC: G06V20/52 ,  A01K61/10 ,  A01K61/95 ,  G06T7/20 ,  H04N23/667

Abstract: A fish monitoring system deployed in a particular area to obtain fish images is described. Neural networks and machine-learning techniques may be implemented to periodically train fish monitoring systems and generate monitoring modes to capture high quality images of fish based on the conditions in the determined area. The camera systems may be configured according to the settings, e.g., positions, viewing angles, specified by the monitoring modes when conditions matching the monitoring modes are detected. Each monitoring mode may be associated with one or more fish activities, such as sleeping, eating, swimming alone, and one or more parameters, such as time, location, and fish type.