# US 11,113,539 B2 · Fish measurement station keeping > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,113,539 B2 - Document ID: US11113539B2 - Status: Granted - Kind: B2 - Application number: 16/701,853 - Filed: 2019-12-03 - Published: 2020-04-02 - Granted: 2021-09-07 - Earliest claimed: 2018-05-03 - Assignees: X Development LLC - Applicants: X Development LLC - Inventors: Joel Fraser Atwater; Barnaby John James; Matthew Messana - Web page: https://patentgrep.com/patent/US11113539B2 ## CPC - G06V 20/52: Surveillance or monitoring of activities, e.g. for recognising suspicious objects (recognising microscopic objects G06V20/69) - G16H 40/67: for remote operation - A01K 61/10: of fish - A01K 61/13: Prevention or treatment of fish diseases - A01K 61/60: Floating cultivation devices, e.g. rafts or floating fish-farms - A01K 61/80: Feeding devices - A01K 61/90: Sorting, grading, counting or marking live aquatic animals, e.g. sex determination - A01K 61/95: specially adapted for fish - G06T 7/20: Analysis of motion (motion estimation for coding, decoding, compressing or decompressing digital video signals H04N19/43, H04N19/51) - H04N 23/667: Camera operation mode switching, e.g. between still and video, sport and normal or high- and low-resolution modes - Y02A 40/81: Aquaculture, e.g. of fish - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA - A01K: ANIMAL HUSBANDRY; AVICULTURE; APICULTURE; PISCICULTURE; FISHING; REARING OR BREEDING ANIMALS, NOT OTHERWISE PROVIDED FOR; NEW BREEDS OF ANIMALS - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - H04N: PICTORIAL COMMUNICATION, e.g. TELEVISION - Y02A: TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE ## 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. ## Claims ### Claim 1 A computer-implemented method comprising: obtaining data that reflects present, sensed, environmental conditions that are associated with a fish pen; providing, to a model that is trained to output, for given, input parameters that reflect sensed environmental conditions that are associated with the fish pen, a given set of output parameters for generating images of fish that are contained within the fish pen using one or more underwater cameras, particular parameters that reflect the present, sensed, environmental conditions that are associated with the fish pen; obtaining, from the model, a particular set of output parameters; and adjusting a position of one or more underwater cameras based on the particular set of output parameters. ### Claim 2 (depends on 1) The method of claim 1, wherein the input parameters indicate a fish type of fish that are enclosed within the fish pen, a sensed water current within the fish pen, or a sensed light level within the fish pen. ### Claim 3 (depends on 1) The method of claim 1, wherein the particular set of output parameters identifies a first subset of the underwater cameras that are to be used to generate the images of the fish, and a second subset of the underwater cameras that are not to be used to generate the images of the fish. ### Claim 4 (depends on 1) The method of claim 1, wherein the particular set of output parameters identifies one or more camera angles associated with the underwater cameras that are to be used to generate the images of the fish, and wherein the position of the one or more of the underwater cameras is adjusted to match the one or more camera angles. ### Claim 5 (depends on 1) The method of claim 1, comprising training the model for outputting parameters for generating images of fish that are contained within the fish pen using machine learning. ### Claim 6 (depends on 1) The method of claim 1, wherein the particular set of output of output parameters specifies a particular mode of the one or more underwater cameras, selected from among multiple modes that are pre-associated with the one or more underwater cameras and that are each associated with a different fish activity type. ### Claim 7 (depends on 1) The method of claim 1, comprising adjusting an orientation of one or more of the underwater cameras with respect to the fish pen. ### Claim 8 A system comprising: one or more computing devices and one or more storage devices that store instructions which, when executed by the one or more computing devices, cause the one or more computing devices to perform operations comprising: obtaining data that reflects present, sensed, environmental conditions that are associated with a fish pen; providing, to a model that is trained to output, for given, input parameters that reflect sensed environmental conditions that are associated with the fish pen, a given set of output parameters for generating images of fish that are contained within the fish pen using one or more underwater cameras, particular parameters that reflect the present, sensed, environmental conditions that are associated with the fish pen; obtaining, from the model, a particular set of output parameters; and adjusting a position of one or more underwater cameras based on the particular set of output parameters. ### Claim 9 (depends on 8) The system of claim 8, wherein the input parameters indicate a fish type of fish that are enclosed within the fish pen, a sensed water current within the fish pen, or a sensed light level within the fish pen. ### Claim 10 (depends on 8) The system of claim 8, wherein the particular set of output parameters identifies a first subset of the underwater cameras that are to be used to generate the images of the fish, and a second subset of the underwater cameras that are not to be used to generate the images of the fish. ### Claim 11 (depends on 8) The system of claim 8, wherein the particular set of output parameters identifies one or more camera angles associated with the underwater cameras that are to be used to generate the images of the fish, and wherein the position of the one or more of the underwater cameras is adjusted to match the one or more camera angles. ### Claim 12 (depends on 8) The system of claim 8, wherein the operations comprise training the model for outputting parameters for generating images of fish that are contained within the fish pen using machine learning. ### Claim 13 (depends on 8) The system of claim 8, wherein the particular set of output of output parameters specifies a particular mode of the one or more underwater cameras, selected from among multiple modes that are pre-associated with the one or more underwater cameras and that are each associated with a different fish activity type. ### Claim 14 (depends on 8) The system of claim 8, wherein the operations comprise adjusting an orientation of one or more of the underwater cameras with respect to the fish pen. ### Claim 15 One or more non-transitory computer-readable storage media comprising instructions, which, when executed by one or more computing devices, cause the one or more computing devices to perform operations comprising: obtaining data that reflects present, sensed, environmental conditions that are associated with a fish pen; providing, to a model that is trained to output, for given, input parameters that reflect sensed environmental conditions that are associated with the fish pen, a given set of output parameters for generating images of fish that are contained within the fish pen using one or more underwater cameras, particular parameters that reflect the present, sensed, environmental conditions that are associated with the fish pen; obtaining, from the model, a particular set of output parameters; and adjusting a position of one or more underwater cameras based on the particular set of output parameters. ### Claim 16 (depends on 15) The media of claim 15, wherein the input parameters indicate a fish type of fish that are enclosed within the fish pen, a sensed water current within the fish pen, or a sensed light level within the fish pen. ### Claim 17 (depends on 15) The media of claim 15, wherein the particular set of output parameters identifies a first subset of the underwater cameras that are to be used to generate the images of the fish, and a second subset of the underwater cameras that are not to be used to generate the images of the fish. ### Claim 18 (depends on 15) The media of claim 15, wherein the particular set of output parameters identifies one or more camera angles associated with the underwater cameras that are to be used to generate the images of the fish, and wherein the position of the one or more of the underwater cameras is adjusted to match the one or more camera angles. ### Claim 19 (depends on 15) The media of claim 15, wherein the operations comprise training the model for outputting parameters for generating images of fish that are contained within the fish pen using machine learning. ### Claim 20 (depends on 15) The media of claim 15, wherein the particular set of output of output parameters specifies a particular mode of the one or more underwater cameras, selected from among multiple modes that are pre-associated with the one or more underwater cameras and that are each associated with a different fish activity type.