Mobile and web application for fishing

Abstract

The present invention is for a web or mobile based application comprising a predictive tool for helping users find the highest probability locations for catching or studying aquatic species. The application accomplishes this by compiling large amounts of data from public and private entities, as well as crowdsourcing data from trained scientist-anglers users, and using a machine learning program to analyze the data and predict the location of aquatic populations. The program incorporates data such as local weather conditions, date, time, tidal information, solunar calendar information, and so forth, as well as the information related to the captured species, such as size, species. The application further functions to synchronize with fishing tournaments and fish tagging and recapture programs for the supply and cross reference of data.

Description

IN THE UNITED STATES PATENT AND TRADEMARK OEFICE- PCT Patent Specification -Prepared by:Jared K. Rovira (Reg. 81,471) Intellectual Property’ Consulting, L.L. C.400 Poydras StreetSuite 1400New Orleans, LA 70130(Telephone: 504-322-7166)(Facsimile: 504-322-7184)Inventor: Randall Pausina: Cherie Pausina - 1 - Attorney File No.: 1969 PCTTitle: Mobile and Web Application for FishingMobile and Web Application for FishingFIELD OF THE INVENTION

[0001] The present invention relates generally to a web and mobile application for fishing.GENERAL BACKGROUND

[0002] There are nearly 60 million anglers in the United States alone. As any avid angler knows, the location of one’s fishing spot, the place where fish are always biting, is a closely guarded secret. Typically, fishing spots are discovered through the diligent and time-consuming method of trial and error, which explains why they are so highly valued. While fish spots are usually a go-to for anglers, they are not foolproof; some days the fish just aren’t there. Fortunately, data analytics and artificial intelligence can be leveraged to help predict fish spots in real-time. The present invention provides anglers without the necessary free-time to develop their own fishing spots and avid anglers looking to switch things up, with a data-driven solution to finding the highest probability locations for their favorite fish.SUMMARY OF THE INVENTION

[0003] In accordance with some embodiments, the present invention is a web or mobile based application for anglers that includes several useful features and programs. The application comprises a predictive tool for helping users find the highest probability locations for catching fish or even a certain species of fish. The application accomplishes this by compiling large amounts of data from public and private entities, as well as crowdsourcing data from trained scientist-anglers users, and using a machine learning program to analyze the data and predict where fish are. The program incorporates all types of data related to a capture such as local weather conditions, date, time, tidal information, solunar calendar information, and so forth, as well as the information related to the capture, such as size, species The application also provides a personal journal typeprogram, that allows users to document their own captures and the related details such as time, date, location, species, size and so forth. The angler’s journal is equipped with an interactive map, so that anglers can see the locations of all their captures, weather, fish forecasts, captures by other users and many other features. The application further functions to synchronize with fishing tournaments and fish tagging and recapture programs (for example TAG Louisiana) for the supply and cross reference of data, and the tracking and easy access of tournament and tagging information and data by the user.BRIEF DESCRIPTION OF THE DRAWINGS

[0004] The foregoing and other objects, features, and advantages of the invention are apparent from the following detailed description taken in conjunction with the accompanying drawings in which like parts are given like reference numerals and, wherein:

[0005] FIG. 1 depicts a flowchart of steps of a computer-implemented method for forecasting behavior of one or more aquatic life species in accordance with some embodiments of the invention.

[0006] FIG. 2 depicts a flowchart of steps of a computer-implemented method for identifying an aquatic life species based on an image in accordance with some embodiments of the invention.

[0007] FIG. 3 depicts a system for forecasting behavior of one or more aquatic life species in accordance with some embodiments of the invention.

[0008] The images in the drawings are simplified for illustrative purposes and are not depicted to scale. Within the descriptions of the figures, similar elements are provided similar names and reference numerals as those of the previous figure(s). The specific numerals assigned to the elements are provided solely to aid in the description and are not meant to imply any limitations(structural or functional) on the invention.

[0009] The appended drawings illustrate exemplary configurations of the invention and, as such, should not be considered as limiting the scope of the invention that may admit to other equally effective configurations. It is contemplated that features of one configuration may be beneficially incorporated in other configurations without further recitation.DETAILED DESCRIPTION

[0010] The embodiments of the disclosure will be best understood by reference to the Figures, wherein like parts are designated by like numerals throughout. It will be readily understood that the components, as generally described and illustrated in the Figures herein, could be arranged and designed in a wide variety of different configurations or be entirely separate. Thus, the following more detailed description of the embodiments of the system of the disclosure, as represented in the Figures is not intended to limit the scope of the disclosure, as claimed, but is merely representative of possible embodiments of the disclosure.

[0011] The following description sets forth numerous embodiments and parameters. It should be recognized, however, that such description is not intended as a limitation on the scope of the present invention but is instead provided as a description of exemplary embodiments. Various modifications to the examples described will be readily apparent to those of ordinary skill in the art, and the general principles defined may be applied to other examples and applications without departing from the spirit and scope of the invention. Thus, the present invention is not intended to be limited to the examples described herein but is to be accorded a scope consistent with the claims.

[0012] The present invention is generally a web or phone-based application for several functions related to recreational and competitive fishing. In one function, the application is for documenting, plotting, predicting and forecasting the location of many types of fish species. This is accomplished by compiling data (both input by scientist-anglers and automatically gatheredfrom measurement devices) such as weather, temperature, fish type and dimensions, location, salinity at the time of a successful catch by trained anglers. The application then uses machine learning to process these many data points so it can accurately predict the best location, time and / or weather conditions for catching a specific type of fish. Another function of the application allows users to record, document and share their own captures. The application offers a fish recognition feature that allows the application to identify the fish in images taken through the application or images uploaded to the application without any additional input from the user. Users may then share their captures with other users through chat and other related social features. In some embodiments the application will generate real-time data, increasing sample size and accuracy for offshore fishing effort estimates and critical biological metrics, including species identification, fish length, catch disposition, and release condition while minimizing reporting burden on anglers.

[0013] The application allows users to input data associated with the capture of a fish such as date, time, weight, length, species, location etc. The application may be in communication with a user device’s camera so that the user may photograph their catches with their phone. The application may be in communication with the location function (GPS) of the user device so that the exact location of capture can documented in real time. The application may incorporate artificial intelligence to automatically measure a fish and / or identify the species of a fish in the image taken through the application or an image uploaded to the application. Users and citizen scientists can verify the identifications made by artificial intelligence in order to assist the artificial intelligence in achieving greater accuracy. The application may be in communication with local weather stations, government agencies, databases, etc., so that real-time information may be acquired for the application’s functions.

[0014] One function of the application is the personal angler’s journal function. This function allows users to document all their captures in one place. The function allows users to record and store and image of the capture, the species, size, weight and other measurements related to the capture as well as the associated location, date, time, weather information. The captures and the related information may be represented in lists, maps, graphs or another suitable format. The angler’s journal may be linked to an interactive map within the application that displays all of the captures of the user with the precise locations of those captures. The past captures of an angler may be represented by a pin, fish icon or other mark on the interactive map. Within the interactive map, a user may select an icon, pin or other mark representative of a capture in order to display further information associated with the capture, such as species, date, time etc. The interactive map may include several information overlays and may incorporate past or real-time weather information, graphs or charts. In the personal angler’s journal function, users may input their own fishing tags associated with a fish tagging program. The personal angler’s journal function also allows user to download their capture history and associated information so that it can be accessed offline.

[0015] The personal angler’s journal function also assists users in understanding why and how they captured a fish. When a user records their capture through the application, the current weather, atmospheric, hydrologic and other data are pulled from linked sources (government agencies, etc.) and the recorded along with the capture. This allows users to analyze the conditions around their captures to seek trends and patterns to use to their advantage.

[0016] The personal angler’s journal function also incorporates share and chat features, the ability to add friends and the ability to organize groups. This allows users to share images, fish capture information, locations and other related information. The application may include featuresallowing automatic updates to other user’s friends indicating if the primary user is using the application, at a likely fishing location and / or has added a new capture to their records. The application is capable of syncing with other social media platforms so that users may upload or share their captures and other information to those platforms. The personal journal function may be synchronized with the tournament function of the application, such that users may record their captures for both their personal angler’s journal and to submit the capture to a tournament via the application.

[0017] The application may further incorporate and encyclopedia function that provides information about fish species and other fish related subjects, such as fish sleeping, mating and diet habits, seasonal trends and activities, bait preferences. The encyclopedia may also provide information regarding fish equipment, strategies and instructions. In some embodiments, the encyclopedia is stored off the application and the application provides a link that directs the user to the encyclopedia.

[0018] In some embodiments, the application may comprise a primary database for the recordation and storage of data related to fish captures. The type of data related to the fish capture includes the data of the specific fish captures including size, species, tag number, location, date, time, etc. The application may receive fish specific data from trained citizen scientists, fish tournaments and leaderboards, surveys and / or information published by national, state or local governmental agencies, (such as the Louisiana Department of Wildlife and Fisheries, National Marine Fisheries Service, etc.) and / or private companies. The application may optionally source data from normal users of the application. Surveys can include surveys of fisherman run by government agencies.

[0019] The data collected may also include weather, climate, atmospheric, tidal and / or hydrologic data surrounding the fish capture. The application may be in connection with local, regional or national stations or databases so that it may source the data based on the time and location of the fish capture. This process may be done in real-time or at a time when such data is published and the application may retroactively reference the published data when it becomes available based on the documented location and time of the fish capture. The climate, weather, atmospheric, tidal and hydrologic data may include, but is not limited to: air and water temperature, wind and water speed, wind and water direction, cloud coverage, sunlight amount, humidity, rainfall, water salinity, water pH, water hardness, alkalinity, water depth, tides, and so forth. The application may verify conditions by cross-referencing one or more of the sources of data.

[0020] The primary database may also incorporate data from solunar calendars and tables, fishing almanacs, fishing guides, or any other source of information related to fishing. The primary database may also incorporate geographical information such as depths.

[0021] The application uses machine learning and / or artificial intelligence to analyze the plurality of provided data to predict high-probability areas of fish capture, the best locations for catching specific species of fish, the best times, dates and locations for certain species, the locations of bait schools of fish, etc. The predictive function of the application is linked with the interactive map of the application. The application’s predictions may be displayed through the interactive map, or elsewhere, in any suitable format, such as heat maps, pins, icons, graphs or charts. A user may select any number of parameters for the predictive tool to curate predictions best suited to the user’s fishing needs. For example, if a user inputs one or more species of fish, the application can predict what time, location and weather conditions the user will have the highest chance of success. Alternatively, a user may input a specific time frame and the application can tell the user whatlocations are best for any number of species at those times. The predictive capability offers predictions on multiple time frames including hours, days, weeks or months away. The collection and analysis of the fishing related data may not only assist anglers in more effective fishing but may be utilized to help maintain and protect wildlife populations and aid in conservation efforts.

[0022] The application may function to alert users to any number of conditions. For example, the application may alert users as to updates in local weather conditions, the emergence of nearby high-probability fishing locations, if a friended user is nearby, etc.

[0023] The application’s interactive map may feature established fishing locations such as fishing spots, landings, piers, and / or marinas. Users may select these locations on the map and access further information such as fish availability, species availability and local weather, hydrologic and atmospheric conditions as well as the predictions for the locations. For example, the application may relay that a particular pier may have a high probability of flounder.

[0024] The application is also configured to access and synchronize with Louisiana TAG, and / or other fish tagging programs. In some embodiments, the application is configured to access and synchronize with fishing tournaments. Users can easily monitor changes tournament leaderboards, and receive alerts based on changes. Additionally, the application allows users to submit captures to tournaments by taking images through the application and entering related information into the application. This allows anglers to centralize their tournament, tagging and personal fishing experiences in a single, convenient location.

[0025] The application may be implemented on an operating environment that may include a network, a computing device, a server, and / or any other means standard in the art. In general, the network enables the computing device, the server, and / or data sources to communicate with eachother. The computing device may be, for example, a desktop computer, tablet, smartphone, a laptop, etc.

[0026] In some embodiments, the mobile or web application utilizes a computer-implemented method for forecasting behavior of one or more aquatic life species comprising the steps of providing a network 102, providing a database 104 such as a primary database, and providing an Al (artificial intelligence) program 106. The database may be connected to the internet. The process may further comprise providing one or more servers for operation of the network and other elements of the computer implemented method. The process may further comprise the steps of compiling at least one form of data related to an aquatic species into the primary database 108 and then providing that data to the Al program 110 for analysis. Based on the analysis of the data, the Al program is then directed to produce a predictive result 112. The predictive result may comprise the identification of one or more locations of a species of aquatic life at a specific time selected by a user or during a period of time selected by a user. In some embodiments, the predictive result may comprise different or additional information such as total estimated population size in an area or location, estimated population size per estimated group or population cluster, estimated likelihood of successful capture, bait or capture method suggestion or weather forecasts. In some embodiments the computer-implemented method may further comprise the step of providing the predictive result to the user via a graphic display 114. In some embodiments, the computer- implemented method involves providing the predictive result to a user on a graphic display via a geographical map or interactive geographical map overlayed with one or more of heatmaps, plotting, points, markers, pins, icons, graphs, charts etc.

[0027] In some embodiments, the computer implemented method may further comprise the step of allowing a user to provide feedback regarding the accuracy of the predictive result to theAl program 116. The application may provide a prompt to a user or the user may select an option wherein the user can grade the accuracy of a predictive result on a pass / fail or score based grading system. The results of the user feedback system can be stored and provided to the Al program in order to allow the Al program to machine learn and provide the ability to improve the analysis of data and the accuracy of the predictive results.

[0028] In one embodiment of the computer implemented method, the process may further comprise the step of providing a secondary database and allowing a user to upload data related to a capture of an aquatic species into storage on the secondary database for later retrieval by the user 118. In this embodiment, the computer implemented method may further comprise the step of incorporating the user-uploaded data related to the capture of an aquatic species into the primary database for analysis by the Al program in producing the predictive result 120. In this embodiment, the data related to a capture of an aquatic species comprises one or more of the species, size, length, width, weight, whether the capture was released or kept by the user, tag number, health, presence of parasites, location of capture, date of capture or time of capture.

[0029] In another embodiment of the computer implemented method, the process may further comprise the step of obtaining the time and geolocation data at the time an aquatic species is captured by a user and cross-referencing the time and geolocation data with one or more online third-party databases, websites or services to obtain environmental conditions data for the time and location of the user’s aquatic species capture 122. In this embodiment, the computer implemented method may further comprise the step of providing the weather and environmental data resources data, together with the capture time and location data, to the Al program for analysis and used in generating predictive results 124. The weather and environmental data resources data, together with the capture time and location data, may be stored on a primary database. Thisprovides the environmental and weather data associated with the capture or sighting of a specific aquatic species, which may help produce reliable indicators for predicting the location of those species when attempting to capture or study them. This process provides a way for the application and associated process of generating their own useful data that is especially useful to the purposes of systems and methods, i.e. predicting the location and activity of aquatic species and training an Al program to do the same. In this embodiment, the environmental conditions data is one or more of weather salinity, air and water temperature, wind and water speed, wind and water direction, cloud coverage, sunlight amount, humidity, rainfall, water salinity, water pH, water hardness, alkalinity, water depth or tides. In this embodiment, the time and geolocation data at the time of the aquatic species capture may be obtained from a user device’s location or timekeeping functionality, such as GPS.

[0030] In some embodiments of the computer-implemented method, the data stored on the primary database and / or provided to the Al program for analysis is data from solunar calendars and tables, fishing almanacs or fishing guides. In some embodiments of the computer-implemented method, the data stored on the primary database and / or provided to the Al program for analysis is data from solunar calendars and tables, fishing almanacs or fishing guides. In some embodiments of the computer-implemented method, the data stored on the primary database and / or provided to the Al program for analysis is documented times and locations of previous sightings an aquatic species, the times and locations of previous captures of the aquatic species or the physical dimensions of a previously captured aquatic species, historical records for aquatic species populations, etc. In some embodiments of the computer-implemented method, the data stored on the primary database and / or provided to the Al program for analysis is obtained from trained citizen scientists, fish tournaments and leaderboards, surveys and / or information published bynational, state or local governmental agencies, private companies or users. In some embodiments of the computer-implemented method, the data stored on the primary database and / or provided to the Al program for analysis is weather data, location, salinity, air and water temperature, wind and water speed, wind and water direction, cloud coverage, sunlight amount, humidity, rainfall, water salinity, water pH, water hardness, alkalinity, water depth or tides.

[0031] In some embodiments, the invention may provide a computer-implemented method for identifying an aquatic life species comprising the steps of providing a network 202, providing a primary database 204, providing an Al (artificial intelligence) program 206, compiling at least one form of data related to the physical features of one or more aquatic life species into the primary database 208, providing a photograph or image of an unknown aquatic life sample to the primary database 210, directing the Al program to produce a predictive result wherein the predictive result is the identification of the species of the unknown aquatic life sample in the photograph or image based the Al program’s analysis of the data related to the physical features of one or more aquatic life species 212. Finally the computer implanted method comprises the step of providing the identification of the species of the unknown aquatic life sample to a user via a graphic display 214. The method may employ storage and analysis by the Al program of any of the data types from any of the sources discussed herein. The method may be improved by allowing users to grade the accuracy of the Al program’s identification and providing the feedback data to the Al program to aid in machine learning.

[0032] For the purposes of promoting an understanding of the principles of the invention, reference has been made to the preferred embodiments illustrated in the drawings, and specific language has been used to describe these embodiments. However, this specific language intends no limitation of the scope of the invention, and the invention should be construed to encompass allembodiments that would normally occur to one of ordinary skill in the art. The particular implementations shown and described herein are illustrative examples of the invention and are not intended to otherwise limit the scope of the invention in any way. For the sake of brevity, conventional aspects of the system (and components of the individual operating components of the system) may not be described in detail. Furthermore, the connecting lines, or connectors shown in the various figures presented are intended to represent exemplary functional relationships and / or physical or logical couplings between the various elements. It should be noted that many alternative or additional functional relationships, physical connections or logical connections may be present in a practical device. Moreover, no item or component is essential to the practice of the invention unless the element is specifically described as “essential” or “critical”. Numerous modifications and adaptations will be readily apparent to those skilled in this art without departing from the spirit and scope of the present invention.

Claims

CLAIMS1. A computer-implemented method for forecasting behavior of one or more aquatic life species comprising; providing a network; providing a primary database; providing an Al (artificial intelligence) program; compiling at least one form of data related to an aquatic species into the primary database; providing the at least one type of data related to an aquatic species to the Al program from the primary database; directing the Al program to produce a predictive result, wherein the predictive result comprises the identification of one or more locations of a species of aquatic life at a specific time or during period of time selected by a user based on the at least one type of data related to an aquatic species; providing the predictive result to the user via a graphic display.

2. The computer-implemented method of Claim 1 further comprising, the step of allowing the user to provide feedback regarding the accuracy of the predictive result and providing the feedback to the Al program to improve results.

3. The computer-implemented method of Claim 1, wherein the predictive result is produced as a plotting on a map.

4. The computer-implemented method of Claim 1, wherein the predictive result is produced as a heatmap overlayed on a geographical map.

5. The computer-implemented method of Claim 1 , wherein the database is an online database.

6. The computer-implemented method of Claim 1, wherein the at least one form of data is compiled from information from solunar calendars and tables, fishing almanacs or fishing guides.

7. The computer-implemented method of Claim 1, wherein the at least one form of data is obtained from information from solunar calendars and tables, fishing almanacs or fishing guides.

8. The computer-implemented method of Claim 1, wherein the at least one form of data is one or more of the time and location of previous sightings of the aquatic species, the time and location of previous captures of the aquatic species or the physical dimensions of a previously captured aquatic species,9. The computer-implemented method of Claim 1, wherein the at least one form of data is one or more of weather data, location, salinity, air and water temperature, wind and water speed, wind and water direction, cloud coverage, sunlight amount, humidity, rainfall, water salinity, water pH, water hardness, alkalinity, water depth or tides.

10. The computer-implemented method of Claim 1, wherein the at least one form of data is obtained from one or more of trained citizen scientists, fish tournaments and leaderboards, surveys and / or information published by national, state or local governmental agencies, private companies or program users.

11. The computer-implemented method of Claim 1, wherein the graphic display comprises one or more of an interactive map, heat maps, pins, icons, graphs or charts.

12. The computer-implemented method of Claim 1, further comprising the step of providing a secondary database and allowing a user to upload data related to a capture of an aquatic species into storage on the secondary database for later retrieval by the user.

13. The computer-implemented method of Claim 12, further comprising the step of incorporating the user-uploaded data related to the capture of an aquatic species into the primary database for analysis by the Al program in producing the predictive result.

14. The computer-implemented method of Claim 13, wherein the data related to a capture of an aquatic species comprises one or more of the species, size, length, width, weight, whether the capture was released or kept by the user, tag number, health, presence of parasites, location of capture, date of capture or time of capture.

15. The computer-implemented method of Claim 12, further comprising the step of obtaining the time and geolocation data at the time the aquatic species capture by a user and crossreferencing the time and geolocation data with one or more online third-party databases, websites or services to obtain environmental conditions data for the time and location of the user’s aquatic species capture.

16. The computer-implemented method of Claim 15, wherein the environmental conditions data is one or more of weather salinity, air and water temperature, wind and water speed, wind and water direction, cloud coverage, sunlight amount, humidity, rainfall, water salinity, water pH, water hardness, alkalinity, water depth or tides,17. The computer-implemented method of Claim 15, wherein time and geolocation data at the time of the aquatic species capture is obtained from a user device’s location or timekeeping functionality.

18. The computer-implemented method of Claim 15, further comprising the step of incorporating the time and geolocation data at the time the aquatic species capture by a user and the associated environmental conditions data into the primary database for analysis by the Al program in producing the predictive result.

9. A computer-implemented method for identifying an aquatic life species comprising; providing a network; providing a primary database; providing an Al (artificial intelligence) program; compiling at least one form of data related to the physical features of one or more aquatic life species into the primary database; providing a photograph or image of an unknown aquatic life sample to the primary database; directing the Al program to produce an identification of the species of the unknown aquatic life sample in the photograph or image based the Al program’s analysis of the data related to the physical features of one or more aquatic life species; providing the identification of the species of the unknown aquatic life sample to a user via a graphic display.

Images