Patent ID: 12201106

DETAILED DESCRIPTION

The subject matter of the invention now will be described more fully hereinafter with reference to the accompanying drawings, in which some, but not all embodiments of the subject matter of the invention are shown. Like numbers refer to like elements throughout. The subject matter of the invention may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will satisfy applicable legal requirements. Indeed, many modifications and other embodiments of the subject matter of the invention set forth herein will come to mind to one skilled in the art to which the subject matter of the invention pertains having the benefit of the teachings presented in the foregoing descriptions and the associated drawings. Therefore, it is to be understood that the subject matter of the invention is not to be limited to the specific embodiments disclosed and that modifications and other embodiments are intended to be included within the scope of the appended claims.

In some embodiments, the subject matter of the present invention provides a fishing and hunting prediction system and method including waypoints (including virtual buoys) and utilizing personalized user attributes.

In some embodiments, the fishing and hunting prediction system and method may be applicable to, for example, offshore, inshore, freshwater, saltwater fishing, hunting, and/or other like activities.

In some embodiments, the fishing and hunting prediction system and method may be implemented using a client-server architecture in which a server-based fishing and hunting prediction application may be running on an application server and a client-based fishing and hunting prediction application (e.g., a fishing prediction mobile app) may be running on a networked user computer and wherein the client-based fishing and hunting prediction application is the counterpart to the server-based fishing and hunting prediction application.

In some embodiments, the fishing and hunting prediction system and method may provide a fishing and hunting monitoring and forecasting system, which may be a personalized fishing and hunting analytics tool for saltwater (inshore/offshore) fisherman, freshwater fishermen, and/or hunters.

In some embodiments, the fishing and hunting prediction system and method may utilize multiple types of data, such as, but not limited to, satellite data (e.g., sea surface temperature, altimetry, currents, bathymetry, chlorophyll/plankton), forecast weather data (e.g., wind, wave, weather), tides, astronomy, and/or other data of interest and wherein the data may be processed with respect to the personal preferences (called attributes) of the user.

In some embodiments, the fishing and hunting prediction system and method may provide a waypoint system, which may include “virtual buoys” that can pull data from different sources like forecast data providers (forecasted) and satellite data providers (near real-time data readings) to provide data that users may use to plan out a fishing and hunting trip and wherein the data may be use to (1) provide daily forecasts and (2) look forward and forecast upcoming good fishing and hunting days based on the user's configured attributes.

In some embodiments, the fishing and hunting prediction system and method may provide a waypoint system that may include, but is not limited to, any number and/or combinations of virtual buoys (virtually marking specific locations) and/or NOAA buoys (i.e., actual buoys).

In some embodiments, the fishing and hunting prediction system and method may provide the ability to build a monitoring and forecasting model based on (1) an individual user profile and (2) a waypoint system.

In some embodiments, the fishing and hunting prediction system and method may provide a web-based fishing and hunting prediction application and/or a fishing and hunting prediction mobile app by which users may create, upload, and/or manage waypoints (i.e., any geographic locations of interest) that may be used for forecasting fishing and hunting days and routes and wherein the waypoints may include, but are not limited to, actual buoys (e.g., NOAA buoys), virtual buoys, fish sites, wreck sites, rocks sites, ledge sites, sandbar sites, hunting sites, bodies of water, fields, trails, and/or any other specific locations, and the like.

In some embodiments, the fishing and hunting prediction system and method may provide a fishing and hunting prediction application that utilizes an analytics engine that includes modern machine learning, analytics, and data modeling capabilities to capture various data inputs from waypoints (e.g., actual buoys, virtual buoys), forecasts, and/or actual user input that allows the models to continuously improve overtime.

Additionally, a method of using the fishing and hunting prediction system including a waypoint system and utilizing personalized user attributes is provided.

For the ease of describing the subject matter of the invention it is discussed below in the manner of a fishing prediction system and method. However, the subject matter of the invention may further apply to other activities, such as hunting. In that regard, the system and method as described herein, would operate in essentially the same manner as a hunting prediction system and method as it would a fishing prediction system and method.

Referring now toFIG.1is a block diagram of an example of a fishing prediction system100including virtual buoys and utilizing personalized user attributes, in accordance with an embodiment of the invention. Fishing prediction system100may be applicable to, for example, offshore, inshore, freshwater, and/or saltwater fishing. In this example, fishing prediction system100may be provided in a networked computing configuration that includes a fishing prediction application110and a data store140running on an application server150.

At application server150, fishing prediction application110may further include a user interface (UI) module112, an authentication module114, a security module116, a profile module118, a weather module120, a waypoint module122, a satellite module124, an astronomy module126, a tides module128, and a fishing (and/or hunting) analytics module130(e.g., analytics monitoring and forecasting module). Further, fishing analytics module130may be informed by feedback132(e.g., user feedback) for machine learning purposes to improve accuracy.

Further, user account data142including personalized user attributes144may be stored at data store140. Additionally, application server150may be accessible via a network155. Network155may be, for example, a local area network (LAN) and/or a wide area network (WAN) for connecting to the Internet or to an Intranet. Application server150may connect to network155by any wired and/or wireless means. For example, application server150may include a communications interface148.

A plurality of users105may be associated with the fishing prediction system100. In one example, users105of fishing prediction system100may be fishermen interested in being informed in real time when good fishing conditions may exist. Further, users105may be any saltwater (inshore/offshore) and/or freshwater fishermen. The users105may access fishing prediction application110at application server150via their respective user computers160and network155. User computers160may be any computing device, such as, but not limited to, a desktop computer, a laptop computer, a handheld computing device, a mobile phone (or smart phone), a tablet device, a smartwatch, and/or the like. Any information about users105may be stored in user account data142at data store140of application server150. User account data142may include, for example, account information, user name, user group name, user/group credentials, user payment information, and the like. Further, a user profile (seeFIG.6) for each user105may be generated using profile module118and stored in user account data142.

Further, user attributes144may be set by each user105and thus may be personalized to the user105's preferences. Each user attribute144may specify a certain condition, parameter, and/or setting. Examples of user attributes144may include, but are not limited to, the following.

Wind measurements (knots/mph)—User sets preferred way of displaying wind speed measurements;

Units of Distance (feet/meters)—User sets preferred way of displaying distances;

Virtual Buoys (latitude/longitude)—Identifies a specific waypoint, area, or location the user desires to monitor/forecast;

Sea Surface Temperature (SST) Range (degrees C./F)—User sets preferred range of SST (e.g., min SST, max SST). This value may be used by fishing analytics module130(i.e., analytics) for monitoring/forecasting;

Max Wave Height (feet/meters)—User sets maximum wave height they are willing to fish in. This value may be used by fishing analytics module130(i.e., analytics) for monitoring/forecasting;

Max Wind Speed (knots/mph)—User sets maximum wind speed they are willing to fish in. This value may be used by fishing analytics module130(i.e., analytics) for monitoring/forecasting;

Current Flow (speed (knots/mph) and direction (N/S/E/W/NE/NW/SE/SW))—User sets preferred current speed range (e.g., min speed, max speed) and direction;

Water chlorophyll level—The concentration of chlorophyll is an indicator for the amount of photosynthetic plankton and/or phytoplankton present in a body of water. Based on the satellite data received, the levels of chlorophyll in the water may be expressed graphically on the map, for example, represented by colors on the map;

Water Depth (feet/meters)—User sets preferred water depth (e.g., min depth, max depth) for their fishing. This value may be used by fishing analytics module130(i.e., analytics) for monitoring/forecasting;

Wind Direction (N/S/E/W/NE/NW/SE/SW)—User sets preferred wind direction for their fishing. This value may be used by fishing analytics module130(i.e., analytics) for monitoring/forecasting;

Worst Wind Direction (N/S/E/W/NE/NW/SE/SW)—User sets least preferred wind direction for their fishing. This value may be used by fishing analytics module130(i.e., analytics) for monitoring/forecasting;

Moon Phases (new, quarter, half, three quarters, full)—Tracks the various moon phases. Moon phases play an important role in fishing and hunting. This data may be used as another attribute in the analytics engine (e.g., fishing analytics module130);

Lure used (types of lures)—User identifies what lures that were used on a given fishing day. This data may be used as feedback132to fishing analytics module130(i.e., analytics);

Bait used (e.g., live bait, ballyhoo)—User sets the type of baits that they used on a given fishing day. Such things are live baits to things such as squid, ballyhoo, etc.;

Boat used (e.g., type, length, size)—User sets the type of boat they use to fish; and

Actual Fishing Data—This may be a list of data the user can add to actual conditions (wind, wave, surface temps, etc.) and/or lures used, baits used, and so on. This information allows for actual data to be added and plugged into models for analytics.

In one example, users105may interact with the fishing prediction application110using UI module112at application server150. In this example, UI module112may be a web-based UI that is accessible via network155. For example, fishing prediction application110at application server150may be a software application that may be implemented as a web application and run in a web browser, such as Google Chrome or Microsoft Edge.

A fishing desktop application162or fishing mobile app162may be installed and running on each of the user computers160. Fishing desktop application162and fishing mobile app162is hereafter called fishing mobile app162. In this example, fishing mobile app162may be implemented, for example, as a .NET application, a desktop application, a mobile app, an application program interface (API), and the like known now or in the future.

When configured as a mobile app, fishing mobile app162may be designed to operate on any device platform, including for example, Windows, Android, Apple, and the like known now or in the future. Accordingly, users105may interact with the fishing prediction application110using fishing mobile app162of their user computer160(e.g., smart phone or tablet device). More details of an example of fishing mobile app162are shown and described hereinbelow with reference toFIG.4throughFIG.23.

Referring still toFIG.1, fishing prediction system100may also include one or more data sources170. Data sources170may include any sources of information that may be useful to fishing prediction application110. Data sources170may include, for example, National Weather Service (NWS)172, National Oceanic and Atmospheric Administration (NOAA)174, the National Aeronautics and Space

Administration (NASA)176, and/or any other data sources178known now or in the future. Other data sources178may include, for example, any third-party entities that may process and provide information from NWS172, NOAA174, and/or NASA176. Data sources170(e.g., NWS172, NOAA174, and/or NASA176) may be accessible to fishing prediction application110via network155.

NWS172is an agency of the United States federal government that is tasked with providing weather forecasts, warnings of hazardous weather, and other weather-related products to organizations and the public for the purposes of protection, safety, and general information.

NOAA174is an American scientific and regulatory agency within the United States Department of Commerce that forecasts weather, monitors oceanic and atmospheric conditions, charts the seas, conducts deep sea exploration, and manages fishing and protection of marine mammals and endangered species in the U.S. exclusive economic zone.

NASA176is an independent agency of the U.S. federal government responsible for the civilian space program, as well as aeronautics and space research. NASA176may be the source of satellite data, such as, but not limited to, satellite imagery and its related data.

Authentication module114of fishing prediction application110may be used to manage the authentication process of any entities of fishing prediction system100, such as users105and any of the data sources170. For example, when the user105signs into the fishing prediction application110, a standard authentication process may be performed that allows the user105to access fishing prediction application110. User-sign in may occur a number of ways. In one example, the user105may use a web browser to access UI module112of fishing prediction application110and enter credentials (e.g., username and password).

In another example, the user105may use fishing mobile app162of their user computer160to enter his/her credentials. In yet another example, the user sign-in process may occur automatically when the user105starts fishing mobile app162. As users105are authorized to access fishing prediction system100, user information may be stored in user account data142in data store140.

Security module116of fishing prediction application110may be used to perform any system security functions with respect to keeping secure the contents of data store140and/or any other information with respect to fishing prediction system100. Security module116may use standard security techniques, such as encryption, secure hashtags (or hash tags), and the like. Data store140may be, for example, data repositories (like databases) and/or flat files that can store data. Further, fishing prediction system100is not limited to one data store140only. Fishing prediction system100may include multiple data stores140. Further, data store140may be provided on a data server that is separate from application server150.

Again, profile module118may be a software component of fishing prediction application110that may be used to manage user profile information (seeFIG.6) that may be stored in user account data142of users105.

Weather module120may be a software component of fishing prediction application110that may be used to manage any weather information received from data sources170. For example, with respect to fishing prediction application110, weather module120may be used to manage any weather information from NWS172, NOAA174, NASA176, and/or any other weather information source.

Waypoint module122may be a software component of fishing prediction application110that may be used to manage any waypoints of fishing prediction application110. Generally, a waypoint may be any geographic location of interest to any user105. A waypoint may be specified via a GPS location (i.e., latitude, longitude location). Referring now toFIG.2, in fishing prediction application110the types of waypoints may include, for example, NOAA buoys210(i.e., actual buoys), virtual buoys212, fish sites214, wreck sites216, rocks sites218, ledge sites220, sandbar sites222, other waypoint sites224, and the like.

Waypoint module122may be used to upload, download, create, and/or delete any types of waypoints. Each waypoint may have certain data associated with it. For example, associated with each waypoint may be GPS information, weather information, wind information, wave information, water temperature and depth information, satellite information, astronomy information, tides information, and the like.

Referring now again toFIG.1, satellite module124may be a software component of fishing prediction application110that may be used to manage any satellite information received from data sources170. For example, with respect to fishing prediction application110, satellite module124may be used to manage any satellite information from NWS172, NOAA174, NASA176, and/or any other satellite information source.

Astronomy module126may be a software component of fishing prediction application110that may be used to manage any astronomical information, e.g., lunar information, received from data sources170. For example, with respect to fishing prediction application110, astronomy module126may be used to manage any lunar information (e.g., moon phase information) from NWS172, NOAA174, NASA176, and/or any other astronomical information source.

Tides module128may be a software component of fishing prediction application110that may be used to manage any tides information received from data sources170. For example, with respect to fishing prediction application110, tides module128may be used to manage any tides information from NWS172, NOAA174, NASA176, and/or any other tide information source.

Fishing analytics module130may be a software component of fishing prediction application110that may be used as the analytics engine for monitoring and forecasting the fishing conditions. Again, fishing analytics module130may be informed by feedback132for machine learning purposes to improve accuracy. Feedback132may be, for example, any feedback information provided by users105via fishing mobile app162. Feedback132may include, for example, the degree of fishing success and/or fishing failure with respect to a given day's location and conditions. Feedback132may include things such as previous trip data consisting of tracking waypoints of where the user fished, previous weather, satellite, astronomy, tide data, and the like. Along with the degree of fishing success and what the fisherman used for bait.

Further, because any predictive system may rely on historical data, waypoint module122and/or fishing analytics module130may be used to process historical data. Accordingly, fishing prediction application110may provide and store all previous trip data associated with each user105in their user data142at data store140. This historical data is valuable information that current systems do not have easy access to, other than the GPS unit on boats. In fishing prediction system100, fishing prediction application110may be used to overlay this historical data on the maps. In this way, users105can see in real time where they have been tied to the data like SST, and so on.

Further, waypoint module122and/or fishing analytics module130may provide a “trip/track” feature wherein it records where the user105has fished and upload trip data.

Communications interface148at application server150may be any wired and/or wireless communication interface for connecting to a network (e.g., network155) and by which information may be exchanged with other devices connected to the network. Examples of wired communication interfaces may include, but are not limited to, USB ports, RS232 connectors, RJ45 connectors, Ethernet, and any combinations thereof. Examples of wireless communication interfaces may include, but are not limited to, an Intranet connection, Internet, ISM, Bluetooth® technology, Bluetooth® Low Energy (BLE) technology, Wi-Fi, Wi-Max, IEEE 402.11 technology, ZigBee technology, Z-Wave technology, 6LoWPAN technology (i.e., IPv6 over Low Power Wireless Area Network (6LoWPAN)), ANT or ANT+ (Advanced Network Tools) technology, radio frequency (RF), Infrared Data Association (IrDA) compatible protocols, Local Area Networks (LAN), Wide Area Networks (WAN), Shared Wireless Access Protocol (SWAP), any combinations thereof, and other types of wireless networking protocols known now or in the future.

Referring still toFIG.1, fishing prediction system100may operate in a client/server computing architecture, which is well known. In this example, fishing prediction application110at the application server150may be the server component of fishing prediction system100, while fishing mobile app162at each of the user computers160may be the client component of fishing prediction system100. In other words, fishing mobile app162at each of the user computers160is the counterpart to fishing prediction application110at application server150.

Additionally, application server150may be any networked computing configuration as long as it is accessible via network155by other entities of fishing prediction system100, such as users105and any of the data sources170. For example, fishing prediction system100, and more particularly the fishing prediction application110on application server150, may support a cloud computing environment. In a cloud computing environment, application server150may be the cloud server. Further, fishing prediction application110is not limited to running on one application server150only. Fishing prediction system100may include multiple application servers140(or cloud servers) in order to ensure high-availability of computing resources.

Generally, and referring still toFIG.1, fishing prediction application110may be a software application that provides a means of processing waypoint information in real time and then predict good fishing to users105based on their personalized user attributes144. Fishing mobile app162provides the means for users105to interact with fishing prediction application110. More specifically, using fishing mobile app162of fishing prediction application110, users105may receive automatic notifications about fishing conditions. Likewise, using fishing mobile app162, users105may return information to fishing analytics module130of fishing prediction application110for learning purposes.

Referring now toFIG.3is a schematic diagram of an example of a virtual buoy system250that may be created using and within the fishing prediction system100shown inFIG.1andFIG.2. In this example, virtual buoy system250may include any number and/or combinations of NOAA buoys210(i.e., actual buoys) and/or virtual buoys212. However, virtual buoy system250is not limited to NOAA buoys210and virtual buoys212only. In other embodiments, virtual buoy system250may include any number and/or combinations of NOAA buoys210, virtual buoys212, fish sites214, wreck sites216, rocks sites218, ledge sites220, sandbar sites222, other waypoint sites224, and the like. Examples of virtual buoy system250are shown and described hereinbelow with reference toFIG.11throughFIG.18.

Referring now toFIG.4throughFIG.23is various views and/or screenshots of an example of fishing mobile app162of the fishing prediction system100shown inFIG.1andFIG.2. WhileFIG.4throughFIG.23show an example of fishing mobile app162, it is understood the that same information may be provided using, for example, a web-based fishing desktop application162.

For example,FIG.4shows an example of a home page of an example of the fishing prediction system instantiated as mobile app162.FIG.5shows an example of a login page of fishing mobile app162.FIG.6shows an example of a user profile page of fishing mobile app162by which a user may enter information that may be processed using profile module118of fishing prediction application110. Generally, the user profile stores personal data like email, password, home address, as well as the user's personalized attributes. For example, user105may use the user profile page of fishing mobile app162to enter his/her personalized attributes that reflect their personal fishing preferences. In one example, a user105may set his/her wave information to max wave is 3 feet at not less than every 5 seconds.

Further,FIG.7shows an example of a maps page of fishing mobile app162. The maps page may be used to display various maps of any user selected geographic location and indicating, for example, sea surface temperature (SST), chlorophyll, altimetry, bathymetry, and the like. For example, the maps page may allow users105to drive overlays with parameters, such as changing the range of SST data they are targeting (e.g., daily or weekly SST). For example,FIG.8shows an example of daily SST andFIG.9shows an example of an SST 8-day summary. Additionally, using the maps page, fishing prediction application110may have the ability to overlay previous fishing trips.

FIG.10throughFIG.18show examples of a managed waypoints and routes page of fishing mobile app162. Generally, using the managed waypoints and routes page, users105may be used to import/export waypoints, create new waypoints, delete waypoints, edit waypoints, and the like. Information of managed waypoints and routes page may be processed using waypoint module122of fishing prediction application110. In fishing mobile app162, waypoints are associated with a certain user105and are not accessible by other users105unless the certain user105chooses to share. Using managed waypoints and routes page of fishing mobile app162, users105may create the “virtual buoy” system that is applied to the prediction modeling.

In one example,FIG.10shows a list of a certain user105's waypoints. In this example, the waypoints include one NOAA buoy (e.g., NB1) and three virtual buoys (e.g., VB1, VB2, VB3). NB1is an example of NOAA buoys210shown inFIG.2. VB1, VB2, and VB3are examples of virtual buoys212shown inFIG.2. Further to the example,FIG.11shows NB1, VB1, VB2, and VB3overlaid geographically on a map and wherein NB1is an actual buoy and VB1, VB2, VB3are virtual buoys that have been previously created by the user105. Again, the waypoints are managed using waypoint module122.FIG.12shows that, in one example, when hovering over a certain waypoint (e.g., VB2) a snapshot of information about the waypoint may be displayed. However,FIG.13shows that by selecting the waypoint, all information about the waypoint may be displayed. In this example, the waypoint details menu indicates that VB2is a virtual buoy. Here, a user105may enter/edit the name the waypoint.

Further to the example,FIG.14throughFIG.18show an example of using the managed waypoints and routes page of fishing mobile app162in a process of creating a virtual buoy212. For example, a user105opens a beginning maps page. In this example, the map shows NB1, VB1, VB2, and VB3previously described inFIG.10throughFIG.13. A user105may then click one any location on the map to initiate the creation of a new virtual buoy212, in this case VB4. Next, and referring now toFIG.15andFIG.16, the waypoint details menu is displayed showing the geographic location (latitude, longitude) of the virtual buoy212. Here, user105may enter, for example, the waypoint name (e.g., VB4) and may select whether a virtual buoy (seeFIG.16). Further,FIG.17shows that the type of waypoint may be selected. Next, and referring now toFIG.18, by selecting the “save” button on the waypoint details menu a new virtual buoy212(e.g., VB4) appears on the map.

Further to the example, the screenshots of fishing mobile app162shown inFIG.11throughFIG.18show pictorially examples of virtual buoy system250shown inFIG.3. Further,FIG.11throughFIG.18show examples of creating a virtual buoy system250.

Next,FIG.19throughFIG.23shows an example dashboard page of fishing mobile app162. In one example, the user105's home page may be the dashboard for them to quickly see analyzed data, weather data, and forecasted data and monitor waypoints (e.g., NB1, VB1, VB2, VB3, VB4) for an easy access view of data. This dashboard page may show various data inputs from such things as graphs showing the various wave heights and periods (significant, wind, swell) overtime, currents, forecast summaries, to maps for visualizations. The information shown on the dashboard page may be driven from fishing analytics module130(i.e., the analytics/machine learning engine) based on user profile information (e.g., personalized user attributes) and/or the virtual buoy system.

InFIG.19, the graph may show, for example, the aggregate data across one or more waypoints (e.g., NB1, VB1, VB2, VB3, VB4). The dashboard page provides analytics data on forecasted days to fish. In one example, the data may show the aggregate of every 3 hours for 10 days across the user105s waypoints. The graph may be, for example, an average of two or more closely located waypoints.FIG.20shows an example of a summary display by hovering over a certain curve.FIG.21shows an example of a further drill down of the data.FIG.22shows another example of the dashboard information. Further, in one example,FIG.23shows an example of three closely located waypoints (e.g., NB1, VB1, VB4) on a map showing six waypoints, and wherein the data from these closely located waypoints (e.g., NB1, VB1, VB4) may be averaged and displayed on the dashboard page of fishing mobile app162.

With respect to fishing forecasting and monitoring in fishing prediction application110including fishing mobile app162, based on the personalized user attributes and/or any other attributes for each waypoint (e.g., NOAA buoys210, virtual buoys212), fishing prediction application110will monitor data from, for example, current weather data, forecasting to satellite data and forecast upcoming potential upcoming good fishing days. The forecasting system can also monitor surrounding areas for other data features based on the personalized user attributes plus, for example, altimetry/sea surface height (SSH) to identify upwellings and down dwellings, sea surface temperatures (SSTs), currents, weather data to help locate potential good fishing spots near the any of the user105s waypoints.

Further, inFIG.19throughFIG.23, the information of dashboard page of fishing mobile app162may be generated by accessing any and all data sources170with respect to all of the user105's waypoints (e.g., NOAA buoys210, virtual buoys212).

FIG.24illustrates a flow diagram of an example of a method300of using the fishing prediction system including a virtual buoy system and utilizing personalized user attributes, in accordance with an embodiment of the invention. Method300may include, but is not limited to, one or more of the following steps, and may be conducted in a different order than indicated below.

At a step310, a fishing prediction system is provided. For example, fishing prediction system100including fishing prediction application110and fishing mobile app162as described hereinabove with reference toFIG.1throughFIG.23is provided.

At a step315, the fishing prediction system is accessed and the user selects and/or creates waypoints of interest and accordingly a virtual buoy system is created. For example, using fishing mobile app162, a certain user105may access fishing prediction application110of fishing prediction system100. Then, the user105may select and/or create any waypoints of interest, such as, but not limited to, any NOAA buoys210and/or virtual buoys212in any bodies of water of interest. Accordingly, a virtual buoy system250, such as shown inFIG.3, may be created.

At a step320, the waypoints information may be collected in real time from any data sources. For example, fishing prediction application110may be used to collect information from any waypoints of interest (e.g., NOAA buoys210and/or virtual buoys212) in real time from any data sources170, such as NWS172, NOAA174, and/or NASA176.

At a step325, the waypoints information is processed in real time for forecasting and/or monitoring upcoming good fishing days. For example, fishing analytics module130(i.e., the analytics/machine learning engine) of fishing prediction application110may be used to process in real time any waypoints information collected in step320for forecasting and/or monitoring upcoming good fishing days.

At a step330, fishing notifications are sent to the user. For example, when fishing analytics module130identifies a potential good fishing day, a notification may be sent to the user105via fishing mobile app162. Further, using ongoing analytics of data sources170, any changes to the forecast may be pushed to the user105.

At a step335, feedback may be provided to fishing prediction system. For example, at any time, user105may provide feedback132to fishing analytics module130for the purpose of improved learning. Feedback132may be, for example, any feedback information provided by users105via fishing mobile app162and/or a web-based fishing desktop application162. Feedback132may include, for example, the degree of fishing success and/or fishing failure with respect to a given day's location and conditions, the type of bait used, any actual conditions, and so on.

In summary and referring now again toFIG.1throughFIG.24, fishing prediction system100and method300including virtual buoys and utilizing personalized user attributes is provided. In one example, fishing prediction system100and method300may be applicable to, for example, offshore, inshore, freshwater, and/or saltwater fishing.

In some embodiments, fishing prediction system100and method300may be implemented using a client-server architecture in which server-based fishing prediction application110may be running on application server150and client-based fishing mobile app162may be running on a networked user computer160and wherein the client-based fishing mobile app162is the counterpart to the server-based fishing prediction application110.

In some embodiments, fishing prediction system100and method300may utilize multiple data sources170, such as NWS172, NOAA174, and/or NASA176, that provide multiple types of data, such as, but not limited to, satellite data (e.g., sea surface temperature, altimetry, currents, bathymetry, chlorophyll/plankton), forecast weather data (e.g., wind, wave, weather), tides and astronomy data and wherein the data may be processed with respect to the personal preferences (called attributes) of users105.

In some embodiments, fishing prediction system100and method300may provide a “virtual buoy” system that may include, but is not limited to, one or more virtual buoys212that can pull data from different sources like forecast data providers (forecasted) and satellite data providers (near real-time data readings) to provide data that users may use to plan out a fishing trip and wherein the data may be used to (1) provide daily forecasts and (2) look forward and forecast upcoming good fishing days based on the user105's configured attributes. Accordingly, fishing prediction system100and method300may provide the ability to build a forecasting model based on (1) an individual user profile and (2) a virtual buoy system.

In some embodiments, fishing prediction system100and method300may provide fishing mobile app162by which users105may create, upload, and/or manage waypoints (i.e., any geographic locations of interest) that may be used for forecasting fishing days and routes and wherein the waypoints may include, but are not limited to, actual buoys (e.g., NOAA buoys210), virtual buoys212, fish sites214, wreck sites216, rocks sites218, ledge sites220, sandbar sites222, any other waypoint sites224, and the like.

In some embodiments, fishing prediction system100and method300may provide a fishing mobile app162that utilizes fishing analytics module130that includes modern machine learning, analytics, and data modeling capabilities to capture various data inputs from actual buoys, virtual buoys, forecasts, and/or actual user input that allows the models to continuously improve overtime.

Again, the subject matter of the invention has been discussed above primarily in the manner of a fishing prediction system and method. However, the subject matter of the invention may further apply to other activities, such as hunting. The system and method as described above, would operate in essentially the same manner as a hunting prediction system and method. For example, waypoints would still be geographic locations of interest, and used for forecasting hunting days and routes and wherein the waypoints may still include, but are not limited to, actual buoys (e.g., NOAA buoys210), virtual buoys212(for example, for waterfowl hunting), and may further include terrestrial waypoints, e.g., fields, trails, stands, blinds, feeder sites, landmarks, and/or any other waypoint sites. In some embodiments, in the hunting prediction embodiment and method, the system and method may utilize the same or similar ones of the multiple data sources (e.g., NWS172, NOAA174, and/or NASA176), and/or any other data sources that provide multiple types of data relevant to and/or related to hunting, such as, but not limited to, satellite data, forecast weather data, astronomy data, breeding cycles, rut, migration patterns, sunrise/sunset, and the like.

Following long-standing patent law convention, the terms “a,” “an,” and “the” refer to “one or more” when used in this application, including the claims. Thus, for example, reference to “a subject” includes a plurality of subjects, unless the context clearly is to the contrary (e.g., a plurality of subjects), and so forth.

Throughout this specification and the claims, the terms “comprise,” “comprises,” and “comprising” are used in a non-exclusive sense, except where the context requires otherwise. Likewise, the term “include” and its grammatical variants are intended to be non-limiting, such that recitation of items in a list is not to the exclusion of other like items that can be substituted or added to the listed items.

For the purposes of this specification and appended claims, unless otherwise indicated, all numbers expressing amounts, sizes, dimensions, proportions, shapes, formulations, parameters, percentages, quantities, characteristics, and other numerical values used in the specification and claims, are to be understood as being modified in all instances by the term “about” even though the term “about” may not expressly appear with the value, amount or range. Accordingly, unless indicated to the contrary, the numerical parameters set forth in the following specification and attached claims are not and need not be exact, but may be approximate and/or larger or smaller as desired, reflecting tolerances, conversion factors, rounding off, measurement error and the like, and other factors known to those of skill in the art depending on the desired properties sought to be obtained by the subject matter of the present invention. For example, the term “about,” when referring to a value can be meant to encompass variations of, in some embodiments ±100%, in some embodiments ±50%, in some embodiments ±20%, in some embodiments ±10%, in some embodiments ±5%, in some embodiments ±1%, in some embodiments ±0.5%, and in some embodiments ±0.1% from the specified amount, as such variations are appropriate to perform the disclosed methods or employ the disclosed compositions.

Further, the term “about” when used in connection with one or more numbers or numerical ranges, should be understood to refer to all such numbers, including all numbers in a range and modifies that range by extending the boundaries above and below the numerical values set forth. The recitation of numerical ranges by endpoints includes all numbers, e.g., whole integers, including fractions thereof, subsumed within that range (for example, the recitation of 1 to 5 includes 1, 2, 3, 4, and 5, as well as fractions thereof, e.g., 1.5, 2.25, 3.75, 4.1, and the like) and any range within that range.

Although the foregoing subject matter has been described in some detail by way of illustration and example for purposes of clarity of understanding, it will be understood by those skilled in the art that certain changes and modifications can be practiced within the scope of the appended claims.