System

Abstract

A system is provided.SOLUTION: This fishing result prediction system includes a means for designating a spot where a user wants to fish on a map, a means for inputting a time zone desired by the user, a means for executing a prediction model on the basis of past fishing result data, weather information and tide data in order to generate fishing result prediction data corresponding to the designated spot and time zone, and a means for displaying the generated fishing result prediction data to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] When fishing, it is important to choose the right location and time to maximize catches. However, conventional fishing result prediction systems have insufficient linkage with map information, making it difficult for users to intuitively obtain predicted information about the location and time of day to fish. Furthermore, the prediction accuracy is low, making it difficult to effectively anticipate catches. For this reason, there has been a demand for a system that allows users to easily link map information of planned fishing locations with catch prediction information, and that can provide more accurate fishing result predictions. [Means for solving the problem]

[0005] This invention provides a fishing result prediction system that includes a means for a user to specify a fishing spot on a map, a means for the user to input a desired time period, a means for executing a prediction model based on past fishing result data, meteorological information, and tidal data to generate fishing result prediction data for the specified location and time period, and a means for displaying the generated fishing result prediction data to the user. This system includes a means for acquiring latitude and longitude information for the specified location on the map, a server for generating fishing result prediction data, and a terminal for receiving and displaying the data, allowing the user to obtain fishing result information intuitively and with high accuracy.

[0006] "User" refers to an individual who uses the system to specify a location on a map and input a time period to obtain fishing spot and catch forecast information.

[0007] A "map" is a visual display of geographical location information and provides an interface for a user to specify a location where they would like to fish.

[0008] The "location where you want to fish" refers to a specific location where you want to obtain fishing result prediction information by tapping or other operations on the map.

[0009] A "time period" refers to a specific period during which a user wishes to obtain fishing result prediction information, and is defined by a start time and an end time.

[0010] "Catch prediction data" refers to forecast information about fishing results at a specific location and time period, generated by a prediction model based on past catch data, weather information, tidal data, etc.

[0011] "Past fishing data" is information relating to previous fishing results, including information such as the date, time, location, and the type and number of fish caught.

[0012] "Weather information" refers to information about the weather, wind speed, temperature, precipitation, and other climatic factors that affect fishing.

[0013] "Tidal data" refers to information that indicates the state and changes of the ocean surface tides, and is an important factor in predicting fishing results.

[0014] "Predictive model" refers to a mathematical or statistical algorithm or machine learning model that predicts future fishing results based on past data and various information.

[0015] "Means for generating" refers to the functionality including the process of executing the data processing and algorithms necessary for the system to generate fishing forecast data based on the specified location and time period.

[0016] "Display means" refers to the interface or technology that enables the terminal to visually present the fishing result prediction data to the user.

[0017] "Server" refers to a computer system that generates fishing result prediction data and processes the data to provide it to terminals, as well as providing services.

[0018] A "terminal" is a device that a user uses to operate the system and check fishing forecast information, including smartphones and PCs. [Brief explanation of the drawings]

[0019] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6]FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0020] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0021] First, the terms used in the following description will be explained.

[0022] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0023] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0024] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0025] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0026] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0030] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0031] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0032] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0033] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0034] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0037] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0038] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0039] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0040] A specific embodiment of the fishing result prediction system of the present invention will be described below.

[0041] System Overview

[0042] This system allows users to specify a fishing spot based on map information and obtain a catch forecast for a specific time period. Based on the location and time period specified by the user, the server uses past fishing data, weather information, and tide data to predict the catch, and displays the results on the terminal.

[0043] Overall program flow

[0044] User operations

[0045] The user launches the application

[0046] The user launches the fishing result prediction application on their device (smartphone or PC) and selects the fishing result prediction mode.

[0047] The device uses GPS to display a map centered on the user's current location.

[0048] User specifies a location

[0049] The user taps or clicks on the map to specify the location where they want to fish.

[0050] The device obtains the latitude and longitude information of the specified location and stores it in its internal memory.

[0051] The user enters the time zone

[0052] The terminal displays a dialog box that prompts the user to enter the desired time zone.

[0053] When the user enters a specific time period (e.g., 13:00-15:00), the device saves that information.

[0054] Server Processing

[0055] The device sends a request to the server

[0056] The device packages information about the specified location and time period and sends it to the server as an HTTP request.

[0057] The server receives the request and runs the predictive model.

[0058] The server receives the request from the terminal and analyzes the data.

[0059] The server retrieves past fishing data, weather information, and tidal data from a database and runs a predictive model based on this data.

[0060] For example, if the specified location is "off the coast of Aoshima" and the time period is "13:00-15:00," the system will refer to past fishing results for the same time period, weather information, and tidal data, and calculate information on when the best fishing results can be expected.

[0061] The server generates the prediction results and converts them into JSON format.

[0062] The server sends the prediction results to the device.

[0063] The prediction results generated by the server are sent to the terminal as an HTTP response.

[0064] Display on device

[0065] The device receives the prediction results and displays them to the user.

[0066] The terminal analyzes the prediction results received from the server.

[0067] Based on the analysis results, fishing results are visually displayed on a map.

[0068] Icons and colored markers will be displayed around the designated location, and text information such as "13:00-15:00: You can expect to catch some bluefish" will also be displayed.

[0069] Specific examples

[0070] For example, if a user uses the fishing result prediction system to specify a location "off the coast of Aoshima" and enter a time period of "13:00-15:00," the system will operate as follows.

[0071] 1. The user taps on "Offshore Qingdao" on the map, and latitude and longitude information is obtained.

[0072] 2. The user enters the time period as "13:00-15:00".

[0073] 3. The data is sent to the server, which makes a prediction based on past fishing data and weather information.

[0074] 4. The server generates a prediction result, such as "You can expect to catch some bluefish," and sends it to the device.

[0075] 5. The device displays the results on a map, visually providing the user with the information, "13:00-15:00: You can expect to catch some bluefish."

[0076] In this way, the present invention can provide users with intuitive and highly accurate fishing result prediction information, making it a useful system for maximizing fishing results.

[0077] The processing flow will be explained below.

[0078] Step 1:

[0079] The user launches the application. The device displays the application's home screen. The device obtains the user's current location and displays a map centered on the current location.

[0080] Step 2:

[0081] The user taps on the map the spot where they want to fish. The device retrieves the latitude and longitude of the tapped spot.

[0082] Step 3:

[0083] The device displays a time zone input dialog. The user inputs the time zone they want to fish. The device confirms the user's input and saves the specified location information and time zone information in its internal memory.

[0084] Step 4:

[0085] The terminal packages the specified location information and time zone information and sends it to the server as an HTTP request.

[0086] Step 5:

[0087] The server receives the HTTP request and analyzes the latitude, longitude, and time zone information of the specified location.

[0088] Step 6:

[0089] The server retrieves past fishing data, weather information, and tide data from the database, and then runs a predictive model based on this data to predict fishing results for a specified location and time period.

[0090] Step 7:

[0091] The server generates a prediction result. For example, "You can expect to catch bluefish off the coast of Aoshima between 1:00 PM and 3:00 PM." The server converts the prediction result into JSON format.

[0092] Step 8:

[0093] The server sends the prediction result to the terminal as an HTTP response.

[0094] Step 9:

[0095] The device receives the HTTP response, analyzes the prediction results, and prepares the data to be presented visually to the user.

[0096] Step 10:

[0097] The device displays the forecast results on a map, with colored markers and icons around the specified location, and the text "1:00 PM - 3:00 PM: Bluefish catches are expected."

[0098] Example 1

[0099] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0100] Conventional fishing result prediction systems have had issues such as a non-intuitive interface for users to specify fishing locations and time periods, and low accuracy in fishing result predictions. Furthermore, they lacked a sufficient mechanism for efficiently transmitting user-entered information to a server, making accurate predictions on the server side, and quickly displaying the results. Therefore, there is a need for a system that can be operated intuitively by users and provides highly accurate fishing result prediction information.

[0101] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0102] In this invention, the server includes a means for transmitting information on a location and time period specified by a terminal to the server, a means for the server to receive a request from the terminal and analyze the data, and a means for the server to generate a prediction result and transmit it to the terminal, which allows the user to intuitively operate the server and quickly obtain highly accurate fishing result prediction information.

[0103] A "user" is a person who uses the fishing result prediction system to specify a fishing spot and time period.

[0104] A "terminal" is a device operated by a user, and refers to information and communication equipment such as smartphones and PCs.

[0105] A "map" is a visual representation of geographic information that is displayed for a user to specify a fishing spot.

[0106] A "fishing spot" is a point on a map that a user designates as a place to go fishing.

[0107] A "time period" refers to the range of time during which a user plans to fish, and indicates the start and end of a particular time.

[0108] "Catch prediction data" is data that shows prediction results based on past catch data, weather information, tidal data, etc.

[0109] The term "server" refers to an information processing device that receives a request from a terminal, analyzes the request, generates a prediction result, and transmits the result to the terminal.

[0110] "Past fishing data" refers to data collected to date regarding fishing results.

[0111] "Weather information" refers to data related to weather, such as weather, temperature, and wind speed.

[0112] "Tidal data" is data that includes information about the high and low tides of the ocean.

[0113] A "predictive model" refers to an algorithm or calculation method that predicts fishing results based on past data.

[0114] An "HTTP request" is a type of communication protocol used when a terminal sends data to a server.

[0115] The "JSON format" is a format for expressing data in text format, and is suitable for handling structured data.

[0116] A "dialog box" is an input window that is displayed on a terminal for a user to enter information.

[0117] A specific embodiment of the fishing result prediction system of the present invention will be described below. This system allows a user to specify a fishing spot based on map information and obtain a fishing result prediction for a specific time period. Based on the location and time period specified by the user, the server uses past fishing result data, weather information, and tidal data to predict the fishing result, and displays the result on the terminal.

[0118] User operations

[0119] The user launches the application

[0120] The user launches the fishing prediction application on their smartphone or PC and selects the fishing prediction mode. The device uses GPS to display a map centered on the user's current location.

[0121] User specifies a location

[0122] The user taps or clicks on the map to specify a fishing spot. The device obtains the latitude and longitude information of the specified spot and stores it in its internal memory.

[0123] The user enters the time zone

[0124] The terminal displays a dialog box and asks the user to enter the desired time period. If the user enters a specific time period (e.g., 13:00-15:00), the terminal saves that information.

[0125] Server Processing

[0126] The device sends a request to the server

[0127] The device packages the information for the specified location and time period and sends it as an HTTP request to the server. The server receives the request from the device.

[0128] The server retrieves data from the database

[0129] The server retrieves past fishing data, weather information, and tide data from the database.

[0130] The server runs the predictive model

[0131] The server runs a fishing prediction model based on the data it has acquired. For example, if the specified location is "off the coast of Aoshima" and the time period is "1:00 PM to 3:00 PM," it will refer to past fishing data for the same time period, weather information, and tidal data to calculate the best fishing results.

[0132] The server generates prediction results and sends them to the device.

[0133] The server generates prediction results, converts them into JSON format, and sends them to the device as an HTTP response.

[0134] Display on device

[0135] The device receives the prediction results and displays them to the user.

[0136] The device analyzes the prediction results received from the server. Based on the analysis results, the fishing results are visually displayed on a map. Specifically, icons and colored markers are displayed around the specified location, and text information such as "1:00 PM - 3:00 PM: Bluefish fishing is expected." is also displayed.

[0137] Specific examples

[0138] For example, if a user uses the fishing result prediction system to specify a location "off the coast of Aoshima" and enter a time period of "13:00-15:00," the system will operate as follows.

[0139] 1. The user taps on "Offshore Qingdao" on the map, and latitude and longitude information is obtained.

[0140] 2. The user enters the time period "13:00 - 15:00".

[0141] 3. The device sends the information for the specified location and time period to the server as an HTTP request.

[0142] 4. The server receives the request and retrieves past fishing data, weather information, and tide data from the database.

[0143] 5. The server runs the fishing prediction model, generates a prediction such as "You can expect to catch some bluefish," and converts it into JSON format.

[0144] 6. The server sends the generated prediction results to the device.

[0145] 7. The device analyzes the forecast results received, displays icons and markers on the map, and displays text information such as "13:00-15:00: You can expect to catch some bluefish."

[0146] Prompt Sentence Examples

[0147] Please predict the type of fish that can be caught at the location "off the coast of Aoshima" between 1pm and 3pm.

[0148] In this way, the present invention can provide users with intuitive and highly accurate fishing result prediction information, making it a useful system for maximizing fishing results.

[0149] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0150] Step 1:

[0151] The user launches the application

[0152] The user launches a fishing result prediction application on their smartphone or PC and selects fishing result prediction mode. The device uses GPS to display a map centered on the user's current location. The input for this process is the application start operation, and the output is the display of a map centered on the current location.

[0153] Step 2:

[0154] User specifies a location

[0155] The user taps or clicks on the map to specify a fishing spot. The device obtains the latitude and longitude information of the specified spot and stores it in its internal memory. The input to this process is the user's tap operation, and the output is the acquisition and storage of the latitude and longitude information.

[0156] Step 3:

[0157] The user enters the time zone

[0158] The terminal displays a dialog box and asks the user to enter the desired time zone. When the user enters a specific time zone (e.g., 13:00-15:00), the terminal saves that information. The input to this process is the user's time zone entry, and the output is the saved time zone information.

[0159] Step 4:

[0160] The device sends a request to the server

[0161] The terminal packages the information for the specified location and time zone and sends it to the server as an HTTP request. The input for this process is latitude and longitude information and time zone information, and the output is an HTTP request to the server.

[0162] Step 5:

[0163] The server receives and parses the request

[0164] The server receives an HTTP request from a terminal. The server analyzes the request and extracts the specified location and time zone information. The input to this process is the request received by the server, and the output is the extracted location and time zone information.

[0165] Step 6:

[0166] The server retrieves data from the database

[0167] The server retrieves past fishing data, weather information, and tide data from the database. The input for this process is information on the location and time period, and the output is the retrieved past data.

[0168] Step 7:

[0169] The server runs the predictive model

[0170] The server runs a fishing result prediction model based on the data it has acquired. For example, if the specified location is "off the coast of Aoshima" and the time period is "1:00 PM to 3:00 PM," it references past fishing result data for the same time period, weather information, and tidal data to calculate the best fishing result. The input for this process is the acquired data, and the output is the predicted result.

[0171] Step 8:

[0172] The server generates prediction results and sends them to the device.

[0173] The server generates prediction results, converts them to JSON format, and sends them to the device as an HTTP response. The input to this process is the prediction results, and the output is the HTTP response to the device.

[0174] Step 9:

[0175] The device receives and analyzes the prediction results.

[0176] The device analyzes the prediction results received from the server. The input of this process is the HTTP response from the server, and the output is the analyzed prediction results.

[0177] Step 10:

[0178] The device displays the prediction results to the user.

[0179] Based on the analysis results, the device visually displays a catch forecast on a map. Specifically, icons and colored markers are displayed around the specified location, and text information such as "1:00 PM - 3:00 PM: Bluefish catches are expected." The input to this process is the analyzed prediction results, and the output is a visual display to the user.

[0180] (Application example 1)

[0181] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0182] Today's anglers are seeking fishing forecast information to increase their fishing success. However, current systems only provide fishing forecast data and lack navigation to actual fishing locations or a more intuitive way to provide that information. This makes it difficult for users to accurately determine fishing locations, and there are issues with underutilizing the fishing forecast information.

[0183] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0184] In this invention, the server includes means for acquiring the user's current location information and providing navigation to a specified location, means for executing a prediction model based on past fishing result data, weather information, and tidal data to generate fishing result prediction data for the specified location and time period, and means for providing the user with visual and audio guidance of new fishing result prediction data. This allows the user to not only obtain fishing result prediction information, but also receive navigation to the actual fishing location, enabling fishing while intuitively checking the prediction information visually and audibly.

[0185] "User" refers to a general user who uses the fishing result prediction system.

[0186] "Location" refers to the specific location on the map where the user designates they would like to fish.

[0187] "Time period" refers to a particular range of time during which a user wishes to fish.

[0188] "Catch prediction data" refers to forecast information regarding fishing results generated based on past catch data, weather information, tidal data, etc.

[0189] "Predictive Model" refers to the algorithms and statistical models used to generate catch prediction data.

[0190] "Visual" refers to information being presented in a form that can be seen by the user.

[0191] "Audio" refers to information being presented in a form that can be heard by the user.

[0192] "Current location information" refers to location information such as the user's current latitude and longitude.

[0193] "Navigation" refers to a function that provides guidance to a user's designated location.

[0194] "Smart glasses" refers to a type of wearable device that displays information visually and provides audio guidance when worn by the user.

[0195] A specific embodiment of the invention will be described. This system allows users to specify the location where they want to fish on a map and input the time period for fishing, and provides fishing results forecasts based on past fishing results, weather information, and tidal data. The main components of this system include smart glasses, a server, and a user terminal.

[0196] System configuration

[0197] The system includes the following major components:

[0198] User device: A smartphone, tablet, or PC used to perform basic operations.

[0199] Server: A central processing unit that runs predictive models using historical data to generate catch prediction data.

[0200] Smart glasses: Wearable devices such as Google Glass that provide visual and audio information.

[0201] Overall program flow

[0202] User operations

[0203] The user launches the fishing result prediction application on their device, specifies the fishing spot and inputs the time period. The device uses GPS to obtain the current location information and displays a map based on this information. The user specifies the spot on the map and inputs the desired time period. This data is saved in the device's internal memory and sent to the server.

[0204] Server Processing

[0205] The server receives the location and time information sent from the device. It then retrieves past fishing data, weather information, and tidal data from the database and runs a prediction model. The prediction model generates fishing forecast data based on the specified location and time period and converts it into JSON format. The generated data is then sent to the user's device and smart glasses.

[0206] Display on device

[0207] The user device and smart glasses analyze the fishing result prediction data received from the server and provide it to the user visually and audibly. For example, a marker showing the fishing result prediction is displayed on a map, along with text information such as "1:00 PM - 3:00 PM: Bluefish fishing is expected." The smart glasses also provide audio guidance, allowing the user to receive navigation to the fishing spot.

[0208] Hardware and software used

[0209] Hardware: Smart glasses (e.g., Google Glass), user devices (smartphones, tablets, PCs)

[0210] Software: Predictive models (machine learning algorithms, etc.), HTTP request library, GPS module

[0211] Specific examples

[0212] For example, if a user uses the fishing prediction system to specify the location "offshore Aoshima" and the time period "13:00-15:00", the flow will be as follows:

[0213] 1. The user taps on "Offshore Qingdao" on the map, and latitude and longitude information is obtained.

[0214] 2. The user enters the time period as "13:00-15:00".

[0215] 3. The data is sent to the server, which makes a prediction based on past fishing results and weather information.

[0216] 4. The server generates a prediction result, such as "You can expect to catch some bluefish," and sends it to the smart glasses and the user's device.

[0217] 5. The device will display the results on a map and the smart glasses will also provide voice guidance.

[0218] Example prompts for generative AI models

[0219] "If a user selects the area off the coast of Aoshima on the map and enters the time period as 1:00 PM to 3:00 PM, the fishing prediction system will predict that 'you can expect to catch some bluefish' based on past fishing data, weather information, and tidal data. This prediction result will be displayed on the smart glasses' display and provided to the user visually and audibly."

[0220] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0221] Step 1:

[0222] The user launches the application and selects the fishing result prediction mode.

[0223] Input: The action of a user launching an application on a device such as a smartphone.

[0224] Action: The user launches the application and selects catch prediction mode.

[0225] Output: The application is launched and the catch prediction mode interface is displayed.

[0226] Step 2:

[0227] The device uses GPS to display a map centered on the user's current location.

[0228] Input: User's current location (GPS data).

[0229] How it works: The device's GPS module obtains the current latitude and longitude and loads map data.

[0230] Output: A map centered on the user's current location is displayed on the device screen.

[0231] Step 3:

[0232] The user specifies the location on the map where they want to fish.

[0233] Input: What the user does on the map: tap or click.

[0234] Operation: Obtains the latitude and longitude information of the location specified by the user and saves it in internal memory.

[0235] Output: The latitude and longitude of the specified location will be saved to the device.

[0236] Step 4:

[0237] The user enters the desired time period

[0238] Input: Enter the specific time period (e.g. 13:00-15:00) when the user wishes to fish.

[0239] Action: The terminal displays a dialog box in which the user enters the time zone.

[0240] Output: The entered time zone information is saved to the terminal.

[0241] Step 5:

[0242] The device sends a request to the server for information about the specified location and time period.

[0243] Input: The latitude and longitude of the specified location, and the time zone information.

[0244] How it works: The device packages this data and sends it to the server as an HTTP request.

[0245] Output: The server receives an HTTP request containing the specified location and time zone information.

[0246] Step 6:

[0247] The server collects historical data and runs predictive models

[0248] Input: Latitude and longitude information of the specified location, time zone information, past fishing results data, weather information, and tidal data.

[0249] How it works: The server retrieves the necessary data from the database and runs the predictive model.

[0250] Output: Generates fishing forecast data based on the specified location and time period.

[0251] Step 7:

[0252] The server sends the prediction results to the device.

[0253] Input: Generated fishing prediction data.

[0254] Operation: The server converts the prediction results generated by the server into JSON format and sends them to the device as an HTTP response.

[0255] Output: The device receives the prediction results from the server.

[0256] Step 8:

[0257] The device analyzes the received prediction results and displays them to the user.

[0258] Input: Received catch prediction data.

[0259] How it works: The device analyzes the prediction results, visually displays the catch forecast on a map, and starts audio guidance.

[0260] Output: The user can see the fishing forecast displayed on the map and the audio guidance.

[0261] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0262] The following describes an embodiment of a fishing result prediction system of the present invention. This system not only allows a user to specify a fishing spot based on map information and predicts fishing results for a specific time period, but also recognizes the user's emotions using an emotion engine and adjusts the display of fishing result prediction data and recommendations accordingly.

[0263] System Overview

[0264] In addition to the basic functionality of providing catch prediction information based on the user's designated fishing location and time period, this system also incorporates an emotion engine that recognizes the user's emotions. This aims to improve the fishing experience by providing optimal catch prediction data according to the user's emotional state.

[0265] Overall program flow

[0266] User operations

[0267] The user launches the application

[0268] The user starts a fishing result prediction application on the device and selects emotion recognition mode.

[0269] Your device will display a map centered on your current location.

[0270] User specifies a location

[0271] The user taps on the map the spot where they want to fish.

[0272] The device obtains the latitude and longitude information of the location and stores it in its internal memory.

[0273] The user enters the time zone

[0274] The terminal displays a dialog box in which the user inputs the time period during which they would like to fish.

[0275] The terminal confirms the user's input and saves the specified location information and time period information.

[0276] Manipulating the Emotion Engine

[0277] The device recognizes the user's emotions

[0278] The emotion engine analyzes the user's voice input and facial expressions to recognize the user's emotions.

[0279] For example, if a user inputs "I'm looking forward to fishing today," the emotion of joy is recognized.

[0280] Emotion-based data adjustment

[0281] The display format of fishing prediction data and recommended content are adjusted based on the recognized emotion.

[0282] For example, if the user is in a fun mood, a preferred fishing spot and time of day will be recommended.

[0283] Server Processing

[0284] The device sends a request to the server

[0285] The terminal packages the specified location information, time period information, and recognized emotion information and transmits the packaged information to the server.

[0286] The server receives the request and runs the predictive model.

[0287] The server retrieves past fishing data, weather information, and tidal data, and runs a predictive model based on this.

[0288] The server generates prediction results and optimizes them based on emotion information.

[0289] For example, the system generates a result such as "You can expect to catch bluefish off the coast of Aoshima between 1:00 PM and 3:00 PM," but also adds a recommendation comment based on the user's feelings.

[0290] The server sends the prediction results to the device.

[0291] The server sends the generated prediction results and emotion adjustment information to the terminal as a response.

[0292] Display on device

[0293] The device receives the prediction results and displays them to the user.

[0294] The terminal analyzes the prediction results received from the server and provides them visually to the user.

[0295] Colored markers and icons are displayed around designated locations on the map, and emotional messages such as "13:00-15:00: You can expect to catch some bluefish. Have fun!" are also displayed as text information.

[0296] Specific examples

[0297] If a user uses the fishing result prediction system to specify "offshore Aoshima," inputs the time period "13:00-15:00," and then voice-inputs "I want to relax today," the specific actions will be as follows.

[0298] 1. The user taps on "Offshore Qingdao" on the map, and latitude and longitude information is obtained.

[0299] 2. The user enters the time period as "13:00-15:00".

[0300] 3. The user says, "I want to relax today."

[0301] 4. The emotion engine analyzes the user's voice and recognizes the desire to relax.

[0302] 5. The device sends the specified location, time period, and user emotion information to the server.

[0303] 6. The server analyzes the data and performs fishing predictions.

[0304] 7. The server generates the result, "You can expect to catch some bluefish off the coast of Aoshima between 1:00 PM and 3:00 PM. Relax in a quiet spot and enjoy fishing."

[0305] 8. The device displays the results on a map, providing a visual for the user.

[0306] In this way, the user can obtain fishing result prediction information that is adjusted based on emotional information, and can enjoy a more satisfying fishing experience.

[0307] The processing flow will be explained below.

[0308] Step 1:

[0309] The user launches the application. The device displays the home screen of the fishing result prediction application. The device uses GPS to obtain the user's current location and displays a map centered on the current location.

[0310] Step 2:

[0311] The user taps on the map the location where they want to fish. The device obtains the latitude and longitude information of the tapped location and stores it in its internal memory.

[0312] Step 3:

[0313] The device displays a time zone input dialog. The user inputs the time zone they want to fish (e.g., 13:00-15:00). The device confirms the user's input and saves the specified location and time zone information in its internal memory.

[0314] Step 4:

[0315] The device displays an emotion recognition dialog to accept the user's voice input. The user voice-inputs, "I want to relax today." The device collects the voice data.

[0316] Step 5:

[0317] The device activates an emotion engine and analyzes the voice data to recognize the user's emotion. For example, the device may recognize that the user wants to relax through voice analysis.

[0318] Step 6:

[0319] The terminal packages the specified location information, time zone information, and emotion information and transmits the packaged information to the server as an HTTP request.

[0320] Step 7:

[0321] The server receives the HTTP request and analyzes the data. The server retrieves past fishing data, weather information, and tide data from the database.

[0322] Step 8:

[0323] The server runs a prediction model based on the acquired data to predict catches at a specified location and time period. It also adjusts the display format and content of the prediction results based on the user's emotional information.

[0324] Step 9:

[0325] The server generates a prediction result, for example, "Bluefish can be expected to be caught between 1:00 PM and 3:00 PM off the coast of Aoshima. Relax in a quiet place and enjoy fishing," and converts it into JSON format.

[0326] Step 10:

[0327] The prediction results and emotion adjustment information generated by the server are sent to the terminal as an HTTP response.

[0328] Step 11:

[0329] The device receives the HTTP response and analyzes the prediction results. The device displays colored markers and icons around the specified location on the map, along with the text "1:00 PM - 3:00 PM: Good catches of bluefish are expected. Relax and enjoy fishing in a quiet location."

[0330] Example 2

[0331] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0332] Conventional fishing result prediction systems only provide fishing result prediction data based on the location and time period specified by the user, and do not sufficiently consider emotional factors to improve the quality of the fishing experience. As a result, it is not possible to recommend fishing spots or display data that matches the user's emotions, which leads to the problem of not being able to improve user satisfaction.

[0333] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for the user to specify a location on a map where they would like to fish, a means for the user to input their desired time period, a means for executing a prediction model based on past fishing data, weather information, and tidal data to generate fishing result prediction data corresponding to the specified location and time period, a means for displaying the generated fishing result prediction data to the user, and a means for recognizing the user's emotions and adjusting the display content and recommended content based on the emotions. This makes it possible to provide fishing result prediction data that is adapted to the user's emotions.

[0334] "User" refers to an individual who uses the fishing result prediction system to obtain fishing result prediction data for a specific location and time period.

[0335] "Map" refers to a visual interface that displays geographic information on a computer terminal or mobile device screen.

[0336] "Location" refers to a specific geographic location where you wish to fish, expressed as latitude and longitude information.

[0337] "Time period" refers to a specific time range for which a user desires a catch prediction.

[0338] "Catch prediction data" refers to predicted information about fishing results at a particular location and time period.

[0339] "Past fishing data" refers to records of fishing results at specific locations and times in the past.

[0340] "Weather information" refers to weather data relating to a particular location and time period.

[0341] "Tidal Data" refers to data relating to the ebb and flow of tides at a particular location.

[0342] A "predictive model" refers to an algorithm or mathematical model for predicting fishing results based on past fishing results, weather information, and tidal data.

[0343] "Emotion recognition" refers to the process of analyzing a user's voice and facial expressions to identify their mental state.

[0344] "Adjusting display content and recommendations" refers to changing the format of the fishing result prediction data presented, recommended fishing spots, and time periods based on the user's emotional state.

[0345] The following describes an embodiment of a fishing result prediction system of the present invention. This system not only allows a user to specify a fishing spot based on map information and predicts fishing results for a specific time period, but also recognizes the user's emotions using an emotion engine and adjusts the display of fishing result prediction data and recommendations accordingly.

[0346] System Overview

[0347] In addition to the basic functionality of providing catch prediction information based on the user's designated fishing location and time period, this system also incorporates an emotion engine that recognizes the user's emotions. This aims to improve the fishing experience by providing optimal catch prediction data according to the user's emotional state.

[0348] The system is activated when the user launches the application and selects emotion recognition mode. The device displays a map centered on the user's current location, and when the user taps the spot where they wish to fish, the device obtains the latitude and longitude information of that spot and stores it in internal memory. The user also enters the time of day they wish to fish in a dialog box in the application, and the specified location and time information is saved.

[0349] This application is equipped with an emotion engine that recognizes emotions through user voice input and facial expression analysis. For example, if a user voice-inputs, "I'm looking forward to fishing today," the engine recognizes the emotion of joy. Based on the recognized emotion, the engine then adjusts the display format of the catch prediction data and the recommended content. If the user is feeling excited, the emotion engine recommends a suitable fishing spot and time of day.

[0350] The device sends the specified location information, time zone information, and recognized emotion information to the server. The server retrieves past fishing results, weather information, and tide data, and runs a prediction model based on these. The server then generates prediction results and optimizes them based on the emotion information. For example, the server might generate a result such as "Bluefish can be expected to be caught between 1:00 PM and 3:00 PM off the coast of Aoshima," and add a recommendation comment based on the user's emotion.

[0351] The server generates a prediction result and sends the emotion adjustment information to the device as a response, and the device analyzes the prediction result received from the server and provides it visually to the user. Specifically, colored markers and icons are displayed around the specified point on the map, and emotion-based messages such as "1:00 PM - 3:00 PM: Bluefish catches are expected. Have fun!" are also displayed as text information.

[0352] Specific examples

[0353] If a user uses the fishing result prediction system to specify "offshore Aoshima," inputs the time period "13:00-15:00," and then voice-inputs "I want to relax today," the specific actions will be as follows.

[0354] First, the user taps "Offshore Aoshima" on the map, and the system obtains latitude and longitude information. Next, the user enters the time period, "1:00 PM - 3:00 PM," which is saved. After that, when the user voice-inputs, "I want to relax today," the emotion engine analyzes the voice and recognizes the emotion of wanting to relax. The device sends the specified location, time period, and user emotion information to the server, which analyzes it and performs a fishing result prediction.

[0355] The server generates the result, "You can expect to catch some bluefish off the coast of Aoshima between 1:00 PM and 3:00 PM. Relax in a quiet place and enjoy fishing," and sends this to the terminal.

[0356] Finally, the terminal displays the results on a map and provides them visually to the user, allowing the user to obtain fishing result prediction information adjusted based on emotional information and enjoy a more satisfying fishing experience.

[0357] Prompt Sentence Examples

[0358] "Off the coast of Aoshima" "1pm-3pm" "I want to relax"

[0359] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0360] Step 1:

[0361] The user launches the application and selects the emotion recognition mode.

[0362] Input: User actions (app launch and mode selection)

[0363] Output: Emotion recognition mode is enabled and the map is displayed.

[0364] Specific operation: The user taps the app icon on the device's home screen to launch the app, then selects emotion recognition mode. The device activates emotion recognition mode and displays a map centered on the user's current location on the screen.

[0365] Step 2:

[0366] The user specifies the location on the map where they wish to fish.

[0367] Input: User tap action (selecting a point on the map)

[0368] Output: Latitude and longitude information is saved to the device

[0369] Specific operation: The user taps "Offshore Qingdao" on the map. The device obtains the latitude and longitude information of this tapped location and stores it in its internal memory.

[0370] Step 3:

[0371] The user inputs the time period during which he or she will be fishing.

[0372] Input: The time period (start and end times) entered by the user in the dialog box

[0373] Output: The time zone information entered by the user is saved on the device.

[0374] Specific operation: The device displays "Please enter the time period for fishing," and the user enters "13:00-15:00." The device stores this information in its internal memory.

[0375] Step 4:

[0376] The device analyzes voice input and facial expressions to recognize the user's emotions.

[0377] Input: User's voice input or facial expression (emotion data)

[0378] Output: Emotion recognition results (e.g., feeling of wanting to relax)

[0379] Specific operation: The user inputs "I want to relax today" by voice. The emotion engine analyzes this voice data and recognizes the user's emotion as "I want to relax."

[0380] Step 5:

[0381] The display format of fishing prediction data and recommended content are adjusted based on the recognized emotion.

[0382] Input: Emotion recognition results (user emotion data)

[0383] Output: Adjusted recommendations (e.g., quiet location)

[0384] What it does: The emotion engine adjusts the data to recommend quiet fishing spots based on the emotion "I want to relax."

[0385] Step 6:

[0386] The terminal transmits the specified location information, time period information, and emotion information to the server.

[0387] Input: Latitude and longitude information, time zone information, emotion information

[0388] Output: Packaged request data sent to the server

[0389] Specific operation: The terminal packages the information "Off the coast of Qingdao," "13:00-15:00," and "Relax" and sends it to the server.

[0390] Step 7:

[0391] The server receives the request, retrieves past fishing data, weather information, and tidal data, and runs a predictive model based on that information.

[0392] Input: Packaged request data

[0393] Output: Prediction results and adjustment information

[0394] Specific operation: The server receives the request data from the user, and retrieves and analyzes past fishing results, weather information, and tide data. Based on this data, the server generates a prediction result that "bluefish can be expected to be caught off the coast of Aoshima between 1:00 PM and 3:00 PM," and adds a comment saying, "Please relax and enjoy fishing in a quiet place."

[0395] Step 8:

[0396] The server sends the generated prediction results and emotion adjustment information to the terminal as a response.

[0397] Input: Prediction results and emotion regulation information

[0398] Output: Response data to the terminal

[0399] Specific operation: The server sends response data including the prediction results and recommended comments to the terminal.

[0400] Step 9:

[0401] The terminal receives the prediction result and visually presents it to the user.

[0402] Input: Response data from the server

[0403] Output: Visual display of information to the user

[0404] Specific operation: The device analyzes the prediction results received from the server and provides them visually to the user. Colored markers and icons are displayed around the designated location on the map, and messages such as "1:00 PM - 3:00 PM: Bluefish catches are expected. Please relax in a quiet place and enjoy fishing." are also displayed.

[0405] (Application example 2)

[0406] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0407] Conventional fishing result prediction systems and product recommendation systems are unable to respond to users' emotions or real-time needs and are limited to providing uniform information. This results in a poor user experience and an inability to provide appropriate advice and recommendations. In particular, there is a need for systems that can provide optimal information according to the user's emotional state.

[0408] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0409] In this invention, the server includes a means for a user to specify a desired section on a map, a means for the user to input desired information by voice, a means for executing a predictive model based on past data, weather information, and emotion information to generate recommended data according to the specified section and voice input, and a means for displaying the generated recommended data based on the recognized emotion to the user, thereby enabling the provision of highly personalized information that is in line with the user's emotions and needs.

[0410] "User" refers to a person who uses this system.

[0411] "Map" means a visual representation of a particular place or area.

[0412] A "section" refers to a subdivision of a destination or area.

[0413] A "means" refers to a method or device used to accomplish a particular purpose.

[0414] "Voice input" refers to the act of the system recognizing the voice spoken by the user through a microphone and capturing it as data.

[0415] "Recommendation data" refers to suggested information provided based on the user's input information and emotional state.

[0416] "Historical Data" refers to information previously collected.

[0417] "Weather information" refers to data about the weather at that time or in the past.

[0418] "Emotional information" refers to data that represents the user's emotional state.

[0419] A "predictive model" refers to an algorithm or computational method for predicting future outcomes based on input data.

[0420] "Recognition" refers to the act of the system analyzing the user's emotions and voice and understanding their meaning.

[0421] "Display" refers to the act of visually presenting information to a user.

[0422] "Server" refers to a computer that performs central data processing and transmits the results to clients.

[0423] "Terminal" refers to a device through which a user accesses the system.

[0424] System Overview

[0425] This invention is a system that acquires information desired by a user and provides optimal recommended data based on their emotions. The user specifies a desired section on a map, and recommendations are made based on voice input about that section using emotional information and past data.

[0426] Hardware and Software

[0427] Hardware: Smartphone (camera, microphone, GPS)

[0428] software

[0429] OpenCV (for face recognition and facial expression analysis)

[0430] TensorFlow (emotion recognition model)

[0431] Google Maps API (store map display)

[0432] Node.js (server-side processing)

[0433] Flask (API request processing)

[0434] Google Speech-to-Text API (voice recognition)

[0435] User operation procedure

[0436] 1. Designating sections on the map:

[0437] The user starts the map application and taps on the map to specify the section they want to view, which then retrieves the latitude and longitude information for the specified section.

[0438] 2. Voice input:

[0439] The user inputs the desired information by voice. For example, the user may input a request such as, "I want some clothes that will cheer me up today."

[0440] 3. Emotion recognition:

[0441] Based on the user's voice input and facial expression data acquired from the camera, the system uses TensorFlow and OpenCV to recognize the user's emotions. For example, it analyzes the user's emotions such as "joy" or "energetic" from the voice input.

[0442] Server-side processing

[0443] 1. Data Receipt and Analysis:

[0444] The server receives the latitude and longitude, voice data, and emotion information sent by the user, and then performs appropriate database queries to retrieve historical data and related information.

[0445] 2. Run the predictive model:

[0446] Using the acquired data as input, the server runs a predictive model using TensorFlow to generate recommendation data that matches the user's emotions.

[0447] 3. Submit recommended data:

[0448] The generated recommendation data is sent to the user's device, and a visual display is also provided along with recommendation information such as "Try a bright-colored shirt."

[0449] Display on device

[0450] 1. Display recommended data:

[0451] The device analyzes the recommendation data received from the server and provides it to the user as a map and text information. The desired section is highlighted on the map, and a message such as "Here's a cheerful color that's perfect for you today!" is displayed as text information.

[0452] Specific examples

[0453] Example of user action:

[0454] 1. The user uses the store app to select the "shirt section" and tap on the map.

[0455] 2. The user speaks, "I want to have a good day today."

[0456] 3. The emotion engine analyzes the voice and recognizes the emotion of "fun."

[0457] 4. The app sends the specified section, voice, and emotion information to the server.

[0458] 5. The server analyzes the data and generates appropriate product suggestions, such as "Try a brightly colored shirt."

[0459] 6. The app displays the results on a map, providing a visual experience for the user.

[0460] Example prompt sentence:

[0461] If you say, "I want to have a good day today," suggestions based on that emotion will be displayed.

[0462] In this way, users can receive product suggestions tailored based on their emotional information, resulting in a more satisfying purchasing experience.

[0463] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0464] Step 1:

[0465] The user launches the map application and taps on the map to specify the section they want to view.

[0466] Input: User taps

[0467] Output: Latitude and longitude information for sections on the map

[0468] How it works: By operating the map displayed on the smartphone screen and tapping on a specific section, the latitude and longitude information of that area is obtained. The device temporarily stores this information in its internal memory.

[0469] Step 2:

[0470] The user inputs the desired information by voice.

[0471] Input: User voice input

[0472] Output: Audio data

[0473] Specific operation: The user's voice is captured through the microphone and converted into text data using the Google Speech-to-Text API. For example, a speech such as "I want to have a good day today" is obtained as text data.

[0474] Step 3:

[0475] The device performs emotion recognition using the user's voice data and facial expression data acquired from the camera.

[0476] Input: Voice data, facial expression data

[0477] Output: Emotional information

[0478] Specific operation: The user's facial expressions captured by the camera are analyzed using OpenCV, and the audio data is input into a TensorFlow model to predict the emotional state. Emotional information such as "enjoyment" or "relaxation" is generated as a result of the analysis.

[0479] Step 4:

[0480] The device sends latitude and longitude information, emotion information, and voice data to the server.

[0481] Input: Latitude and longitude information, emotion information, voice data

[0482] Output: Request sent to server

[0483] What happens: The device packages the above information into JSON format and sends it to the server as an HTTP request. The request is sent using Flask.

[0484] Step 5:

[0485] The server receives information from users and runs a predictive model based on past data, weather information, and emotional information.

[0486] Input: Latitude and longitude information, emotion information, voice data

[0487] Output: Recommendation data

[0488] How it works: The server receives requests using Node.js, executes database queries to retrieve historical data and weather information, and then inputs this data into a TensorFlow emotion recognition model to generate optimal recommendations.

[0489] Step 6:

[0490] The server transmits the generated recommendation data to the terminal.

[0491] Input: Recommended data

[0492] Output: Sending a response to the device

[0493] What it does: The server packages the recommendation data in JSON format and sends it to the device as an HTTP response. The response is sent using Flask.

[0494] Step 7:

[0495] The device analyzes the recommendation data received from the server and visually presents it to the user.

[0496] Input: Recommended data

[0497] Output: Visual display

[0498] What it does: Your device receives the recommendation data, highlights the desired section on the map, and displays a recommendation message on the screen, such as "Here's a cheerful color that's perfect for you today!"

[0499] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0500] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0501] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0502] [Second embodiment]

[0503] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0504] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0505] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0506] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0507] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0508] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0509] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0510] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0511] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0512] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0513] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0514] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0515] A specific embodiment of the fishing result prediction system of the present invention will be described below.

[0516] System Overview

[0517] This system allows users to specify a fishing spot based on map information and obtain a catch forecast for a specific time period. Based on the location and time period specified by the user, the server uses past fishing data, weather information, and tide data to predict the catch, and displays the results on the terminal.

[0518] Overall program flow

[0519] User operations

[0520] The user launches the application

[0521] The user launches the fishing result prediction application on their device (smartphone or PC) and selects the fishing result prediction mode.

[0522] The device uses GPS to display a map centered on the user's current location.

[0523] User specifies a location

[0524] The user taps or clicks on the map to specify the location where they want to fish.

[0525] The device obtains the latitude and longitude information of the specified location and stores it in its internal memory.

[0526] The user enters the time zone

[0527] The terminal displays a dialog box that prompts the user to enter the desired time zone.

[0528] When the user enters a specific time period (e.g., 13:00-15:00), the device saves that information.

[0529] Server Processing

[0530] The device sends a request to the server

[0531] The device packages information about the specified location and time period and sends it to the server as an HTTP request.

[0532] The server receives the request and runs the predictive model.

[0533] The server receives the request from the terminal and analyzes the data.

[0534] The server retrieves past fishing data, weather information, and tidal data from a database and runs a predictive model based on this data.

[0535] For example, if the specified location is "off the coast of Aoshima" and the time period is "13:00-15:00," the system will refer to past fishing results for the same time period, weather information, and tidal data, and calculate information on when the best fishing results can be expected.

[0536] The server generates the prediction results and converts them into JSON format.

[0537] The server sends the prediction results to the device.

[0538] The prediction results generated by the server are sent to the terminal as an HTTP response.

[0539] Display on device

[0540] The device receives the prediction results and displays them to the user.

[0541] The terminal analyzes the prediction results received from the server.

[0542] Based on the analysis results, fishing results are visually displayed on a map.

[0543] Icons and colored markers will be displayed around the designated location, and text information such as "13:00-15:00: You can expect to catch some bluefish" will also be displayed.

[0544] Specific examples

[0545] For example, if a user uses the fishing result prediction system to specify a location "off the coast of Aoshima" and enter a time period of "13:00-15:00," the system will operate as follows.

[0546] 1. The user taps on "Offshore Qingdao" on the map, and latitude and longitude information is obtained.

[0547] 2. The user enters the time period as "13:00-15:00".

[0548] 3. The data is sent to the server, which makes a prediction based on past fishing data and weather information.

[0549] 4. The server generates a prediction result, such as "You can expect to catch some bluefish," and sends it to the device.

[0550] 5. The device displays the results on a map, visually providing the user with the information, "13:00-15:00: You can expect to catch some bluefish."

[0551] In this way, the present invention can provide users with intuitive and highly accurate fishing result prediction information, making it a useful system for maximizing fishing results.

[0552] The processing flow will be explained below.

[0553] Step 1:

[0554] The user launches the application. The device displays the application's home screen. The device obtains the user's current location and displays a map centered on the current location.

[0555] Step 2:

[0556] The user taps on the map the spot where they want to fish. The device retrieves the latitude and longitude of the tapped spot.

[0557] Step 3:

[0558] The device displays a time zone input dialog. The user inputs the time zone they want to fish. The device confirms the user's input and saves the specified location information and time zone information in its internal memory.

[0559] Step 4:

[0560] The terminal packages the specified location information and time zone information and sends it to the server as an HTTP request.

[0561] Step 5:

[0562] The server receives the HTTP request and analyzes the latitude, longitude, and time zone information of the specified location.

[0563] Step 6:

[0564] The server retrieves past fishing data, weather information, and tide data from the database, and then runs a predictive model based on this data to predict fishing results for a specified location and time period.

[0565] Step 7:

[0566] The server generates a prediction result. For example, "You can expect to catch bluefish off the coast of Aoshima between 1:00 PM and 3:00 PM." The server converts the prediction result into JSON format.

[0567] Step 8:

[0568] The server sends the prediction result to the terminal as an HTTP response.

[0569] Step 9:

[0570] The device receives the HTTP response, analyzes the prediction results, and prepares the data to be presented visually to the user.

[0571] Step 10:

[0572] The device displays the forecast results on a map, with colored markers and icons around the specified location, and the text "1:00 PM - 3:00 PM: Bluefish catches are expected."

[0573] Example 1

[0574] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0575] Conventional fishing result prediction systems have had issues such as a non-intuitive interface for users to specify fishing locations and time periods, and low accuracy in fishing result predictions. Furthermore, they lacked a sufficient mechanism for efficiently transmitting user-entered information to a server, making accurate predictions on the server side, and quickly displaying the results. Therefore, there is a need for a system that can be operated intuitively by users and provides highly accurate fishing result prediction information.

[0576] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0577] In this invention, the server includes a means for transmitting information on a location and time period specified by a terminal to the server, a means for the server to receive a request from the terminal and analyze the data, and a means for the server to generate a prediction result and transmit it to the terminal, which allows the user to intuitively operate the server and quickly obtain highly accurate fishing result prediction information.

[0578] A "user" is a person who uses the fishing result prediction system to specify a fishing spot and time period.

[0579] A "terminal" is a device operated by a user, and refers to information and communication equipment such as smartphones and PCs.

[0580] A "map" is a visual representation of geographic information that is displayed for a user to specify a fishing spot.

[0581] A "fishing spot" is a point on a map that a user designates as a place to go fishing.

[0582] A "time period" refers to the range of time during which a user plans to fish, and indicates the start and end of a particular time.

[0583] "Catch prediction data" is data that shows prediction results based on past catch data, weather information, tidal data, etc.

[0584] The term "server" refers to an information processing device that receives a request from a terminal, analyzes the request, generates a prediction result, and transmits the result to the terminal.

[0585] "Past fishing data" refers to data collected to date regarding fishing results.

[0586] "Weather information" refers to data related to weather, such as weather, temperature, and wind speed.

[0587] "Tidal data" is data that includes information about the high and low tides of the ocean.

[0588] A "predictive model" refers to an algorithm or calculation method that predicts fishing results based on past data.

[0589] An "HTTP request" is a type of communication protocol used when a terminal sends data to a server.

[0590] The "JSON format" is a format for expressing data in text format, and is suitable for handling structured data.

[0591] A "dialog box" is an input window that is displayed on a terminal for a user to enter information.

[0592] A specific embodiment of the fishing result prediction system of the present invention will be described below. This system allows a user to specify a fishing spot based on map information and obtain a fishing result prediction for a specific time period. Based on the location and time period specified by the user, the server uses past fishing result data, weather information, and tidal data to predict the fishing result, and displays the result on the terminal.

[0593] User operations

[0594] The user launches the application

[0595] The user launches the fishing prediction application on their smartphone or PC and selects the fishing prediction mode. The device uses GPS to display a map centered on the user's current location.

[0596] User specifies a location

[0597] The user taps or clicks on the map to specify a fishing spot. The device obtains the latitude and longitude information of the specified spot and stores it in its internal memory.

[0598] The user enters the time zone

[0599] The terminal displays a dialog box and asks the user to enter the desired time period. If the user enters a specific time period (e.g., 13:00-15:00), the terminal saves that information.

[0600] Server Processing

[0601] The device sends a request to the server

[0602] The device packages the information for the specified location and time period and sends it as an HTTP request to the server. The server receives the request from the device.

[0603] The server retrieves data from the database

[0604] The server retrieves past fishing data, weather information, and tide data from the database.

[0605] The server runs the predictive model

[0606] The server runs a fishing prediction model based on the data it has acquired. For example, if the specified location is "off the coast of Aoshima" and the time period is "1:00 PM to 3:00 PM," it will refer to past fishing data for the same time period, weather information, and tidal data to calculate the best fishing results.

[0607] The server generates prediction results and sends them to the device.

[0608] The server generates prediction results, converts them into JSON format, and sends them to the device as an HTTP response.

[0609] Display on device

[0610] The device receives the prediction results and displays them to the user.

[0611] The device analyzes the prediction results received from the server. Based on the analysis results, the fishing results are visually displayed on a map. Specifically, icons and colored markers are displayed around the specified location, and text information such as "1:00 PM - 3:00 PM: Bluefish fishing is expected." is also displayed.

[0612] Specific examples

[0613] For example, if a user uses the fishing result prediction system to specify a location "off the coast of Aoshima" and enter a time period of "13:00-15:00," the system will operate as follows.

[0614] 1. The user taps on "Offshore Qingdao" on the map, and latitude and longitude information is obtained.

[0615] 2. The user enters the time period "13:00 - 15:00".

[0616] 3. The device sends the information for the specified location and time period to the server as an HTTP request.

[0617] 4. The server receives the request and retrieves past fishing data, weather information, and tide data from the database.

[0618] 5. The server runs the fishing prediction model, generates a prediction such as "You can expect to catch some bluefish," and converts it into JSON format.

[0619] 6. The server sends the generated prediction results to the device.

[0620] 7. The device analyzes the forecast results received, displays icons and markers on the map, and displays text information such as "13:00-15:00: You can expect to catch some bluefish."

[0621] Prompt Sentence Examples

[0622] Please predict the type of fish that can be caught at the location "off the coast of Aoshima" between 1pm and 3pm.

[0623] In this way, the present invention can provide users with intuitive and highly accurate fishing result prediction information, making it a useful system for maximizing fishing results.

[0624] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0625] Step 1:

[0626] The user launches the application

[0627] The user launches a fishing result prediction application on their smartphone or PC and selects fishing result prediction mode. The device uses GPS to display a map centered on the user's current location. The input for this process is the application start operation, and the output is the display of a map centered on the current location.

[0628] Step 2:

[0629] User specifies a location

[0630] The user taps or clicks on the map to specify a fishing spot. The device obtains the latitude and longitude information of the specified spot and stores it in its internal memory. The input to this process is the user's tap operation, and the output is the acquisition and storage of the latitude and longitude information.

[0631] Step 3:

[0632] The user enters the time zone

[0633] The terminal displays a dialog box and asks the user to enter the desired time zone. When the user enters a specific time zone (e.g., 13:00-15:00), the terminal saves that information. The input to this process is the user's time zone entry, and the output is the saved time zone information.

[0634] Step 4:

[0635] The device sends a request to the server

[0636] The terminal packages the information for the specified location and time zone and sends it to the server as an HTTP request. The input for this process is latitude and longitude information and time zone information, and the output is an HTTP request to the server.

[0637] Step 5:

[0638] The server receives and parses the request

[0639] The server receives an HTTP request from a terminal. The server analyzes the request and extracts the specified location and time zone information. The input to this process is the request received by the server, and the output is the extracted location and time zone information.

[0640] Step 6:

[0641] The server retrieves data from the database

[0642] The server retrieves past fishing data, weather information, and tide data from the database. The input for this process is information on the location and time period, and the output is the retrieved past data.

[0643] Step 7:

[0644] The server runs the predictive model

[0645] The server runs a fishing result prediction model based on the data it has acquired. For example, if the specified location is "off the coast of Aoshima" and the time period is "1:00 PM to 3:00 PM," it references past fishing result data for the same time period, weather information, and tidal data to calculate the best fishing result. The input for this process is the acquired data, and the output is the predicted result.

[0646] Step 8:

[0647] The server generates prediction results and sends them to the device.

[0648] The server generates prediction results, converts them to JSON format, and sends them to the device as an HTTP response. The input to this process is the prediction results, and the output is the HTTP response to the device.

[0649] Step 9:

[0650] The device receives and analyzes the prediction results.

[0651] The device analyzes the prediction results received from the server. The input of this process is the HTTP response from the server, and the output is the analyzed prediction results.

[0652] Step 10:

[0653] The device displays the prediction results to the user.

[0654] Based on the analysis results, the device visually displays a catch forecast on a map. Specifically, icons and colored markers are displayed around the specified location, and text information such as "1:00 PM - 3:00 PM: Bluefish catches are expected." The input to this process is the analyzed prediction results, and the output is a visual display to the user.

[0655] (Application example 1)

[0656] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0657] Today's anglers are seeking fishing forecast information to increase their fishing success. However, current systems only provide fishing forecast data and lack navigation to actual fishing locations or a more intuitive way to provide that information. This makes it difficult for users to accurately determine fishing locations, and there are issues with underutilizing the fishing forecast information.

[0658] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0659] In this invention, the server includes means for acquiring the user's current location information and providing navigation to a specified location, means for executing a prediction model based on past fishing result data, weather information, and tidal data to generate fishing result prediction data for the specified location and time period, and means for providing the user with visual and audio guidance of new fishing result prediction data. This allows the user to not only obtain fishing result prediction information, but also receive navigation to the actual fishing location, enabling fishing while intuitively checking the prediction information visually and audibly.

[0660] "User" refers to a general user who uses the fishing result prediction system.

[0661] "Location" refers to the specific location on the map where the user designates they would like to fish.

[0662] "Time period" refers to a particular range of time during which a user wishes to fish.

[0663] "Catch prediction data" refers to forecast information regarding fishing results generated based on past catch data, weather information, tidal data, etc.

[0664] "Predictive Model" refers to the algorithms and statistical models used to generate catch prediction data.

[0665] "Visual" refers to information being presented in a form that can be seen by the user.

[0666] "Audio" refers to information being presented in a form that can be heard by the user.

[0667] "Current location information" refers to location information such as the user's current latitude and longitude.

[0668] "Navigation" refers to a function that provides guidance to a user's designated location.

[0669] "Smart glasses" refers to a type of wearable device that displays information visually and provides audio guidance when worn by the user.

[0670] A specific embodiment of the invention will be described. This system allows users to specify the location where they want to fish on a map and input the time period for fishing, and provides fishing results forecasts based on past fishing results, weather information, and tidal data. The main components of this system include smart glasses, a server, and a user terminal.

[0671] System configuration

[0672] The system includes the following major components:

[0673] User device: A smartphone, tablet, or PC used to perform basic operations.

[0674] Server: A central processing unit that runs predictive models using historical data to generate catch prediction data.

[0675] Smart glasses: Wearable devices such as Google Glass that provide visual and audio information.

[0676] Overall program flow

[0677] User operations

[0678] The user launches the fishing result prediction application on their device, specifies the fishing spot and inputs the time period. The device uses GPS to obtain the current location information and displays a map based on this information. The user specifies the spot on the map and inputs the desired time period. This data is saved in the device's internal memory and sent to the server.

[0679] Server Processing

[0680] The server receives the location and time information sent from the device. It then retrieves past fishing data, weather information, and tidal data from the database and runs a prediction model. The prediction model generates fishing forecast data based on the specified location and time period and converts it into JSON format. The generated data is then sent to the user's device and smart glasses.

[0681] Display on device

[0682] The user device and smart glasses analyze the fishing result prediction data received from the server and provide it to the user visually and audibly. For example, a marker showing the fishing result prediction is displayed on a map, along with text information such as "1:00 PM - 3:00 PM: Bluefish fishing is expected." The smart glasses also provide audio guidance, allowing the user to receive navigation to the fishing spot.

[0683] Hardware and software used

[0684] Hardware: Smart glasses (e.g., Google Glass), user devices (smartphones, tablets, PCs)

[0685] Software: Predictive models (machine learning algorithms, etc.), HTTP request library, GPS module

[0686] Specific examples

[0687] For example, if a user uses the fishing prediction system to specify the location "offshore Aoshima" and the time period "13:00-15:00", the flow will be as follows:

[0688] 1. The user taps on "Offshore Qingdao" on the map, and latitude and longitude information is obtained.

[0689] 2. The user enters the time period as "13:00-15:00".

[0690] 3. The data is sent to the server, which makes a prediction based on past fishing results and weather information.

[0691] 4. The server generates a prediction result, such as "You can expect to catch some bluefish," and sends it to the smart glasses and the user's device.

[0692] 5. The device will display the results on a map and the smart glasses will also provide voice guidance.

[0693] Example prompts for generative AI models

[0694] "If a user selects the area off the coast of Aoshima on the map and enters the time period as 1:00 PM to 3:00 PM, the fishing prediction system will predict that 'you can expect to catch some bluefish' based on past fishing data, weather information, and tidal data. This prediction result will be displayed on the smart glasses' display and provided to the user visually and audibly."

[0695] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0696] Step 1:

[0697] The user launches the application and selects the fishing result prediction mode.

[0698] Input: The action of a user launching an application on a device such as a smartphone.

[0699] Action: The user launches the application and selects catch prediction mode.

[0700] Output: The application is launched and the catch prediction mode interface is displayed.

[0701] Step 2:

[0702] The device uses GPS to display a map centered on the user's current location.

[0703] Input: User's current location (GPS data).

[0704] How it works: The device's GPS module obtains the current latitude and longitude and loads map data.

[0705] Output: A map centered on the user's current location is displayed on the device screen.

[0706] Step 3:

[0707] The user specifies the location on the map where they want to fish.

[0708] Input: What the user does on the map: tap or click.

[0709] Operation: Obtains the latitude and longitude information of the location specified by the user and saves it in internal memory.

[0710] Output: The latitude and longitude of the specified location will be saved to the device.

[0711] Step 4:

[0712] The user enters the desired time period

[0713] Input: Enter the specific time period (e.g. 13:00-15:00) when the user wishes to fish.

[0714] Action: The terminal displays a dialog box in which the user enters the time zone.

[0715] Output: The entered time zone information is saved to the terminal.

[0716] Step 5:

[0717] The device sends a request to the server for information about the specified location and time period.

[0718] Input: The latitude and longitude of the specified location, and the time zone information.

[0719] How it works: The device packages this data and sends it to the server as an HTTP request.

[0720] Output: The server receives an HTTP request containing the specified location and time zone information.

[0721] Step 6:

[0722] The server collects historical data and runs predictive models

[0723] Input: Latitude and longitude information of the specified location, time zone information, past fishing results data, weather information, and tidal data.

[0724] How it works: The server retrieves the necessary data from the database and runs the predictive model.

[0725] Output: Generates fishing forecast data based on the specified location and time period.

[0726] Step 7:

[0727] The server sends the prediction results to the device.

[0728] Input: Generated fishing prediction data.

[0729] Operation: The server converts the prediction results generated by the server into JSON format and sends them to the device as an HTTP response.

[0730] Output: The device receives the prediction results from the server.

[0731] Step 8:

[0732] The device analyzes the received prediction results and displays them to the user.

[0733] Input: Received catch prediction data.

[0734] How it works: The device analyzes the prediction results, visually displays the catch forecast on a map, and starts audio guidance.

[0735] Output: The user can see the fishing forecast displayed on the map and the audio guidance.

[0736] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0737] The following describes an embodiment of a fishing result prediction system of the present invention. This system not only allows a user to specify a fishing spot based on map information and predicts fishing results for a specific time period, but also recognizes the user's emotions using an emotion engine and adjusts the display of fishing result prediction data and recommendations accordingly.

[0738] System Overview

[0739] In addition to the basic functionality of providing catch prediction information based on the user's designated fishing location and time period, this system also incorporates an emotion engine that recognizes the user's emotions. This aims to improve the fishing experience by providing optimal catch prediction data according to the user's emotional state.

[0740] Overall program flow

[0741] User operations

[0742] The user launches the application

[0743] The user starts a fishing result prediction application on the device and selects emotion recognition mode.

[0744] Your device will display a map centered on your current location.

[0745] User specifies a location

[0746] The user taps on the map the spot where they want to fish.

[0747] The device obtains the latitude and longitude information of the location and stores it in its internal memory.

[0748] The user enters the time zone

[0749] The terminal displays a dialog box in which the user inputs the time period during which they would like to fish.

[0750] The terminal confirms the user's input and saves the specified location information and time period information.

[0751] Manipulating the Emotion Engine

[0752] The device recognizes the user's emotions

[0753] The emotion engine analyzes the user's voice input and facial expressions to recognize the user's emotions.

[0754] For example, if a user inputs "I'm looking forward to fishing today," the emotion of joy is recognized.

[0755] Emotion-based data adjustment

[0756] The display format of fishing prediction data and recommended content are adjusted based on the recognized emotion.

[0757] For example, if the user is in a fun mood, a preferred fishing spot and time of day will be recommended.

[0758] Server Processing

[0759] The device sends a request to the server

[0760] The terminal packages the specified location information, time period information, and recognized emotion information and transmits the packaged information to the server.

[0761] The server receives the request and runs the predictive model.

[0762] The server retrieves past fishing data, weather information, and tidal data, and runs a predictive model based on this.

[0763] The server generates prediction results and optimizes them based on emotion information.

[0764] For example, the system generates a result such as "You can expect to catch bluefish off the coast of Aoshima between 1:00 PM and 3:00 PM," but also adds a recommendation comment based on the user's feelings.

[0765] The server sends the prediction results to the device.

[0766] The server sends the generated prediction results and emotion adjustment information to the terminal as a response.

[0767] Display on device

[0768] The device receives the prediction results and displays them to the user.

[0769] The terminal analyzes the prediction results received from the server and provides them visually to the user.

[0770] Colored markers and icons are displayed around designated locations on the map, and emotional messages such as "13:00-15:00: You can expect to catch some bluefish. Have fun!" are also displayed as text information.

[0771] Specific examples

[0772] If a user uses the fishing result prediction system to specify "offshore Aoshima," inputs the time period "13:00-15:00," and then voice-inputs "I want to relax today," the specific actions will be as follows.

[0773] 1. The user taps on "Offshore Qingdao" on the map, and latitude and longitude information is obtained.

[0774] 2. The user enters the time period as "13:00-15:00".

[0775] 3. The user says, "I want to relax today."

[0776] 4. The emotion engine analyzes the user's voice and recognizes the desire to relax.

[0777] 5. The device sends the specified location, time period, and user emotion information to the server.

[0778] 6. The server analyzes the data and performs fishing predictions.

[0779] 7. The server generates the result, "You can expect to catch some bluefish off the coast of Aoshima between 1:00 PM and 3:00 PM. Relax in a quiet spot and enjoy fishing."

[0780] 8. The device displays the results on a map, providing a visual for the user.

[0781] In this way, the user can obtain fishing result prediction information that is adjusted based on emotional information, and can enjoy a more satisfying fishing experience.

[0782] The processing flow will be explained below.

[0783] Step 1:

[0784] The user launches the application. The device displays the home screen of the fishing result prediction application. The device uses GPS to obtain the user's current location and displays a map centered on the current location.

[0785] Step 2:

[0786] The user taps on the map the location where they want to fish. The device obtains the latitude and longitude information of the tapped location and stores it in its internal memory.

[0787] Step 3:

[0788] The device displays a time zone input dialog. The user inputs the time zone they want to fish (e.g., 13:00-15:00). The device confirms the user's input and saves the specified location and time zone information in its internal memory.

[0789] Step 4:

[0790] The device displays an emotion recognition dialog to accept the user's voice input. The user voice-inputs, "I want to relax today." The device collects the voice data.

[0791] Step 5:

[0792] The device activates an emotion engine and analyzes the voice data to recognize the user's emotion. For example, the device may recognize that the user wants to relax through voice analysis.

[0793] Step 6:

[0794] The terminal packages the specified location information, time zone information, and emotion information and transmits the packaged information to the server as an HTTP request.

[0795] Step 7:

[0796] The server receives the HTTP request and analyzes the data. The server retrieves past fishing data, weather information, and tide data from the database.

[0797] Step 8:

[0798] The server runs a prediction model based on the acquired data to predict catches at a specified location and time period. It also adjusts the display format and content of the prediction results based on the user's emotional information.

[0799] Step 9:

[0800] The server generates a prediction result, for example, "Bluefish can be expected to be caught between 1:00 PM and 3:00 PM off the coast of Aoshima. Relax in a quiet place and enjoy fishing," and converts it into JSON format.

[0801] Step 10:

[0802] The prediction results and emotion adjustment information generated by the server are sent to the terminal as an HTTP response.

[0803] Step 11:

[0804] The device receives the HTTP response and analyzes the prediction results. The device displays colored markers and icons around the specified location on the map, along with the text "1:00 PM - 3:00 PM: Good catches of bluefish are expected. Relax and enjoy fishing in a quiet location."

[0805] Example 2

[0806] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0807] Conventional fishing result prediction systems only provide fishing result prediction data based on the location and time period specified by the user, and do not sufficiently consider emotional factors to improve the quality of the fishing experience. As a result, it is not possible to recommend fishing spots or display data that matches the user's emotions, which leads to the problem of not being able to improve user satisfaction.

[0808] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for the user to specify a location on a map where they would like to fish, a means for the user to input their desired time period, a means for executing a prediction model based on past fishing data, weather information, and tidal data to generate fishing result prediction data corresponding to the specified location and time period, a means for displaying the generated fishing result prediction data to the user, and a means for recognizing the user's emotions and adjusting the display content and recommended content based on the emotions. This makes it possible to provide fishing result prediction data that is adapted to the user's emotions.

[0809] "User" refers to an individual who uses the fishing result prediction system to obtain fishing result prediction data for a specific location and time period.

[0810] "Map" refers to a visual interface that displays geographic information on a computer terminal or mobile device screen.

[0811] "Location" refers to a specific geographic location where you wish to fish, expressed as latitude and longitude information.

[0812] "Time period" refers to a specific time range for which a user desires a catch prediction.

[0813] "Catch prediction data" refers to predicted information about fishing results at a particular location and time period.

[0814] "Past fishing data" refers to records of fishing results at specific locations and times in the past.

[0815] "Weather information" refers to weather data relating to a particular location and time period.

[0816] "Tidal Data" refers to data relating to the ebb and flow of tides at a particular location.

[0817] A "predictive model" refers to an algorithm or mathematical model for predicting fishing results based on past fishing results, weather information, and tidal data.

[0818] "Emotion recognition" refers to the process of analyzing a user's voice and facial expressions to identify their mental state.

[0819] "Adjusting display content and recommendations" refers to changing the format of the fishing result prediction data presented, recommended fishing spots, and time periods based on the user's emotional state.

[0820] The following describes an embodiment of a fishing result prediction system of the present invention. This system not only allows a user to specify a fishing spot based on map information and predicts fishing results for a specific time period, but also recognizes the user's emotions using an emotion engine and adjusts the display of fishing result prediction data and recommendations accordingly.

[0821] System Overview

[0822] In addition to the basic functionality of providing catch prediction information based on the user's designated fishing location and time period, this system also incorporates an emotion engine that recognizes the user's emotions. This aims to improve the fishing experience by providing optimal catch prediction data according to the user's emotional state.

[0823] The system is activated when the user launches the application and selects emotion recognition mode. The device displays a map centered on the user's current location, and when the user taps the spot where they wish to fish, the device obtains the latitude and longitude information of that spot and stores it in internal memory. The user also enters the time of day they wish to fish in a dialog box in the application, and the specified location and time information is saved.

[0824] This application is equipped with an emotion engine that recognizes emotions through user voice input and facial expression analysis. For example, if a user voice-inputs, "I'm looking forward to fishing today," the engine recognizes the emotion of joy. Based on the recognized emotion, the engine then adjusts the display format of the catch prediction data and the recommended content. If the user is feeling excited, the emotion engine recommends a suitable fishing spot and time of day.

[0825] The device sends the specified location information, time zone information, and recognized emotion information to the server. The server retrieves past fishing results, weather information, and tide data, and runs a prediction model based on these. The server then generates prediction results and optimizes them based on the emotion information. For example, the server might generate a result such as "Bluefish can be expected to be caught between 1:00 PM and 3:00 PM off the coast of Aoshima," and add a recommendation comment based on the user's emotion.

[0826] The server generates a prediction result and sends the emotion adjustment information to the device as a response, and the device analyzes the prediction result received from the server and provides it visually to the user. Specifically, colored markers and icons are displayed around the specified point on the map, and emotion-based messages such as "1:00 PM - 3:00 PM: Bluefish catches are expected. Have fun!" are also displayed as text information.

[0827] Specific examples

[0828] If a user uses the fishing result prediction system to specify "offshore Aoshima," inputs the time period "13:00-15:00," and then voice-inputs "I want to relax today," the specific actions will be as follows.

[0829] First, the user taps "Offshore Aoshima" on the map, and the system obtains latitude and longitude information. Next, the user enters the time period, "1:00 PM - 3:00 PM," which is saved. After that, when the user voice-inputs, "I want to relax today," the emotion engine analyzes the voice and recognizes the emotion of wanting to relax. The device sends the specified location, time period, and user emotion information to the server, which analyzes it and performs a fishing result prediction.

[0830] The server generates the result, "You can expect to catch some bluefish off the coast of Aoshima between 1:00 PM and 3:00 PM. Relax in a quiet place and enjoy fishing," and sends this to the terminal.

[0831] Finally, the terminal displays the results on a map and provides them visually to the user, allowing the user to obtain fishing result prediction information adjusted based on emotional information and enjoy a more satisfying fishing experience.

[0832] Prompt Sentence Examples

[0833] "Off the coast of Aoshima" "1pm-3pm" "I want to relax"

[0834] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0835] Step 1:

[0836] The user launches the application and selects the emotion recognition mode.

[0837] Input: User actions (app launch and mode selection)

[0838] Output: Emotion recognition mode is enabled and the map is displayed.

[0839] Specific operation: The user taps the app icon on the device's home screen to launch the app, then selects emotion recognition mode. The device activates emotion recognition mode and displays a map centered on the user's current location on the screen.

[0840] Step 2:

[0841] The user specifies the location on the map where they wish to fish.

[0842] Input: User tap action (selecting a point on the map)

[0843] Output: Latitude and longitude information is saved to the device

[0844] Specific operation: The user taps "Offshore Qingdao" on the map. The device obtains the latitude and longitude information of this tapped location and stores it in its internal memory.

[0845] Step 3:

[0846] The user inputs the time period during which he or she will be fishing.

[0847] Input: The time period (start and end times) entered by the user in the dialog box

[0848] Output: The time zone information entered by the user is saved on the device.

[0849] Specific operation: The device displays "Please enter the time period for fishing," and the user enters "13:00-15:00." The device stores this information in its internal memory.

[0850] Step 4:

[0851] The device analyzes voice input and facial expressions to recognize the user's emotions.

[0852] Input: User's voice input or facial expression (emotion data)

[0853] Output: Emotion recognition results (e.g., feeling of wanting to relax)

[0854] Specific operation: The user inputs "I want to relax today" by voice. The emotion engine analyzes this voice data and recognizes the user's emotion as "I want to relax."

[0855] Step 5:

[0856] The display format of fishing prediction data and recommended content are adjusted based on the recognized emotion.

[0857] Input: Emotion recognition results (user emotion data)

[0858] Output: Adjusted recommendations (e.g., quiet location)

[0859] What it does: The emotion engine adjusts the data to recommend quiet fishing spots based on the emotion "I want to relax."

[0860] Step 6:

[0861] The terminal transmits the specified location information, time period information, and emotion information to the server.

[0862] Input: Latitude and longitude information, time zone information, emotion information

[0863] Output: Packaged request data sent to the server

[0864] Specific operation: The terminal packages the information "Off the coast of Qingdao," "13:00-15:00," and "Relax" and sends it to the server.

[0865] Step 7:

[0866] The server receives the request, retrieves past fishing data, weather information, and tidal data, and runs a predictive model based on that information.

[0867] Input: Packaged request data

[0868] Output: Prediction results and adjustment information

[0869] Specific operation: The server receives the request data from the user, and retrieves and analyzes past fishing results, weather information, and tide data. Based on this data, the server generates a prediction result that "bluefish can be expected to be caught off the coast of Aoshima between 1:00 PM and 3:00 PM," and adds a comment saying, "Please relax and enjoy fishing in a quiet place."

[0870] Step 8:

[0871] The server sends the generated prediction results and emotion adjustment information to the terminal as a response.

[0872] Input: Prediction results and emotion regulation information

[0873] Output: Response data to the terminal

[0874] Specific operation: The server sends response data including the prediction results and recommended comments to the terminal.

[0875] Step 9:

[0876] The terminal receives the prediction result and visually presents it to the user.

[0877] Input: Response data from the server

[0878] Output: Visual display of information to the user

[0879] Specific operation: The device analyzes the prediction results received from the server and provides them visually to the user. Colored markers and icons are displayed around the designated location on the map, and messages such as "1:00 PM - 3:00 PM: Bluefish catches are expected. Please relax in a quiet place and enjoy fishing." are also displayed.

[0880] (Application example 2)

[0881] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0882] Conventional fishing result prediction systems and product recommendation systems are unable to respond to users' emotions or real-time needs and are limited to providing uniform information. This results in a poor user experience and an inability to provide appropriate advice and recommendations. In particular, there is a need for systems that can provide optimal information according to the user's emotional state.

[0883] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0884] In this invention, the server includes a means for a user to specify a desired section on a map, a means for the user to input desired information by voice, a means for executing a predictive model based on past data, weather information, and emotion information to generate recommended data according to the specified section and voice input, and a means for displaying the generated recommended data based on the recognized emotion to the user, thereby enabling the provision of highly personalized information that is in line with the user's emotions and needs.

[0885] "User" refers to a person who uses this system.

[0886] "Map" means a visual representation of a particular place or area.

[0887] A "section" refers to a subdivision of a destination or area.

[0888] A "means" refers to a method or device used to accomplish a particular purpose.

[0889] "Voice input" refers to the act of the system recognizing the voice spoken by the user through a microphone and capturing it as data.

[0890] "Recommendation data" refers to suggested information provided based on the user's input information and emotional state.

[0891] "Historical Data" refers to information previously collected.

[0892] "Weather information" refers to data about the weather at that time or in the past.

[0893] "Emotional information" refers to data that represents the user's emotional state.

[0894] A "predictive model" refers to an algorithm or computational method for predicting future outcomes based on input data.

[0895] "Recognition" refers to the act of the system analyzing the user's emotions and voice and understanding their meaning.

[0896] "Display" refers to the act of visually presenting information to a user.

[0897] "Server" refers to a computer that performs central data processing and transmits the results to clients.

[0898] "Terminal" refers to a device through which a user accesses the system.

[0899] System Overview

[0900] This invention is a system that acquires information desired by a user and provides optimal recommended data based on their emotions. The user specifies a desired section on a map, and recommendations are made based on voice input about that section using emotional information and past data.

[0901] Hardware and Software

[0902] Hardware: Smartphone (camera, microphone, GPS)

[0903] software

[0904] OpenCV (for face recognition and facial expression analysis)

[0905] TensorFlow (emotion recognition model)

[0906] Google Maps API (store map display)

[0907] Node.js (server-side processing)

[0908] Flask (API request processing)

[0909] Google Speech-to-Text API (voice recognition)

[0910] User operation procedure

[0911] 1. Designating sections on the map:

[0912] The user starts the map application and taps on the map to specify the section they want to view, which then retrieves the latitude and longitude information for the specified section.

[0913] 2. Voice input:

[0914] The user inputs the desired information by voice. For example, the user may input a request such as, "I want some clothes that will cheer me up today."

[0915] 3. Emotion recognition:

[0916] Based on the user's voice input and facial expression data acquired from the camera, the system uses TensorFlow and OpenCV to recognize the user's emotions. For example, it analyzes the user's emotions such as "joy" or "energetic" from the voice input.

[0917] Server-side processing

[0918] 1. Data Receipt and Analysis:

[0919] The server receives the latitude and longitude, voice data, and emotion information sent by the user, and then performs appropriate database queries to retrieve historical data and related information.

[0920] 2. Run the predictive model:

[0921] Using the acquired data as input, the server runs a predictive model using TensorFlow to generate recommendation data that matches the user's emotions.

[0922] 3. Submit recommended data:

[0923] The generated recommendation data is sent to the user's device, and a visual display is also provided along with recommendation information such as "Try a bright-colored shirt."

[0924] Display on device

[0925] 1. Display recommended data:

[0926] The device analyzes the recommendation data received from the server and provides it to the user as a map and text information. The desired section is highlighted on the map, and a message such as "Here's a cheerful color that's perfect for you today!" is displayed as text information.

[0927] Specific examples

[0928] Example of user action:

[0929] 1. The user uses the store app to select the "shirt section" and tap on the map.

[0930] 2. The user speaks, "I want to have a good day today."

[0931] 3. The emotion engine analyzes the voice and recognizes the emotion of "fun."

[0932] 4. The app sends the specified section, voice, and emotion information to the server.

[0933] 5. The server analyzes the data and generates appropriate product suggestions, such as "Try a brightly colored shirt."

[0934] 6. The app displays the results on a map, providing a visual experience for the user.

[0935] Example prompt sentence:

[0936] If you say, "I want to have a good day today," suggestions based on that emotion will be displayed.

[0937] In this way, users can receive product suggestions tailored based on their emotional information, resulting in a more satisfying purchasing experience.

[0938] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0939] Step 1:

[0940] The user launches the map application and taps on the map to specify the section they want to view.

[0941] Input: User taps

[0942] Output: Latitude and longitude information for sections on the map

[0943] How it works: By operating the map displayed on the smartphone screen and tapping on a specific section, the latitude and longitude information of that area is obtained. The device temporarily stores this information in its internal memory.

[0944] Step 2:

[0945] The user inputs the desired information by voice.

[0946] Input: User voice input

[0947] Output: Audio data

[0948] Specific operation: The user's voice is captured through the microphone and converted into text data using the Google Speech-to-Text API. For example, a speech such as "I want to have a good day today" is obtained as text data.

[0949] Step 3:

[0950] The device performs emotion recognition using the user's voice data and facial expression data acquired from the camera.

[0951] Input: Voice data, facial expression data

[0952] Output: Emotional information

[0953] Specific operation: The user's facial expressions captured by the camera are analyzed using OpenCV, and the audio data is input into a TensorFlow model to predict the emotional state. Emotional information such as "enjoyment" or "relaxation" is generated as a result of the analysis.

[0954] Step 4:

[0955] The device sends latitude and longitude information, emotion information, and voice data to the server.

[0956] Input: Latitude and longitude information, emotion information, voice data

[0957] Output: Request sent to server

[0958] What happens: The device packages the above information into JSON format and sends it to the server as an HTTP request. The request is sent using Flask.

[0959] Step 5:

[0960] The server receives information from users and runs a predictive model based on past data, weather information, and emotional information.

[0961] Input: Latitude and longitude information, emotion information, voice data

[0962] Output: Recommendation data

[0963] How it works: The server receives requests using Node.js, executes database queries to retrieve historical data and weather information, and then inputs this data into a TensorFlow emotion recognition model to generate optimal recommendations.

[0964] Step 6:

[0965] The server transmits the generated recommendation data to the terminal.

[0966] Input: Recommended data

[0967] Output: Sending a response to the device

[0968] What it does: The server packages the recommendation data in JSON format and sends it to the device as an HTTP response. The response is sent using Flask.

[0969] Step 7:

[0970] The device analyzes the recommendation data received from the server and visually presents it to the user.

[0971] Input: Recommended data

[0972] Output: Visual display

[0973] What it does: Your device receives the recommendation data, highlights the desired section on the map, and displays a recommendation message on the screen, such as "Here's a cheerful color that's perfect for you today!"

[0974] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0975] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0976] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0977] [Third embodiment]

[0978] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0979] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0980] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0981] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0982] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0983] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0984] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0985] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0986] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0987] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0988] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0989] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0990] A specific embodiment of the fishing result prediction system of the present invention will be described below.

[0991] System Overview

[0992] This system allows users to specify a fishing spot based on map information and obtain a catch forecast for a specific time period. Based on the location and time period specified by the user, the server uses past fishing data, weather information, and tide data to predict the catch, and displays the results on the terminal.

[0993] Overall program flow

[0994] User operations

[0995] The user launches the application

[0996] The user launches the fishing result prediction application on their device (smartphone or PC) and selects the fishing result prediction mode.

[0997] The device uses GPS to display a map centered on the user's current location.

[0998] User specifies a location

[0999] The user taps or clicks on the map to specify the location where they want to fish.

[1000] The device obtains the latitude and longitude information of the specified location and stores it in its internal memory.

[1001] The user enters the time zone

[1002] The terminal displays a dialog box that prompts the user to enter the desired time zone.

[1003] When the user enters a specific time period (e.g., 13:00-15:00), the device saves that information.

[1004] Server Processing

[1005] The device sends a request to the server

[1006] The device packages information about the specified location and time period and sends it to the server as an HTTP request.

[1007] The server receives the request and runs the predictive model.

[1008] The server receives the request from the terminal and analyzes the data.

[1009] The server retrieves past fishing data, weather information, and tidal data from a database and runs a predictive model based on this data.

[1010] For example, if the specified location is "off the coast of Aoshima" and the time period is "13:00-15:00," the system will refer to past fishing results for the same time period, weather information, and tidal data, and calculate information on when the best fishing results can be expected.

[1011] The server generates the prediction results and converts them into JSON format.

[1012] The server sends the prediction results to the device.

[1013] The prediction results generated by the server are sent to the terminal as an HTTP response.

[1014] Display on device

[1015] The device receives the prediction results and displays them to the user.

[1016] The terminal analyzes the prediction results received from the server.

[1017] Based on the analysis results, fishing results are visually displayed on a map.

[1018] Icons and colored markers will be displayed around the designated location, and text information such as "13:00-15:00: You can expect to catch some bluefish" will also be displayed.

[1019] Specific examples

[1020] For example, if a user uses the fishing result prediction system to specify a location "off the coast of Aoshima" and enter a time period of "13:00-15:00," the system will operate as follows.

[1021] 1. The user taps on "Offshore Qingdao" on the map, and latitude and longitude information is obtained.

[1022] 2. The user enters the time period as "13:00-15:00".

[1023] 3. The data is sent to the server, which makes a prediction based on past fishing data and weather information.

[1024] 4. The server generates a prediction result, such as "You can expect to catch some bluefish," and sends it to the device.

[1025] 5. The device displays the results on a map, visually providing the user with the information, "13:00-15:00: You can expect to catch some bluefish."

[1026] In this way, the present invention can provide users with intuitive and highly accurate fishing result prediction information, making it a useful system for maximizing fishing results.

[1027] The processing flow will be explained below.

[1028] Step 1:

[1029] The user launches the application. The device displays the application's home screen. The device obtains the user's current location and displays a map centered on the current location.

[1030] Step 2:

[1031] The user taps on the map the spot where they want to fish. The device retrieves the latitude and longitude of the tapped spot.

[1032] Step 3:

[1033] The device displays a time zone input dialog. The user inputs the time zone they want to fish. The device confirms the user's input and saves the specified location information and time zone information in its internal memory.

[1034] Step 4:

[1035] The terminal packages the specified location information and time zone information and sends it to the server as an HTTP request.

[1036] Step 5:

[1037] The server receives the HTTP request and analyzes the latitude, longitude, and time zone information of the specified location.

[1038] Step 6:

[1039] The server retrieves past fishing data, weather information, and tide data from the database, and then runs a predictive model based on this data to predict fishing results for a specified location and time period.

[1040] Step 7:

[1041] The server generates a prediction result. For example, "You can expect to catch bluefish off the coast of Aoshima between 1:00 PM and 3:00 PM." The server converts the prediction result into JSON format.

[1042] Step 8:

[1043] The server sends the prediction result to the terminal as an HTTP response.

[1044] Step 9:

[1045] The device receives the HTTP response, analyzes the prediction results, and prepares the data to be presented visually to the user.

[1046] Step 10:

[1047] The device displays the forecast results on a map, with colored markers and icons around the specified location, and the text "1:00 PM - 3:00 PM: Bluefish catches are expected."

[1048] Example 1

[1049] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1050] Conventional fishing result prediction systems have had issues such as a non-intuitive interface for users to specify fishing locations and time periods, and low accuracy in fishing result predictions. Furthermore, they lacked a sufficient mechanism for efficiently transmitting user-entered information to a server, making accurate predictions on the server side, and quickly displaying the results. Therefore, there is a need for a system that can be operated intuitively by users and provides highly accurate fishing result prediction information.

[1051] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1052] In this invention, the server includes a means for transmitting information on a location and time period specified by a terminal to the server, a means for the server to receive a request from the terminal and analyze the data, and a means for the server to generate a prediction result and transmit it to the terminal, which allows the user to intuitively operate the server and quickly obtain highly accurate fishing result prediction information.

[1053] A "user" is a person who uses the fishing result prediction system to specify a fishing spot and time period.

[1054] A "terminal" is a device operated by a user, and refers to information and communication equipment such as smartphones and PCs.

[1055] A "map" is a visual representation of geographic information that is displayed for a user to specify a fishing spot.

[1056] A "fishing spot" is a point on a map that a user designates as a place to go fishing.

[1057] A "time period" refers to the range of time during which a user plans to fish, and indicates the start and end of a particular time.

[1058] "Catch prediction data" is data that shows prediction results based on past catch data, weather information, tidal data, etc.

[1059] The term "server" refers to an information processing device that receives a request from a terminal, analyzes the request, generates a prediction result, and transmits the result to the terminal.

[1060] "Past fishing data" refers to data collected to date regarding fishing results.

[1061] "Weather information" refers to data related to weather, such as weather, temperature, and wind speed.

[1062] "Tidal data" is data that includes information about the high and low tides of the ocean.

[1063] A "predictive model" refers to an algorithm or calculation method that predicts fishing results based on past data.

[1064] An "HTTP request" is a type of communication protocol used when a terminal sends data to a server.

[1065] The "JSON format" is a format for expressing data in text format, and is suitable for handling structured data.

[1066] A "dialog box" is an input window that is displayed on a terminal for a user to enter information.

[1067] A specific embodiment of the fishing result prediction system of the present invention will be described below. This system allows a user to specify a fishing spot based on map information and obtain a fishing result prediction for a specific time period. Based on the location and time period specified by the user, the server uses past fishing result data, weather information, and tidal data to predict the fishing result, and displays the result on the terminal.

[1068] User operations

[1069] The user launches the application

[1070] The user launches the fishing prediction application on their smartphone or PC and selects the fishing prediction mode. The device uses GPS to display a map centered on the user's current location.

[1071] User specifies a location

[1072] The user taps or clicks on the map to specify a fishing spot. The device obtains the latitude and longitude information of the specified spot and stores it in its internal memory.

[1073] The user enters the time zone

[1074] The terminal displays a dialog box and asks the user to enter the desired time period. If the user enters a specific time period (e.g., 13:00-15:00), the terminal saves that information.

[1075] Server Processing

[1076] The device sends a request to the server

[1077] The device packages the information for the specified location and time period and sends it as an HTTP request to the server. The server receives the request from the device.

[1078] The server retrieves data from the database

[1079] The server retrieves past fishing data, weather information, and tide data from the database.

[1080] The server runs the predictive model

[1081] The server runs a fishing prediction model based on the data it has acquired. For example, if the specified location is "off the coast of Aoshima" and the time period is "1:00 PM to 3:00 PM," it will refer to past fishing data for the same time period, weather information, and tidal data to calculate the best fishing results.

[1082] The server generates prediction results and sends them to the device.

[1083] The server generates prediction results, converts them into JSON format, and sends them to the device as an HTTP response.

[1084] Display on device

[1085] The device receives the prediction results and displays them to the user.

[1086] The device analyzes the prediction results received from the server. Based on the analysis results, the fishing results are visually displayed on a map. Specifically, icons and colored markers are displayed around the specified location, and text information such as "1:00 PM - 3:00 PM: Bluefish fishing is expected." is also displayed.

[1087] Specific examples

[1088] For example, if a user uses the fishing result prediction system to specify a location "off the coast of Aoshima" and enter a time period of "13:00-15:00," the system will operate as follows.

[1089] 1. The user taps on "Offshore Qingdao" on the map, and latitude and longitude information is obtained.

[1090] 2. The user enters the time period "13:00 - 15:00".

[1091] 3. The device sends the information for the specified location and time period to the server as an HTTP request.

[1092] 4. The server receives the request and retrieves past fishing data, weather information, and tide data from the database.

[1093] 5. The server runs the fishing prediction model, generates a prediction such as "You can expect to catch some bluefish," and converts it into JSON format.

[1094] 6. The server sends the generated prediction results to the device.

[1095] 7. The device analyzes the forecast results received, displays icons and markers on the map, and displays text information such as "13:00-15:00: You can expect to catch some bluefish."

[1096] Prompt Sentence Examples

[1097] Please predict the type of fish that can be caught at the location "off the coast of Aoshima" between 1pm and 3pm.

[1098] In this way, the present invention can provide users with intuitive and highly accurate fishing result prediction information, making it a useful system for maximizing fishing results.

[1099] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1100] Step 1:

[1101] The user launches the application

[1102] The user launches a fishing result prediction application on their smartphone or PC and selects fishing result prediction mode. The device uses GPS to display a map centered on the user's current location. The input for this process is the application start operation, and the output is the display of a map centered on the current location.

[1103] Step 2:

[1104] User specifies a location

[1105] The user taps or clicks on the map to specify a fishing spot. The device obtains the latitude and longitude information of the specified spot and stores it in its internal memory. The input to this process is the user's tap operation, and the output is the acquisition and storage of the latitude and longitude information.

[1106] Step 3:

[1107] The user enters the time zone

[1108] The terminal displays a dialog box and asks the user to enter the desired time zone. When the user enters a specific time zone (e.g., 13:00-15:00), the terminal saves that information. The input to this process is the user's time zone entry, and the output is the saved time zone information.

[1109] Step 4:

[1110] The device sends a request to the server

[1111] The terminal packages the information for the specified location and time zone and sends it to the server as an HTTP request. The input for this process is latitude and longitude information and time zone information, and the output is an HTTP request to the server.

[1112] Step 5:

[1113] The server receives and parses the request

[1114] The server receives an HTTP request from a terminal. The server analyzes the request and extracts the specified location and time zone information. The input to this process is the request received by the server, and the output is the extracted location and time zone information.

[1115] Step 6:

[1116] The server retrieves data from the database

[1117] The server retrieves past fishing data, weather information, and tide data from the database. The input for this process is information on the location and time period, and the output is the retrieved past data.

[1118] Step 7:

[1119] The server runs the predictive model

[1120] The server runs a fishing result prediction model based on the data it has acquired. For example, if the specified location is "off the coast of Aoshima" and the time period is "1:00 PM to 3:00 PM," it references past fishing result data for the same time period, weather information, and tidal data to calculate the best fishing result. The input for this process is the acquired data, and the output is the predicted result.

[1121] Step 8:

[1122] The server generates prediction results and sends them to the device.

[1123] The server generates prediction results, converts them to JSON format, and sends them to the device as an HTTP response. The input to this process is the prediction results, and the output is the HTTP response to the device.

[1124] Step 9:

[1125] The device receives and analyzes the prediction results.

[1126] The device analyzes the prediction results received from the server. The input of this process is the HTTP response from the server, and the output is the analyzed prediction results.

[1127] Step 10:

[1128] The device displays the prediction results to the user.

[1129] Based on the analysis results, the device visually displays a catch forecast on a map. Specifically, icons and colored markers are displayed around the specified location, and text information such as "1:00 PM - 3:00 PM: Bluefish catches are expected." The input to this process is the analyzed prediction results, and the output is a visual display to the user.

[1130] (Application example 1)

[1131] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1132] Today's anglers are seeking fishing forecast information to increase their fishing success. However, current systems only provide fishing forecast data and lack navigation to actual fishing locations or a more intuitive way to provide that information. This makes it difficult for users to accurately determine fishing locations, and there are issues with underutilizing the fishing forecast information.

[1133] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1134] In this invention, the server includes means for acquiring the user's current location information and providing navigation to a specified location, means for executing a prediction model based on past fishing result data, weather information, and tidal data to generate fishing result prediction data for the specified location and time period, and means for providing the user with visual and audio guidance of new fishing result prediction data. This allows the user to not only obtain fishing result prediction information, but also receive navigation to the actual fishing location, enabling fishing while intuitively checking the prediction information visually and audibly.

[1135] "User" refers to a general user who uses the fishing result prediction system.

[1136] "Location" refers to the specific location on the map where the user designates they would like to fish.

[1137] "Time period" refers to a particular range of time during which a user wishes to fish.

[1138] "Catch prediction data" refers to forecast information regarding fishing results generated based on past catch data, weather information, tidal data, etc.

[1139] "Predictive Model" refers to the algorithms and statistical models used to generate catch prediction data.

[1140] "Visual" refers to information being presented in a form that can be seen by the user.

[1141] "Audio" refers to information being presented in a form that can be heard by the user.

[1142] "Current location information" refers to location information such as the user's current latitude and longitude.

[1143] "Navigation" refers to a function that provides guidance to a user's designated location.

[1144] "Smart glasses" refers to a type of wearable device that displays information visually and provides audio guidance when worn by the user.

[1145] A specific embodiment of the invention will be described. This system allows users to specify the location where they want to fish on a map and input the time period for fishing, and provides fishing results forecasts based on past fishing results, weather information, and tidal data. The main components of this system include smart glasses, a server, and a user terminal.

[1146] System configuration

[1147] The system includes the following major components:

[1148] User device: A smartphone, tablet, or PC used to perform basic operations.

[1149] Server: A central processing unit that runs predictive models using historical data to generate catch prediction data.

[1150] Smart glasses: Wearable devices such as Google Glass that provide visual and audio information.

[1151] Overall program flow

[1152] User operations

[1153] The user launches the fishing result prediction application on their device, specifies the fishing spot and inputs the time period. The device uses GPS to obtain the current location information and displays a map based on this information. The user specifies the spot on the map and inputs the desired time period. This data is saved in the device's internal memory and sent to the server.

[1154] Server Processing

[1155] The server receives the location and time information sent from the device. It then retrieves past fishing data, weather information, and tidal data from the database and runs a prediction model. The prediction model generates fishing forecast data based on the specified location and time period and converts it into JSON format. The generated data is then sent to the user's device and smart glasses.

[1156] Display on device

[1157] The user device and smart glasses analyze the fishing result prediction data received from the server and provide it to the user visually and audibly. For example, a marker showing the fishing result prediction is displayed on a map, along with text information such as "1:00 PM - 3:00 PM: Bluefish fishing is expected." The smart glasses also provide audio guidance, allowing the user to receive navigation to the fishing spot.

[1158] Hardware and software used

[1159] Hardware: Smart glasses (e.g., Google Glass), user devices (smartphones, tablets, PCs)

[1160] Software: Predictive models (machine learning algorithms, etc.), HTTP request library, GPS module

[1161] Specific examples

[1162] For example, if a user uses the fishing prediction system to specify the location "offshore Aoshima" and the time period "13:00-15:00", the flow will be as follows:

[1163] 1. The user taps on "Offshore Qingdao" on the map, and latitude and longitude information is obtained.

[1164] 2. The user enters the time period as "13:00-15:00".

[1165] 3. The data is sent to the server, which makes a prediction based on past fishing results and weather information.

[1166] 4. The server generates a prediction result, such as "You can expect to catch some bluefish," and sends it to the smart glasses and the user's device.

[1167] 5. The device will display the results on a map and the smart glasses will also provide voice guidance.

[1168] Example prompts for generative AI models

[1169] "If a user selects the area off the coast of Aoshima on the map and enters the time period as 1:00 PM to 3:00 PM, the fishing prediction system will predict that 'you can expect to catch some bluefish' based on past fishing data, weather information, and tidal data. This prediction result will be displayed on the smart glasses' display and provided to the user visually and audibly."

[1170] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1171] Step 1:

[1172] The user launches the application and selects the fishing result prediction mode.

[1173] Input: The action of a user launching an application on a device such as a smartphone.

[1174] Action: The user launches the application and selects catch prediction mode.

[1175] Output: The application is launched and the catch prediction mode interface is displayed.

[1176] Step 2:

[1177] The device uses GPS to display a map centered on the user's current location.

[1178] Input: User's current location (GPS data).

[1179] How it works: The device's GPS module obtains the current latitude and longitude and loads map data.

[1180] Output: A map centered on the user's current location is displayed on the device screen.

[1181] Step 3:

[1182] The user specifies the location on the map where they want to fish.

[1183] Input: What the user does on the map: tap or click.

[1184] Operation: Obtains the latitude and longitude information of the location specified by the user and saves it in internal memory.

[1185] Output: The latitude and longitude of the specified location will be saved to the device.

[1186] Step 4:

[1187] The user enters the desired time period

[1188] Input: Enter the specific time period (e.g. 13:00-15:00) when the user wishes to fish.

[1189] Action: The terminal displays a dialog box in which the user enters the time zone.

[1190] Output: The entered time zone information is saved to the terminal.

[1191] Step 5:

[1192] The device sends a request to the server for information about the specified location and time period.

[1193] Input: The latitude and longitude of the specified location, and the time zone information.

[1194] How it works: The device packages this data and sends it to the server as an HTTP request.

[1195] Output: The server receives an HTTP request containing the specified location and time zone information.

[1196] Step 6:

[1197] The server collects historical data and runs predictive models

[1198] Input: Latitude and longitude information of the specified location, time zone information, past fishing results data, weather information, and tidal data.

[1199] How it works: The server retrieves the necessary data from the database and runs the predictive model.

[1200] Output: Generates fishing forecast data based on the specified location and time period.

[1201] Step 7:

[1202] The server sends the prediction results to the device.

[1203] Input: Generated fishing prediction data.

[1204] Operation: The server converts the prediction results generated by the server into JSON format and sends them to the device as an HTTP response.

[1205] Output: The device receives the prediction results from the server.

[1206] Step 8:

[1207] The device analyzes the received prediction results and displays them to the user.

[1208] Input: Received catch prediction data.

[1209] How it works: The device analyzes the prediction results, visually displays the catch forecast on a map, and starts audio guidance.

[1210] Output: The user can see the fishing forecast displayed on the map and the audio guidance.

[1211] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1212] The following describes an embodiment of a fishing result prediction system of the present invention. This system not only allows a user to specify a fishing spot based on map information and predicts fishing results for a specific time period, but also recognizes the user's emotions using an emotion engine and adjusts the display of fishing result prediction data and recommendations accordingly.

[1213] System Overview

[1214] In addition to the basic functionality of providing catch prediction information based on the user's designated fishing location and time period, this system also incorporates an emotion engine that recognizes the user's emotions. This aims to improve the fishing experience by providing optimal catch prediction data according to the user's emotional state.

[1215] Overall program flow

[1216] User operations

[1217] The user launches the application

[1218] The user starts a fishing result prediction application on the device and selects emotion recognition mode.

[1219] Your device will display a map centered on your current location.

[1220] User specifies a location

[1221] The user taps on the map the spot where they want to fish.

[1222] The device obtains the latitude and longitude information of the location and stores it in its internal memory.

[1223] The user enters the time zone

[1224] The terminal displays a dialog box in which the user inputs the time period during which they would like to fish.

[1225] The terminal confirms the user's input and saves the specified location information and time period information.

[1226] Manipulating the Emotion Engine

[1227] The device recognizes the user's emotions

[1228] The emotion engine analyzes the user's voice input and facial expressions to recognize the user's emotions.

[1229] For example, if a user inputs "I'm looking forward to fishing today," the emotion of joy is recognized.

[1230] Emotion-based data adjustment

[1231] The display format of fishing prediction data and recommended content are adjusted based on the recognized emotion.

[1232] For example, if the user is in a fun mood, a preferred fishing spot and time of day will be recommended.

[1233] Server Processing

[1234] The device sends a request to the server

[1235] The terminal packages the specified location information, time period information, and recognized emotion information and transmits the packaged information to the server.

[1236] The server receives the request and runs the predictive model.

[1237] The server retrieves past fishing data, weather information, and tidal data, and runs a predictive model based on this.

[1238] The server generates prediction results and optimizes them based on emotion information.

[1239] For example, the system generates a result such as "You can expect to catch bluefish off the coast of Aoshima between 1:00 PM and 3:00 PM," but also adds a recommendation comment based on the user's feelings.

[1240] The server sends the prediction results to the device.

[1241] The server sends the generated prediction results and emotion adjustment information to the terminal as a response.

[1242] Display on device

[1243] The device receives the prediction results and displays them to the user.

[1244] The terminal analyzes the prediction results received from the server and provides them visually to the user.

[1245] Colored markers and icons are displayed around designated locations on the map, and emotional messages such as "13:00-15:00: You can expect to catch some bluefish. Have fun!" are also displayed as text information.

[1246] Specific examples

[1247] If a user uses the fishing result prediction system to specify "offshore Aoshima," inputs the time period "13:00-15:00," and then voice-inputs "I want to relax today," the specific actions will be as follows.

[1248] 1. The user taps on "Offshore Qingdao" on the map, and latitude and longitude information is obtained.

[1249] 2. The user enters the time period as "13:00-15:00".

[1250] 3. The user says, "I want to relax today."

[1251] 4. The emotion engine analyzes the user's voice and recognizes the desire to relax.

[1252] 5. The device sends the specified location, time period, and user emotion information to the server.

[1253] 6. The server analyzes the data and performs fishing predictions.

[1254] 7. The server generates the result, "You can expect to catch some bluefish off the coast of Aoshima between 1:00 PM and 3:00 PM. Relax in a quiet spot and enjoy fishing."

[1255] 8. The device displays the results on a map, providing a visual for the user.

[1256] In this way, the user can obtain fishing result prediction information that is adjusted based on emotional information, and can enjoy a more satisfying fishing experience.

[1257] The processing flow will be explained below.

[1258] Step 1:

[1259] The user launches the application. The device displays the home screen of the fishing result prediction application. The device uses GPS to obtain the user's current location and displays a map centered on the current location.

[1260] Step 2:

[1261] The user taps on the map the location where they want to fish. The device obtains the latitude and longitude information of the tapped location and stores it in its internal memory.

[1262] Step 3:

[1263] The device displays a time zone input dialog. The user inputs the time zone they want to fish (e.g., 13:00-15:00). The device confirms the user's input and saves the specified location and time zone information in its internal memory.

[1264] Step 4:

[1265] The device displays an emotion recognition dialog to accept the user's voice input. The user voice-inputs, "I want to relax today." The device collects the voice data.

[1266] Step 5:

[1267] The device activates an emotion engine and analyzes the voice data to recognize the user's emotion. For example, the device may recognize that the user wants to relax through voice analysis.

[1268] Step 6:

[1269] The terminal packages the specified location information, time zone information, and emotion information and transmits the packaged information to the server as an HTTP request.

[1270] Step 7:

[1271] The server receives the HTTP request and analyzes the data. The server retrieves past fishing data, weather information, and tide data from the database.

[1272] Step 8:

[1273] The server runs a prediction model based on the acquired data to predict catches at a specified location and time period. It also adjusts the display format and content of the prediction results based on the user's emotional information.

[1274] Step 9:

[1275] The server generates a prediction result, for example, "Bluefish can be expected to be caught between 1:00 PM and 3:00 PM off the coast of Aoshima. Relax in a quiet place and enjoy fishing," and converts it into JSON format.

[1276] Step 10:

[1277] The prediction results and emotion adjustment information generated by the server are sent to the terminal as an HTTP response.

[1278] Step 11:

[1279] The device receives the HTTP response and analyzes the prediction results. The device displays colored markers and icons around the specified location on the map, along with the text "1:00 PM - 3:00 PM: Good catches of bluefish are expected. Relax and enjoy fishing in a quiet location."

[1280] Example 2

[1281] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1282] Conventional fishing result prediction systems only provide fishing result prediction data based on the location and time period specified by the user, and do not sufficiently consider emotional factors to improve the quality of the fishing experience. As a result, it is not possible to recommend fishing spots or display data that matches the user's emotions, which leads to the problem of not being able to improve user satisfaction.

[1283] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for the user to specify a location on a map where they would like to fish, a means for the user to input their desired time period, a means for executing a prediction model based on past fishing data, weather information, and tidal data to generate fishing result prediction data corresponding to the specified location and time period, a means for displaying the generated fishing result prediction data to the user, and a means for recognizing the user's emotions and adjusting the display content and recommended content based on the emotions. This makes it possible to provide fishing result prediction data that is adapted to the user's emotions.

[1284] "User" refers to an individual who uses the fishing result prediction system to obtain fishing result prediction data for a specific location and time period.

[1285] "Map" refers to a visual interface that displays geographic information on a computer terminal or mobile device screen.

[1286] "Location" refers to a specific geographic location where you wish to fish, expressed as latitude and longitude information.

[1287] "Time period" refers to a specific time range for which a user desires a catch prediction.

[1288] "Catch prediction data" refers to predicted information about fishing results at a particular location and time period.

[1289] "Past fishing data" refers to records of fishing results at specific locations and times in the past.

[1290] "Weather information" refers to weather data relating to a particular location and time period.

[1291] "Tidal Data" refers to data relating to the ebb and flow of tides at a particular location.

[1292] A "predictive model" refers to an algorithm or mathematical model for predicting fishing results based on past fishing results, weather information, and tidal data.

[1293] "Emotion recognition" refers to the process of analyzing a user's voice and facial expressions to identify their mental state.

[1294] "Adjusting display content and recommendations" refers to changing the format of the fishing result prediction data presented, recommended fishing spots, and time periods based on the user's emotional state.

[1295] The following describes an embodiment of a fishing result prediction system of the present invention. This system not only allows a user to specify a fishing spot based on map information and predicts fishing results for a specific time period, but also recognizes the user's emotions using an emotion engine and adjusts the display of fishing result prediction data and recommendations accordingly.

[1296] System Overview

[1297] In addition to the basic functionality of providing catch prediction information based on the user's designated fishing location and time period, this system also incorporates an emotion engine that recognizes the user's emotions. This aims to improve the fishing experience by providing optimal catch prediction data according to the user's emotional state.

[1298] The system is activated when the user launches the application and selects emotion recognition mode. The device displays a map centered on the user's current location, and when the user taps the spot where they wish to fish, the device obtains the latitude and longitude information of that spot and stores it in internal memory. The user also enters the time of day they wish to fish in a dialog box in the application, and the specified location and time information is saved.

[1299] This application is equipped with an emotion engine that recognizes emotions through user voice input and facial expression analysis. For example, if a user voice-inputs, "I'm looking forward to fishing today," the engine recognizes the emotion of joy. Based on the recognized emotion, the engine then adjusts the display format of the catch prediction data and the recommended content. If the user is feeling excited, the emotion engine recommends a suitable fishing spot and time of day.

[1300] The device sends the specified location information, time zone information, and recognized emotion information to the server. The server retrieves past fishing results, weather information, and tide data, and runs a prediction model based on these. The server then generates prediction results and optimizes them based on the emotion information. For example, the server might generate a result such as "Bluefish can be expected to be caught between 1:00 PM and 3:00 PM off the coast of Aoshima," and add a recommendation comment based on the user's emotion.

[1301] The server generates a prediction result and sends the emotion adjustment information to the device as a response, and the device analyzes the prediction result received from the server and provides it visually to the user. Specifically, colored markers and icons are displayed around the specified point on the map, and emotion-based messages such as "1:00 PM - 3:00 PM: Bluefish catches are expected. Have fun!" are also displayed as text information.

[1302] Specific examples

[1303] If a user uses the fishing result prediction system to specify "offshore Aoshima," inputs the time period "13:00-15:00," and then voice-inputs "I want to relax today," the specific actions will be as follows.

[1304] First, the user taps "Offshore Aoshima" on the map, and the system obtains latitude and longitude information. Next, the user enters the time period, "1:00 PM - 3:00 PM," which is saved. After that, when the user voice-inputs, "I want to relax today," the emotion engine analyzes the voice and recognizes the emotion of wanting to relax. The device sends the specified location, time period, and user emotion information to the server, which analyzes it and performs a fishing result prediction.

[1305] The server generates the result, "You can expect to catch some bluefish off the coast of Aoshima between 1:00 PM and 3:00 PM. Relax in a quiet place and enjoy fishing," and sends this to the terminal.

[1306] Finally, the terminal displays the results on a map and provides them visually to the user, allowing the user to obtain fishing result prediction information adjusted based on emotional information and enjoy a more satisfying fishing experience.

[1307] Prompt Sentence Examples

[1308] "Off the coast of Aoshima" "1pm-3pm" "I want to relax"

[1309] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1310] Step 1:

[1311] The user launches the application and selects the emotion recognition mode.

[1312] Input: User actions (app launch and mode selection)

[1313] Output: Emotion recognition mode is enabled and the map is displayed.

[1314] Specific operation: The user taps the app icon on the device's home screen to launch the app, then selects emotion recognition mode. The device activates emotion recognition mode and displays a map centered on the user's current location on the screen.

[1315] Step 2:

[1316] The user specifies the location on the map where they wish to fish.

[1317] Input: User tap action (selecting a point on the map)

[1318] Output: Latitude and longitude information is saved to the device

[1319] Specific operation: The user taps "Offshore Qingdao" on the map. The device obtains the latitude and longitude information of this tapped location and stores it in its internal memory.

[1320] Step 3:

[1321] The user inputs the time period during which he or she will be fishing.

[1322] Input: The time period (start and end times) entered by the user in the dialog box

[1323] Output: The time zone information entered by the user is saved on the device.

[1324] Specific operation: The device displays "Please enter the time period for fishing," and the user enters "13:00-15:00." The device stores this information in its internal memory.

[1325] Step 4:

[1326] The device analyzes voice input and facial expressions to recognize the user's emotions.

[1327] Input: User's voice input or facial expression (emotion data)

[1328] Output: Emotion recognition results (e.g., feeling of wanting to relax)

[1329] Specific operation: The user inputs "I want to relax today" by voice. The emotion engine analyzes this voice data and recognizes the user's emotion as "I want to relax."

[1330] Step 5:

[1331] The display format of fishing prediction data and recommended content are adjusted based on the recognized emotion.

[1332] Input: Emotion recognition results (user emotion data)

[1333] Output: Adjusted recommendations (e.g., quiet location)

[1334] What it does: The emotion engine adjusts the data to recommend quiet fishing spots based on the emotion "I want to relax."

[1335] Step 6:

[1336] The terminal transmits the specified location information, time period information, and emotion information to the server.

[1337] Input: Latitude and longitude information, time zone information, emotion information

[1338] Output: Packaged request data sent to the server

[1339] Specific operation: The terminal packages the information "Off the coast of Qingdao," "13:00-15:00," and "Relax" and sends it to the server.

[1340] Step 7:

[1341] The server receives the request, retrieves past fishing data, weather information, and tidal data, and runs a predictive model based on that information.

[1342] Input: Packaged request data

[1343] Output: Prediction results and adjustment information

[1344] Specific operation: The server receives the request data from the user, and retrieves and analyzes past fishing results, weather information, and tide data. Based on this data, the server generates a prediction result that "bluefish can be expected to be caught off the coast of Aoshima between 1:00 PM and 3:00 PM," and adds a comment saying, "Please relax and enjoy fishing in a quiet place."

[1345] Step 8:

[1346] The server sends the generated prediction results and emotion adjustment information to the terminal as a response.

[1347] Input: Prediction results and emotion regulation information

[1348] Output: Response data to the terminal

[1349] Specific operation: The server sends response data including the prediction results and recommended comments to the terminal.

[1350] Step 9:

[1351] The terminal receives the prediction result and visually presents it to the user.

[1352] Input: Response data from the server

[1353] Output: Visual display of information to the user

[1354] Specific operation: The device analyzes the prediction results received from the server and provides them visually to the user. Colored markers and icons are displayed around the designated location on the map, and messages such as "1:00 PM - 3:00 PM: Bluefish catches are expected. Please relax in a quiet place and enjoy fishing." are also displayed.

[1355] (Application example 2)

[1356] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1357] Conventional fishing result prediction systems and product recommendation systems are unable to respond to users' emotions or real-time needs and are limited to providing uniform information. This results in a poor user experience and an inability to provide appropriate advice and recommendations. In particular, there is a need for systems that can provide optimal information according to the user's emotional state.

[1358] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1359] In this invention, the server includes a means for a user to specify a desired section on a map, a means for the user to input desired information by voice, a means for executing a predictive model based on past data, weather information, and emotion information to generate recommended data according to the specified section and voice input, and a means for displaying the generated recommended data based on the recognized emotion to the user, thereby enabling the provision of highly personalized information that is in line with the user's emotions and needs.

[1360] "User" refers to a person who uses this system.

[1361] "Map" means a visual representation of a particular place or area.

[1362] A "section" refers to a subdivision of a destination or area.

[1363] A "means" refers to a method or device used to accomplish a particular purpose.

[1364] "Voice input" refers to the act of the system recognizing the voice spoken by the user through a microphone and capturing it as data.

[1365] "Recommendation data" refers to suggested information provided based on the user's input information and emotional state.

[1366] "Historical Data" refers to information previously collected.

[1367] "Weather information" refers to data about the weather at that time or in the past.

[1368] "Emotional information" refers to data that represents the user's emotional state.

[1369] A "predictive model" refers to an algorithm or computational method for predicting future outcomes based on input data.

[1370] "Recognition" refers to the act of the system analyzing the user's emotions and voice and understanding their meaning.

[1371] "Display" refers to the act of visually presenting information to a user.

[1372] "Server" refers to a computer that performs central data processing and transmits the results to clients.

[1373] "Terminal" refers to a device through which a user accesses the system.

[1374] System Overview

[1375] This invention is a system that acquires information desired by a user and provides optimal recommended data based on their emotions. The user specifies a desired section on a map, and recommendations are made based on voice input about that section using emotional information and past data.

[1376] Hardware and Software

[1377] Hardware: Smartphone (camera, microphone, GPS)

[1378] software

[1379] OpenCV (for face recognition and facial expression analysis)

[1380] TensorFlow (emotion recognition model)

[1381] Google Maps API (store map display)

[1382] Node.js (server-side processing)

[1383] Flask (API request processing)

[1384] Google Speech-to-Text API (voice recognition)

[1385] User operation procedure

[1386] 1. Designating sections on the map:

[1387] The user starts the map application and taps on the map to specify the section they want to view, which then retrieves the latitude and longitude information for the specified section.

[1388] 2. Voice input:

[1389] The user inputs the desired information by voice. For example, the user may input a request such as, "I want some clothes that will cheer me up today."

[1390] 3. Emotion recognition:

[1391] Based on the user's voice input and facial expression data acquired from the camera, the system uses TensorFlow and OpenCV to recognize the user's emotions. For example, it analyzes the user's emotions such as "joy" or "energetic" from the voice input.

[1392] Server-side processing

[1393] 1. Data Receipt and Analysis:

[1394] The server receives the latitude and longitude, voice data, and emotion information sent by the user, and then performs appropriate database queries to retrieve historical data and related information.

[1395] 2. Run the predictive model:

[1396] Using the acquired data as input, the server runs a predictive model using TensorFlow to generate recommendation data that matches the user's emotions.

[1397] 3. Submit recommended data:

[1398] The generated recommendation data is sent to the user's device, and a visual display is also provided along with recommendation information such as "Try a bright-colored shirt."

[1399] Display on device

[1400] 1. Display recommended data:

[1401] The device analyzes the recommendation data received from the server and provides it to the user as a map and text information. The desired section is highlighted on the map, and a message such as "Here's a cheerful color that's perfect for you today!" is displayed as text information.

[1402] Specific examples

[1403] Example of user action:

[1404] 1. The user uses the store app to select the "shirt section" and tap on the map.

[1405] 2. The user speaks, "I want to have a good day today."

[1406] 3. The emotion engine analyzes the voice and recognizes the emotion of "fun."

[1407] 4. The app sends the specified section, voice, and emotion information to the server.

[1408] 5. The server analyzes the data and generates appropriate product suggestions, such as "Try a brightly colored shirt."

[1409] 6. The app displays the results on a map, providing a visual experience for the user.

[1410] Example prompt sentence:

[1411] If you say, "I want to have a good day today," suggestions based on that emotion will be displayed.

[1412] In this way, users can receive product suggestions tailored based on their emotional information, resulting in a more satisfying purchasing experience.

[1413] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1414] Step 1:

[1415] The user launches the map application and taps on the map to specify the section they want to view.

[1416] Input: User taps

[1417] Output: Latitude and longitude information for sections on the map

[1418] How it works: By operating the map displayed on the smartphone screen and tapping on a specific section, the latitude and longitude information of that area is obtained. The device temporarily stores this information in its internal memory.

[1419] Step 2:

[1420] The user inputs the desired information by voice.

[1421] Input: User voice input

[1422] Output: Audio data

[1423] Specific operation: The user's voice is captured through the microphone and converted into text data using the Google Speech-to-Text API. For example, a speech such as "I want to have a good day today" is obtained as text data.

[1424] Step 3:

[1425] The device performs emotion recognition using the user's voice data and facial expression data acquired from the camera.

[1426] Input: Voice data, facial expression data

[1427] Output: Emotional information

[1428] Specific operation: The user's facial expressions captured by the camera are analyzed using OpenCV, and the audio data is input into a TensorFlow model to predict the emotional state. Emotional information such as "enjoyment" or "relaxation" is generated as a result of the analysis.

[1429] Step 4:

[1430] The device sends latitude and longitude information, emotion information, and voice data to the server.

[1431] Input: Latitude and longitude information, emotion information, voice data

[1432] Output: Request sent to server

[1433] What happens: The device packages the above information into JSON format and sends it to the server as an HTTP request. The request is sent using Flask.

[1434] Step 5:

[1435] The server receives information from users and runs a predictive model based on past data, weather information, and emotional information.

[1436] Input: Latitude and longitude information, emotion information, voice data

[1437] Output: Recommendation data

[1438] How it works: The server receives requests using Node.js, executes database queries to retrieve historical data and weather information, and then inputs this data into a TensorFlow emotion recognition model to generate optimal recommendations.

[1439] Step 6:

[1440] The server transmits the generated recommendation data to the terminal.

[1441] Input: Recommended data

[1442] Output: Sending a response to the device

[1443] What it does: The server packages the recommendation data in JSON format and sends it to the device as an HTTP response. The response is sent using Flask.

[1444] Step 7:

[1445] The device analyzes the recommendation data received from the server and visually presents it to the user.

[1446] Input: Recommended data

[1447] Output: Visual display

[1448] What it does: Your device receives the recommendation data, highlights the desired section on the map, and displays a recommendation message on the screen, such as "Here's a cheerful color that's perfect for you today!"

[1449] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1450] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1451] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1452] [Fourth embodiment]

[1453] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1454] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1455] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1456] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1457] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1458] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1459] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1460] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1461] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1462] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1463] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1464] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1465] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1466] A specific embodiment of the fishing result prediction system of the present invention will be described below.

[1467] System Overview

[1468] This system allows users to specify a fishing spot based on map information and obtain a catch forecast for a specific time period. Based on the location and time period specified by the user, the server uses past fishing data, weather information, and tide data to predict the catch, and displays the results on the terminal.

[1469] Overall program flow

[1470] User operations

[1471] The user launches the application

[1472] The user launches the fishing result prediction application on their device (smartphone or PC) and selects the fishing result prediction mode.

[1473] The device uses GPS to display a map centered on the user's current location.

[1474] User specifies a location

[1475] The user taps or clicks on the map to specify the location where they want to fish.

[1476] The device obtains the latitude and longitude information of the specified location and stores it in its internal memory.

[1477] The user enters the time zone

[1478] The terminal displays a dialog box that prompts the user to enter the desired time zone.

[1479] When the user enters a specific time period (e.g., 13:00-15:00), the device saves that information.

[1480] Server Processing

[1481] The device sends a request to the server

[1482] The device packages information about the specified location and time period and sends it to the server as an HTTP request.

[1483] The server receives the request and runs the predictive model.

[1484] The server receives the request from the terminal and analyzes the data.

[1485] The server retrieves past fishing data, weather information, and tidal data from a database and runs a predictive model based on this data.

[1486] For example, if the specified location is "off the coast of Aoshima" and the time period is "13:00-15:00," the system will refer to past fishing results for the same time period, weather information, and tidal data, and calculate information on when the best fishing results can be expected.

[1487] The server generates the prediction results and converts them into JSON format.

[1488] The server sends the prediction results to the device.

[1489] The prediction results generated by the server are sent to the terminal as an HTTP response.

[1490] Display on device

[1491] The device receives the prediction results and displays them to the user.

[1492] The terminal analyzes the prediction results received from the server.

[1493] Based on the analysis results, fishing results are visually displayed on a map.

[1494] Icons and colored markers will be displayed around the designated location, and text information such as "13:00-15:00: You can expect to catch some bluefish" will also be displayed.

[1495] Specific examples

[1496] For example, if a user uses the fishing result prediction system to specify a location "off the coast of Aoshima" and enter a time period of "13:00-15:00," the system will operate as follows.

[1497] 1. The user taps on "Offshore Qingdao" on the map, and latitude and longitude information is obtained.

[1498] 2. The user enters the time period as "13:00-15:00".

[1499] 3. The data is sent to the server, which makes a prediction based on past fishing data and weather information.

[1500] 4. The server generates a prediction result, such as "You can expect to catch some bluefish," and sends it to the device.

[1501] 5. The device displays the results on a map, visually providing the user with the information, "13:00-15:00: You can expect to catch some bluefish."

[1502] In this way, the present invention can provide users with intuitive and highly accurate fishing result prediction information, making it a useful system for maximizing fishing results.

[1503] The processing flow will be explained below.

[1504] Step 1:

[1505] The user launches the application. The device displays the application's home screen. The device obtains the user's current location and displays a map centered on the current location.

[1506] Step 2:

[1507] The user taps on the map the spot where they want to fish. The device retrieves the latitude and longitude of the tapped spot.

[1508] Step 3:

[1509] The device displays a time zone input dialog. The user inputs the time zone they want to fish. The device confirms the user's input and saves the specified location information and time zone information in its internal memory.

[1510] Step 4:

[1511] The terminal packages the specified location information and time zone information and sends it to the server as an HTTP request.

[1512] Step 5:

[1513] The server receives the HTTP request and analyzes the latitude, longitude, and time zone information of the specified location.

[1514] Step 6:

[1515] The server retrieves past fishing data, weather information, and tide data from the database, and then runs a predictive model based on this data to predict fishing results for a specified location and time period.

[1516] Step 7:

[1517] The server generates a prediction result. For example, "You can expect to catch bluefish off the coast of Aoshima between 1:00 PM and 3:00 PM." The server converts the prediction result into JSON format.

[1518] Step 8:

[1519] The server sends the prediction result to the terminal as an HTTP response.

[1520] Step 9:

[1521] The device receives the HTTP response, analyzes the prediction results, and prepares the data to be presented visually to the user.

[1522] Step 10:

[1523] The device displays the forecast results on a map, with colored markers and icons around the specified location, and the text "1:00 PM - 3:00 PM: Bluefish catches are expected."

[1524] Example 1

[1525] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1526] Conventional fishing result prediction systems have had issues such as a non-intuitive interface for users to specify fishing locations and time periods, and low accuracy in fishing result predictions. Furthermore, they lacked a sufficient mechanism for efficiently transmitting user-entered information to a server, making accurate predictions on the server side, and quickly displaying the results. Therefore, there is a need for a system that can be operated intuitively by users and provides highly accurate fishing result prediction information.

[1527] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1528] In this invention, the server includes a means for transmitting information on a location and time period specified by a terminal to the server, a means for the server to receive a request from the terminal and analyze the data, and a means for the server to generate a prediction result and transmit it to the terminal, which allows the user to intuitively operate the server and quickly obtain highly accurate fishing result prediction information.

[1529] A "user" is a person who uses the fishing result prediction system to specify a fishing spot and time period.

[1530] A "terminal" is a device operated by a user, and refers to information and communication equipment such as smartphones and PCs.

[1531] A "map" is a visual representation of geographic information that is displayed for a user to specify a fishing spot.

[1532] A "fishing spot" is a point on a map that a user designates as a place to go fishing.

[1533] A "time period" refers to the range of time during which a user plans to fish, and indicates the start and end of a particular time.

[1534] "Catch prediction data" is data that shows prediction results based on past catch data, weather information, tidal data, etc.

[1535] The term "server" refers to an information processing device that receives a request from a terminal, analyzes the request, generates a prediction result, and transmits the result to the terminal.

[1536] "Past fishing data" refers to data collected to date regarding fishing results.

[1537] "Weather information" refers to data related to weather, such as weather, temperature, and wind speed.

[1538] "Tidal data" is data that includes information about the high and low tides of the ocean.

[1539] A "predictive model" refers to an algorithm or calculation method that predicts fishing results based on past data.

[1540] An "HTTP request" is a type of communication protocol used when a terminal sends data to a server.

[1541] The "JSON format" is a format for expressing data in text format, and is suitable for handling structured data.

[1542] A "dialog box" is an input window that is displayed on a terminal for a user to enter information.

[1543] A specific embodiment of the fishing result prediction system of the present invention will be described below. This system allows a user to specify a fishing spot based on map information and obtain a fishing result prediction for a specific time period. Based on the location and time period specified by the user, the server uses past fishing result data, weather information, and tidal data to predict the fishing result, and displays the result on the terminal.

[1544] User operations

[1545] The user launches the application

[1546] The user launches the fishing prediction application on their smartphone or PC and selects the fishing prediction mode. The device uses GPS to display a map centered on the user's current location.

[1547] User specifies a location

[1548] The user taps or clicks on the map to specify a fishing spot. The device obtains the latitude and longitude information of the specified spot and stores it in its internal memory.

[1549] The user enters the time zone

[1550] The terminal displays a dialog box and asks the user to enter the desired time period. If the user enters a specific time period (e.g., 13:00-15:00), the terminal saves that information.

[1551] Server Processing

[1552] The device sends a request to the server

[1553] The device packages the information for the specified location and time period and sends it as an HTTP request to the server. The server receives the request from the device.

[1554] The server retrieves data from the database

[1555] The server retrieves past fishing data, weather information, and tide data from the database.

[1556] The server runs the predictive model

[1557] The server runs a fishing prediction model based on the data it has acquired. For example, if the specified location is "off the coast of Aoshima" and the time period is "1:00 PM to 3:00 PM," it will refer to past fishing data for the same time period, weather information, and tidal data to calculate the best fishing results.

[1558] The server generates prediction results and sends them to the device.

[1559] The server generates prediction results, converts them into JSON format, and sends them to the device as an HTTP response.

[1560] Display on device

[1561] The device receives the prediction results and displays them to the user.

[1562] The device analyzes the prediction results received from the server. Based on the analysis results, the fishing results are visually displayed on a map. Specifically, icons and colored markers are displayed around the specified location, and text information such as "1:00 PM - 3:00 PM: Bluefish fishing is expected." is also displayed.

[1563] Specific examples

[1564] For example, if a user uses the fishing result prediction system to specify a location "off the coast of Aoshima" and enter a time period of "13:00-15:00," the system will operate as follows.

[1565] 1. The user taps on "Offshore Qingdao" on the map, and latitude and longitude information is obtained.

[1566] 2. The user enters the time period "13:00 - 15:00".

[1567] 3. The device sends the information for the specified location and time period to the server as an HTTP request.

[1568] 4. The server receives the request and retrieves past fishing data, weather information, and tide data from the database.

[1569] 5. The server runs the fishing prediction model, generates a prediction such as "You can expect to catch some bluefish," and converts it into JSON format.

[1570] 6. The server sends the generated prediction results to the device.

[1571] 7. The device analyzes the forecast results received, displays icons and markers on the map, and displays text information such as "13:00-15:00: You can expect to catch some bluefish."

[1572] Prompt Sentence Examples

[1573] Please predict the type of fish that can be caught at the location "off the coast of Aoshima" between 1pm and 3pm.

[1574] In this way, the present invention can provide users with intuitive and highly accurate fishing result prediction information, making it a useful system for maximizing fishing results.

[1575] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1576] Step 1:

[1577] The user launches the application

[1578] The user launches a fishing result prediction application on their smartphone or PC and selects fishing result prediction mode. The device uses GPS to display a map centered on the user's current location. The input for this process is the application start operation, and the output is the display of a map centered on the current location.

[1579] Step 2:

[1580] User specifies a location

[1581] The user taps or clicks on the map to specify a fishing spot. The device obtains the latitude and longitude information of the specified spot and stores it in its internal memory. The input to this process is the user's tap operation, and the output is the acquisition and storage of the latitude and longitude information.

[1582] Step 3:

[1583] The user enters the time zone

[1584] The terminal displays a dialog box and asks the user to enter the desired time zone. When the user enters a specific time zone (e.g., 13:00-15:00), the terminal saves that information. The input to this process is the user's time zone entry, and the output is the saved time zone information.

[1585] Step 4:

[1586] The device sends a request to the server

[1587] The terminal packages the information for the specified location and time zone and sends it to the server as an HTTP request. The input for this process is latitude and longitude information and time zone information, and the output is an HTTP request to the server.

[1588] Step 5:

[1589] The server receives and parses the request

[1590] The server receives an HTTP request from a terminal. The server analyzes the request and extracts the specified location and time zone information. The input to this process is the request received by the server, and the output is the extracted location and time zone information.

[1591] Step 6:

[1592] The server retrieves data from the database

[1593] The server retrieves past fishing data, weather information, and tide data from the database. The input for this process is information on the location and time period, and the output is the retrieved past data.

[1594] Step 7:

[1595] The server runs the predictive model

[1596] The server runs a fishing result prediction model based on the data it has acquired. For example, if the specified location is "off the coast of Aoshima" and the time period is "1:00 PM to 3:00 PM," it references past fishing result data for the same time period, weather information, and tidal data to calculate the best fishing result. The input for this process is the acquired data, and the output is the predicted result.

[1597] Step 8:

[1598] The server generates prediction results and sends them to the device.

[1599] The server generates prediction results, converts them to JSON format, and sends them to the device as an HTTP response. The input to this process is the prediction results, and the output is the HTTP response to the device.

[1600] Step 9:

[1601] The device receives and analyzes the prediction results.

[1602] The device analyzes the prediction results received from the server. The input of this process is the HTTP response from the server, and the output is the analyzed prediction results.

[1603] Step 10:

[1604] The device displays the prediction results to the user.

[1605] Based on the analysis results, the device visually displays a catch forecast on a map. Specifically, icons and colored markers are displayed around the specified location, and text information such as "1:00 PM - 3:00 PM: Bluefish catches are expected." The input to this process is the analyzed prediction results, and the output is a visual display to the user.

[1606] (Application example 1)

[1607] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1608] Today's anglers are seeking fishing forecast information to increase their fishing success. However, current systems only provide fishing forecast data and lack navigation to actual fishing locations or a more intuitive way to provide that information. This makes it difficult for users to accurately determine fishing locations, and there are issues with underutilizing the fishing forecast information.

[1609] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1610] In this invention, the server includes means for acquiring the user's current location information and providing navigation to a specified location, means for executing a prediction model based on past fishing result data, weather information, and tidal data to generate fishing result prediction data for the specified location and time period, and means for providing the user with visual and audio guidance of new fishing result prediction data. This allows the user to not only obtain fishing result prediction information, but also receive navigation to the actual fishing location, enabling fishing while intuitively checking the prediction information visually and audibly.

[1611] "User" refers to a general user who uses the fishing result prediction system.

[1612] "Location" refers to the specific location on the map where the user designates they would like to fish.

[1613] "Time period" refers to a particular range of time during which a user wishes to fish.

[1614] "Catch prediction data" refers to forecast information regarding fishing results generated based on past catch data, weather information, tidal data, etc.

[1615] "Predictive Model" refers to the algorithms and statistical models used to generate catch prediction data.

[1616] "Visual" refers to information being presented in a form that can be seen by the user.

[1617] "Audio" refers to information being presented in a form that can be heard by the user.

[1618] "Current location information" refers to location information such as the user's current latitude and longitude.

[1619] "Navigation" refers to a function that provides guidance to a user's designated location.

[1620] "Smart glasses" refers to a type of wearable device that displays information visually and provides audio guidance when worn by the user.

[1621] A specific embodiment of the invention will be described. This system allows users to specify the location where they want to fish on a map and input the time period for fishing, and provides fishing results forecasts based on past fishing results, weather information, and tidal data. The main components of this system include smart glasses, a server, and a user terminal.

[1622] System configuration

[1623] The system includes the following major components:

[1624] User device: A smartphone, tablet, or PC used to perform basic operations.

[1625] Server: A central processing unit that runs predictive models using historical data to generate catch prediction data.

[1626] Smart glasses: Wearable devices such as Google Glass that provide visual and audio information.

[1627] Overall program flow

[1628] User operations

[1629] The user launches the fishing result prediction application on their device, specifies the fishing spot and inputs the time period. The device uses GPS to obtain the current location information and displays a map based on this information. The user specifies the spot on the map and inputs the desired time period. This data is saved in the device's internal memory and sent to the server.

[1630] Server Processing

[1631] The server receives the location and time information sent from the device. It then retrieves past fishing data, weather information, and tidal data from the database and runs a prediction model. The prediction model generates fishing forecast data based on the specified location and time period and converts it into JSON format. The generated data is then sent to the user's device and smart glasses.

[1632] Display on device

[1633] The user device and smart glasses analyze the fishing result prediction data received from the server and provide it to the user visually and audibly. For example, a marker showing the fishing result prediction is displayed on a map, along with text information such as "1:00 PM - 3:00 PM: Bluefish fishing is expected." The smart glasses also provide audio guidance, allowing the user to receive navigation to the fishing spot.

[1634] Hardware and software used

[1635] Hardware: Smart glasses (e.g., Google Glass), user devices (smartphones, tablets, PCs)

[1636] Software: Predictive models (machine learning algorithms, etc.), HTTP request library, GPS module

[1637] Specific examples

[1638] For example, if a user uses the fishing prediction system to specify the location "offshore Aoshima" and the time period "13:00-15:00", the flow will be as follows:

[1639] 1. The user taps on "Offshore Qingdao" on the map, and latitude and longitude information is obtained.

[1640] 2. The user enters the time period as "13:00-15:00".

[1641] 3. The data is sent to the server, which makes a prediction based on past fishing results and weather information.

[1642] 4. The server generates a prediction result, such as "You can expect to catch some bluefish," and sends it to the smart glasses and the user's device.

[1643] 5. The device will display the results on a map and the smart glasses will also provide voice guidance.

[1644] Example prompts for generative AI models

[1645] "If a user selects the area off the coast of Aoshima on the map and enters the time period as 1:00 PM to 3:00 PM, the fishing prediction system will predict that 'you can expect to catch some bluefish' based on past fishing data, weather information, and tidal data. This prediction result will be displayed on the smart glasses' display and provided to the user visually and audibly."

[1646] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1647] Step 1:

[1648] The user launches the application and selects the fishing result prediction mode.

[1649] Input: The action of a user launching an application on a device such as a smartphone.

[1650] Action: The user launches the application and selects catch prediction mode.

[1651] Output: The application is launched and the catch prediction mode interface is displayed.

[1652] Step 2:

[1653] The device uses GPS to display a map centered on the user's current location.

[1654] Input: User's current location (GPS data).

[1655] How it works: The device's GPS module obtains the current latitude and longitude and loads map data.

[1656] Output: A map centered on the user's current location is displayed on the device screen.

[1657] Step 3:

[1658] The user specifies the location on the map where they want to fish.

[1659] Input: What the user does on the map: tap or click.

[1660] Operation: Obtains the latitude and longitude information of the location specified by the user and saves it in internal memory.

[1661] Output: The latitude and longitude of the specified location will be saved to the device.

[1662] Step 4:

[1663] The user enters the desired time period

[1664] Input: Enter the specific time period (e.g. 13:00-15:00) when the user wishes to fish.

[1665] Action: The terminal displays a dialog box in which the user enters the time zone.

[1666] Output: The entered time zone information is saved to the terminal.

[1667] Step 5:

[1668] The device sends a request to the server for information about the specified location and time period.

[1669] Input: The latitude and longitude of the specified location, and the time zone information.

[1670] How it works: The device packages this data and sends it to the server as an HTTP request.

[1671] Output: The server receives an HTTP request containing the specified location and time zone information.

[1672] Step 6:

[1673] The server collects historical data and runs predictive models

[1674] Input: Latitude and longitude information of the specified location, time zone information, past fishing results data, weather information, and tidal data.

[1675] How it works: The server retrieves the necessary data from the database and runs the predictive model.

[1676] Output: Generates fishing forecast data based on the specified location and time period.

[1677] Step 7:

[1678] The server sends the prediction results to the device.

[1679] Input: Generated fishing prediction data.

[1680] Operation: The server converts the prediction results generated by the server into JSON format and sends them to the device as an HTTP response.

[1681] Output: The device receives the prediction results from the server.

[1682] Step 8:

[1683] The device analyzes the received prediction results and displays them to the user.

[1684] Input: Received catch prediction data.

[1685] How it works: The device analyzes the prediction results, visually displays the catch forecast on a map, and starts audio guidance.

[1686] Output: The user can see the fishing forecast displayed on the map and the audio guidance.

[1687] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1688] The following describes an embodiment of a fishing result prediction system of the present invention. This system not only allows a user to specify a fishing spot based on map information and predicts fishing results for a specific time period, but also recognizes the user's emotions using an emotion engine and adjusts the display of fishing result prediction data and recommendations accordingly.

[1689] System Overview

[1690] In addition to the basic functionality of providing catch prediction information based on the user's designated fishing location and time period, this system also incorporates an emotion engine that recognizes the user's emotions. This aims to improve the fishing experience by providing optimal catch prediction data according to the user's emotional state.

[1691] Overall program flow

[1692] User operations

[1693] The user launches the application

[1694] The user starts a fishing result prediction application on the device and selects emotion recognition mode.

[1695] Your device will display a map centered on your current location.

[1696] User specifies a location

[1697] The user taps on the map the spot where they want to fish.

[1698] The device obtains the latitude and longitude information of the location and stores it in its internal memory.

[1699] The user enters the time zone

[1700] The terminal displays a dialog box in which the user inputs the time period during which they would like to fish.

[1701] The terminal confirms the user's input and saves the specified location information and time period information.

[1702] Manipulating the Emotion Engine

[1703] The device recognizes the user's emotions

[1704] The emotion engine analyzes the user's voice input and facial expressions to recognize the user's emotions.

[1705] For example, if a user inputs "I'm looking forward to fishing today," the emotion of joy is recognized.

[1706] Emotion-based data adjustment

[1707] The display format of fishing prediction data and recommended content are adjusted based on the recognized emotion.

[1708] For example, if the user is in a fun mood, a preferred fishing spot and time of day will be recommended.

[1709] Server Processing

[1710] The device sends a request to the server

[1711] The terminal packages the specified location information, time period information, and recognized emotion information and transmits the packaged information to the server.

[1712] The server receives the request and runs the predictive model.

[1713] The server retrieves past fishing data, weather information, and tidal data, and runs a predictive model based on this.

[1714] The server generates prediction results and optimizes them based on emotion information.

[1715] For example, the system generates a result such as "You can expect to catch bluefish off the coast of Aoshima between 1:00 PM and 3:00 PM," but also adds a recommendation comment based on the user's feelings.

[1716] The server sends the prediction results to the device.

[1717] The server sends the generated prediction results and emotion adjustment information to the terminal as a response.

[1718] Display on device

[1719] The device receives the prediction results and displays them to the user.

[1720] The terminal analyzes the prediction results received from the server and provides them visually to the user.

[1721] Colored markers and icons are displayed around designated locations on the map, and emotional messages such as "13:00-15:00: You can expect to catch some bluefish. Have fun!" are also displayed as text information.

[1722] Specific examples

[1723] If a user uses the fishing result prediction system to specify "offshore Aoshima," inputs the time period "13:00-15:00," and then voice-inputs "I want to relax today," the specific actions will be as follows.

[1724] 1. The user taps on "Offshore Qingdao" on the map, and latitude and longitude information is obtained.

[1725] 2. The user enters the time period as "13:00-15:00".

[1726] 3. The user says, "I want to relax today."

[1727] 4. The emotion engine analyzes the user's voice and recognizes the desire to relax.

[1728] 5. The device sends the specified location, time period, and user emotion information to the server.

[1729] 6. The server analyzes the data and performs fishing predictions.

[1730] 7. The server generates the result, "You can expect to catch some bluefish off the coast of Aoshima between 1:00 PM and 3:00 PM. Relax in a quiet spot and enjoy fishing."

[1731] 8. The device displays the results on a map, providing a visual for the user.

[1732] In this way, the user can obtain fishing result prediction information that is adjusted based on emotional information, and can enjoy a more satisfying fishing experience.

[1733] The processing flow will be explained below.

[1734] Step 1:

[1735] The user launches the application. The device displays the home screen of the fishing result prediction application. The device uses GPS to obtain the user's current location and displays a map centered on the current location.

[1736] Step 2:

[1737] The user taps on the map the location where they want to fish. The device obtains the latitude and longitude information of the tapped location and stores it in its internal memory.

[1738] Step 3:

[1739] The device displays a time zone input dialog. The user inputs the time zone they want to fish (e.g., 13:00-15:00). The device confirms the user's input and saves the specified location and time zone information in its internal memory.

[1740] Step 4:

[1741] The device displays an emotion recognition dialog to accept the user's voice input. The user voice-inputs, "I want to relax today." The device collects the voice data.

[1742] Step 5:

[1743] The device activates an emotion engine and analyzes the voice data to recognize the user's emotion. For example, the device may recognize that the user wants to relax through voice analysis.

[1744] Step 6:

[1745] The terminal packages the specified location information, time zone information, and emotion information and transmits the packaged information to the server as an HTTP request.

[1746] Step 7:

[1747] The server receives the HTTP request and analyzes the data. The server retrieves past fishing data, weather information, and tide data from the database.

[1748] Step 8:

[1749] The server runs a prediction model based on the acquired data to predict catches at a specified location and time period. It also adjusts the display format and content of the prediction results based on the user's emotional information.

[1750] Step 9:

[1751] The server generates a prediction result, for example, "Bluefish can be expected to be caught between 1:00 PM and 3:00 PM off the coast of Aoshima. Relax in a quiet place and enjoy fishing," and converts it into JSON format.

[1752] Step 10:

[1753] The prediction results and emotion adjustment information generated by the server are sent to the terminal as an HTTP response.

[1754] Step 11:

[1755] The device receives the HTTP response and analyzes the prediction results. The device displays colored markers and icons around the specified location on the map, along with the text "1:00 PM - 3:00 PM: Good catches of bluefish are expected. Relax and enjoy fishing in a quiet location."

[1756] Example 2

[1757] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1758] Conventional fishing result prediction systems only provide fishing result prediction data based on the location and time period specified by the user, and do not sufficiently consider emotional factors to improve the quality of the fishing experience. As a result, it is not possible to recommend fishing spots or display data that matches the user's emotions, which leads to the problem of not being able to improve user satisfaction.

[1759] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for the user to specify a location on a map where they would like to fish, a means for the user to input their desired time period, a means for executing a prediction model based on past fishing data, weather information, and tidal data to generate fishing result prediction data corresponding to the specified location and time period, a means for displaying the generated fishing result prediction data to the user, and a means for recognizing the user's emotions and adjusting the display content and recommended content based on the emotions. This makes it possible to provide fishing result prediction data that is adapted to the user's emotions.

[1760] "User" refers to an individual who uses the fishing result prediction system to obtain fishing result prediction data for a specific location and time period.

[1761] "Map" refers to a visual interface that displays geographic information on a computer terminal or mobile device screen.

[1762] "Location" refers to a specific geographic location where you wish to fish, expressed as latitude and longitude information.

[1763] "Time period" refers to a specific time range for which a user desires a catch prediction.

[1764] "Catch prediction data" refers to predicted information about fishing results at a particular location and time period.

[1765] "Past fishing data" refers to records of fishing results at specific locations and times in the past.

[1766] "Weather information" refers to weather data relating to a particular location and time period.

[1767] "Tidal Data" refers to data relating to the ebb and flow of tides at a particular location.

[1768] A "predictive model" refers to an algorithm or mathematical model for predicting fishing results based on past fishing results, weather information, and tidal data.

[1769] "Emotion recognition" refers to the process of analyzing a user's voice and facial expressions to identify their mental state.

[1770] "Adjusting display content and recommendations" refers to changing the format of the fishing result prediction data presented, recommended fishing spots, and time periods based on the user's emotional state.

[1771] The following describes an embodiment of a fishing result prediction system of the present invention. This system not only allows a user to specify a fishing spot based on map information and predicts fishing results for a specific time period, but also recognizes the user's emotions using an emotion engine and adjusts the display of fishing result prediction data and recommendations accordingly.

[1772] System Overview

[1773] In addition to the basic functionality of providing catch prediction information based on the user's designated fishing location and time period, this system also incorporates an emotion engine that recognizes the user's emotions. This aims to improve the fishing experience by providing optimal catch prediction data according to the user's emotional state.

[1774] The system is activated when the user launches the application and selects emotion recognition mode. The device displays a map centered on the user's current location, and when the user taps the spot where they wish to fish, the device obtains the latitude and longitude information of that spot and stores it in internal memory. The user also enters the time of day they wish to fish in a dialog box in the application, and the specified location and time information is saved.

[1775] This application is equipped with an emotion engine that recognizes emotions through user voice input and facial expression analysis. For example, if a user voice-inputs, "I'm looking forward to fishing today," the engine recognizes the emotion of joy. Based on the recognized emotion, the engine then adjusts the display format of the catch prediction data and the recommended content. If the user is feeling excited, the emotion engine recommends a suitable fishing spot and time of day.

[1776] The device sends the specified location information, time zone information, and recognized emotion information to the server. The server retrieves past fishing results, weather information, and tide data, and runs a prediction model based on these. The server then generates prediction results and optimizes them based on the emotion information. For example, the server might generate a result such as "Bluefish can be expected to be caught between 1:00 PM and 3:00 PM off the coast of Aoshima," and add a recommendation comment based on the user's emotion.

[1777] The server generates a prediction result and sends the emotion adjustment information to the device as a response, and the device analyzes the prediction result received from the server and provides it visually to the user. Specifically, colored markers and icons are displayed around the specified point on the map, and emotion-based messages such as "1:00 PM - 3:00 PM: Bluefish catches are expected. Have fun!" are also displayed as text information.

[1778] Specific examples

[1779] If a user uses the fishing result prediction system to specify "offshore Aoshima," inputs the time period "13:00-15:00," and then voice-inputs "I want to relax today," the specific actions will be as follows.

[1780] First, the user taps "Offshore Aoshima" on the map, and the system obtains latitude and longitude information. Next, the user enters the time period, "1:00 PM - 3:00 PM," which is saved. After that, when the user voice-inputs, "I want to relax today," the emotion engine analyzes the voice and recognizes the emotion of wanting to relax. The device sends the specified location, time period, and user emotion information to the server, which analyzes it and performs a fishing result prediction.

[1781] The server generates the result, "You can expect to catch some bluefish off the coast of Aoshima between 1:00 PM and 3:00 PM. Relax in a quiet place and enjoy fishing," and sends this to the terminal.

[1782] Finally, the terminal displays the results on a map and provides them visually to the user, allowing the user to obtain fishing result prediction information adjusted based on emotional information and enjoy a more satisfying fishing experience.

[1783] Prompt Sentence Examples

[1784] "Off the coast of Aoshima" "1pm-3pm" "I want to relax"

[1785] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1786] Step 1:

[1787] The user launches the application and selects the emotion recognition mode.

[1788] Input: User actions (app launch and mode selection)

[1789] Output: Emotion recognition mode is enabled and the map is displayed.

[1790] Specific operation: The user taps the app icon on the device's home screen to launch the app, then selects emotion recognition mode. The device activates emotion recognition mode and displays a map centered on the user's current location on the screen.

[1791] Step 2:

[1792] The user specifies the location on the map where they wish to fish.

[1793] Input: User tap action (selecting a point on the map)

[1794] Output: Latitude and longitude information is saved to the device

[1795] Specific operation: The user taps "Offshore Qingdao" on the map. The device obtains the latitude and longitude information of this tapped location and stores it in its internal memory.

[1796] Step 3:

[1797] The user inputs the time period during which he or she will be fishing.

[1798] Input: The time period (start and end times) entered by the user in the dialog box

[1799] Output: The time zone information entered by the user is saved on the device.

[1800] Specific operation: The device displays "Please enter the time period for fishing," and the user enters "13:00-15:00." The device stores this information in its internal memory.

[1801] Step 4:

[1802] The device analyzes voice input and facial expressions to recognize the user's emotions.

[1803] Input: User's voice input or facial expression (emotion data)

[1804] Output: Emotion recognition results (e.g., feeling of wanting to relax)

[1805] Specific operation: The user inputs "I want to relax today" by voice. The emotion engine analyzes this voice data and recognizes the user's emotion as "I want to relax."

[1806] Step 5:

[1807] The display format of fishing prediction data and recommended content are adjusted based on the recognized emotion.

[1808] Input: Emotion recognition results (user emotion data)

[1809] Output: Adjusted recommendations (e.g., quiet location)

[1810] What it does: The emotion engine adjusts the data to recommend quiet fishing spots based on the emotion "I want to relax."

[1811] Step 6:

[1812] The terminal transmits the specified location information, time period information, and emotion information to the server.

[1813] Input: Latitude and longitude information, time zone information, emotion information

[1814] Output: Packaged request data sent to the server

[1815] Specific operation: The terminal packages the information "Off the coast of Qingdao," "13:00-15:00," and "Relax" and sends it to the server.

[1816] Step 7:

[1817] The server receives the request, retrieves past fishing data, weather information, and tidal data, and runs a predictive model based on that information.

[1818] Input: Packaged request data

[1819] Output: Prediction results and adjustment information

[1820] Specific operation: The server receives the request data from the user, and retrieves and analyzes past fishing results, weather information, and tide data. Based on this data, the server generates a prediction result that "bluefish can be expected to be caught off the coast of Aoshima between 1:00 PM and 3:00 PM," and adds a comment saying, "Please relax and enjoy fishing in a quiet place."

[1821] Step 8:

[1822] The server sends the generated prediction results and emotion adjustment information to the terminal as a response.

[1823] Input: Prediction results and emotion regulation information

[1824] Output: Response data to the terminal

[1825] Specific operation: The server sends response data including the prediction results and recommended comments to the terminal.

[1826] Step 9:

[1827] The terminal receives the prediction result and visually presents it to the user.

[1828] Input: Response data from the server

[1829] Output: Visual display of information to the user

[1830] Specific operation: The device analyzes the prediction results received from the server and provides them visually to the user. Colored markers and icons are displayed around the designated location on the map, and messages such as "1:00 PM - 3:00 PM: Bluefish catches are expected. Please relax in a quiet place and enjoy fishing." are also displayed.

[1831] (Application example 2)

[1832] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1833] Conventional fishing result prediction systems and product recommendation systems are unable to respond to users' emotions or real-time needs and are limited to providing uniform information. This results in a poor user experience and an inability to provide appropriate advice and recommendations. In particular, there is a need for systems that can provide optimal information according to the user's emotional state.

[1834] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1835] In this invention, the server includes a means for a user to specify a desired section on a map, a means for the user to input desired information by voice, a means for executing a predictive model based on past data, weather information, and emotion information to generate recommended data according to the specified section and voice input, and a means for displaying the generated recommended data based on the recognized emotion to the user, thereby enabling the provision of highly personalized information that is in line with the user's emotions and needs.

[1836] "User" refers to a person who uses this system.

[1837] "Map" means a visual representation of a particular place or area.

[1838] A "section" refers to a subdivision of a destination or area.

[1839] A "means" refers to a method or device used to accomplish a particular purpose.

[1840] "Voice input" refers to the act of the system recognizing the voice spoken by the user through a microphone and capturing it as data.

[1841] "Recommendation data" refers to suggested information provided based on the user's input information and emotional state.

[1842] "Historical Data" refers to information previously collected.

[1843] "Weather information" refers to data about the weather at that time or in the past.

[1844] "Emotional information" refers to data that represents the user's emotional state.

[1845] A "predictive model" refers to an algorithm or computational method for predicting future outcomes based on input data.

[1846] "Recognition" refers to the act of the system analyzing the user's emotions and voice and understanding their meaning.

[1847] "Display" refers to the act of visually presenting information to a user.

[1848] "Server" refers to a computer that performs central data processing and transmits the results to clients.

[1849] "Terminal" refers to a device through which a user accesses the system.

[1850] System Overview

[1851] This invention is a system that acquires information desired by a user and provides optimal recommended data based on their emotions. The user specifies a desired section on a map, and recommendations are made based on voice input about that section using emotional information and past data.

[1852] Hardware and Software

[1853] Hardware: Smartphone (camera, microphone, GPS)

[1854] software

[1855] OpenCV (for face recognition and facial expression analysis)

[1856] TensorFlow (emotion recognition model)

[1857] Google Maps API (store map display)

[1858] Node.js (server-side processing)

[1859] Flask (API request processing)

[1860] Google Speech-to-Text API (voice recognition)

[1861] User operation procedure

[1862] 1. Designating sections on the map:

[1863] The user starts the map application and taps on the map to specify the section they want to view, which then retrieves the latitude and longitude information for the specified section.

[1864] 2. Voice input:

[1865] The user inputs the desired information by voice. For example, the user may input a request such as, "I want some clothes that will cheer me up today."

[1866] 3. Emotion recognition:

[1867] Based on the user's voice input and facial expression data acquired from the camera, the system uses TensorFlow and OpenCV to recognize the user's emotions. For example, it analyzes the user's emotions such as "joy" or "energetic" from the voice input.

[1868] Server-side processing

[1869] 1. Data Receipt and Analysis:

[1870] The server receives the latitude and longitude, voice data, and emotion information sent by the user, and then performs appropriate database queries to retrieve historical data and related information.

[1871] 2. Run the predictive model:

[1872] Using the acquired data as input, the server runs a predictive model using TensorFlow to generate recommendation data that matches the user's emotions.

[1873] 3. Submit recommended data:

[1874] The generated recommendation data is sent to the user's device, and a visual display is also provided along with recommendation information such as "Try a bright-colored shirt."

[1875] Display on device

[1876] 1. Display recommended data:

[1877] The device analyzes the recommendation data received from the server and provides it to the user as a map and text information. The desired section is highlighted on the map, and a message such as "Here's a cheerful color that's perfect for you today!" is displayed as text information.

[1878] Specific examples

[1879] Example of user action:

[1880] 1. The user uses the store app to select the "shirt section" and tap on the map.

[1881] 2. The user speaks, "I want to have a good day today."

[1882] 3. The emotion engine analyzes the voice and recognizes the emotion of "fun."

[1883] 4. The app sends the specified section, voice, and emotion information to the server.

[1884] 5. The server analyzes the data and generates appropriate product suggestions, such as "Try a brightly colored shirt."

[1885] 6. The app displays the results on a map, providing a visual experience for the user.

[1886] Example prompt sentence:

[1887] If you say, "I want to have a good day today," suggestions based on that emotion will be displayed.

[1888] In this way, users can receive product suggestions tailored based on their emotional information, resulting in a more satisfying purchasing experience.

[1889] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1890] Step 1:

[1891] The user launches the map application and taps on the map to specify the section they want to view.

[1892] Input: User taps

[1893] Output: Latitude and longitude information for sections on the map

[1894] How it works: By operating the map displayed on the smartphone screen and tapping on a specific section, the latitude and longitude information of that area is obtained. The device temporarily stores this information in its internal memory.

[1895] Step 2:

[1896] The user inputs the desired information by voice.

[1897] Input: User voice input

[1898] Output: Audio data

[1899] Specific operation: The user's voice is captured through the microphone and converted into text data using the Google Speech-to-Text API. For example, a speech such as "I want to have a good day today" is obtained as text data.

[1900] Step 3:

[1901] The device performs emotion recognition using the user's voice data and facial expression data acquired from the camera.

[1902] Input: Voice data, facial expression data

[1903] Output: Emotional information

[1904] Specific operation: The user's facial expressions captured by the camera are analyzed using OpenCV, and the audio data is input into a TensorFlow model to predict the emotional state. Emotional information such as "enjoyment" or "relaxation" is generated as a result of the analysis.

[1905] Step 4:

[1906] The device sends latitude and longitude information, emotion information, and voice data to the server.

[1907] Input: Latitude and longitude information, emotion information, voice data

[1908] Output: Request sent to server

[1909] What happens: The device packages the above information into JSON format and sends it to the server as an HTTP request. The request is sent using Flask.

[1910] Step 5:

[1911] The server receives information from users and runs a predictive model based on past data, weather information, and emotional information.

[1912] Input: Latitude and longitude information, emotion information, voice data

[1913] Output: Recommendation data

[1914] How it works: The server receives requests using Node.js, executes database queries to retrieve historical data and weather information, and then inputs this data into a TensorFlow emotion recognition model to generate optimal recommendations.

[1915] Step 6:

[1916] The server transmits the generated recommendation data to the terminal.

[1917] Input: Recommended data

[1918] Output: Sending a response to the device

[1919] What it does: The server packages the recommendation data in JSON format and sends it to the device as an HTTP response. The response is sent using Flask.

[1920] Step 7:

[1921] The device analyzes the recommendation data received from the server and visually presents it to the user.

[1922] Input: Recommended data

[1923] Output: Visual display

[1924] What it does: Your device receives the recommendation data, highlights the desired section on the map, and displays a recommendation message on the screen, such as "Here's a cheerful color that's perfect for you today!"

[1925] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1926] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1927] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1928] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1929] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1930] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1931] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1932] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1933] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1934] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1935] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1936] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1937] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1938] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1939] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1940] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1941] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1942] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1943] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1944] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1945] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1946] The following is further disclosed regarding the above embodiment.

[1947] (Claim 1)

[1948] a means for a user to specify a location on a map where they would like to fish;

[1949] a means for the user to input a desired time period;

[1950] means for executing a prediction model based on past fishing data, weather information, and tidal data to generate fishing prediction data for a specified location and time period;

[1951] a means for displaying the generated fishing result prediction data to a user;

[1952] A fishing prediction system including.

[1953] (Claim 2)

[1954] 2. The fishing result prediction system according to claim 1, further comprising means for acquiring latitude and longitude information of a point designated on a map.

[1955] (Claim 3)

[1956] 2. The fishing result prediction system according to claim 1, wherein the server generates fishing result prediction data, and the terminal has means for receiving and displaying the data.

[1957] "Example 1"

[1958] (Claim 1)

[1959] a means for a user to specify a location on a map where they would like to fish;

[1960] a means for the user to input a desired time period;

[1961] means for executing a prediction model based on past fishing data, weather information, and tidal data to generate fishing prediction data for a specified location and time period;

[1962] a means for displaying the generated fishing result prediction data to a user;

[1963] A means for the terminal to transmit information of a designated location and time period to a server;

[1964] A means for the server to receive a request from the terminal and analyze the data;

[1965] A means for the server to generate a prediction result and transmit it to the terminal;

[1966] means for analyzing the prediction result received by the terminal and visually displaying it to the user;

[1967] A system including:

[1968] (Claim 2)

[1969] 2. The system according to claim 1, further comprising means for acquiring latitude and longitude information of a point designated on a map.

[1970] (Claim 3)

[1971] 2. The system according to claim 1, wherein the server generates fishing result prediction data, and the terminal has means for receiving and displaying the data.

[1972] "Application Example 1"

[1973] (Claim 1)

[1974] a means for a user to specify on a map where they would like to fish;

[1975] a means for the user to input a desired time period;

[1976] means for executing a prediction model based on past fishing data, weather information, and tidal data to generate fishing prediction data for a specified location and time period;

[1977] a means for displaying the generated fishing result prediction data to a user;

[1978] A means for acquiring the user's current location information and providing navigation to a specified location;

[1979] means for visually and audibly informing the user of new fishing result forecast data;

[1980] A system including:

[1981] (Claim 2)

[1982] 2. The system according to claim 1, further comprising means for acquiring latitude and longitude information of a location specified on a map.

[1983] (Claim 3)

[1984] 10. The system of claim 1, wherein the server generates catch prediction data, and the smart glasses have means for receiving and displaying the data.

[1985] "Example 2: Combining Emotion Engines"

[1986] (Claim 1)

[1987] a means for a user to specify a location on a map where they would like to fish;

[1988] a means for the user to input a desired time period;

[1989] means for executing a prediction model based on past fishing data, weather information, and tidal data to generate fishing prediction data for a specified location and time period;

[1990] a means for displaying the generated fishing result prediction data to a user;

[1991] means for recognizing a user's emotion and adjusting the displayed content or recommendations based on the emotion;

[1992] A system including:

[1993] (Claim 2)

[1994] 2. The system according to claim 1, further comprising means for acquiring latitude and longitude information of a point designated on a map.

[1995] (Claim 3)

[1996] 2. The system according to claim 1, wherein the server generates fishing result prediction data, and the terminal has means for receiving and displaying the data.

[1997] "Application example 2 when combining emotion engines"

[1998] (Claim 1)

[1999] a means for a user to specify a desired section on the map;

[2000] A means for a user to input desired information by voice;

[2001] means for executing a predictive model based on historical data, weather information, and sentiment information to generate recommendations based on the specified section and voice input;

[2002] means for displaying to a user recommendation data generated based on the recognized emotion;

[2003] A system including:

[2004] (Claim 2)

[2005] 2. The system according to claim 1, further comprising means for acquiring latitude and longitude information of a position specified on a map.

[2006] (Claim 3)

[2007] 2. The system of claim 1, wherein the server generates the recommendation data, and the terminal has means for receiving and displaying the data. [Explanation of symbols]

[2008] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for a user to specify a location on a map where they would like to fish; a means for the user to input a desired time period; means for executing a prediction model based on past fishing data, weather information, and tidal data to generate fishing prediction data for a specified location and time period; a means for displaying the generated fishing result prediction data to a user; A fishing prediction system including.

2. 2. The fishing result prediction system according to claim 1, further comprising means for acquiring latitude and longitude information of a point designated on a map.

3. 2. A fishing result prediction system according to claim 1, wherein the server generates fishing result prediction data, and the terminal has means for receiving and displaying the data.

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