System

Abstract

A system is provided.SOLUTION: This system includes a means for acquiring weather information, a means for acquiring atmospheric temperature information, a means for acquiring wind information, a means for acquiring high tide / low tide information, a means for collecting past fishing result information, a means for predicting a future fishing result on the basis of the information, and a means for displaying the prediction result on a map in a heat map format by time.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] In the past, fishing users found it difficult to take efficient fishing actions due to insufficient information for predicting catches. In particular, factors such as weather, temperature, wind, and high and low tides significantly affect catches, so there was a need for a method to collect and integrate this information individually to predict catches. As a result, finding the optimal time and location for fishing required a lot of effort, reducing user convenience. Therefore, to solve these problems, the present invention proposes a system that integrates the necessary information and provides future catch predictions. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems with a system that includes the following means: a means for acquiring weather information, a means for acquiring temperature information, a means for acquiring wind information, a means for acquiring high tide and low tide information, a means for collecting past fishing results, a means for predicting future fishing results based on this information, and a means for displaying the predicted results in the form of a heat map by hour on a map. The system also includes a means for a user to input the date, time, and location of a planned fishing trip, and collects and integrates information based on the input to generate a fishing result prediction that is then displayed in the form of a heat map on a map. The system also includes means for acquiring weather information, temperature information, wind information, and high tide and low tide information from an external database and for acquiring past fishing results from an internal database, and uses an algorithm for generating a fishing result prediction based on this acquired information to display the predicted fishing result in the form of a heat map by hour on a map.

[0006] "Weather information" is data that indicates the weather conditions at a fishing spot, and specifically includes weather conditions such as rain, cloudy weather, and fine weather.

[0007] "Temperature information" is data indicating the temperature at a specified fishing spot, including the temperature at a specific time period.

[0008] "Wind information" is data that indicates the wind speed and direction near the designated fishing spot.

[0009] "High tide and low tide information" is data that indicates the time and magnitude of the tides at a specified fishing spot.

[0010] "Past fishing information" is data relating to fishing results at specific times and locations in the past, and specifically includes the types and numbers of fish caught.

[0011] "Means for predicting future catches" refers to an algorithm for predicting future catches based on various collected information.

[0012] The "heat map format" is a method of visually representing information on a map by using different shades and colors of data.

[0013] "Means for displaying on a map" refers to systems and software for visualizing the prediction results on a map. [Brief explanation of the drawings]

[0014] [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

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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."

[0035] This system integrates the information necessary for users to enjoy fishing efficiently and provides future fishing results predictions. It collects and integrates weather information, temperature information, wind information, high tide / low tide information, and past fishing results, and displays them on a map in the form of a heat map by time, helping users make decisions about their fishing activities.

[0036] System Overview:

[0037] The system consists of a dedicated map app or website where users can input the date, time, and location of their fishing plans, a backend server, and an external API and internal database for collecting and processing various data. Based on the user's specified date, time, and location, the system visually displays the predicted results on a map.

[0038] Embodiment Details:

[0039] The user accesses a dedicated map app or website and enters the date, time, and location of the planned fishing trip. The device then sends this information to the server. Based on the received date, time, and location, the server collects the necessary weather information, temperature information, wind information, high and low tide information, and past fishing results information from external APIs and internal databases.

[0040] For example, weather information is obtained using a weather API. Data from the weather API includes the weather condition (sunny, cloudy, rainy, etc.) at a given date and time. Similarly, tide information is obtained via an ocean data API, and past fishing results are retrieved from the system's internal database.

[0041] The server uses the collected information to run an algorithm that predicts future catches. This algorithm combines information such as weather, temperature, wind, tides, and past fishing patterns to calculate the likelihood of a catch at a specific date and time. The prediction is expressed as a score that indicates the probability of a successful catch at a specific time.

[0042] The calculated prediction results are sent to the device and displayed in the user's map app in the form of a heat map. The heat map visualizes the predicted fishing results by color-coding them on a map, allowing users to intuitively identify the best time and place to fish.

[0043] For example, the heat map will show "red" during times when the fishing yield is expected to be high at a particular fishing spot, and "blue" during times when the fishing yield is low. This allows users to intuitively decide when to select a fishing spot and when to go fishing.

[0044] Based on the information provided by this system, users can efficiently plan their fishing trips. Furthermore, the system is capable of continuously collecting data and improving its algorithms to improve the accuracy of catch predictions, which is expected to further improve the user experience.

[0045] The processing flow will be explained below.

[0046] Step 1:

[0047] Users access a dedicated map app or website and input the date, time, and location of their planned fishing trip. The device then sends this information to the server.

[0048] Step 2:

[0049] The server receives the date, time, and location information sent by the user, and uses this information to prepare to collect the necessary data from external APIs and internal databases.

[0050] Step 3:

[0051] The server retrieves weather information using an external weather API. This process retrieves weather data including the weather condition (sunny, cloudy, rainy, etc.) associated with the specified location and date / time.

[0052] Step 4:

[0053] The server also uses an external ocean data API to obtain high and low tide information, including the time and magnitude of the tides at the specified fishing spot.

[0054] Step 5:

[0055] The server collects historical fishing information from an internal database, which contains historical fishing data for a specified date, time, and location, including the type and number of fish caught.

[0056] Step 6:

[0057] The server also retrieves wind and temperature information using an external API, including wind speed, wind direction, and temperature for a specified location and date and time.

[0058] Step 7:

[0059] The server combines the collected weather, temperature, wind, high and low tide information, and past fishing results, and then runs an algorithm to predict future fishing results. This algorithm weights each piece of information and calculates the likelihood of a fishing result.

[0060] Step 8:

[0061] The server generates predictions and stores them as catch prediction scores for each specified time slot, which indicate the likelihood of fishing success during that particular time slot.

[0062] Step 9:

[0063] The server transmits the generated fishing result prediction score to the terminal.

[0064] Step 10:

[0065] Based on the fishing prediction score received by the device, the results are displayed on a map in the form of a heat map. This heat map shows areas with high fishing predictions in "red" and areas with low fishing predictions in "blue," allowing users to intuitively identify the best time and place to fish.

[0066] Example 1

[0067] 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."

[0068] Conventional fishing planning systems require users to manually collect weather information, tide information, past fishing results, and other information from multiple sources and integrate that information to make predictions. This makes the information collection and integration process cumbersome, and often results in inaccurate predictions. Furthermore, there are limited ways to visually display fishing results, making it difficult for users to intuitively determine the optimal fishing timing and location. The present invention aims to solve these problems and provide a system that allows users to efficiently plan fishing.

[0069] 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.

[0070] In this invention, the server includes means for acquiring weather information, means for acquiring temperature information, means for acquiring wind information, means for acquiring tide information, means for collecting past fishing results, an algorithm for predicting future fishing results based on this information, means for displaying the prediction results in a heat map format by time on a map, means for a user to input the date, time, and location of the planned fishing trip, means for transmitting the input information to the server, means for collecting necessary information from external and internal databases, means for integrating the collected information and performing prediction calculations, means for transmitting the prediction results to a user terminal, and means for displaying the prediction results on a user terminal. This allows a user to automatically collect and integrate necessary information from multiple information sources, obtain highly accurate fishing results predictions, and visualize the results in a form that is easy to understand intuitively.

[0071] "Means for obtaining weather information" refers to the function of the system to automatically collect weather data for a specified date, time and location from an external database.

[0072] "Means for obtaining temperature information" refers to the function of the system to automatically collect temperature data for a specified date, time and location from an external database.

[0073] "Means for obtaining wind information" refers to the function of the system to automatically collect wind speed and direction at a specified date, time and location from an external database.

[0074] "Means for obtaining tidal information" refers to the system's ability to automatically collect high and low tide data for specified dates, times, and locations from an external database.

[0075] "Means for collecting past fishing information" refers to the system's ability to obtain previously recorded fishing data from an internal database.

[0076] "Future fishing prediction algorithm" refers to a calculation procedure that integrates collected weather, temperature, wind, tide, and past fishing information to predict the probability of fishing at a specified date, time, and location.

[0077] "Means for displaying the prediction results on a map in the form of a heat map by time period" refers to a function that visually displays the probability of catching a fish obtained by the prediction algorithm on a map by color-coding it according to different time periods.

[0078] "Means for inputting the date, time and location of planned fishing by the user" refers to an interface that allows the user to specify to the system the time and location of planned fishing.

[0079] The "means for transmitting the input information to the server" refers to a function for transmitting the date, time, and location information input by the user to the server.

[0080] "Means for collecting necessary information from external and internal databases" refers to a function for automatically collecting necessary weather information, temperature information, wind information, tide information, and fishing results information based on a specified date, time, and location.

[0081] The "means for integrating the collected information and performing predictive calculations" refers to a function that integrates various collected data and performs calculations to predict future fishing results.

[0082] "Means for sending prediction results to the user's terminal" refers to the function of sending the results of the fishing result prediction calculated by the server to the user's terminal.

[0083] "Means for displaying the prediction results on the user terminal" refers to a function for displaying the prediction results on the user terminal in a format that can be intuitively understood.

[0084] This is a system that integrates the information necessary for users to enjoy fishing efficiently and provides future fishing results. The system integrates and forecasts weather, temperature, wind, high and low tide, and past fishing results, and displays the results in a heat map format by time to help users make decisions about their fishing activities.

[0085] System configuration

[0086] The system consists of the following elements:

[0087] 1. A dedicated mapping app or website where users can input the date, time and location where they plan to fish.

[0088] 2. A backend server that processes the user input.

[0089] 3. External APIs and internal databases for collecting and processing various data.

[0090] Hardware and Software

[0091] Hardware: Server, user device (PC or smartphone).

[0092] software:

[0093] Weather API for obtaining weather information (e.g. OpenWeatherMap API).

[0094] Ocean data APIs for retrieving tide information (e.g., NOAA Tides & Currents API).

[0095] An internal database (e.g. MySQL) that maintains historical catch information and other statistical data.

[0096] Data collection and processing

[0097] The user accesses a dedicated map app or website and inputs the date, time and location of the planned fishing trip. This information is sent by the device to the server, which then performs the following operations:

[0098] 1. Use the weather API to get weather information for a specified date, time, and location. For example, get data including the weather (sunny, cloudy, rainy, etc.) for a specified date and time.

[0099] 2. Use the ocean data API to collect tidal information and obtain the tides for a specified date and time.

[0100] 3. Retrieve historical fishing information from an internal database based on the specified date, time and location.

[0101] Prediction Algorithm

[0102] The server uses the collected information to run an algorithm that predicts future catches. The algorithm combines weather, temperature, wind, tides, and past fishing information to calculate the likelihood of a catch. The prediction is expressed as a score that indicates the probability of a successful catch at a particular time.

[0103] Display in heatmap format

[0104] The calculated prediction results are sent from the server to the device and displayed in the user's map app in the form of a heat map. The heat map visualizes the fishing prediction score using color coding, allowing users to intuitively identify the best time and place to fish.

[0105] Specific examples

[0106] For example, if a user plans to go fishing in Yokohama at 6am on July 15th, they might use the following prompt:

[0107] "I'm planning to go fishing in Yokohama at 6:00 AM on July 15th. What are the fishing results forecasts for this date, time, and location?"

[0108] Based on this prompt, the system will perform the following steps:

[0109] 1. The user accesses a dedicated map app or website and enters the date, time and location where they plan to fish.

[0110] 2. The terminal sends this input information to the server.

[0111] 3. The server collects the necessary information from external APIs and internal databases.

[0112] 4. The server runs a catch prediction algorithm based on the collected information and calculates a predicted score.

[0113] 5. The server sends the results to the device and displays them as a heat map.

[0114] In this way, users can plan their fishing trips efficiently. The system also continuously collects data and improves its algorithms, which is expected to further improve the accuracy of fishing predictions.

[0115] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0116] Step 1:

[0117] A user accesses a dedicated mapping app or website and enters the date, time, and location where they plan to fish. The entered date, time, and location are captured by the user's device. Specifically, the user selects a date and time from the calendar function and pins the desired fishing location on the map. This information is stored locally and sent for further processing.

[0118] Input: User-selected date, time, and location.

[0119] Output: The date, time, and location information entered.

[0120] Step 2:

[0121] The device sends the date, time, and location information entered by the user to the server. In this case, the information is sent to the server using an HTTP POST request. Specifically, the device packages the information and sends it to the server when triggered by a click event by the user (for example, pressing the "Send" button).

[0122] Input: Date, time, and location information entered by the user.

[0123] Output: HTTP POST request to the server.

[0124] Step 3:

[0125] The server collects the necessary data based on the received date, time, and location information. This includes retrieving weather information from the weather API, tidal information from the ocean data API, and past fishing results from an internal database. Specifically, the server sends an HTTP GET request to the API endpoint to retrieve the necessary information.

[0126] Input: Date, time, and location information sent from your device.

[0127] Output: Collected weather information, tide information, and past fishing results.

[0128] Step 4:

[0129] The server integrates the collected data and runs an algorithm to predict future catches based on this information. Specifically, the server integrates each piece of information, analyzes the data using machine learning models, and calculates a prediction score.

[0130] Input: Collected weather information, tide information, and past fishing results.

[0131] Output: Prediction score.

[0132] Step 5:

[0133] The server then sends the calculated prediction score to the user's device. This process uses an HTTP POST request. Specifically, the server serializes the prediction score in JSON format and sends it to the user's device.

[0134] Input: The calculated prediction score.

[0135] Output: HTTP POST request to the user device.

[0136] Step 6:

[0137] The device then displays the received prediction scores as a heat map on the map app. Specifically, the device analyzes the received data and uses a heat map library to display it in different colors on the map.

[0138] Input: Prediction score sent by the server.

[0139] Output: Prediction scores displayed in a heatmap format.

[0140] (Application example 1)

[0141] 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."

[0142] Conventional fishing information systems provide limited information for users to enjoy fishing, making it difficult to accurately predict catches at specific times, dates, and locations. Furthermore, these systems lack intuitive visual information displays, providing insufficient support for users to identify optimal fishing times and locations. The present invention aims to solve these problems by providing a visual display of future catches, allowing users to enjoy fishing more efficiently.

[0143] 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.

[0144] In this invention, the server includes means for acquiring weather information, means for acquiring temperature information, means for acquiring wind information, means for acquiring high tide and low tide information, means for collecting past fishing results, means for predicting future fishing results based on this information, means for displaying the prediction results in a heat map format by time on a map, means for calculating a fishing result prediction score based on the weather information, high tide and low tide information, and past fishing results information, and means for generating a color-coded heat map on a map based on the calculated fishing result prediction score. This allows users to intuitively understand the fishing result prediction score and identify the optimal fishing timing and location.

[0145] "Weather information" refers to weather data at a specific date and time, and generally includes weather conditions such as sunny, cloudy, rainy, and snowy.

[0146] "Temperature information" is data relating to the temperature at a specific location during a specific time period.

[0147] "Wind information" refers to data regarding wind speed and direction at a specific date and time.

[0148] "High and low tide information" refers to data regarding the times and heights of high and low tides at a specific location.

[0149] "Past fishing information" refers to fishing data recorded in the past based on a specific location and time period.

[0150] The "means for predicting future catches" is an algorithm or computational model for calculating the likelihood of future catches based on various collected information.

[0151] The "means of displaying on a map in a heat map format by time period" is a method of intuitively visualizing the fishing result prediction results and displaying them on a map in a color-coded format for specific time periods.

[0152] A "fishing prediction score" is a numerical value that represents the expected fishing result at a specific date, time and location, and is evaluated based on certain criteria.

[0153] A "heat map" is a visual representation that visualizes two-dimensional data using color variations to show the concentration and variation of data in a specific area.

[0154] This invention is a system that integrates the information necessary for users to enjoy fishing efficiently and provides future fishing results predictions. This system collects and integrates weather information, temperature information, wind information, high tide / low tide information, and past fishing results information, and displays them on a map in the form of a heat map by time to help users make decisions about their fishing activities.

[0155] System Overview

[0156] This system consists of a dedicated application that allows users to input the date, time, and location of their fishing plans, a back-end server, and an external API and internal database for collecting and processing various data. The details are explained below.

[0157] Program Description

[0158] In this invention, the server includes means for acquiring weather information, means for acquiring temperature information, means for acquiring wind information, means for acquiring high tide and low tide information, means for collecting past fishing results information, means for predicting future fishing results based on this information, means for displaying the prediction results in a heat map format by time on a map, means for calculating a fishing result prediction score based on the weather information, high tide and low tide information, and past fishing results information, and means for generating a color-coded heat map on a map based on the calculated fishing result prediction score. Specifically, the following hardware and software are used.

[0159] Hardware and Software Use

[0160] End-user device: A device used by a user, such as a smartphone or PC.

[0161] Server: A computer system that processes collected data and makes fishing predictions.

[0162] Internet environment: A network environment that allows access to various data APIs.

[0163] The software used is as follows:

[0164] Requests library: Used to retrieve data from external APIs.

[0165] Folium library: Used to display heatmaps on maps.

[0166] Weather Data API: An API for obtaining weather and temperature information.

[0167] Marine Data API: API for obtaining high and low tide information.

[0168] Internal Database: A database system for storing and retrieving past fishing information.

[0169] Data processing and calculation

[0170] The server collects various data from external APIs and internal databases based on the date, time, and location entered by the user into the application. For example, weather information is obtained from the weather data API, and tide information is obtained from the ocean data API. Based on the collected data, the server runs an algorithm to calculate a fishing prediction score and displays the results on a map as a heat map.

[0171] Examples of concrete examples and prompts

[0172] For example, if a user plans to go fishing in Tokyo on October 10, 2023, the system will collect the necessary data based on the specified date and location, and calculate a catch prediction score. Based on this score, specific areas on the map will be color-coded, with red indicating high catch potential and blue indicating low catch potential.

[0173] Example prompt sentence:

[0174] input_date: 2023-10-10

[0175] input_location: lat: 35.6895, lon: 139.6917

[0176] By feeding these prompts into a generative AI model, a real-time heat map will display the optimal fishing times and locations for the specified date, time and location.

[0177] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0178] Step 1:

[0179] A user uses a dedicated application to input the date, time, and location of the planned fishing trip. This input information is the latitude and longitude coordinates of the specified date and location. For example, a user inputs "October 10, 2023, Tokyo (latitude 35.6895, longitude 139.6917)."

[0180] Step 2:

[0181] The device sends the entered date, time, and location information to the server. The entered data includes "input_date: 2023-10-10, input_location: lat: 35.6895, lon: 139.6917".

[0182] Step 3:

[0183] The server collects weather information, temperature information, wind information, and high tide and low tide information from external APIs based on the received date, time, and location. For example, it uses the weather data API to obtain weather information (sunny, cloudy, rainy, etc.), temperature information (Celsius), and wind information (wind speed and direction) for the date, time, and location specified by the user. It also uses the ocean data API to obtain the times and heights of high and low tides.

[0184] Step 4:

[0185] The server retrieves past fishing information from its internal database. Specifically, it extracts past fishing data (e.g., fish species, time of fishing, location, etc.) for the specified location from the database.

[0186] Step 5:

[0187] The server integrates collected weather, temperature, wind, high and low tide information, and past fishing results, and then runs a fishing prediction algorithm. Based on the input data, the server calculates a fishing result prediction score for a specific date, time, and location, taking into account various conditions (for example, high temperatures mean fish are more active).

[0188] Step 6:

[0189] The server generates data to display on a map in the form of a heat map by time based on the calculated catch prediction score. For example, the server generates heat map data so that high catch prediction scores are displayed in red and low catch prediction scores are displayed in blue.

[0190] Step 7:

[0191] The device displays the heat map data received from the server on a dedicated application. The application allows users to visually check the locations and time periods with high fishing prediction scores. Specifically, a color-coded heat map is displayed on the map, with red areas indicating high fishing prospects and blue areas indicating low fishing prospects.

[0192] 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.

[0193] This invention is a system that integrates information for efficient fishing behavior and provides future catch predictions for users who enjoy fishing. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to recommend fishing spots and weather conditions according to the user's emotional state.

[0194] System Overview:

[0195] The system consists of a map app or website where users input the date, time, and location of their fishing plans, a backend server, an emotion engine, and an external API and internal database for collecting and processing various data. Based on the user's specified date, time, and location, the system visually displays prediction results on a map and makes appropriate suggestions based on their emotional state.

[0196] Embodiment Details:

[0197] The user accesses a dedicated map app or website and enters the date, time, and location of the planned fishing trip. The device then sends this information to the server. Based on the received date, time, and location information, the server collects weather information, temperature information, wind information, high and low tide information, and past fishing results information from external APIs and internal databases.

[0198] For example, weather information is obtained using a weather API. Data from the weather API includes the weather condition (sunny, cloudy, rainy, etc.) at a given date and time. Similarly, tide information is obtained via an ocean data API, and past fishing results are retrieved from the system's internal database.

[0199] The server runs an algorithm that uses the collected information to predict future catches. This algorithm combines information such as weather, temperature, wind, tides, and past fishing patterns to calculate the likelihood of catching a fish at a specific date and time. The calculated prediction is expressed as a score that indicates the probability of fishing success at a specific time.

[0200] The calculated prediction results are sent to the device and displayed in the user's map app in the form of a heat map. The heat map visualizes the predicted fishing results by color-coding them on a map, allowing users to intuitively identify the best time and place to fish.

[0201] Furthermore, this system incorporates an emotion engine, which analyzes the user's emotional state by inputting their own emotions. For example, if the user is feeling stressed, the emotion engine will use this information to recommend fishing spots and conditions that are likely to have a relaxing effect.

[0202] Once the user has completed entering their emotion, the device also sends this emotion data to the server. The server then re-runs the prediction algorithm based on the emotion data to predict catches and suggest fishing activities based on the user's emotional state. For example, if the user enters the emotion "I want to relax," the server will adjust its prediction results to prioritize recommending fishing spots with calm weather and few people.

[0203] For example, if a user inputs "I want to escape the stress of busy daily life," the emotion engine will analyze this and identify the best place and time for relaxation. Based on this information, the server will re-run the fishing prediction algorithm and may indicate that "daytime with calm winds and clear weather" is the best time and place. This prediction result is displayed on a map in heat map format, allowing users to intuitively understand the information.

[0204] With the above mechanism, the present invention not only provides catch predictions but also proposes more personalized fishing actions that take into account the user's emotional state, allowing the user to enjoy an efficient and satisfying fishing experience.

[0205] The processing flow will be explained below.

[0206] Step 1:

[0207] The user accesses a dedicated map app or website and inputs the date, time, and location of the planned fishing trip, as well as their emotional state (e.g., wanting to relax, wanting to relieve stress, etc.), and the device then sends this input information to the server.

[0208] Step 2:

[0209] The server receives the date, time, location, and emotional state information sent by the user. The server first sends a request to an external weather API to collect weather information.

[0210] Step 3:

[0211] The server receives the data returned from the weather API and retrieves the weather information, including the weather conditions (sunny, cloudy, rainy, etc.) for the given date, time, and location.

[0212] Step 4:

[0213] The server then sends a request to an external ocean data API to retrieve high and low tide information, which includes the times and magnitudes of tides for the specified fishing spot.

[0214] Step 5:

[0215] The server accesses an internal database to retrieve historical fishing information related to the specified date, time, and location, including the type and number of fish caught during a particular time period.

[0216] Step 6:

[0217] The server uses an external API to retrieve wind and temperature information for the specified location and date and time, including wind speed, wind direction, and temperature.

[0218] Step 7:

[0219] The server combines collected weather, temperature, wind, high and low tide information, and past fishing results to run an algorithm that predicts future fishing results. This algorithm weights each piece of information and calculates the likelihood of a fishing result at a specific date and time.

[0220] Step 8:

[0221] The server uses an emotion engine to analyze the user's emotional state. Based on the emotional state input by the user, the emotion engine evaluates the user's current emotional state and reflects it in the recommendation of fishing spots and conditions.

[0222] Step 9:

[0223] The server re-runs the prediction algorithm based on the analysis results of the emotion engine to generate a fishing forecast that reflects the user's emotional state. For example, if a user inputs "I want to relax," the server will adjust the prediction results to prioritize fishing spots with calm weather and few people.

[0224] Step 10:

[0225] The server generates the final prediction results and stores them as a catch prediction score for the specified time slot, which indicates the probability of fishing success during that particular time slot.

[0226] Step 11:

[0227] The server sends the prediction results to the device.

[0228] Step 12:

[0229] Based on the fishing prediction score received by the device, the results are displayed on a map in the form of a heat map. This heat map shows areas with high fishing predictions in "red" and areas with low fishing predictions in "blue," allowing users to intuitively identify the best time and place to fish.

[0230] Step 13:

[0231] Users can check the heat map on the map, select the best time and place to fish, and plan their actual fishing activities. By taking suggestions from the emotion engine into consideration, users can enjoy a more satisfying fishing experience.

[0232] Example 2

[0233] 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."

[0234] Conventional fishing prediction systems make predictions based on environmental information such as weather, temperature, wind, and high and low tides, but many of them do not take into account the user's emotional state or psychological needs. As a result, they are unable to select or suggest fishing spots that suit the user's emotional state, and they are unable to fully increase user satisfaction. In addition, existing systems lack an effective means of visually displaying the results of fishing predictions in an easy-to-understand manner. These issues need to be resolved.

[0235] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0236] In this invention, the server includes means for acquiring weather information, means for acquiring temperature information, means for acquiring wind information, means for acquiring high tide and low tide information, means for collecting past fishing results, means for inputting and analyzing user emotions, means for predicting future fishing results based on this information and the user's emotional state, and means for displaying the predicted results on a map in the form of a heat map by time. This makes it possible to predict fishing results and suggest fishing activities according to the user's emotional state, thereby improving user satisfaction and the value of the experience.

[0237] "Weather Information" refers to weather conditions (sunny, cloudy, rainy, snowy, etc.) and related meteorological parameters at a specified date, time and location.

[0238] "Temperature Information" refers to temperature (in Celsius or Fahrenheit) data for a specified date, time and location.

[0239] "Wind Information" refers to data on wind speed and direction at a specified date, time and location.

[0240] "High and low tide information" refers to data regarding the fluctuation of sea level height at a specified date, time and location.

[0241] "Past fishing information" refers to records and patterns of fishing results previously obtained at a particular date, time and location.

[0242] "Emotion input" refers to the act of a user inputting their current emotional state or psychological goal (e.g., relaxation, stress relief).

[0243] "Emotion analysis" refers to the process of analyzing a user's emotional input and identifying appropriate fishing spots and conditions based on that emotional state.

[0244] "Catch prediction" refers to integrating various collected data and the user's emotional state to calculate the likelihood of catching a fish at a future date and time.

[0245] A "heat map" is a visual display format that uses color coding to show fishing results on a map.

[0246] This system integrates information for users to plan fishing trips and supports efficient fishing activities. Its special feature is that it takes into account the user's emotional state and suggests optimal fishing spots and trips. This embodiment uses a map app or website used by the user, a backend server, an emotion engine, an external API, and an internal database.

[0247] System configuration

[0248] 1. User Interface

[0249] Users access a dedicated map app or website. This interface allows them to input the date, time, and location of their planned fishing trip. Additionally, they are also provided with a screen to input their current emotional state.

[0250] 2. Data transmission by the terminal

[0251] The device transmits the user-entered date, time, location, and emotional state data to a server using a secure communication protocol (e.g., HTTPS).

[0252] 3. Data processing by the server

[0253] The server collects information using the following external APIs and internal databases:

[0254] Weather information: Uses a weather API (e.g., OpenWeatherMap API).

[0255] Temperature information: Also obtained from the weather API.

[0256] Wind Information: Obtains wind speed and direction data from the weather API.

[0257] High and low tide information: Obtained from ocean data APIs (e.g., NOAA Tides & Currents API).

[0258] Past fishing information: Obtained from an internal database.

[0259] 4. Data integration and analysis

[0260] The server combines the collected data with the user's emotional state and runs a fishing prediction algorithm. The algorithm takes into account weather, temperature, wind, tides, past fishing patterns, and the user's emotional state to calculate the likelihood of catching a fish at a specific date and time. The result is expressed as a score indicating the probability of fishing success at a specific time.

[0261] 5. Visualizing the results

[0262] The calculated prediction results are sent to the device, which then displays this data in the form of a heat map on the user's map app. The heat map visualizes the predicted fishing results by color-coding them on a map, allowing users to intuitively identify the best time and location for fishing.

[0263] 6. Use of Emotion Engines

[0264] The server analyzes the emotional data entered by the user and suggests appropriate fishing spots and conditions based on that emotional state. For example, if a user enters "I want to relax," the server will adjust its prediction results to prioritize fishing spots with calm weather and few people.

[0265] Specific examples

[0266] User Scenarios

[0267] The user enters "I'm going fishing at Yokohama Port at 8am on May 20th, 2023" and the emotion "I want to relax."

[0268] Device behavior

[0269] The device sends date, time, location, and emotion data to the server, and the received prediction results are displayed in heat map format.

[0270] Server Operation

[0271] The server collects necessary data from external APIs and internal databases, runs a fishing prediction algorithm, and analyzes emotional data to generate recommendations that prioritize relaxing fishing spots.

[0272] Prompt Sentence Examples

[0273] "I want to relax and unwind from the stress of busy everyday life. Could you please tell me the best fishing spots and timings?"

[0274] "Tell me where fishing is most successful on a sunny day. The emotional goal is relaxation."

[0275] "Recommend a fishing spot where I can de-stress on a cloudy afternoon."

[0276] By operating the system in accordance with these specific examples and prompts, users can enjoy an efficient and satisfying fishing experience.

[0277] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0278] Step 1:

[0279] The user inputs the date, time, and location of the planned fishing trip, as well as their emotional state, into a dedicated map app or website. For example, they input "I'm going fishing at Yokohama Port at 8:00 a.m. on May 20, 2023," and input the emotion "I want to relax." The input data includes the date, time, location, and emotional state.

[0280] Step 2:

[0281] The device receives the user's date, time, location, and emotional state data as input and sends it to the server using a secure communication protocol (e.g., HTTPS).

[0282] Step 3:

[0283] Based on the received date, time, and location information, the server collects weather information, temperature information, wind information, high and low tide information, and past fishing results information from external APIs (e.g., weather API, ocean data API) and internal databases. It receives date, time, and location information as input and obtains this environmental data as output.

[0284] Step 4:

[0285] The server combines collected weather, temperature, wind, high and low tide information, and past fishing results to run a fishing prediction algorithm. The algorithm takes each piece of information as input and calculates the likelihood of catching a fish at a specific time. As an output, it generates a predicted score indicating the fishing success rate at a specific date and time.

[0286] Step 5:

[0287] The server analyzes the prediction score and the user's emotional state data and adjusts the prediction result. For example, if the user inputs "I want to relax," the server adjusts the prediction result to prioritize recommending fishing spots with calm weather and few people. The server receives the prediction score and emotional state as input and obtains the adjusted prediction score as output.

[0288] Step 6:

[0289] The server sends the adjusted prediction result to the terminal, and sends the adjusted prediction score as output, which the terminal receives.

[0290] Step 7:

[0291] The device displays the received prediction scores in the form of a heat map on the user's map app. The heat map visualizes the predicted catch scores on a map by color-coding them, allowing users to intuitively identify the best time and place to fish. The device receives the prediction scores as input and displays the heat map as output.

[0292] Specifically, if a user inputs "I'm going fishing at Yokohama Port at 8:00 AM on May 20, 2023" and emotionally inputs "I want to relax," the device will send this information to the server. The server collects weather information, temperature information, wind information, high and low tide information, and past fishing results, and runs a fishing result prediction algorithm to calculate a fishing result prediction score. The server then adjusts the prediction result taking into account the user's emotional state and sends it to the device. Finally, the device displays the adjusted prediction score on a map in heat map format, allowing the user to intuitively understand the optimal fishing time and location for relaxation.

[0293] (Application example 2)

[0294] 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."

[0295] In the operation of factory robots, conventional systems make predictions based on weather information and past data, but do not optimize operations by taking into account the emotional state of the manager. This increases the burden on the manager and reduces overall operational efficiency. The present invention aims to solve these problems and provide a flexible operation plan based on the emotional state of the factory manager.

[0296] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring weather information, means for acquiring temperature information, means for acquiring wind information, means for acquiring high tide and low tide information, means for collecting past fishing results, means for predicting future fishing results based on this information, means for displaying the prediction results on a map in the form of a heat map by time, means including an emotion engine for inputting and analyzing the user's emotional state, means for adjusting the operation plan based on the emotion data, and means for optimizing production management and operation schedules. This makes it possible to provide optimal operation schedules and work plans that correspond to the manager's emotional state.

[0297] "Weather information" is information about weather conditions such as sunny, cloudy, rainy, etc., obtained from forecasting agencies and weather data providing services.

[0298] "Temperature information" is information about the temperature value at a specific date, time and location.

[0299] "Wind information" is information about wind conditions at a specific date, time, and location, such as wind speed and direction.

[0300] "High tide and low tide information" is information about the rise and fall of the ocean tides on a specific date and time.

[0301] "Past fishing information" is information previously acquired about fishing results at a specific location and date.

[0302] An "emotion engine" is software or a system for analyzing a user's emotional state.

[0303] The "heat map format" is a method for intuitively visualizing data by representing it using shades of color.

[0304] A "predictive algorithm" is a computational procedure or model that predicts future outcomes based on collected data.

[0305] A "database" is a system for systematically storing various types of information.

[0306] "Server" means a primarily network-based computer system that collects and processes data and provides the results to users.

[0307] An "operational plan" is a plan that determines how work is to be carried out in a factory or other work environment.

[0308] "Production control" is the process of planning, implementing, and monitoring production activities to ensure their efficiency.

[0309] An "operation schedule" is a timetable for planning and adjusting work and operations.

[0310] In a system for realizing this invention, a factory manager, who is a user, first accesses an interface for inputting an operation plan. The user inputs the date, time, and location of the factory operation, the current operating status of the machine, past operation data, and their own emotional state. This emotional state is input through simple text input.

[0311] The device sends this input information to the server, which then uses external databases such as weather APIs to obtain weather, temperature, and wind information. It also obtains past fishing results from its internal database. This completes the necessary operational data.

[0312] The server then uses the collected information to run predictive algorithms that combine multiple factors to predict future operational outcomes, such as weather, temperature, and wind data to calculate how efficiently a machine will operate at a specific time and date.

[0313] Additionally, the server includes an emotion engine that analyzes the user's emotional state. If the manager is feeling stressed, the prediction results are adjusted to provide a less demanding operational plan or break recommendations. This emotion analysis is performed using natural language processing (NLP) technology.

[0314] The forecast results generated by the server are displayed on the device in the form of a heat map, allowing users to intuitively understand which areas are best suited for which time of day.

[0315] As a concrete example, when a factory manager writes the next day's operations, he or she might enter the following information:

[0316] Location: Tokyo

[0317] Date: 2023-10-02

[0318] Machine status: Normal operation

[0319] Historical data: Average performance score of 0.7 last week

[0320] Emotion text: "I want to be free from the stress of busy days"

[0321] An example prompt is:

[0322] Calculate an operational prediction score based on weather information for the location and date / time specified by the user, the machine's operating status, and past data. Also, adjust the prediction score according to the user's emotional state and propose the optimal operational plan. Please use the following information.

[0323] Location: Tokyo

[0324] Date: 2023-10-02

[0325] Machine status: Normal operation

[0326] Historical data: Average performance score of 0.7 last week

[0327] Emotion text: "I want to be free from the stress of busy days"

[0328] Calculate the appropriate operational forecast score and present the optimal operational plan based on it.

[0329] This system enables factory managers to create efficient operational plans that are in line with emotional intelligence. Through the entire process, from data acquisition and operational prediction to emotion analysis, the system aims to improve factory operational efficiency and manager satisfaction.

[0330] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0331] Step 1:

[0332] The user inputs the date, time, and location of the planned fishing trip, the machine's operating status, past operation data, and emotion text.

[0333] The entered information is prepared to be sent to the server in the next step, and the resulting data becomes the basis for creating operational plans.

[0334] Step 2:

[0335] The terminal transmits the user's input information to the server.

[0336] The transmitted information includes date and time, location, machine operating status, historical operational data, and emotion text, based on which the server processes the data and proceeds to the next step.

[0337] Step 3:

[0338] The server retrieves weather information, temperature information, and wind information from the weather API.

[0339] Through API requests, various weather data related to a specific date, time, and location is collected, which serves as the basis for operational forecasts.

[0340] Step 4:

[0341] The server retrieves past fishing information from an internal database.

[0342] Database queries are used to extract the necessary historical data and feed it into operational forecasting algorithms, which then generate forecasts based on past performance.

[0343] Step 5:

[0344] The server runs the prediction algorithm.

[0345] A prediction algorithm is run using inputs such as weather information, temperature information, wind information, high and low tide information, and past fishing results. The algorithm integrates the data from each element and outputs a score that predicts operational efficiency for a specific date and time.

[0346] Step 6:

[0347] The server analyzes the user's emotional state using an emotion engine.

[0348] Using natural language processing technology, the input emotional text is analyzed to determine the user's emotional state, which is then used to adjust the prediction results in the next step.

[0349] Step 7:

[0350] The server adjusts the prediction results depending on the emotional state.

[0351] Based on the analyzed emotional information, the system recalculates operational prediction results and generates plans that correspond to the user's emotional state. For example, it presents a low-stress operational schedule to a user who is feeling stressed.

[0352] Step 8:

[0353] The server generates the final prediction results and sends them to the device in the form of a heat map.

[0354] The generated plans and prediction scores are visualized and output as a color-coded heat map, allowing users to intuitively understand which areas are optimal for which time periods.

[0355] Step 9:

[0356] The device displays the heat map to the user.

[0357] It displays a heat map and provides users with a visual representation, based on which they can implement optimal operational plans.

[0358] Step 10:

[0359] The user makes the final decision.

[0360] Based on the presented information, the user finalizes the operational plan and moves on to specific actions, thereby achieving efficient and effective operations that take into consideration the emotional state of the manager.

[0361] 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.

[0362] 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.

[0363] 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.

[0364] [Second embodiment]

[0365] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0366] 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.

[0367] 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).

[0368] 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.

[0369] 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.

[0370] 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).

[0371] 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.

[0372] 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.

[0373] 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.

[0374] 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.

[0375] In the smart glasses 214, 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.

[0376] 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."

[0377] This system integrates the information necessary for users to enjoy fishing efficiently and provides future fishing results predictions. It collects and integrates weather information, temperature information, wind information, high tide / low tide information, and past fishing results, and displays them on a map in the form of a heat map by time, helping users make decisions about their fishing activities.

[0378] System Overview:

[0379] The system consists of a dedicated map app or website where users can input the date, time, and location of their fishing plans, a backend server, and an external API and internal database for collecting and processing various data. Based on the user's specified date, time, and location, the system visually displays the predicted results on a map.

[0380] Embodiment Details:

[0381] The user accesses a dedicated map app or website and enters the date, time, and location of the planned fishing trip. The device then sends this information to the server. Based on the received date, time, and location, the server collects the necessary weather information, temperature information, wind information, high and low tide information, and past fishing results information from external APIs and internal databases.

[0382] For example, weather information is obtained using a weather API. Data from the weather API includes the weather condition (sunny, cloudy, rainy, etc.) at a given date and time. Similarly, tide information is obtained via an ocean data API, and past fishing results are retrieved from the system's internal database.

[0383] The server uses the collected information to run an algorithm that predicts future catches. This algorithm combines information such as weather, temperature, wind, tides, and past fishing patterns to calculate the likelihood of a catch at a specific date and time. The prediction is expressed as a score that indicates the probability of a successful catch at a specific time.

[0384] The calculated prediction results are sent to the device and displayed in the user's map app in the form of a heat map. The heat map visualizes the predicted fishing results by color-coding them on a map, allowing users to intuitively identify the best time and place to fish.

[0385] For example, the heat map will show "red" during times when the fishing yield is expected to be high at a particular fishing spot, and "blue" during times when the fishing yield is low. This allows users to intuitively decide when to select a fishing spot and when to go fishing.

[0386] Based on the information provided by this system, users can efficiently plan their fishing trips. Furthermore, the system is capable of continuously collecting data and improving its algorithms to improve the accuracy of catch predictions, which is expected to further improve the user experience.

[0387] The processing flow will be explained below.

[0388] Step 1:

[0389] Users access a dedicated map app or website and input the date, time, and location of their planned fishing trip. The device then sends this information to the server.

[0390] Step 2:

[0391] The server receives the date, time, and location information sent by the user, and uses this information to prepare to collect the necessary data from external APIs and internal databases.

[0392] Step 3:

[0393] The server retrieves weather information using an external weather API. This process retrieves weather data including the weather condition (sunny, cloudy, rainy, etc.) associated with the specified location and date / time.

[0394] Step 4:

[0395] The server also uses an external ocean data API to obtain high and low tide information, including the time and magnitude of the tides at the specified fishing spot.

[0396] Step 5:

[0397] The server collects historical fishing information from an internal database, which contains historical fishing data for a specified date, time, and location, including the type and number of fish caught.

[0398] Step 6:

[0399] The server also retrieves wind and temperature information using an external API, including wind speed, wind direction, and temperature for a specified location and date and time.

[0400] Step 7:

[0401] The server combines the collected weather, temperature, wind, high and low tide information, and past fishing results, and then runs an algorithm to predict future fishing results. This algorithm weights each piece of information and calculates the likelihood of a fishing result.

[0402] Step 8:

[0403] The server generates predictions and stores them as catch prediction scores for each specified time slot, which indicate the likelihood of fishing success during that particular time slot.

[0404] Step 9:

[0405] The server transmits the generated fishing result prediction score to the terminal.

[0406] Step 10:

[0407] Based on the fishing prediction score received by the device, the results are displayed on a map in the form of a heat map. This heat map shows areas with high fishing predictions in "red" and areas with low fishing predictions in "blue," allowing users to intuitively identify the best time and place to fish.

[0408] Example 1

[0409] 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."

[0410] Conventional fishing planning systems require users to manually collect weather information, tide information, past fishing results, and other information from multiple sources and integrate that information to make predictions. This makes the information collection and integration process cumbersome, and often results in inaccurate predictions. Furthermore, there are limited ways to visually display fishing results, making it difficult for users to intuitively determine the optimal fishing timing and location. The present invention aims to solve these problems and provide a system that allows users to efficiently plan fishing.

[0411] 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.

[0412] In this invention, the server includes means for acquiring weather information, means for acquiring temperature information, means for acquiring wind information, means for acquiring tide information, means for collecting past fishing results, an algorithm for predicting future fishing results based on this information, means for displaying the prediction results in a heat map format by time on a map, means for a user to input the date, time, and location of the planned fishing trip, means for transmitting the input information to the server, means for collecting necessary information from external and internal databases, means for integrating the collected information and performing prediction calculations, means for transmitting the prediction results to a user terminal, and means for displaying the prediction results on a user terminal. This allows a user to automatically collect and integrate necessary information from multiple information sources, obtain highly accurate fishing results predictions, and visualize the results in a form that is easy to understand intuitively.

[0413] "Means for obtaining weather information" refers to the function of the system to automatically collect weather data for a specified date, time and location from an external database.

[0414] "Means for obtaining temperature information" refers to the function of the system to automatically collect temperature data for a specified date, time and location from an external database.

[0415] "Means for obtaining wind information" refers to the function of the system to automatically collect wind speed and direction at a specified date, time and location from an external database.

[0416] "Means for obtaining tidal information" refers to the system's ability to automatically collect high and low tide data for specified dates, times, and locations from an external database.

[0417] "Means for collecting past fishing information" refers to the system's ability to obtain previously recorded fishing data from an internal database.

[0418] "Future fishing prediction algorithm" refers to a calculation procedure that integrates collected weather, temperature, wind, tide, and past fishing information to predict the probability of fishing at a specified date, time, and location.

[0419] "Means for displaying the prediction results on a map in the form of a heat map by time period" refers to a function that visually displays the probability of catching a fish obtained by the prediction algorithm on a map by color-coding it according to different time periods.

[0420] "Means for inputting the date, time and location of planned fishing by the user" refers to an interface that allows the user to specify to the system the time and location of planned fishing.

[0421] The "means for transmitting the input information to the server" refers to a function for transmitting the date, time, and location information input by the user to the server.

[0422] "Means for collecting necessary information from external and internal databases" refers to a function for automatically collecting necessary weather information, temperature information, wind information, tide information, and fishing results information based on a specified date, time, and location.

[0423] The "means for integrating the collected information and performing predictive calculations" refers to a function that integrates various collected data and performs calculations to predict future fishing results.

[0424] "Means for sending prediction results to the user's terminal" refers to the function of sending the results of the fishing result prediction calculated by the server to the user's terminal.

[0425] "Means for displaying the prediction results on the user terminal" refers to a function for displaying the prediction results on the user terminal in a format that can be intuitively understood.

[0426] This is a system that integrates the information necessary for users to enjoy fishing efficiently and provides future fishing results. The system integrates and forecasts weather, temperature, wind, high and low tide, and past fishing results, and displays the results in a heat map format by time to help users make decisions about their fishing activities.

[0427] System configuration

[0428] The system consists of the following elements:

[0429] 1. A dedicated mapping app or website where users can input the date, time and location where they plan to fish.

[0430] 2. A backend server that processes the user input.

[0431] 3. External APIs and internal databases for collecting and processing various data.

[0432] Hardware and Software

[0433] Hardware: Server, user device (PC or smartphone).

[0434] software:

[0435] Weather API for obtaining weather information (e.g. OpenWeatherMap API).

[0436] Ocean data APIs for retrieving tide information (e.g., NOAA Tides & Currents API).

[0437] An internal database (e.g. MySQL) that maintains historical catch information and other statistical data.

[0438] Data collection and processing

[0439] The user accesses a dedicated map app or website and inputs the date, time and location of the planned fishing trip. This information is sent by the device to the server, which then performs the following operations:

[0440] 1. Use the weather API to get weather information for a specified date, time, and location. For example, get data including the weather (sunny, cloudy, rainy, etc.) for a specified date and time.

[0441] 2. Use the ocean data API to collect tidal information and obtain the tides for a specified date and time.

[0442] 3. Retrieve historical fishing information from an internal database based on the specified date, time and location.

[0443] Prediction Algorithm

[0444] The server uses the collected information to run an algorithm that predicts future catches. The algorithm combines weather, temperature, wind, tides, and past fishing information to calculate the likelihood of a catch. The prediction is expressed as a score that indicates the probability of a successful catch at a particular time.

[0445] Display in heatmap format

[0446] The calculated prediction results are sent from the server to the device and displayed in the user's map app in the form of a heat map. The heat map visualizes the fishing prediction score using color coding, allowing users to intuitively identify the best time and place to fish.

[0447] Specific examples

[0448] For example, if a user plans to go fishing in Yokohama at 6am on July 15th, they might use the following prompt:

[0449] "I'm planning to go fishing in Yokohama at 6:00 AM on July 15th. What are the fishing results forecasts for this date, time, and location?"

[0450] Based on this prompt, the system will perform the following steps:

[0451] 1. The user accesses a dedicated map app or website and enters the date, time and location where they plan to fish.

[0452] 2. The terminal sends this input information to the server.

[0453] 3. The server collects the necessary information from external APIs and internal databases.

[0454] 4. The server runs a catch prediction algorithm based on the collected information and calculates a predicted score.

[0455] 5. The server sends the results to the device and displays them as a heat map.

[0456] In this way, users can plan their fishing trips efficiently. The system also continuously collects data and improves its algorithms, which is expected to further improve the accuracy of fishing predictions.

[0457] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0458] Step 1:

[0459] A user accesses a dedicated mapping app or website and enters the date, time, and location where they plan to fish. The entered date, time, and location are captured by the user's device. Specifically, the user selects a date and time from the calendar function and pins the desired fishing location on the map. This information is stored locally and sent for further processing.

[0460] Input: User-selected date, time, and location.

[0461] Output: The date, time, and location information entered.

[0462] Step 2:

[0463] The device sends the date, time, and location information entered by the user to the server. In this case, the information is sent to the server using an HTTP POST request. Specifically, the device packages the information and sends it to the server when triggered by a click event by the user (for example, pressing the "Send" button).

[0464] Input: Date, time, and location information entered by the user.

[0465] Output: HTTP POST request to the server.

[0466] Step 3:

[0467] The server collects the necessary data based on the received date, time, and location information. This includes retrieving weather information from the weather API, tidal information from the ocean data API, and past fishing results from an internal database. Specifically, the server sends an HTTP GET request to the API endpoint to retrieve the necessary information.

[0468] Input: Date, time, and location information sent from your device.

[0469] Output: Collected weather information, tide information, and past fishing results.

[0470] Step 4:

[0471] The server integrates the collected data and runs an algorithm to predict future catches based on this information. Specifically, the server integrates each piece of information, analyzes the data using machine learning models, and calculates a prediction score.

[0472] Input: Collected weather information, tide information, and past fishing results.

[0473] Output: Prediction score.

[0474] Step 5:

[0475] The server then sends the calculated prediction score to the user's device. This process uses an HTTP POST request. Specifically, the server serializes the prediction score in JSON format and sends it to the user's device.

[0476] Input: The calculated prediction score.

[0477] Output: HTTP POST request to the user device.

[0478] Step 6:

[0479] The device then displays the received prediction scores as a heat map on the map app. Specifically, the device analyzes the received data and uses a heat map library to display it in different colors on the map.

[0480] Input: Prediction score sent by the server.

[0481] Output: Prediction scores displayed in a heatmap format.

[0482] (Application example 1)

[0483] 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."

[0484] Conventional fishing information systems provide limited information for users to enjoy fishing, making it difficult to accurately predict catches at specific times, dates, and locations. Furthermore, these systems lack intuitive visual information displays, providing insufficient support for users to identify optimal fishing times and locations. The present invention aims to solve these problems by providing a visual display of future catches, allowing users to enjoy fishing more efficiently.

[0485] 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.

[0486] In this invention, the server includes means for acquiring weather information, means for acquiring temperature information, means for acquiring wind information, means for acquiring high tide and low tide information, means for collecting past fishing results, means for predicting future fishing results based on this information, means for displaying the prediction results in a heat map format by time on a map, means for calculating a fishing result prediction score based on the weather information, high tide and low tide information, and past fishing results information, and means for generating a color-coded heat map on a map based on the calculated fishing result prediction score. This allows users to intuitively understand the fishing result prediction score and identify the optimal fishing timing and location.

[0487] "Weather information" refers to weather data at a specific date and time, and generally includes weather conditions such as sunny, cloudy, rainy, and snowy.

[0488] "Temperature information" is data relating to the temperature at a specific location during a specific time period.

[0489] "Wind information" refers to data regarding wind speed and direction at a specific date and time.

[0490] "High and low tide information" refers to data regarding the times and heights of high and low tides at a specific location.

[0491] "Past fishing information" refers to fishing data recorded in the past based on a specific location and time period.

[0492] The "means for predicting future catches" is an algorithm or computational model for calculating the likelihood of future catches based on various collected information.

[0493] The "means of displaying on a map in a heat map format by time period" is a method of intuitively visualizing the fishing result prediction results and displaying them on a map in a color-coded format for specific time periods.

[0494] A "fishing prediction score" is a numerical value that represents the expected fishing result at a specific date, time and location, and is evaluated based on certain criteria.

[0495] A "heat map" is a visual representation that visualizes two-dimensional data using color variations to show the concentration and variation of data in a specific area.

[0496] This invention is a system that integrates the information necessary for users to enjoy fishing efficiently and provides future fishing results predictions. This system collects and integrates weather information, temperature information, wind information, high tide / low tide information, and past fishing results information, and displays them on a map in the form of a heat map by time to help users make decisions about their fishing activities.

[0497] System Overview

[0498] This system consists of a dedicated application that allows users to input the date, time, and location of their fishing plans, a back-end server, and an external API and internal database for collecting and processing various data. The details are explained below.

[0499] Program Description

[0500] In this invention, the server includes means for acquiring weather information, means for acquiring temperature information, means for acquiring wind information, means for acquiring high tide and low tide information, means for collecting past fishing results information, means for predicting future fishing results based on this information, means for displaying the prediction results in a heat map format by time on a map, means for calculating a fishing result prediction score based on the weather information, high tide and low tide information, and past fishing results information, and means for generating a color-coded heat map on a map based on the calculated fishing result prediction score. Specifically, the following hardware and software are used.

[0501] Hardware and Software Use

[0502] End-user device: A device used by a user, such as a smartphone or PC.

[0503] Server: A computer system that processes collected data and makes fishing predictions.

[0504] Internet environment: A network environment that allows access to various data APIs.

[0505] The software used is as follows:

[0506] Requests library: Used to retrieve data from external APIs.

[0507] Folium library: Used to display heatmaps on maps.

[0508] Weather Data API: An API for obtaining weather and temperature information.

[0509] Marine Data API: API for obtaining high and low tide information.

[0510] Internal Database: A database system for storing and retrieving past fishing information.

[0511] Data processing and calculation

[0512] The server collects various data from external APIs and internal databases based on the date, time, and location entered by the user into the application. For example, weather information is obtained from the weather data API, and tide information is obtained from the ocean data API. Based on the collected data, the server runs an algorithm to calculate a fishing prediction score and displays the results on a map as a heat map.

[0513] Examples of concrete examples and prompts

[0514] For example, if a user plans to go fishing in Tokyo on October 10, 2023, the system will collect the necessary data based on the specified date and location, and calculate a catch prediction score. Based on this score, specific areas on the map will be color-coded, with red indicating high catch potential and blue indicating low catch potential.

[0515] Example prompt sentence:

[0516] input_date: 2023-10-10

[0517] input_location: lat: 35.6895, lon: 139.6917

[0518] By feeding these prompts into a generative AI model, a real-time heat map will display the optimal fishing times and locations for the specified date, time and location.

[0519] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0520] Step 1:

[0521] A user uses a dedicated application to input the date, time, and location of the planned fishing trip. This input information is the latitude and longitude coordinates of the specified date and location. For example, a user inputs "October 10, 2023, Tokyo (latitude 35.6895, longitude 139.6917)."

[0522] Step 2:

[0523] The device sends the entered date, time, and location information to the server. The entered data includes "input_date: 2023-10-10, input_location: lat: 35.6895, lon: 139.6917".

[0524] Step 3:

[0525] The server collects weather information, temperature information, wind information, and high tide and low tide information from external APIs based on the received date, time, and location. For example, it uses the weather data API to obtain weather information (sunny, cloudy, rainy, etc.), temperature information (Celsius), and wind information (wind speed and direction) for the date, time, and location specified by the user. It also uses the ocean data API to obtain the times and heights of high and low tides.

[0526] Step 4:

[0527] The server retrieves past fishing information from its internal database. Specifically, it extracts past fishing data (e.g., fish species, time of fishing, location, etc.) for the specified location from the database.

[0528] Step 5:

[0529] The server integrates collected weather, temperature, wind, high and low tide information, and past fishing results, and then runs a fishing prediction algorithm. Based on the input data, the server calculates a fishing result prediction score for a specific date, time, and location, taking into account various conditions (for example, high temperatures mean fish are more active).

[0530] Step 6:

[0531] The server generates data to display on a map in the form of a heat map by time based on the calculated catch prediction score. For example, the server generates heat map data so that high catch prediction scores are displayed in red and low catch prediction scores are displayed in blue.

[0532] Step 7:

[0533] The device displays the heat map data received from the server on a dedicated application. The application allows users to visually check the locations and time periods with high fishing prediction scores. Specifically, a color-coded heat map is displayed on the map, with red areas indicating high fishing prospects and blue areas indicating low fishing prospects.

[0534] 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.

[0535] This invention is a system that integrates information for efficient fishing behavior and provides future catch predictions for users who enjoy fishing. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to recommend fishing spots and weather conditions according to the user's emotional state.

[0536] System Overview:

[0537] The system consists of a map app or website where users input the date, time, and location of their fishing plans, a backend server, an emotion engine, and an external API and internal database for collecting and processing various data. Based on the user's specified date, time, and location, the system visually displays prediction results on a map and makes appropriate suggestions based on their emotional state.

[0538] Embodiment Details:

[0539] The user accesses a dedicated map app or website and enters the date, time, and location of the planned fishing trip. The device then sends this information to the server. Based on the received date, time, and location information, the server collects weather information, temperature information, wind information, high and low tide information, and past fishing results information from external APIs and internal databases.

[0540] For example, weather information is obtained using a weather API. Data from the weather API includes the weather condition (sunny, cloudy, rainy, etc.) at a given date and time. Similarly, tide information is obtained via an ocean data API, and past fishing results are retrieved from the system's internal database.

[0541] The server runs an algorithm that uses the collected information to predict future catches. This algorithm combines information such as weather, temperature, wind, tides, and past fishing patterns to calculate the likelihood of catching a fish at a specific date and time. The calculated prediction is expressed as a score that indicates the probability of fishing success at a specific time.

[0542] The calculated prediction results are sent to the device and displayed in the user's map app in the form of a heat map. The heat map visualizes the predicted fishing results by color-coding them on a map, allowing users to intuitively identify the best time and place to fish.

[0543] Furthermore, this system incorporates an emotion engine, which analyzes the user's emotional state by inputting their own emotions. For example, if the user is feeling stressed, the emotion engine will use this information to recommend fishing spots and conditions that are likely to have a relaxing effect.

[0544] Once the user has completed entering their emotion, the device also sends this emotion data to the server. The server then re-runs the prediction algorithm based on the emotion data to predict catches and suggest fishing activities based on the user's emotional state. For example, if the user enters the emotion "I want to relax," the server will adjust its prediction results to prioritize recommending fishing spots with calm weather and few people.

[0545] For example, if a user inputs "I want to escape the stress of busy daily life," the emotion engine will analyze this and identify the best place and time for relaxation. Based on this information, the server will re-run the fishing prediction algorithm and may indicate that "daytime with calm winds and clear weather" is the best time and place. This prediction result is displayed on a map in heat map format, allowing users to intuitively understand the information.

[0546] With the above mechanism, the present invention not only provides catch predictions but also proposes more personalized fishing actions that take into account the user's emotional state, allowing the user to enjoy an efficient and satisfying fishing experience.

[0547] The processing flow will be explained below.

[0548] Step 1:

[0549] The user accesses a dedicated map app or website and inputs the date, time, and location of the planned fishing trip, as well as their emotional state (e.g., wanting to relax, wanting to relieve stress, etc.), and the device then sends this input information to the server.

[0550] Step 2:

[0551] The server receives the date, time, location, and emotional state information sent by the user. The server first sends a request to an external weather API to collect weather information.

[0552] Step 3:

[0553] The server receives the data returned from the weather API and retrieves the weather information, including the weather conditions (sunny, cloudy, rainy, etc.) for the given date, time, and location.

[0554] Step 4:

[0555] The server then sends a request to an external ocean data API to retrieve high and low tide information, which includes the times and magnitudes of tides for the specified fishing spot.

[0556] Step 5:

[0557] The server accesses an internal database to retrieve historical fishing information related to the specified date, time, and location, including the type and number of fish caught during a particular time period.

[0558] Step 6:

[0559] The server uses an external API to retrieve wind and temperature information for the specified location and date and time, including wind speed, wind direction, and temperature.

[0560] Step 7:

[0561] The server combines collected weather, temperature, wind, high and low tide information, and past fishing results to run an algorithm that predicts future fishing results. This algorithm weights each piece of information and calculates the likelihood of a fishing result at a specific date and time.

[0562] Step 8:

[0563] The server uses an emotion engine to analyze the user's emotional state. Based on the emotional state input by the user, the emotion engine evaluates the user's current emotional state and reflects it in the recommendation of fishing spots and conditions.

[0564] Step 9:

[0565] The server re-runs the prediction algorithm based on the analysis results of the emotion engine to generate a fishing forecast that reflects the user's emotional state. For example, if a user inputs "I want to relax," the server will adjust the prediction results to prioritize fishing spots with calm weather and few people.

[0566] Step 10:

[0567] The server generates the final prediction results and stores them as a catch prediction score for the specified time slot, which indicates the probability of fishing success during that particular time slot.

[0568] Step 11:

[0569] The server sends the prediction results to the device.

[0570] Step 12:

[0571] Based on the fishing prediction score received by the device, the results are displayed on a map in the form of a heat map. This heat map shows areas with high fishing predictions in "red" and areas with low fishing predictions in "blue," allowing users to intuitively identify the best time and place to fish.

[0572] Step 13:

[0573] Users can check the heat map on the map, select the best time and place to fish, and plan their actual fishing activities. By taking suggestions from the emotion engine into consideration, users can enjoy a more satisfying fishing experience.

[0574] Example 2

[0575] 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."

[0576] Conventional fishing prediction systems make predictions based on environmental information such as weather, temperature, wind, and high and low tides, but many of them do not take into account the user's emotional state or psychological needs. As a result, they are unable to select or suggest fishing spots that suit the user's emotional state, and they are unable to fully increase user satisfaction. In addition, existing systems lack an effective means of visually displaying the results of fishing predictions in an easy-to-understand manner. These issues need to be resolved.

[0577] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0578] In this invention, the server includes means for acquiring weather information, means for acquiring temperature information, means for acquiring wind information, means for acquiring high tide and low tide information, means for collecting past fishing results, means for inputting and analyzing user emotions, means for predicting future fishing results based on this information and the user's emotional state, and means for displaying the predicted results on a map in the form of a heat map by time. This makes it possible to predict fishing results and suggest fishing activities according to the user's emotional state, thereby improving user satisfaction and the value of the experience.

[0579] "Weather Information" refers to weather conditions (sunny, cloudy, rainy, snowy, etc.) and related meteorological parameters at a specified date, time and location.

[0580] "Temperature Information" refers to temperature (in Celsius or Fahrenheit) data for a specified date, time and location.

[0581] "Wind Information" refers to data on wind speed and direction at a specified date, time and location.

[0582] "High and low tide information" refers to data regarding the fluctuation of sea level height at a specified date, time and location.

[0583] "Past fishing information" refers to records and patterns of fishing results previously obtained at a particular date, time and location.

[0584] "Emotion input" refers to the act of a user inputting their current emotional state or psychological goal (e.g., relaxation, stress relief).

[0585] "Emotion analysis" refers to the process of analyzing a user's emotional input and identifying appropriate fishing spots and conditions based on that emotional state.

[0586] "Catch prediction" refers to integrating various collected data and the user's emotional state to calculate the likelihood of catching a fish at a future date and time.

[0587] A "heat map" is a visual display format that uses color coding to show fishing results on a map.

[0588] This system integrates information for users to plan fishing trips and supports efficient fishing activities. Its special feature is that it takes into account the user's emotional state and suggests optimal fishing spots and trips. This embodiment uses a map app or website used by the user, a backend server, an emotion engine, an external API, and an internal database.

[0589] System configuration

[0590] 1. User Interface

[0591] Users access a dedicated map app or website. This interface allows them to input the date, time, and location of their planned fishing trip. Additionally, they are also provided with a screen to input their current emotional state.

[0592] 2. Data transmission by the terminal

[0593] The device transmits the user-entered date, time, location, and emotional state data to a server using a secure communication protocol (e.g., HTTPS).

[0594] 3. Data processing by the server

[0595] The server collects information using the following external APIs and internal databases:

[0596] Weather information: Uses a weather API (e.g., OpenWeatherMap API).

[0597] Temperature information: Also obtained from the weather API.

[0598] Wind Information: Obtains wind speed and direction data from the weather API.

[0599] High and low tide information: Obtained from ocean data APIs (e.g., NOAA Tides & Currents API).

[0600] Past fishing information: Obtained from an internal database.

[0601] 4. Data integration and analysis

[0602] The server combines the collected data with the user's emotional state and runs a fishing prediction algorithm. The algorithm takes into account weather, temperature, wind, tides, past fishing patterns, and the user's emotional state to calculate the likelihood of catching a fish at a specific date and time. The result is expressed as a score indicating the probability of fishing success at a specific time.

[0603] 5. Visualizing the results

[0604] The calculated prediction results are sent to the device, which then displays this data in the form of a heat map on the user's map app. The heat map visualizes the predicted fishing results by color-coding them on a map, allowing users to intuitively identify the best time and location for fishing.

[0605] 6. Use of Emotion Engines

[0606] The server analyzes the emotional data entered by the user and suggests appropriate fishing spots and conditions based on that emotional state. For example, if a user enters "I want to relax," the server will adjust its prediction results to prioritize fishing spots with calm weather and few people.

[0607] Specific examples

[0608] User Scenarios

[0609] The user enters "I'm going fishing at Yokohama Port at 8am on May 20th, 2023" and the emotion "I want to relax."

[0610] Device behavior

[0611] The device sends date, time, location, and emotion data to the server, and the received prediction results are displayed in heat map format.

[0612] Server Operation

[0613] The server collects necessary data from external APIs and internal databases, runs a fishing prediction algorithm, and analyzes emotional data to generate recommendations that prioritize relaxing fishing spots.

[0614] Prompt Sentence Examples

[0615] "I want to relax and unwind from the stress of busy everyday life. Could you please tell me the best fishing spots and timings?"

[0616] "Tell me where fishing is most successful on a sunny day. The emotional goal is relaxation."

[0617] "Recommend a fishing spot where I can de-stress on a cloudy afternoon."

[0618] By operating the system in accordance with these specific examples and prompts, users can enjoy an efficient and satisfying fishing experience.

[0619] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0620] Step 1:

[0621] The user inputs the date, time, and location of the planned fishing trip, as well as their emotional state, into a dedicated map app or website. For example, they input "I'm going fishing at Yokohama Port at 8:00 a.m. on May 20, 2023," and input the emotion "I want to relax." The input data includes the date, time, location, and emotional state.

[0622] Step 2:

[0623] The device receives the user's date, time, location, and emotional state data as input and sends it to the server using a secure communication protocol (e.g., HTTPS).

[0624] Step 3:

[0625] Based on the received date, time, and location information, the server collects weather information, temperature information, wind information, high and low tide information, and past fishing results information from external APIs (e.g., weather API, ocean data API) and internal databases. It receives date, time, and location information as input and obtains this environmental data as output.

[0626] Step 4:

[0627] The server combines collected weather, temperature, wind, high and low tide information, and past fishing results to run a fishing prediction algorithm. The algorithm takes each piece of information as input and calculates the likelihood of catching a fish at a specific time. As an output, it generates a predicted score indicating the fishing success rate at a specific date and time.

[0628] Step 5:

[0629] The server analyzes the prediction score and the user's emotional state data and adjusts the prediction result. For example, if the user inputs "I want to relax," the server adjusts the prediction result to prioritize recommending fishing spots with calm weather and few people. The server receives the prediction score and emotional state as input and obtains the adjusted prediction score as output.

[0630] Step 6:

[0631] The server sends the adjusted prediction result to the terminal, and sends the adjusted prediction score as output, which the terminal receives.

[0632] Step 7:

[0633] The device displays the received prediction scores in the form of a heat map on the user's map app. The heat map visualizes the predicted catch scores on a map by color-coding them, allowing users to intuitively identify the best time and place to fish. The device receives the prediction scores as input and displays the heat map as output.

[0634] Specifically, if a user inputs "I'm going fishing at Yokohama Port at 8:00 AM on May 20, 2023" and emotionally inputs "I want to relax," the device will send this information to the server. The server collects weather information, temperature information, wind information, high and low tide information, and past fishing results, and runs a fishing result prediction algorithm to calculate a fishing result prediction score. The server then adjusts the prediction result taking into account the user's emotional state and sends it to the device. Finally, the device displays the adjusted prediction score on a map in heat map format, allowing the user to intuitively understand the optimal fishing time and location for relaxation.

[0635] (Application example 2)

[0636] 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."

[0637] In the operation of factory robots, conventional systems make predictions based on weather information and past data, but do not optimize operations by taking into account the emotional state of the manager. This increases the burden on the manager and reduces overall operational efficiency. The present invention aims to solve these problems and provide a flexible operation plan based on the emotional state of the factory manager.

[0638] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring weather information, means for acquiring temperature information, means for acquiring wind information, means for acquiring high tide and low tide information, means for collecting past fishing results, means for predicting future fishing results based on this information, means for displaying the prediction results on a map in the form of a heat map by time, means including an emotion engine for inputting and analyzing the user's emotional state, means for adjusting the operation plan based on the emotion data, and means for optimizing production management and operation schedules. This makes it possible to provide optimal operation schedules and work plans that correspond to the manager's emotional state.

[0639] "Weather information" is information about weather conditions such as sunny, cloudy, rainy, etc., obtained from forecasting agencies and weather data providing services.

[0640] "Temperature information" is information about the temperature value at a specific date, time and location.

[0641] "Wind information" is information about wind conditions at a specific date, time, and location, such as wind speed and direction.

[0642] "High tide and low tide information" is information about the rise and fall of the ocean tides on a specific date and time.

[0643] "Past fishing information" is information previously acquired about fishing results at a specific location and date.

[0644] An "emotion engine" is software or a system for analyzing a user's emotional state.

[0645] The "heat map format" is a method for intuitively visualizing data by representing it using shades of color.

[0646] A "predictive algorithm" is a computational procedure or model that predicts future outcomes based on collected data.

[0647] A "database" is a system for systematically storing various types of information.

[0648] "Server" means a primarily network-based computer system that collects and processes data and provides the results to users.

[0649] An "operational plan" is a plan that determines how work is to be carried out in a factory or other work environment.

[0650] "Production control" is the process of planning, implementing, and monitoring production activities to ensure their efficiency.

[0651] An "operation schedule" is a timetable for planning and adjusting work and operations.

[0652] In a system for realizing this invention, a factory manager, who is a user, first accesses an interface for inputting an operation plan. The user inputs the date, time, and location of the factory operation, the current operating status of the machine, past operation data, and their own emotional state. This emotional state is input through simple text input.

[0653] The device sends this input information to the server, which then uses external databases such as weather APIs to obtain weather, temperature, and wind information. It also obtains past fishing results from its internal database. This completes the necessary operational data.

[0654] The server then uses the collected information to run predictive algorithms that combine multiple factors to predict future operational outcomes, such as weather, temperature, and wind data to calculate how efficiently a machine will operate at a specific time and date.

[0655] Additionally, the server includes an emotion engine that analyzes the user's emotional state. If the manager is feeling stressed, the prediction results are adjusted to provide a less demanding operational plan or break recommendations. This emotion analysis is performed using natural language processing (NLP) technology.

[0656] The forecast results generated by the server are displayed on the device in the form of a heat map, allowing users to intuitively understand which areas are best suited for which time of day.

[0657] As a concrete example, when a factory manager writes the next day's operations, he or she might enter the following information:

[0658] Location: Tokyo

[0659] Date: 2023-10-02

[0660] Machine status: Normal operation

[0661] Historical data: Average performance score of 0.7 last week

[0662] Emotion text: "I want to be free from the stress of busy days"

[0663] An example prompt is:

[0664] Calculate an operational prediction score based on weather information for the location and date / time specified by the user, the machine's operating status, and past data. Also, adjust the prediction score according to the user's emotional state and propose the optimal operational plan. Please use the following information.

[0665] Location: Tokyo

[0666] Date: 2023-10-02

[0667] Machine status: Normal operation

[0668] Historical data: Average performance score of 0.7 last week

[0669] Emotion text: "I want to be free from the stress of busy days"

[0670] Calculate the appropriate operational forecast score and present the optimal operational plan based on it.

[0671] This system enables factory managers to create efficient operational plans that are in line with emotional intelligence. Through the entire process, from data acquisition and operational prediction to emotion analysis, the system aims to improve factory operational efficiency and manager satisfaction.

[0672] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0673] Step 1:

[0674] The user inputs the date, time, and location of the planned fishing trip, the machine's operating status, past operation data, and emotion text.

[0675] The entered information is prepared to be sent to the server in the next step, and the resulting data becomes the basis for creating operational plans.

[0676] Step 2:

[0677] The terminal transmits the user's input information to the server.

[0678] The transmitted information includes date and time, location, machine operating status, historical operational data, and emotion text, based on which the server processes the data and proceeds to the next step.

[0679] Step 3:

[0680] The server retrieves weather information, temperature information, and wind information from the weather API.

[0681] Through API requests, various weather data related to a specific date, time, and location is collected, which serves as the basis for operational forecasts.

[0682] Step 4:

[0683] The server retrieves past fishing information from an internal database.

[0684] Database queries are used to extract the necessary historical data and feed it into operational forecasting algorithms, which then generate forecasts based on past performance.

[0685] Step 5:

[0686] The server runs the prediction algorithm.

[0687] A prediction algorithm is run using inputs such as weather information, temperature information, wind information, high and low tide information, and past fishing results. The algorithm integrates the data from each element and outputs a score that predicts operational efficiency for a specific date and time.

[0688] Step 6:

[0689] The server analyzes the user's emotional state using an emotion engine.

[0690] Using natural language processing technology, the input emotional text is analyzed to determine the user's emotional state, which is then used to adjust the prediction results in the next step.

[0691] Step 7:

[0692] The server adjusts the prediction results depending on the emotional state.

[0693] Based on the analyzed emotional information, the system recalculates operational prediction results and generates plans that correspond to the user's emotional state. For example, it presents a low-stress operational schedule to a user who is feeling stressed.

[0694] Step 8:

[0695] The server generates the final prediction results and sends them to the device in the form of a heat map.

[0696] The generated plans and prediction scores are visualized and output as a color-coded heat map, allowing users to intuitively understand which areas are optimal for which time periods.

[0697] Step 9:

[0698] The device displays the heat map to the user.

[0699] It displays a heat map and provides users with a visual representation, based on which they can implement optimal operational plans.

[0700] Step 10:

[0701] The user makes the final decision.

[0702] Based on the presented information, the user finalizes the operational plan and moves on to specific actions, thereby achieving efficient and effective operations that take into consideration the emotional state of the manager.

[0703] 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.

[0704] 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.

[0705] 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.

[0706] [Third embodiment]

[0707] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0708] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0709] 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).

[0710] 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.

[0711] 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.

[0712] 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).

[0713] 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.

[0714] 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.

[0715] 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.

[0716] 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.

[0717] 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.

[0718] 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."

[0719] This system integrates the information necessary for users to enjoy fishing efficiently and provides future fishing results predictions. It collects and integrates weather information, temperature information, wind information, high tide / low tide information, and past fishing results, and displays them on a map in the form of a heat map by time, helping users make decisions about their fishing activities.

[0720] System Overview:

[0721] The system consists of a dedicated map app or website where users can input the date, time, and location of their fishing plans, a backend server, and an external API and internal database for collecting and processing various data. Based on the user's specified date, time, and location, the system visually displays the predicted results on a map.

[0722] Embodiment Details:

[0723] The user accesses a dedicated map app or website and enters the date, time, and location of the planned fishing trip. The device then sends this information to the server. Based on the received date, time, and location, the server collects the necessary weather information, temperature information, wind information, high and low tide information, and past fishing results information from external APIs and internal databases.

[0724] For example, weather information is obtained using a weather API. Data from the weather API includes the weather condition (sunny, cloudy, rainy, etc.) at a given date and time. Similarly, tide information is obtained via an ocean data API, and past fishing results are retrieved from the system's internal database.

[0725] The server uses the collected information to run an algorithm that predicts future catches. This algorithm combines information such as weather, temperature, wind, tides, and past fishing patterns to calculate the likelihood of a catch at a specific date and time. The prediction is expressed as a score that indicates the probability of a successful catch at a specific time.

[0726] The calculated prediction results are sent to the device and displayed in the user's map app in the form of a heat map. The heat map visualizes the predicted fishing results by color-coding them on a map, allowing users to intuitively identify the best time and place to fish.

[0727] For example, the heat map will show "red" during times when the fishing yield is expected to be high at a particular fishing spot, and "blue" during times when the fishing yield is low. This allows users to intuitively decide when to select a fishing spot and when to go fishing.

[0728] Based on the information provided by this system, users can efficiently plan their fishing trips. Furthermore, the system is capable of continuously collecting data and improving its algorithms to improve the accuracy of catch predictions, which is expected to further improve the user experience.

[0729] The processing flow will be explained below.

[0730] Step 1:

[0731] Users access a dedicated map app or website and input the date, time, and location of their planned fishing trip. The device then sends this information to the server.

[0732] Step 2:

[0733] The server receives the date, time, and location information sent by the user, and uses this information to prepare to collect the necessary data from external APIs and internal databases.

[0734] Step 3:

[0735] The server retrieves weather information using an external weather API. This process retrieves weather data including the weather condition (sunny, cloudy, rainy, etc.) associated with the specified location and date / time.

[0736] Step 4:

[0737] The server also uses an external ocean data API to obtain high and low tide information, including the time and magnitude of the tides at the specified fishing spot.

[0738] Step 5:

[0739] The server collects historical fishing information from an internal database, which contains historical fishing data for a specified date, time, and location, including the type and number of fish caught.

[0740] Step 6:

[0741] The server also retrieves wind and temperature information using an external API, including wind speed, wind direction, and temperature for a specified location and date and time.

[0742] Step 7:

[0743] The server combines the collected weather, temperature, wind, high and low tide information, and past fishing results, and then runs an algorithm to predict future fishing results. This algorithm weights each piece of information and calculates the likelihood of a fishing result.

[0744] Step 8:

[0745] The server generates predictions and stores them as catch prediction scores for each specified time slot, which indicate the likelihood of fishing success during that particular time slot.

[0746] Step 9:

[0747] The server transmits the generated fishing result prediction score to the terminal.

[0748] Step 10:

[0749] Based on the fishing prediction score received by the device, the results are displayed on a map in the form of a heat map. This heat map shows areas with high fishing predictions in "red" and areas with low fishing predictions in "blue," allowing users to intuitively identify the best time and place to fish.

[0750] Example 1

[0751] 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."

[0752] Conventional fishing planning systems require users to manually collect weather information, tide information, past fishing results, and other information from multiple sources and integrate that information to make predictions. This makes the information collection and integration process cumbersome, and often results in inaccurate predictions. Furthermore, there are limited ways to visually display fishing results, making it difficult for users to intuitively determine the optimal fishing timing and location. The present invention aims to solve these problems and provide a system that allows users to efficiently plan fishing.

[0753] 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.

[0754] In this invention, the server includes means for acquiring weather information, means for acquiring temperature information, means for acquiring wind information, means for acquiring tide information, means for collecting past fishing results, an algorithm for predicting future fishing results based on this information, means for displaying the prediction results in a heat map format by time on a map, means for a user to input the date, time, and location of the planned fishing trip, means for transmitting the input information to the server, means for collecting necessary information from external and internal databases, means for integrating the collected information and performing prediction calculations, means for transmitting the prediction results to a user terminal, and means for displaying the prediction results on a user terminal. This allows a user to automatically collect and integrate necessary information from multiple information sources, obtain highly accurate fishing results predictions, and visualize the results in a form that is easy to understand intuitively.

[0755] "Means for obtaining weather information" refers to the function of the system to automatically collect weather data for a specified date, time and location from an external database.

[0756] "Means for obtaining temperature information" refers to the function of the system to automatically collect temperature data for a specified date, time and location from an external database.

[0757] "Means for obtaining wind information" refers to the function of the system to automatically collect wind speed and direction at a specified date, time and location from an external database.

[0758] "Means for obtaining tidal information" refers to the system's ability to automatically collect high and low tide data for specified dates, times, and locations from an external database.

[0759] "Means for collecting past fishing information" refers to the system's ability to obtain previously recorded fishing data from an internal database.

[0760] "Future fishing prediction algorithm" refers to a calculation procedure that integrates collected weather, temperature, wind, tide, and past fishing information to predict the probability of fishing at a specified date, time, and location.

[0761] "Means for displaying the prediction results on a map in the form of a heat map by time period" refers to a function that visually displays the probability of catching a fish obtained by the prediction algorithm on a map by color-coding it according to different time periods.

[0762] "Means for inputting the date, time and location of planned fishing by the user" refers to an interface that allows the user to specify to the system the time and location of planned fishing.

[0763] The "means for transmitting the input information to the server" refers to a function for transmitting the date, time, and location information input by the user to the server.

[0764] "Means for collecting necessary information from external and internal databases" refers to a function for automatically collecting necessary weather information, temperature information, wind information, tide information, and fishing results information based on a specified date, time, and location.

[0765] The "means for integrating the collected information and performing predictive calculations" refers to a function that integrates various collected data and performs calculations to predict future fishing results.

[0766] "Means for sending prediction results to the user's terminal" refers to the function of sending the results of the fishing result prediction calculated by the server to the user's terminal.

[0767] "Means for displaying the prediction results on the user terminal" refers to a function for displaying the prediction results on the user terminal in a format that can be intuitively understood.

[0768] This is a system that integrates the information necessary for users to enjoy fishing efficiently and provides future fishing results. The system integrates and forecasts weather, temperature, wind, high and low tide, and past fishing results, and displays the results in a heat map format by time to help users make decisions about their fishing activities.

[0769] System configuration

[0770] The system consists of the following elements:

[0771] 1. A dedicated mapping app or website where users can input the date, time and location where they plan to fish.

[0772] 2. A backend server that processes the user input.

[0773] 3. External APIs and internal databases for collecting and processing various data.

[0774] Hardware and Software

[0775] Hardware: Server, user device (PC or smartphone).

[0776] software:

[0777] Weather API for obtaining weather information (e.g. OpenWeatherMap API).

[0778] Ocean data APIs for retrieving tide information (e.g., NOAA Tides & Currents API).

[0779] An internal database (e.g. MySQL) that maintains historical catch information and other statistical data.

[0780] Data collection and processing

[0781] The user accesses a dedicated map app or website and inputs the date, time and location of the planned fishing trip. This information is sent by the device to the server, which then performs the following operations:

[0782] 1. Use the weather API to get weather information for a specified date, time, and location. For example, get data including the weather (sunny, cloudy, rainy, etc.) for a specified date and time.

[0783] 2. Use the ocean data API to collect tidal information and obtain the tides for a specified date and time.

[0784] 3. Retrieve historical fishing information from an internal database based on the specified date, time and location.

[0785] Prediction Algorithm

[0786] The server uses the collected information to run an algorithm that predicts future catches. The algorithm combines weather, temperature, wind, tides, and past fishing information to calculate the likelihood of a catch. The prediction is expressed as a score that indicates the probability of a successful catch at a particular time.

[0787] Display in heatmap format

[0788] The calculated prediction results are sent from the server to the device and displayed in the user's map app in the form of a heat map. The heat map visualizes the fishing prediction score using color coding, allowing users to intuitively identify the best time and place to fish.

[0789] Specific examples

[0790] For example, if a user plans to go fishing in Yokohama at 6am on July 15th, they might use the following prompt:

[0791] "I'm planning to go fishing in Yokohama at 6:00 AM on July 15th. What are the fishing results forecasts for this date, time, and location?"

[0792] Based on this prompt, the system will perform the following steps:

[0793] 1. The user accesses a dedicated map app or website and enters the date, time and location where they plan to fish.

[0794] 2. The terminal sends this input information to the server.

[0795] 3. The server collects the necessary information from external APIs and internal databases.

[0796] 4. The server runs a catch prediction algorithm based on the collected information and calculates a predicted score.

[0797] 5. The server sends the results to the device and displays them as a heat map.

[0798] In this way, users can plan their fishing trips efficiently. The system also continuously collects data and improves its algorithms, which is expected to further improve the accuracy of fishing predictions.

[0799] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0800] Step 1:

[0801] A user accesses a dedicated mapping app or website and enters the date, time, and location where they plan to fish. The entered date, time, and location are captured by the user's device. Specifically, the user selects a date and time from the calendar function and pins the desired fishing location on the map. This information is stored locally and sent for further processing.

[0802] Input: User-selected date, time, and location.

[0803] Output: The date, time, and location information entered.

[0804] Step 2:

[0805] The device sends the date, time, and location information entered by the user to the server. In this case, the information is sent to the server using an HTTP POST request. Specifically, the device packages the information and sends it to the server when triggered by a click event by the user (for example, pressing the "Send" button).

[0806] Input: Date, time, and location information entered by the user.

[0807] Output: HTTP POST request to the server.

[0808] Step 3:

[0809] The server collects the necessary data based on the received date, time, and location information. This includes retrieving weather information from the weather API, tidal information from the ocean data API, and past fishing results from an internal database. Specifically, the server sends an HTTP GET request to the API endpoint to retrieve the necessary information.

[0810] Input: Date, time, and location information sent from your device.

[0811] Output: Collected weather information, tide information, and past fishing results.

[0812] Step 4:

[0813] The server integrates the collected data and runs an algorithm to predict future catches based on this information. Specifically, the server integrates each piece of information, analyzes the data using machine learning models, and calculates a prediction score.

[0814] Input: Collected weather information, tide information, and past fishing results.

[0815] Output: Prediction score.

[0816] Step 5:

[0817] The server then sends the calculated prediction score to the user's device. This process uses an HTTP POST request. Specifically, the server serializes the prediction score in JSON format and sends it to the user's device.

[0818] Input: The calculated prediction score.

[0819] Output: HTTP POST request to the user device.

[0820] Step 6:

[0821] The device then displays the received prediction scores as a heat map on the map app. Specifically, the device analyzes the received data and uses a heat map library to display it in different colors on the map.

[0822] Input: Prediction score sent by the server.

[0823] Output: Prediction scores displayed in a heatmap format.

[0824] (Application example 1)

[0825] 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."

[0826] Conventional fishing information systems provide limited information for users to enjoy fishing, making it difficult to accurately predict catches at specific times, dates, and locations. Furthermore, these systems lack intuitive visual information displays, providing insufficient support for users to identify optimal fishing times and locations. The present invention aims to solve these problems by providing a visual display of future catches, allowing users to enjoy fishing more efficiently.

[0827] 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.

[0828] In this invention, the server includes means for acquiring weather information, means for acquiring temperature information, means for acquiring wind information, means for acquiring high tide and low tide information, means for collecting past fishing results, means for predicting future fishing results based on this information, means for displaying the prediction results in a heat map format by time on a map, means for calculating a fishing result prediction score based on the weather information, high tide and low tide information, and past fishing results information, and means for generating a color-coded heat map on a map based on the calculated fishing result prediction score. This allows users to intuitively understand the fishing result prediction score and identify the optimal fishing timing and location.

[0829] "Weather information" refers to weather data at a specific date and time, and generally includes weather conditions such as sunny, cloudy, rainy, and snowy.

[0830] "Temperature information" is data relating to the temperature at a specific location during a specific time period.

[0831] "Wind information" refers to data regarding wind speed and direction at a specific date and time.

[0832] "High and low tide information" refers to data regarding the times and heights of high and low tides at a specific location.

[0833] "Past fishing information" refers to fishing data recorded in the past based on a specific location and time period.

[0834] The "means for predicting future catches" is an algorithm or computational model for calculating the likelihood of future catches based on various collected information.

[0835] The "means of displaying on a map in a heat map format by time period" is a method of intuitively visualizing the fishing result prediction results and displaying them on a map in a color-coded format for specific time periods.

[0836] A "fishing prediction score" is a numerical value that represents the expected fishing result at a specific date, time and location, and is evaluated based on certain criteria.

[0837] A "heat map" is a visual representation that visualizes two-dimensional data using color variations to show the concentration and variation of data in a specific area.

[0838] This invention is a system that integrates the information necessary for users to enjoy fishing efficiently and provides future fishing results predictions. This system collects and integrates weather information, temperature information, wind information, high tide / low tide information, and past fishing results information, and displays them on a map in the form of a heat map by time to help users make decisions about their fishing activities.

[0839] System Overview

[0840] This system consists of a dedicated application that allows users to input the date, time, and location of their fishing plans, a back-end server, and an external API and internal database for collecting and processing various data. The details are explained below.

[0841] Program Description

[0842] In this invention, the server includes means for acquiring weather information, means for acquiring temperature information, means for acquiring wind information, means for acquiring high tide and low tide information, means for collecting past fishing results information, means for predicting future fishing results based on this information, means for displaying the prediction results in a heat map format by time on a map, means for calculating a fishing result prediction score based on the weather information, high tide and low tide information, and past fishing results information, and means for generating a color-coded heat map on a map based on the calculated fishing result prediction score. Specifically, the following hardware and software are used.

[0843] Hardware and Software Use

[0844] End-user device: A device used by a user, such as a smartphone or PC.

[0845] Server: A computer system that processes collected data and makes fishing predictions.

[0846] Internet environment: A network environment that allows access to various data APIs.

[0847] The software used is as follows:

[0848] Requests library: Used to retrieve data from external APIs.

[0849] Folium library: Used to display heatmaps on maps.

[0850] Weather Data API: An API for obtaining weather and temperature information.

[0851] Marine Data API: API for obtaining high and low tide information.

[0852] Internal Database: A database system for storing and retrieving past fishing information.

[0853] Data processing and calculation

[0854] The server collects various data from external APIs and internal databases based on the date, time, and location entered by the user into the application. For example, weather information is obtained from the weather data API, and tide information is obtained from the ocean data API. Based on the collected data, the server runs an algorithm to calculate a fishing prediction score and displays the results on a map as a heat map.

[0855] Examples of concrete examples and prompts

[0856] For example, if a user plans to go fishing in Tokyo on October 10, 2023, the system will collect the necessary data based on the specified date and location, and calculate a catch prediction score. Based on this score, specific areas on the map will be color-coded, with red indicating high catch potential and blue indicating low catch potential.

[0857] Example prompt sentence:

[0858] input_date: 2023-10-10

[0859] input_location: lat: 35.6895, lon: 139.6917

[0860] By feeding these prompts into a generative AI model, a real-time heat map will display the optimal fishing times and locations for the specified date, time and location.

[0861] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0862] Step 1:

[0863] A user uses a dedicated application to input the date, time, and location of the planned fishing trip. This input information is the latitude and longitude coordinates of the specified date and location. For example, a user inputs "October 10, 2023, Tokyo (latitude 35.6895, longitude 139.6917)."

[0864] Step 2:

[0865] The device sends the entered date, time, and location information to the server. The entered data includes "input_date: 2023-10-10, input_location: lat: 35.6895, lon: 139.6917".

[0866] Step 3:

[0867] The server collects weather information, temperature information, wind information, and high tide and low tide information from external APIs based on the received date, time, and location. For example, it uses the weather data API to obtain weather information (sunny, cloudy, rainy, etc.), temperature information (Celsius), and wind information (wind speed and direction) for the date, time, and location specified by the user. It also uses the ocean data API to obtain the times and heights of high and low tides.

[0868] Step 4:

[0869] The server retrieves past fishing information from its internal database. Specifically, it extracts past fishing data (e.g., fish species, time of fishing, location, etc.) for the specified location from the database.

[0870] Step 5:

[0871] The server integrates collected weather, temperature, wind, high and low tide information, and past fishing results, and then runs a fishing prediction algorithm. Based on the input data, the server calculates a fishing result prediction score for a specific date, time, and location, taking into account various conditions (for example, high temperatures mean fish are more active).

[0872] Step 6:

[0873] The server generates data to display on a map in the form of a heat map by time based on the calculated catch prediction score. For example, the server generates heat map data so that high catch prediction scores are displayed in red and low catch prediction scores are displayed in blue.

[0874] Step 7:

[0875] The device displays the heat map data received from the server on a dedicated application. The application allows users to visually check the locations and time periods with high fishing prediction scores. Specifically, a color-coded heat map is displayed on the map, with red areas indicating high fishing prospects and blue areas indicating low fishing prospects.

[0876] 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.

[0877] This invention is a system that integrates information for efficient fishing behavior and provides future catch predictions for users who enjoy fishing. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to recommend fishing spots and weather conditions according to the user's emotional state.

[0878] System Overview:

[0879] The system consists of a map app or website where users input the date, time, and location of their fishing plans, a backend server, an emotion engine, and an external API and internal database for collecting and processing various data. Based on the user's specified date, time, and location, the system visually displays prediction results on a map and makes appropriate suggestions based on their emotional state.

[0880] Embodiment Details:

[0881] The user accesses a dedicated map app or website and enters the date, time, and location of the planned fishing trip. The device then sends this information to the server. Based on the received date, time, and location information, the server collects weather information, temperature information, wind information, high and low tide information, and past fishing results information from external APIs and internal databases.

[0882] For example, weather information is obtained using a weather API. Data from the weather API includes the weather condition (sunny, cloudy, rainy, etc.) at a given date and time. Similarly, tide information is obtained via an ocean data API, and past fishing results are retrieved from the system's internal database.

[0883] The server runs an algorithm that uses the collected information to predict future catches. This algorithm combines information such as weather, temperature, wind, tides, and past fishing patterns to calculate the likelihood of catching a fish at a specific date and time. The calculated prediction is expressed as a score that indicates the probability of fishing success at a specific time.

[0884] The calculated prediction results are sent to the device and displayed in the user's map app in the form of a heat map. The heat map visualizes the predicted fishing results by color-coding them on a map, allowing users to intuitively identify the best time and place to fish.

[0885] Furthermore, this system incorporates an emotion engine, which analyzes the user's emotional state by inputting their own emotions. For example, if the user is feeling stressed, the emotion engine will use this information to recommend fishing spots and conditions that are likely to have a relaxing effect.

[0886] Once the user has completed entering their emotion, the device also sends this emotion data to the server. The server then re-runs the prediction algorithm based on the emotion data to predict catches and suggest fishing activities based on the user's emotional state. For example, if the user enters the emotion "I want to relax," the server will adjust its prediction results to prioritize recommending fishing spots with calm weather and few people.

[0887] For example, if a user inputs "I want to escape the stress of busy daily life," the emotion engine will analyze this and identify the best place and time for relaxation. Based on this information, the server will re-run the fishing prediction algorithm and may indicate that "daytime with calm winds and clear weather" is the best time and place. This prediction result is displayed on a map in heat map format, allowing users to intuitively understand the information.

[0888] With the above mechanism, the present invention not only provides catch predictions but also proposes more personalized fishing actions that take into account the user's emotional state, allowing the user to enjoy an efficient and satisfying fishing experience.

[0889] The processing flow will be explained below.

[0890] Step 1:

[0891] The user accesses a dedicated map app or website and inputs the date, time, and location of the planned fishing trip, as well as their emotional state (e.g., wanting to relax, wanting to relieve stress, etc.), and the device then sends this input information to the server.

[0892] Step 2:

[0893] The server receives the date, time, location, and emotional state information sent by the user. The server first sends a request to an external weather API to collect weather information.

[0894] Step 3:

[0895] The server receives the data returned from the weather API and retrieves the weather information, including the weather conditions (sunny, cloudy, rainy, etc.) for the given date, time, and location.

[0896] Step 4:

[0897] The server then sends a request to an external ocean data API to retrieve high and low tide information, which includes the times and magnitudes of tides for the specified fishing spot.

[0898] Step 5:

[0899] The server accesses an internal database to retrieve historical fishing information related to the specified date, time, and location, including the type and number of fish caught during a particular time period.

[0900] Step 6:

[0901] The server uses an external API to retrieve wind and temperature information for the specified location and date and time, including wind speed, wind direction, and temperature.

[0902] Step 7:

[0903] The server combines collected weather, temperature, wind, high and low tide information, and past fishing results to run an algorithm that predicts future fishing results. This algorithm weights each piece of information and calculates the likelihood of a fishing result at a specific date and time.

[0904] Step 8:

[0905] The server uses an emotion engine to analyze the user's emotional state. Based on the emotional state input by the user, the emotion engine evaluates the user's current emotional state and reflects it in the recommendation of fishing spots and conditions.

[0906] Step 9:

[0907] The server re-runs the prediction algorithm based on the analysis results of the emotion engine to generate a fishing forecast that reflects the user's emotional state. For example, if a user inputs "I want to relax," the server will adjust the prediction results to prioritize fishing spots with calm weather and few people.

[0908] Step 10:

[0909] The server generates the final prediction results and stores them as a catch prediction score for the specified time slot, which indicates the probability of fishing success during that particular time slot.

[0910] Step 11:

[0911] The server sends the prediction results to the device.

[0912] Step 12:

[0913] Based on the fishing prediction score received by the device, the results are displayed on a map in the form of a heat map. This heat map shows areas with high fishing predictions in "red" and areas with low fishing predictions in "blue," allowing users to intuitively identify the best time and place to fish.

[0914] Step 13:

[0915] Users can check the heat map on the map, select the best time and place to fish, and plan their actual fishing activities. By taking suggestions from the emotion engine into consideration, users can enjoy a more satisfying fishing experience.

[0916] Example 2

[0917] 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."

[0918] Conventional fishing prediction systems make predictions based on environmental information such as weather, temperature, wind, and high and low tides, but many of them do not take into account the user's emotional state or psychological needs. As a result, they are unable to select or suggest fishing spots that suit the user's emotional state, and they are unable to fully increase user satisfaction. In addition, existing systems lack an effective means of visually displaying the results of fishing predictions in an easy-to-understand manner. These issues need to be resolved.

[0919] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0920] In this invention, the server includes means for acquiring weather information, means for acquiring temperature information, means for acquiring wind information, means for acquiring high tide and low tide information, means for collecting past fishing results, means for inputting and analyzing user emotions, means for predicting future fishing results based on this information and the user's emotional state, and means for displaying the predicted results on a map in the form of a heat map by time. This makes it possible to predict fishing results and suggest fishing activities according to the user's emotional state, thereby improving user satisfaction and the value of the experience.

[0921] "Weather Information" refers to weather conditions (sunny, cloudy, rainy, snowy, etc.) and related meteorological parameters at a specified date, time and location.

[0922] "Temperature Information" refers to temperature (in Celsius or Fahrenheit) data for a specified date, time and location.

[0923] "Wind Information" refers to data on wind speed and direction at a specified date, time and location.

[0924] "High and low tide information" refers to data regarding the fluctuation of sea level height at a specified date, time and location.

[0925] "Past fishing information" refers to records and patterns of fishing results previously obtained at a particular date, time and location.

[0926] "Emotion input" refers to the act of a user inputting their current emotional state or psychological goal (e.g., relaxation, stress relief).

[0927] "Emotion analysis" refers to the process of analyzing a user's emotional input and identifying appropriate fishing spots and conditions based on that emotional state.

[0928] "Catch prediction" refers to integrating various collected data and the user's emotional state to calculate the likelihood of catching a fish at a future date and time.

[0929] A "heat map" is a visual display format that uses color coding to show fishing results on a map.

[0930] This system integrates information for users to plan fishing trips and supports efficient fishing activities. Its special feature is that it takes into account the user's emotional state and suggests optimal fishing spots and trips. This embodiment uses a map app or website used by the user, a backend server, an emotion engine, an external API, and an internal database.

[0931] System configuration

[0932] 1. User Interface

[0933] Users access a dedicated map app or website. This interface allows them to input the date, time, and location of their planned fishing trip. Additionally, they are also provided with a screen to input their current emotional state.

[0934] 2. Data transmission by the terminal

[0935] The device transmits the user-entered date, time, location, and emotional state data to a server using a secure communication protocol (e.g., HTTPS).

[0936] 3. Data processing by the server

[0937] The server collects information using the following external APIs and internal databases:

[0938] Weather information: Uses a weather API (e.g., OpenWeatherMap API).

[0939] Temperature information: Also obtained from the weather API.

[0940] Wind Information: Obtains wind speed and direction data from the weather API.

[0941] High and low tide information: Obtained from ocean data APIs (e.g., NOAA Tides & Currents API).

[0942] Past fishing information: Obtained from an internal database.

[0943] 4. Data integration and analysis

[0944] The server combines the collected data with the user's emotional state and runs a fishing prediction algorithm. The algorithm takes into account weather, temperature, wind, tides, past fishing patterns, and the user's emotional state to calculate the likelihood of catching a fish at a specific date and time. The result is expressed as a score indicating the probability of fishing success at a specific time.

[0945] 5. Visualizing the results

[0946] The calculated prediction results are sent to the device, which then displays this data in the form of a heat map on the user's map app. The heat map visualizes the predicted fishing results by color-coding them on a map, allowing users to intuitively identify the best time and location for fishing.

[0947] 6. Use of Emotion Engines

[0948] The server analyzes the emotional data entered by the user and suggests appropriate fishing spots and conditions based on that emotional state. For example, if a user enters "I want to relax," the server will adjust its prediction results to prioritize fishing spots with calm weather and few people.

[0949] Specific examples

[0950] User Scenarios

[0951] The user enters "I'm going fishing at Yokohama Port at 8am on May 20th, 2023" and the emotion "I want to relax."

[0952] Device behavior

[0953] The device sends date, time, location, and emotion data to the server, and the received prediction results are displayed in heat map format.

[0954] Server Operation

[0955] The server collects necessary data from external APIs and internal databases, runs a fishing prediction algorithm, and analyzes emotional data to generate recommendations that prioritize relaxing fishing spots.

[0956] Prompt Sentence Examples

[0957] "I want to relax and unwind from the stress of busy everyday life. Could you please tell me the best fishing spots and timings?"

[0958] "Tell me where fishing is most successful on a sunny day. The emotional goal is relaxation."

[0959] "Recommend a fishing spot where I can de-stress on a cloudy afternoon."

[0960] By operating the system in accordance with these specific examples and prompts, users can enjoy an efficient and satisfying fishing experience.

[0961] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0962] Step 1:

[0963] The user inputs the date, time, and location of the planned fishing trip, as well as their emotional state, into a dedicated map app or website. For example, they input "I'm going fishing at Yokohama Port at 8:00 a.m. on May 20, 2023," and input the emotion "I want to relax." The input data includes the date, time, location, and emotional state.

[0964] Step 2:

[0965] The device receives the user's date, time, location, and emotional state data as input and sends it to the server using a secure communication protocol (e.g., HTTPS).

[0966] Step 3:

[0967] Based on the received date, time, and location information, the server collects weather information, temperature information, wind information, high and low tide information, and past fishing results information from external APIs (e.g., weather API, ocean data API) and internal databases. It receives date, time, and location information as input and obtains this environmental data as output.

[0968] Step 4:

[0969] The server combines collected weather, temperature, wind, high and low tide information, and past fishing results to run a fishing prediction algorithm. The algorithm takes each piece of information as input and calculates the likelihood of catching a fish at a specific time. As an output, it generates a predicted score indicating the fishing success rate at a specific date and time.

[0970] Step 5:

[0971] The server analyzes the prediction score and the user's emotional state data and adjusts the prediction result. For example, if the user inputs "I want to relax," the server adjusts the prediction result to prioritize recommending fishing spots with calm weather and few people. The server receives the prediction score and emotional state as input and obtains the adjusted prediction score as output.

[0972] Step 6:

[0973] The server sends the adjusted prediction result to the terminal, and sends the adjusted prediction score as output, which the terminal receives.

[0974] Step 7:

[0975] The device displays the received prediction scores in the form of a heat map on the user's map app. The heat map visualizes the predicted catch scores on a map by color-coding them, allowing users to intuitively identify the best time and place to fish. The device receives the prediction scores as input and displays the heat map as output.

[0976] Specifically, if a user inputs "I'm going fishing at Yokohama Port at 8:00 AM on May 20, 2023" and emotionally inputs "I want to relax," the device will send this information to the server. The server collects weather information, temperature information, wind information, high and low tide information, and past fishing results, and runs a fishing result prediction algorithm to calculate a fishing result prediction score. The server then adjusts the prediction result taking into account the user's emotional state and sends it to the device. Finally, the device displays the adjusted prediction score on a map in heat map format, allowing the user to intuitively understand the optimal fishing time and location for relaxation.

[0977] (Application example 2)

[0978] 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."

[0979] In the operation of factory robots, conventional systems make predictions based on weather information and past data, but do not optimize operations by taking into account the emotional state of the manager. This increases the burden on the manager and reduces overall operational efficiency. The present invention aims to solve these problems and provide a flexible operation plan based on the emotional state of the factory manager.

[0980] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring weather information, means for acquiring temperature information, means for acquiring wind information, means for acquiring high tide and low tide information, means for collecting past fishing results, means for predicting future fishing results based on this information, means for displaying the prediction results on a map in the form of a heat map by time, means including an emotion engine for inputting and analyzing the user's emotional state, means for adjusting the operation plan based on the emotion data, and means for optimizing production management and operation schedules. This makes it possible to provide optimal operation schedules and work plans that correspond to the manager's emotional state.

[0981] "Weather information" is information about weather conditions such as sunny, cloudy, rainy, etc., obtained from forecasting agencies and weather data providing services.

[0982] "Temperature information" is information about the temperature value at a specific date, time and location.

[0983] "Wind information" is information about wind conditions at a specific date, time, and location, such as wind speed and direction.

[0984] "High tide and low tide information" is information about the rise and fall of the ocean tides on a specific date and time.

[0985] "Past fishing information" is information previously acquired about fishing results at a specific location and date.

[0986] An "emotion engine" is software or a system for analyzing a user's emotional state.

[0987] The "heat map format" is a method for intuitively visualizing data by representing it using shades of color.

[0988] A "predictive algorithm" is a computational procedure or model that predicts future outcomes based on collected data.

[0989] A "database" is a system for systematically storing various types of information.

[0990] "Server" means a primarily network-based computer system that collects and processes data and provides the results to users.

[0991] An "operational plan" is a plan that determines how work is to be carried out in a factory or other work environment.

[0992] "Production control" is the process of planning, implementing, and monitoring production activities to ensure their efficiency.

[0993] An "operation schedule" is a timetable for planning and adjusting work and operations.

[0994] In a system for realizing this invention, a factory manager, who is a user, first accesses an interface for inputting an operation plan. The user inputs the date, time, and location of the factory operation, the current operating status of the machine, past operation data, and their own emotional state. This emotional state is input through simple text input.

[0995] The device sends this input information to the server, which then uses external databases such as weather APIs to obtain weather, temperature, and wind information. It also obtains past fishing results from its internal database. This completes the necessary operational data.

[0996] The server then uses the collected information to run predictive algorithms that combine multiple factors to predict future operational outcomes, such as weather, temperature, and wind data to calculate how efficiently a machine will operate at a specific time and date.

[0997] Additionally, the server includes an emotion engine that analyzes the user's emotional state. If the manager is feeling stressed, the prediction results are adjusted to provide a less demanding operational plan or break recommendations. This emotion analysis is performed using natural language processing (NLP) technology.

[0998] The forecast results generated by the server are displayed on the device in the form of a heat map, allowing users to intuitively understand which areas are best suited for which time of day.

[0999] As a concrete example, when a factory manager writes the next day's operations, he or she might enter the following information:

[1000] Location: Tokyo

[1001] Date: 2023-10-02

[1002] Machine status: Normal operation

[1003] Historical data: Average performance score of 0.7 last week

[1004] Emotion text: "I want to be free from the stress of busy days"

[1005] An example prompt is:

[1006] Calculate an operational prediction score based on weather information for the location and date / time specified by the user, the machine's operating status, and past data. Also, adjust the prediction score according to the user's emotional state and propose the optimal operational plan. Please use the following information.

[1007] Location: Tokyo

[1008] Date: 2023-10-02

[1009] Machine status: Normal operation

[1010] Historical data: Average performance score of 0.7 last week

[1011] Emotion text: "I want to be free from the stress of busy days"

[1012] Calculate the appropriate operational forecast score and present the optimal operational plan based on it.

[1013] This system enables factory managers to create efficient operational plans that are in line with emotional intelligence. Through the entire process, from data acquisition and operational prediction to emotion analysis, the system aims to improve factory operational efficiency and manager satisfaction.

[1014] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1015] Step 1:

[1016] The user inputs the date, time, and location of the planned fishing trip, the machine's operating status, past operation data, and emotion text.

[1017] The entered information is prepared to be sent to the server in the next step, and the resulting data becomes the basis for creating operational plans.

[1018] Step 2:

[1019] The terminal transmits the user's input information to the server.

[1020] The transmitted information includes date and time, location, machine operating status, historical operational data, and emotion text, based on which the server processes the data and proceeds to the next step.

[1021] Step 3:

[1022] The server retrieves weather information, temperature information, and wind information from the weather API.

[1023] Through API requests, various weather data related to a specific date, time, and location is collected, which serves as the basis for operational forecasts.

[1024] Step 4:

[1025] The server retrieves past fishing information from an internal database.

[1026] Database queries are used to extract the necessary historical data and feed it into operational forecasting algorithms, which then generate forecasts based on past performance.

[1027] Step 5:

[1028] The server runs the prediction algorithm.

[1029] A prediction algorithm is run using inputs such as weather information, temperature information, wind information, high and low tide information, and past fishing results. The algorithm integrates the data from each element and outputs a score that predicts operational efficiency for a specific date and time.

[1030] Step 6:

[1031] The server analyzes the user's emotional state using an emotion engine.

[1032] Using natural language processing technology, the input emotional text is analyzed to determine the user's emotional state, which is then used to adjust the prediction results in the next step.

[1033] Step 7:

[1034] The server adjusts the prediction results depending on the emotional state.

[1035] Based on the analyzed emotional information, the system recalculates operational prediction results and generates plans that correspond to the user's emotional state. For example, it presents a low-stress operational schedule to a user who is feeling stressed.

[1036] Step 8:

[1037] The server generates the final prediction results and sends them to the device in the form of a heat map.

[1038] The generated plans and prediction scores are visualized and output as a color-coded heat map, allowing users to intuitively understand which areas are optimal for which time periods.

[1039] Step 9:

[1040] The device displays the heat map to the user.

[1041] It displays a heat map and provides users with a visual representation, based on which they can implement optimal operational plans.

[1042] Step 10:

[1043] The user makes the final decision.

[1044] Based on the presented information, the user finalizes the operational plan and moves on to specific actions, thereby achieving efficient and effective operations that take into consideration the emotional state of the manager.

[1045] 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.

[1046] 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.

[1047] 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.

[1048] [Fourth embodiment]

[1049] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1050] 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.

[1051] 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).

[1052] 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.

[1053] 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.

[1054] 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).

[1055] 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.

[1056] 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.

[1057] 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.

[1058] 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.

[1059] 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.

[1060] 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.

[1061] 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."

[1062] This system integrates the information necessary for users to enjoy fishing efficiently and provides future fishing results predictions. It collects and integrates weather information, temperature information, wind information, high tide / low tide information, and past fishing results, and displays them on a map in the form of a heat map by time, helping users make decisions about their fishing activities.

[1063] System Overview:

[1064] The system consists of a dedicated map app or website where users can input the date, time, and location of their fishing plans, a backend server, and an external API and internal database for collecting and processing various data. Based on the user's specified date, time, and location, the system visually displays the predicted results on a map.

[1065] Embodiment Details:

[1066] The user accesses a dedicated map app or website and enters the date, time, and location of the planned fishing trip. The device then sends this information to the server. Based on the received date, time, and location, the server collects the necessary weather information, temperature information, wind information, high and low tide information, and past fishing results information from external APIs and internal databases.

[1067] For example, weather information is obtained using a weather API. Data from the weather API includes the weather condition (sunny, cloudy, rainy, etc.) at a given date and time. Similarly, tide information is obtained via an ocean data API, and past fishing results are retrieved from the system's internal database.

[1068] The server uses the collected information to run an algorithm that predicts future catches. This algorithm combines information such as weather, temperature, wind, tides, and past fishing patterns to calculate the likelihood of a catch at a specific date and time. The prediction is expressed as a score that indicates the probability of a successful catch at a specific time.

[1069] The calculated prediction results are sent to the device and displayed in the user's map app in the form of a heat map. The heat map visualizes the predicted fishing results by color-coding them on a map, allowing users to intuitively identify the best time and place to fish.

[1070] For example, the heat map will show "red" during times when the fishing yield is expected to be high at a particular fishing spot, and "blue" during times when the fishing yield is low. This allows users to intuitively decide when to select a fishing spot and when to go fishing.

[1071] Based on the information provided by this system, users can efficiently plan their fishing trips. Furthermore, the system is capable of continuously collecting data and improving its algorithms to improve the accuracy of catch predictions, which is expected to further improve the user experience.

[1072] The processing flow will be explained below.

[1073] Step 1:

[1074] Users access a dedicated map app or website and input the date, time, and location of their planned fishing trip. The device then sends this information to the server.

[1075] Step 2:

[1076] The server receives the date, time, and location information sent by the user, and uses this information to prepare to collect the necessary data from external APIs and internal databases.

[1077] Step 3:

[1078] The server retrieves weather information using an external weather API. This process retrieves weather data including the weather condition (sunny, cloudy, rainy, etc.) associated with the specified location and date / time.

[1079] Step 4:

[1080] The server also uses an external ocean data API to obtain high and low tide information, including the time and magnitude of the tides at the specified fishing spot.

[1081] Step 5:

[1082] The server collects historical fishing information from an internal database, which contains historical fishing data for a specified date, time, and location, including the type and number of fish caught.

[1083] Step 6:

[1084] The server also retrieves wind and temperature information using an external API, including wind speed, wind direction, and temperature for a specified location and date and time.

[1085] Step 7:

[1086] The server combines the collected weather, temperature, wind, high and low tide information, and past fishing results, and then runs an algorithm to predict future fishing results. This algorithm weights each piece of information and calculates the likelihood of a fishing result.

[1087] Step 8:

[1088] The server generates predictions and stores them as catch prediction scores for each specified time slot, which indicate the likelihood of fishing success during that particular time slot.

[1089] Step 9:

[1090] The server transmits the generated fishing result prediction score to the terminal.

[1091] Step 10:

[1092] Based on the fishing prediction score received by the device, the results are displayed on a map in the form of a heat map. This heat map shows areas with high fishing predictions in "red" and areas with low fishing predictions in "blue," allowing users to intuitively identify the best time and place to fish.

[1093] Example 1

[1094] 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."

[1095] Conventional fishing planning systems require users to manually collect weather information, tide information, past fishing results, and other information from multiple sources and integrate that information to make predictions. This makes the information collection and integration process cumbersome, and often results in inaccurate predictions. Furthermore, there are limited ways to visually display fishing results, making it difficult for users to intuitively determine the optimal fishing timing and location. The present invention aims to solve these problems and provide a system that allows users to efficiently plan fishing.

[1096] 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.

[1097] In this invention, the server includes means for acquiring weather information, means for acquiring temperature information, means for acquiring wind information, means for acquiring tide information, means for collecting past fishing results, an algorithm for predicting future fishing results based on this information, means for displaying the prediction results in a heat map format by time on a map, means for a user to input the date, time, and location of the planned fishing trip, means for transmitting the input information to the server, means for collecting necessary information from external and internal databases, means for integrating the collected information and performing prediction calculations, means for transmitting the prediction results to a user terminal, and means for displaying the prediction results on a user terminal. This allows a user to automatically collect and integrate necessary information from multiple information sources, obtain highly accurate fishing results predictions, and visualize the results in a form that is easy to understand intuitively.

[1098] "Means for obtaining weather information" refers to the function of the system to automatically collect weather data for a specified date, time and location from an external database.

[1099] "Means for obtaining temperature information" refers to the function of the system to automatically collect temperature data for a specified date, time and location from an external database.

[1100] "Means for obtaining wind information" refers to the function of the system to automatically collect wind speed and direction at a specified date, time and location from an external database.

[1101] "Means for obtaining tidal information" refers to the system's ability to automatically collect high and low tide data for specified dates, times, and locations from an external database.

[1102] "Means for collecting past fishing information" refers to the system's ability to obtain previously recorded fishing data from an internal database.

[1103] "Future fishing prediction algorithm" refers to a calculation procedure that integrates collected weather, temperature, wind, tide, and past fishing information to predict the probability of fishing at a specified date, time, and location.

[1104] "Means for displaying the prediction results on a map in the form of a heat map by time period" refers to a function that visually displays the probability of catching a fish obtained by the prediction algorithm on a map by color-coding it according to different time periods.

[1105] "Means for inputting the date, time and location of planned fishing by the user" refers to an interface that allows the user to specify to the system the time and location of planned fishing.

[1106] The "means for transmitting the input information to the server" refers to a function for transmitting the date, time, and location information input by the user to the server.

[1107] "Means for collecting necessary information from external and internal databases" refers to a function for automatically collecting necessary weather information, temperature information, wind information, tide information, and fishing results information based on a specified date, time, and location.

[1108] The "means for integrating the collected information and performing predictive calculations" refers to a function that integrates various collected data and performs calculations to predict future fishing results.

[1109] "Means for sending prediction results to the user's terminal" refers to the function of sending the results of the fishing result prediction calculated by the server to the user's terminal.

[1110] "Means for displaying the prediction results on the user terminal" refers to a function for displaying the prediction results on the user terminal in a format that can be intuitively understood.

[1111] This is a system that integrates the information necessary for users to enjoy fishing efficiently and provides future fishing results. The system integrates and forecasts weather, temperature, wind, high and low tide, and past fishing results, and displays the results in a heat map format by time to help users make decisions about their fishing activities.

[1112] System configuration

[1113] The system consists of the following elements:

[1114] 1. A dedicated mapping app or website where users can input the date, time and location where they plan to fish.

[1115] 2. A backend server that processes the user input.

[1116] 3. External APIs and internal databases for collecting and processing various data.

[1117] Hardware and Software

[1118] Hardware: Server, user device (PC or smartphone).

[1119] software:

[1120] Weather API for obtaining weather information (e.g. OpenWeatherMap API).

[1121] Ocean data APIs for retrieving tide information (e.g., NOAA Tides & Currents API).

[1122] An internal database (e.g. MySQL) that maintains historical catch information and other statistical data.

[1123] Data collection and processing

[1124] The user accesses a dedicated map app or website and inputs the date, time and location of the planned fishing trip. This information is sent by the device to the server, which then performs the following operations:

[1125] 1. Use the weather API to get weather information for a specified date, time, and location. For example, get data including the weather (sunny, cloudy, rainy, etc.) for a specified date and time.

[1126] 2. Use the ocean data API to collect tidal information and obtain the tides for a specified date and time.

[1127] 3. Retrieve historical fishing information from an internal database based on the specified date, time and location.

[1128] Prediction Algorithm

[1129] The server uses the collected information to run an algorithm that predicts future catches. The algorithm combines weather, temperature, wind, tides, and past fishing information to calculate the likelihood of a catch. The prediction is expressed as a score that indicates the probability of a successful catch at a particular time.

[1130] Display in heatmap format

[1131] The calculated prediction results are sent from the server to the device and displayed in the user's map app in the form of a heat map. The heat map visualizes the fishing prediction score using color coding, allowing users to intuitively identify the best time and place to fish.

[1132] Specific examples

[1133] For example, if a user plans to go fishing in Yokohama at 6am on July 15th, they might use the following prompt:

[1134] "I'm planning to go fishing in Yokohama at 6:00 AM on July 15th. What are the fishing results forecasts for this date, time, and location?"

[1135] Based on this prompt, the system will perform the following steps:

[1136] 1. The user accesses a dedicated map app or website and enters the date, time and location where they plan to fish.

[1137] 2. The terminal sends this input information to the server.

[1138] 3. The server collects the necessary information from external APIs and internal databases.

[1139] 4. The server runs a catch prediction algorithm based on the collected information and calculates a predicted score.

[1140] 5. The server sends the results to the device and displays them as a heat map.

[1141] In this way, users can plan their fishing trips efficiently. The system also continuously collects data and improves its algorithms, which is expected to further improve the accuracy of fishing predictions.

[1142] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1143] Step 1:

[1144] A user accesses a dedicated mapping app or website and enters the date, time, and location where they plan to fish. The entered date, time, and location are captured by the user's device. Specifically, the user selects a date and time from the calendar function and pins the desired fishing location on the map. This information is stored locally and sent for further processing.

[1145] Input: User-selected date, time, and location.

[1146] Output: The date, time, and location information entered.

[1147] Step 2:

[1148] The device sends the date, time, and location information entered by the user to the server. In this case, the information is sent to the server using an HTTP POST request. Specifically, the device packages the information and sends it to the server when triggered by a click event by the user (for example, pressing the "Send" button).

[1149] Input: Date, time, and location information entered by the user.

[1150] Output: HTTP POST request to the server.

[1151] Step 3:

[1152] The server collects the necessary data based on the received date, time, and location information. This includes retrieving weather information from the weather API, tidal information from the ocean data API, and past fishing results from an internal database. Specifically, the server sends an HTTP GET request to the API endpoint to retrieve the necessary information.

[1153] Input: Date, time, and location information sent from your device.

[1154] Output: Collected weather information, tide information, and past fishing results.

[1155] Step 4:

[1156] The server integrates the collected data and runs an algorithm to predict future catches based on this information. Specifically, the server integrates each piece of information, analyzes the data using machine learning models, and calculates a prediction score.

[1157] Input: Collected weather information, tide information, and past fishing results.

[1158] Output: Prediction score.

[1159] Step 5:

[1160] The server then sends the calculated prediction score to the user's device. This process uses an HTTP POST request. Specifically, the server serializes the prediction score in JSON format and sends it to the user's device.

[1161] Input: The calculated prediction score.

[1162] Output: HTTP POST request to the user device.

[1163] Step 6:

[1164] The device then displays the received prediction scores as a heat map on the map app. Specifically, the device analyzes the received data and uses a heat map library to display it in different colors on the map.

[1165] Input: Prediction score sent by the server.

[1166] Output: Prediction scores displayed in a heatmap format.

[1167] (Application example 1)

[1168] 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."

[1169] Conventional fishing information systems provide limited information for users to enjoy fishing, making it difficult to accurately predict catches at specific times, dates, and locations. Furthermore, these systems lack intuitive visual information displays, providing insufficient support for users to identify optimal fishing times and locations. The present invention aims to solve these problems by providing a visual display of future catches, allowing users to enjoy fishing more efficiently.

[1170] 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.

[1171] In this invention, the server includes means for acquiring weather information, means for acquiring temperature information, means for acquiring wind information, means for acquiring high tide and low tide information, means for collecting past fishing results, means for predicting future fishing results based on this information, means for displaying the prediction results in a heat map format by time on a map, means for calculating a fishing result prediction score based on the weather information, high tide and low tide information, and past fishing results information, and means for generating a color-coded heat map on a map based on the calculated fishing result prediction score. This allows users to intuitively understand the fishing result prediction score and identify the optimal fishing timing and location.

[1172] "Weather information" refers to weather data at a specific date and time, and generally includes weather conditions such as sunny, cloudy, rainy, and snowy.

[1173] "Temperature information" is data relating to the temperature at a specific location during a specific time period.

[1174] "Wind information" refers to data regarding wind speed and direction at a specific date and time.

[1175] "High and low tide information" refers to data regarding the times and heights of high and low tides at a specific location.

[1176] "Past fishing information" refers to fishing data recorded in the past based on a specific location and time period.

[1177] The "means for predicting future catches" is an algorithm or computational model for calculating the likelihood of future catches based on various collected information.

[1178] The "means of displaying on a map in a heat map format by time period" is a method of intuitively visualizing the fishing result prediction results and displaying them on a map in a color-coded format for specific time periods.

[1179] A "fishing prediction score" is a numerical value that represents the expected fishing result at a specific date, time and location, and is evaluated based on certain criteria.

[1180] A "heat map" is a visual representation that visualizes two-dimensional data using color variations to show the concentration and variation of data in a specific area.

[1181] This invention is a system that integrates the information necessary for users to enjoy fishing efficiently and provides future fishing results predictions. This system collects and integrates weather information, temperature information, wind information, high tide / low tide information, and past fishing results information, and displays them on a map in the form of a heat map by time to help users make decisions about their fishing activities.

[1182] System Overview

[1183] This system consists of a dedicated application that allows users to input the date, time, and location of their fishing plans, a back-end server, and an external API and internal database for collecting and processing various data. The details are explained below.

[1184] Program Description

[1185] In this invention, the server includes means for acquiring weather information, means for acquiring temperature information, means for acquiring wind information, means for acquiring high tide and low tide information, means for collecting past fishing results information, means for predicting future fishing results based on this information, means for displaying the prediction results in a heat map format by time on a map, means for calculating a fishing result prediction score based on the weather information, high tide and low tide information, and past fishing results information, and means for generating a color-coded heat map on a map based on the calculated fishing result prediction score. Specifically, the following hardware and software are used.

[1186] Hardware and Software Use

[1187] End-user device: A device used by a user, such as a smartphone or PC.

[1188] Server: A computer system that processes collected data and makes fishing predictions.

[1189] Internet environment: A network environment that allows access to various data APIs.

[1190] The software used is as follows:

[1191] Requests library: Used to retrieve data from external APIs.

[1192] Folium library: Used to display heatmaps on maps.

[1193] Weather Data API: An API for obtaining weather and temperature information.

[1194] Marine Data API: API for obtaining high and low tide information.

[1195] Internal Database: A database system for storing and retrieving past fishing information.

[1196] Data processing and calculation

[1197] The server collects various data from external APIs and internal databases based on the date, time, and location entered by the user into the application. For example, weather information is obtained from the weather data API, and tide information is obtained from the ocean data API. Based on the collected data, the server runs an algorithm to calculate a fishing prediction score and displays the results on a map as a heat map.

[1198] Examples of concrete examples and prompts

[1199] For example, if a user plans to go fishing in Tokyo on October 10, 2023, the system will collect the necessary data based on the specified date and location, and calculate a catch prediction score. Based on this score, specific areas on the map will be color-coded, with red indicating high catch potential and blue indicating low catch potential.

[1200] Example prompt sentence:

[1201] input_date: 2023-10-10

[1202] input_location: lat: 35.6895, lon: 139.6917

[1203] By feeding these prompts into a generative AI model, a real-time heat map will display the optimal fishing times and locations for the specified date, time and location.

[1204] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1205] Step 1:

[1206] A user uses a dedicated application to input the date, time, and location of the planned fishing trip. This input information is the latitude and longitude coordinates of the specified date and location. For example, a user inputs "October 10, 2023, Tokyo (latitude 35.6895, longitude 139.6917)."

[1207] Step 2:

[1208] The device sends the entered date, time, and location information to the server. The entered data includes "input_date: 2023-10-10, input_location: lat: 35.6895, lon: 139.6917".

[1209] Step 3:

[1210] The server collects weather information, temperature information, wind information, and high tide and low tide information from external APIs based on the received date, time, and location. For example, it uses the weather data API to obtain weather information (sunny, cloudy, rainy, etc.), temperature information (Celsius), and wind information (wind speed and direction) for the date, time, and location specified by the user. It also uses the ocean data API to obtain the times and heights of high and low tides.

[1211] Step 4:

[1212] The server retrieves past fishing information from its internal database. Specifically, it extracts past fishing data (e.g., fish species, time of fishing, location, etc.) for the specified location from the database.

[1213] Step 5:

[1214] The server integrates collected weather, temperature, wind, high and low tide information, and past fishing results, and then runs a fishing prediction algorithm. Based on the input data, the server calculates a fishing result prediction score for a specific date, time, and location, taking into account various conditions (for example, high temperatures mean fish are more active).

[1215] Step 6:

[1216] The server generates data to display on a map in the form of a heat map by time based on the calculated catch prediction score. For example, the server generates heat map data so that high catch prediction scores are displayed in red and low catch prediction scores are displayed in blue.

[1217] Step 7:

[1218] The device displays the heat map data received from the server on a dedicated application. The application allows users to visually check the locations and time periods with high fishing prediction scores. Specifically, a color-coded heat map is displayed on the map, with red areas indicating high fishing prospects and blue areas indicating low fishing prospects.

[1219] 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.

[1220] This invention is a system that integrates information for efficient fishing behavior and provides future catch predictions for users who enjoy fishing. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to recommend fishing spots and weather conditions according to the user's emotional state.

[1221] System Overview:

[1222] The system consists of a map app or website where users input the date, time, and location of their fishing plans, a backend server, an emotion engine, and an external API and internal database for collecting and processing various data. Based on the user's specified date, time, and location, the system visually displays prediction results on a map and makes appropriate suggestions based on their emotional state.

[1223] Embodiment Details:

[1224] The user accesses a dedicated map app or website and enters the date, time, and location of the planned fishing trip. The device then sends this information to the server. Based on the received date, time, and location information, the server collects weather information, temperature information, wind information, high and low tide information, and past fishing results information from external APIs and internal databases.

[1225] For example, weather information is obtained using a weather API. Data from the weather API includes the weather condition (sunny, cloudy, rainy, etc.) at a given date and time. Similarly, tide information is obtained via an ocean data API, and past fishing results are retrieved from the system's internal database.

[1226] The server runs an algorithm that uses the collected information to predict future catches. This algorithm combines information such as weather, temperature, wind, tides, and past fishing patterns to calculate the likelihood of catching a fish at a specific date and time. The calculated prediction is expressed as a score that indicates the probability of fishing success at a specific time.

[1227] The calculated prediction results are sent to the device and displayed in the user's map app in the form of a heat map. The heat map visualizes the predicted fishing results by color-coding them on a map, allowing users to intuitively identify the best time and place to fish.

[1228] Furthermore, this system incorporates an emotion engine, which analyzes the user's emotional state by inputting their own emotions. For example, if the user is feeling stressed, the emotion engine will use this information to recommend fishing spots and conditions that are likely to have a relaxing effect.

[1229] Once the user has completed entering their emotion, the device also sends this emotion data to the server. The server then re-runs the prediction algorithm based on the emotion data to predict catches and suggest fishing activities based on the user's emotional state. For example, if the user enters the emotion "I want to relax," the server will adjust its prediction results to prioritize recommending fishing spots with calm weather and few people.

[1230] For example, if a user inputs "I want to escape the stress of busy daily life," the emotion engine will analyze this and identify the best place and time for relaxation. Based on this information, the server will re-run the fishing prediction algorithm and may indicate that "daytime with calm winds and clear weather" is the best time and place. This prediction result is displayed on a map in heat map format, allowing users to intuitively understand the information.

[1231] With the above mechanism, the present invention not only provides catch predictions but also proposes more personalized fishing actions that take into account the user's emotional state, allowing the user to enjoy an efficient and satisfying fishing experience.

[1232] The processing flow will be explained below.

[1233] Step 1:

[1234] The user accesses a dedicated map app or website and inputs the date, time, and location of the planned fishing trip, as well as their emotional state (e.g., wanting to relax, wanting to relieve stress, etc.), and the device then sends this input information to the server.

[1235] Step 2:

[1236] The server receives the date, time, location, and emotional state information sent by the user. The server first sends a request to an external weather API to collect weather information.

[1237] Step 3:

[1238] The server receives the data returned from the weather API and retrieves the weather information, including the weather conditions (sunny, cloudy, rainy, etc.) for the given date, time, and location.

[1239] Step 4:

[1240] The server then sends a request to an external ocean data API to retrieve high and low tide information, which includes the times and magnitudes of tides for the specified fishing spot.

[1241] Step 5:

[1242] The server accesses an internal database to retrieve historical fishing information related to the specified date, time, and location, including the type and number of fish caught during a particular time period.

[1243] Step 6:

[1244] The server uses an external API to retrieve wind and temperature information for the specified location and date and time, including wind speed, wind direction, and temperature.

[1245] Step 7:

[1246] The server combines collected weather, temperature, wind, high and low tide information, and past fishing results to run an algorithm that predicts future fishing results. This algorithm weights each piece of information and calculates the likelihood of a fishing result at a specific date and time.

[1247] Step 8:

[1248] The server uses an emotion engine to analyze the user's emotional state. Based on the emotional state input by the user, the emotion engine evaluates the user's current emotional state and reflects it in the recommendation of fishing spots and conditions.

[1249] Step 9:

[1250] The server re-runs the prediction algorithm based on the analysis results of the emotion engine to generate a fishing forecast that reflects the user's emotional state. For example, if a user inputs "I want to relax," the server will adjust the prediction results to prioritize fishing spots with calm weather and few people.

[1251] Step 10:

[1252] The server generates the final prediction results and stores them as a catch prediction score for the specified time slot, which indicates the probability of fishing success during that particular time slot.

[1253] Step 11:

[1254] The server sends the prediction results to the device.

[1255] Step 12:

[1256] Based on the fishing prediction score received by the device, the results are displayed on a map in the form of a heat map. This heat map shows areas with high fishing predictions in "red" and areas with low fishing predictions in "blue," allowing users to intuitively identify the best time and place to fish.

[1257] Step 13:

[1258] Users can check the heat map on the map, select the best time and place to fish, and plan their actual fishing activities. By taking suggestions from the emotion engine into consideration, users can enjoy a more satisfying fishing experience.

[1259] Example 2

[1260] 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."

[1261] Conventional fishing prediction systems make predictions based on environmental information such as weather, temperature, wind, and high and low tides, but many of them do not take into account the user's emotional state or psychological needs. As a result, they are unable to select or suggest fishing spots that suit the user's emotional state, and they are unable to fully increase user satisfaction. In addition, existing systems lack an effective means of visually displaying the results of fishing predictions in an easy-to-understand manner. These issues need to be resolved.

[1262] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1263] In this invention, the server includes means for acquiring weather information, means for acquiring temperature information, means for acquiring wind information, means for acquiring high tide and low tide information, means for collecting past fishing results, means for inputting and analyzing user emotions, means for predicting future fishing results based on this information and the user's emotional state, and means for displaying the predicted results on a map in the form of a heat map by time. This makes it possible to predict fishing results and suggest fishing activities according to the user's emotional state, thereby improving user satisfaction and the value of the experience.

[1264] "Weather Information" refers to weather conditions (sunny, cloudy, rainy, snowy, etc.) and related meteorological parameters at a specified date, time and location.

[1265] "Temperature Information" refers to temperature (in Celsius or Fahrenheit) data for a specified date, time and location.

[1266] "Wind Information" refers to data on wind speed and direction at a specified date, time and location.

[1267] "High and low tide information" refers to data regarding the fluctuation of sea level height at a specified date, time and location.

[1268] "Past fishing information" refers to records and patterns of fishing results previously obtained at a particular date, time and location.

[1269] "Emotion input" refers to the act of a user inputting their current emotional state or psychological goal (e.g., relaxation, stress relief).

[1270] "Emotion analysis" refers to the process of analyzing a user's emotional input and identifying appropriate fishing spots and conditions based on that emotional state.

[1271] "Catch prediction" refers to integrating various collected data and the user's emotional state to calculate the likelihood of catching a fish at a future date and time.

[1272] A "heat map" is a visual display format that uses color coding to show fishing results on a map.

[1273] This system integrates information for users to plan fishing trips and supports efficient fishing activities. Its special feature is that it takes into account the user's emotional state and suggests optimal fishing spots and trips. This embodiment uses a map app or website used by the user, a backend server, an emotion engine, an external API, and an internal database.

[1274] System configuration

[1275] 1. User Interface

[1276] Users access a dedicated map app or website. This interface allows them to input the date, time, and location of their planned fishing trip. Additionally, they are also provided with a screen to input their current emotional state.

[1277] 2. Data transmission by the terminal

[1278] The device transmits the user-entered date, time, location, and emotional state data to a server using a secure communication protocol (e.g., HTTPS).

[1279] 3. Data processing by the server

[1280] The server collects information using the following external APIs and internal databases:

[1281] Weather information: Uses a weather API (e.g., OpenWeatherMap API).

[1282] Temperature information: Also obtained from the weather API.

[1283] Wind Information: Obtains wind speed and direction data from the weather API.

[1284] High and low tide information: Obtained from ocean data APIs (e.g., NOAA Tides & Currents API).

[1285] Past fishing information: Obtained from an internal database.

[1286] 4. Data integration and analysis

[1287] The server combines the collected data with the user's emotional state and runs a fishing prediction algorithm. The algorithm takes into account weather, temperature, wind, tides, past fishing patterns, and the user's emotional state to calculate the likelihood of catching a fish at a specific date and time. The result is expressed as a score indicating the probability of fishing success at a specific time.

[1288] 5. Visualizing the results

[1289] The calculated prediction results are sent to the device, which then displays this data in the form of a heat map on the user's map app. The heat map visualizes the predicted fishing results by color-coding them on a map, allowing users to intuitively identify the best time and location for fishing.

[1290] 6. Use of Emotion Engines

[1291] The server analyzes the emotional data entered by the user and suggests appropriate fishing spots and conditions based on that emotional state. For example, if a user enters "I want to relax," the server will adjust its prediction results to prioritize fishing spots with calm weather and few people.

[1292] Specific examples

[1293] User Scenarios

[1294] The user enters "I'm going fishing at Yokohama Port at 8am on May 20th, 2023" and the emotion "I want to relax."

[1295] Device behavior

[1296] The device sends date, time, location, and emotion data to the server, and the received prediction results are displayed in heat map format.

[1297] Server Operation

[1298] The server collects necessary data from external APIs and internal databases, runs a fishing prediction algorithm, and analyzes emotional data to generate recommendations that prioritize relaxing fishing spots.

[1299] Prompt Sentence Examples

[1300] "I want to relax and unwind from the stress of busy everyday life. Could you please tell me the best fishing spots and timings?"

[1301] "Tell me where fishing is most successful on a sunny day. The emotional goal is relaxation."

[1302] "Recommend a fishing spot where I can de-stress on a cloudy afternoon."

[1303] By operating the system in accordance with these specific examples and prompts, users can enjoy an efficient and satisfying fishing experience.

[1304] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1305] Step 1:

[1306] The user inputs the date, time, and location of the planned fishing trip, as well as their emotional state, into a dedicated map app or website. For example, they input "I'm going fishing at Yokohama Port at 8:00 a.m. on May 20, 2023," and input the emotion "I want to relax." The input data includes the date, time, location, and emotional state.

[1307] Step 2:

[1308] The device receives the user's date, time, location, and emotional state data as input and sends it to the server using a secure communication protocol (e.g., HTTPS).

[1309] Step 3:

[1310] Based on the received date, time, and location information, the server collects weather information, temperature information, wind information, high and low tide information, and past fishing results information from external APIs (e.g., weather API, ocean data API) and internal databases. It receives date, time, and location information as input and obtains this environmental data as output.

[1311] Step 4:

[1312] The server combines collected weather, temperature, wind, high and low tide information, and past fishing results to run a fishing prediction algorithm. The algorithm takes each piece of information as input and calculates the likelihood of catching a fish at a specific time. As an output, it generates a predicted score indicating the fishing success rate at a specific date and time.

[1313] Step 5:

[1314] The server analyzes the prediction score and the user's emotional state data and adjusts the prediction result. For example, if the user inputs "I want to relax," the server adjusts the prediction result to prioritize recommending fishing spots with calm weather and few people. The server receives the prediction score and emotional state as input and obtains the adjusted prediction score as output.

[1315] Step 6:

[1316] The server sends the adjusted prediction result to the terminal, and sends the adjusted prediction score as output, which the terminal receives.

[1317] Step 7:

[1318] The device displays the received prediction scores in the form of a heat map on the user's map app. The heat map visualizes the predicted catch scores on a map by color-coding them, allowing users to intuitively identify the best time and place to fish. The device receives the prediction scores as input and displays the heat map as output.

[1319] Specifically, if a user inputs "I'm going fishing at Yokohama Port at 8:00 AM on May 20, 2023" and emotionally inputs "I want to relax," the device will send this information to the server. The server collects weather information, temperature information, wind information, high and low tide information, and past fishing results, and runs a fishing result prediction algorithm to calculate a fishing result prediction score. The server then adjusts the prediction result taking into account the user's emotional state and sends it to the device. Finally, the device displays the adjusted prediction score on a map in heat map format, allowing the user to intuitively understand the optimal fishing time and location for relaxation.

[1320] (Application example 2)

[1321] 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."

[1322] In the operation of factory robots, conventional systems make predictions based on weather information and past data, but do not optimize operations by taking into account the emotional state of the manager. This increases the burden on the manager and reduces overall operational efficiency. The present invention aims to solve these problems and provide a flexible operation plan based on the emotional state of the factory manager.

[1323] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring weather information, means for acquiring temperature information, means for acquiring wind information, means for acquiring high tide and low tide information, means for collecting past fishing results, means for predicting future fishing results based on this information, means for displaying the prediction results on a map in the form of a heat map by time, means including an emotion engine for inputting and analyzing the user's emotional state, means for adjusting the operation plan based on the emotion data, and means for optimizing production management and operation schedules. This makes it possible to provide optimal operation schedules and work plans that correspond to the manager's emotional state.

[1324] "Weather information" is information about weather conditions such as sunny, cloudy, rainy, etc., obtained from forecasting agencies and weather data providing services.

[1325] "Temperature information" is information about the temperature value at a specific date, time and location.

[1326] "Wind information" is information about wind conditions at a specific date, time, and location, such as wind speed and direction.

[1327] "High tide and low tide information" is information about the rise and fall of the ocean tides on a specific date and time.

[1328] "Past fishing information" is information previously acquired about fishing results at a specific location and date.

[1329] An "emotion engine" is software or a system for analyzing a user's emotional state.

[1330] The "heat map format" is a method for intuitively visualizing data by representing it using shades of color.

[1331] A "predictive algorithm" is a computational procedure or model that predicts future outcomes based on collected data.

[1332] A "database" is a system for systematically storing various types of information.

[1333] "Server" means a primarily network-based computer system that collects and processes data and provides the results to users.

[1334] An "operational plan" is a plan that determines how work is to be carried out in a factory or other work environment.

[1335] "Production control" is the process of planning, implementing, and monitoring production activities to ensure their efficiency.

[1336] An "operation schedule" is a timetable for planning and adjusting work and operations.

[1337] In a system for realizing this invention, a factory manager, who is a user, first accesses an interface for inputting an operation plan. The user inputs the date, time, and location of the factory operation, the current operating status of the machine, past operation data, and their own emotional state. This emotional state is input through simple text input.

[1338] The device sends this input information to the server, which then uses external databases such as weather APIs to obtain weather, temperature, and wind information. It also obtains past fishing results from its internal database. This completes the necessary operational data.

[1339] The server then uses the collected information to run predictive algorithms that combine multiple factors to predict future operational outcomes, such as weather, temperature, and wind data to calculate how efficiently a machine will operate at a specific time and date.

[1340] Additionally, the server includes an emotion engine that analyzes the user's emotional state. If the manager is feeling stressed, the prediction results are adjusted to provide a less demanding operational plan or break recommendations. This emotion analysis is performed using natural language processing (NLP) technology.

[1341] The forecast results generated by the server are displayed on the device in the form of a heat map, allowing users to intuitively understand which areas are best suited for which time of day.

[1342] As a concrete example, when a factory manager writes the next day's operations, he or she might enter the following information:

[1343] Location: Tokyo

[1344] Date: 2023-10-02

[1345] Machine status: Normal operation

[1346] Historical data: Average performance score of 0.7 last week

[1347] Emotion text: "I want to be free from the stress of busy days"

[1348] An example prompt is:

[1349] Calculate an operational prediction score based on weather information for the location and date / time specified by the user, the machine's operating status, and past data. Also, adjust the prediction score according to the user's emotional state and propose the optimal operational plan. Please use the following information.

[1350] Location: Tokyo

[1351] Date: 2023-10-02

[1352] Machine status: Normal operation

[1353] Historical data: Average performance score of 0.7 last week

[1354] Emotion text: "I want to be free from the stress of busy days"

[1355] Calculate the appropriate operational forecast score and present the optimal operational plan based on it.

[1356] This system enables factory managers to create efficient operational plans that are in line with emotional intelligence. Through the entire process, from data acquisition and operational prediction to emotion analysis, the system aims to improve factory operational efficiency and manager satisfaction.

[1357] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1358] Step 1:

[1359] The user inputs the date, time, and location of the planned fishing trip, the machine's operating status, past operation data, and emotion text.

[1360] The entered information is prepared to be sent to the server in the next step, and the resulting data becomes the basis for creating operational plans.

[1361] Step 2:

[1362] The terminal transmits the user's input information to the server.

[1363] The transmitted information includes date and time, location, machine operating status, historical operational data, and emotion text, based on which the server processes the data and proceeds to the next step.

[1364] Step 3:

[1365] The server retrieves weather information, temperature information, and wind information from the weather API.

[1366] Through API requests, various weather data related to a specific date, time, and location is collected, which serves as the basis for operational forecasts.

[1367] Step 4:

[1368] The server retrieves past fishing information from an internal database.

[1369] Database queries are used to extract the necessary historical data and feed it into operational forecasting algorithms, which then generate forecasts based on past performance.

[1370] Step 5:

[1371] The server runs the prediction algorithm.

[1372] A prediction algorithm is run using inputs such as weather information, temperature information, wind information, high and low tide information, and past fishing results. The algorithm integrates the data from each element and outputs a score that predicts operational efficiency for a specific date and time.

[1373] Step 6:

[1374] The server analyzes the user's emotional state using an emotion engine.

[1375] Using natural language processing technology, the input emotional text is analyzed to determine the user's emotional state, which is then used to adjust the prediction results in the next step.

[1376] Step 7:

[1377] The server adjusts the prediction results depending on the emotional state.

[1378] Based on the analyzed emotional information, the system recalculates operational prediction results and generates plans that correspond to the user's emotional state. For example, it presents a low-stress operational schedule to a user who is feeling stressed.

[1379] Step 8:

[1380] The server generates the final prediction results and sends them to the device in the form of a heat map.

[1381] The generated plans and prediction scores are visualized and output as a color-coded heat map, allowing users to intuitively understand which areas are optimal for which time periods.

[1382] Step 9:

[1383] The device displays the heat map to the user.

[1384] It displays a heat map and provides users with a visual representation, based on which they can implement optimal operational plans.

[1385] Step 10:

[1386] The user makes the final decision.

[1387] Based on the presented information, the user finalizes the operational plan and moves on to specific actions, thereby achieving efficient and effective operations that take into consideration the emotional state of the manager.

[1388] 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.

[1389] 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.

[1390] 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.

[1391] 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.

[1392] 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.

[1393] 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.

[1394] 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).

[1395] 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.

[1396] 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."

[1397] 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.

[1398] 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).

[1399] 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.

[1400] 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.

[1401] 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.

[1402] 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.

[1403] 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.

[1404] 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.

[1405] 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.

[1406] 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.

[1407] 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.

[1408] 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.

[1409] The following is further disclosed regarding the above embodiment.

[1410] (Claim 1)

[1411] a means for obtaining weather information;

[1412] A means for obtaining temperature information;

[1413] A means for acquiring wind information;

[1414] A means of obtaining high and low tide information;

[1415] A means of collecting past fishing information,

[1416] A method for predicting future catches based on this information,

[1417] means for displaying the prediction results on a map in a heat map format by time;

[1418] A system including:

[1419] (Claim 2)

[1420] a means for the user to input the date, time and location at which the user plans to fish;

[1421] The system of claim 1 provides a system that collects weather information, temperature information, wind information, high tide and low tide information, and past fishing results based on the input, integrates them to predict fishing results, and displays them in heat map format on a map.

[1422] (Claim 3)

[1423] A means of obtaining weather information, temperature information, wind information, high tide and low tide information from an external database, and past fishing results information from an internal database.

[1424] Based on this acquired information, an algorithm is used to generate fishing results predictions.

[1425] A means for displaying a time-based catch forecast in heat map format on a map based on the forecast results generated by the algorithm;

[1426] 10. The system of claim 1, comprising:

[1427] "Example 1"

[1428] (Claim 1)

[1429] a means for obtaining weather information;

[1430] A means for obtaining temperature information;

[1431] A means for acquiring wind information;

[1432] a means for acquiring tidal information;

[1433] A means of collecting past fishing information,

[1434] Based on this information, an algorithm predicts future catches,

[1435] means for displaying the prediction results on a map in a heat map format by time;

[1436] a means for the user to input the date, time and location at which the user plans to fish;

[1437] means for transmitting the input information to a server;

[1438] means of collecting the required information from external and internal databases;

[1439] means for integrating the collected information and performing predictive calculations;

[1440] means for transmitting the prediction result to a user terminal;

[1441] means for displaying the prediction result on a user terminal;

[1442] A system including:

[1443] (Claim 2)

[1444] means for inputting the date, time and location at which the user plans to fish;

[1445] The system according to claim 1, wherein the system collects necessary information from external and internal databases based on the input, integrates the information to predict fishing results, and displays the results in heat map format on a map.

[1446] (Claim 3)

[1447] A means for obtaining weather information, temperature information, wind information, and tide information from an external database, and for obtaining past fishing results information from an internal database;

[1448] Based on this acquired information, an algorithm is used to generate fishing results predictions using an AI model.

[1449] A means for displaying a time-based catch forecast in heat map format on a map based on the forecast results generated by the algorithm;

[1450] means for displaying the prediction result on a user terminal;

[1451] 10. The system of claim 1, comprising:

[1452] "Application Example 1"

[1453] (Claim 1)

[1454] a means for obtaining weather information;

[1455] A means for obtaining temperature information;

[1456] A means for acquiring wind information;

[1457] A means of obtaining high and low tide information;

[1458] A means of collecting past fishing information,

[1459] A method for predicting future catches based on this information,

[1460] means for displaying the prediction results on a map in a heat map format by time;

[1461] A method for calculating a fishing result prediction score based on weather information, high tide and low tide information, and past fishing results information.

[1462] A means for generating a color-coded heat map on a map based on the calculated fishing result prediction score;

[1463] A system including:

[1464] (Claim 2)

[1465] a means for the user to input the date, time and location at which the user plans to fish;

[1466] The system of claim 1 provides a system that collects weather information, temperature information, wind information, high tide and low tide information, and past fishing results based on the input, integrates them to predict fishing results, and displays them in heat map format on a map.

[1467] (Claim 3)

[1468] A means of obtaining weather information, temperature information, wind information, high tide and low tide information from an external database, and past fishing results information from an internal database.

[1469] Based on this acquired information, an algorithm is used to generate fishing results predictions.

[1470] A means for displaying a time-based catch forecast in heat map format on a map based on the forecast results generated by the algorithm;

[1471] A means to continuously update the heat map that visualizes the catch prediction score,

[1472] 10. The system of claim 1, comprising:

[1473] "Example 2: Combining Emotion Engines"

[1474] (Claim 1)

[1475] a means for obtaining weather information;

[1476] A means for obtaining temperature information;

[1477] A means for acquiring wind information;

[1478] A means of obtaining high and low tide information;

[1479] A means of collecting past fishing information,

[1480] A means of inputting and analyzing user emotions,

[1481] A means for predicting future fishing results based on this information and the user's emotional state;

[1482] means for displaying the prediction results on a map in a heat map format by time;

[1483] A system including:

[1484] (Claim 2)

[1485] a means for the user to input the date, time, location and emotional state of the planned fishing trip;

[1486] A means for collecting weather information, temperature information, wind information, high tide / low tide information, and past fishing results information based on the input, analyzing and integrating the user's emotional state, predicting fishing results, and displaying the results in a heat map format on a map;

[1487] 10. The system of claim 1, comprising:

[1488] (Claim 3)

[1489] A means of obtaining weather information, temperature information, wind information, high tide and low tide information from an external database, and past fishing results information from an internal database.

[1490] A means for inputting and analyzing the user's emotional state;

[1491] An algorithm that generates fishing results predictions based on the acquired information and the user's emotional state.

[1492] A means for displaying a time-based catch forecast in heat map format on a map based on the forecast results generated by the algorithm;

[1493] 10. The system of claim 1, comprising:

[1494] "Application example 2 when combining emotion engines"

[1495] (Claim 1)

[1496] a means for obtaining weather information;

[1497] A means for obtaining temperature information;

[1498] A means for acquiring wind information;

[1499] A means of obtaining high and low tide information;

[1500] A means of collecting past fishing information,

[1501] A method for predicting future catches based on this information,

[1502] means for displaying the prediction results on a map in a heat map format by time;

[1503] means including an emotion engine for inputting and analyzing the user's emotional state;

[1504] a means for adjusting operational plans based on sentiment data;

[1505] A means of optimizing production management and operation schedules,

[1506] A system including:

[1507] (Claim 2)

[1508] a means for the user to input the date, time and location at which the user plans to fish;

[1509] The system of claim 1 collects and integrates weather information, temperature information, wind information, high tide and low tide information, and past fishing results based on the input to predict fishing results and display them in heat map format on a map.

[1510] (Claim 3)

[1511] A means of obtaining weather information, temperature information, wind information, high tide and low tide information from an external database, and past fishing results information from an internal database.

[1512] Based on this acquired information, an algorithm is used to generate fishing results predictions.

[1513] A means for displaying a time-based catch forecast in heat map format on a map based on the forecast results generated by the algorithm;

[1514] 10. The system of claim 1, further comprising means for adjusting the operational plan based on the emotion data. [Explanation of symbols]

[1515] 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 obtaining weather information; A means for obtaining temperature information; A means for acquiring wind information; A means of obtaining high and low tide information; A means of collecting past fishing information, A method for predicting future catches based on this information, means for displaying the prediction results on a map in a heat map format by time; A system including:

2. a means for the user to input the date, time and location at which the user plans to fish; The system of claim 1 provides a system that collects weather information, temperature information, wind information, high tide and low tide information, and past fishing results information based on the input, integrates them to predict fishing results, and displays them in heat map format on a map.

3. A means for obtaining weather information, temperature information, wind information, high tide and low tide information from an external database, and a means for obtaining past fishing results information from an internal database; Based on this acquired information, an algorithm is used to generate fishing results predictions. A means for displaying a time-based catch forecast in heat map format on a map based on the forecast results generated by the algorithm; The system of claim 1 , comprising:

Images