System

Abstract

An object of a system according to an embodiment is to enable a fishing lover to efficiently acquire the latest fishing result information.SOLUTION: A system includes a position information acquisition part, a map display part, a fishing result information collection part, and a fishing result information display part. The position information acquisition unit acquires position information of a current location or a designated location of a user. The map display unit displays the position information acquired by the position information acquisition unit on a map. The fishing result information collection part collects fishing result information provided from a fishing lover. The fishing result information display unit displays the fishing result information collected by the fishing result information collection unit on a map.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] Conventional techniques have had the problem that it is difficult for fishing enthusiasts to efficiently obtain information on the most recent catches.

[0005] The system according to the embodiment aims to enable fishing enthusiasts to efficiently obtain information on the most recent catches. [Means for solving the problem]

[0006] The system according to the embodiment includes a location information acquisition unit, a map display unit, a fishing result information collection unit, and a fishing result information display unit. The location information acquisition unit acquires location information of a user's current location or a location specified by the user. The map display unit displays the location information acquired by the location information acquisition unit on a map. The fishing result information collection unit collects fishing result information provided by fishing enthusiasts. The fishing result information display unit displays the fishing result information collected by the fishing result information collection unit on a map. [Effects of the Invention]

[0007] The system according to the embodiment can enable fishing enthusiasts to efficiently obtain the latest fishing result information. [Brief explanation of the drawings]

[0008] [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. DETAILED DESCRIPTION OF THE INVENTION

[0009] 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.

[0010] First, the terms used in the following description will be explained.

[0011] 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, the 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), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] 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.

[0013] 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.

[0014] 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), and Bluetooth (registered trademark).

[0015] 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."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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).

[0019] 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.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. 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 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. 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.

[0022] 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.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The fishing result information acquisition system according to the embodiment of the present invention is a system that provides information on the types and numbers of fish caught in the vicinity and fishing spots based on location information of the user's current location or a specified location. As a result, the fishing result information acquisition system provides information that allows the user to enjoy fishing efficiently and improve their fishing results.

[0029] A fishing result information acquisition system according to an embodiment includes a location information acquisition unit, a map display unit, a fishing result information collection unit, and a fishing result information display unit. The location information acquisition unit acquires location information of a user's current location or a specified location. For example, the location information of the user's current location is acquired using the GPS function of a smartphone. The user can also manually input location information of a specified location. The map display unit displays the location information acquired by the location information acquisition unit on a map. For example, a map app may be used to display the user's current location or a specified location on a map. The fishing result information collection unit collects fishing result information provided by fishing enthusiasts. For example, the fishing enthusiast may input the type and number of fish caught and fishing spot information through the app. The fishing result information display unit displays the fishing result information collected by the fishing result information collection unit on a map. For example, the fishing result information may be displayed as a marker at a specific point on the map, and the user can click to view detailed information. This allows the user to acquire information to enjoy fishing efficiently.

[0030] The location information acquisition unit can acquire the user's current location using the smartphone's GPS function. The location information acquisition unit acquires the user's current location, for example, using the smartphone's built-in GPS function. For example, it receives GPS signals and acquires latitude and longitude information. The location information acquisition unit can also identify the current location using Wi-Fi and cell tower information. For example, it estimates the current location based on the location information of a Wi-Fi access point. Furthermore, the location information acquisition unit can connect an external GPS device to acquire the current location. For example, it acquires location information from an external GPS device connected via Bluetooth. This allows the user's current location to be acquired accurately.

[0031] The catch information collection unit allows fishing enthusiasts to input information about the type and number of fish caught and fishing spots through the app. For example, fishing enthusiasts can use the app's input form to input information about the type and number of fish caught and fishing spots. For example, fishing enthusiasts can select from a list of fish species or input information manually. The catch information collection unit can also use image recognition technology to automatically identify fish species from photos uploaded by fishing enthusiasts. For example, it can analyze the characteristics of the fish and determine the species. Furthermore, the catch information collection unit can add a voice input function so that fishing enthusiasts can input information by voice. For example, it can use voice recognition technology to convert voice into text data. This allows for efficient collection of information from fishing enthusiasts.

[0032] The fishing result information display unit can display the fishing result information collected by the fishing result information collection unit on a map. The fishing result information display unit, for example, displays the fishing result information as markers at specific points on the map. For example, a marker can be placed at the location of a fishing spot to display the type and number of fish caught. The fishing result information display unit can also display the fishing result information as a pop-up window. For example, when a user clicks on a marker, detailed information is displayed. Furthermore, the fishing result information display unit can display the fishing result information in different colors. For example, the color of the marker can be changed depending on the number of fish caught. This allows the fishing result information to be visually confirmed.

[0033] The location information acquisition unit can analyze the user's movement patterns and suggest routes to fishing spots in real time. For example, the location information acquisition unit can use a generation AI to analyze the user's past movement data and suggest the optimal route to the fishing spot. For example, the location information acquisition unit can display the shortest route or a route that takes traffic conditions into account based on data from fishing spots visited in the past. The location information acquisition unit also updates the route from the user's current location to the desired fishing spot in real time, reflecting information such as traffic congestion and road construction. For example, the generation AI can analyze traffic data and suggest the optimal route. Furthermore, the location information acquisition unit can learn the user's movement patterns and customize the route to the fishing spot. For example, the optimal route can be suggested based on the user's preferences and past movement history. This allows the optimal route to be suggested in real time.

[0034] The location information acquisition unit can recommend fishing spots that are individually customized based on the user's past fishing data. For example, the location information acquisition unit analyzes the user's past fishing data and recommends the optimal fishing spot. For example, it can prioritize displaying locations where many fish have been caught in the past. In addition, using generation AI, it can recommend fishing spots where specific fish species are likely to be caught based on the user's fishing data. For example, it can suggest locations where many of the user's favorite fish species can be caught. Furthermore, the location information acquisition unit can recommend fishing spots according to season and weather conditions based on the user's fishing data. For example, it can display locations where many fish can be caught in a specific season. This allows it to individually recommend the optimal fishing spot to the user.

[0035] The location information acquisition unit can integrate weather information and tidal data into the acquisition of location information and map display to provide information to increase the success rate of fishing. The location information acquisition unit, for example, integrates location information and weather information to provide information to increase the success rate of fishing. For example, sunny days and days with little wind are preferentially displayed. Tidal data can also be integrated with location information to provide information to increase the success rate of fishing. For example, the time periods when the tides are suitable for fishing can be displayed. Furthermore, weather information and tidal data can be integrated to provide information to increase the success rate of fishing. For example, fishing spots with optimal weather and tidal conditions can be suggested. In this way, by integrating weather information and tidal data, information to increase the success rate of fishing can be provided.

[0036] The location information acquisition unit can display the congestion status of fishing spots in real time, allowing the user to select a location where they can enjoy fishing comfortably. The location information acquisition unit can, for example, display the congestion status of fishing spots in real time, allowing the user to select a location where they can enjoy fishing comfortably. For example, the congestion level can be displayed in different colors. Furthermore, the generation AI can be used to analyze the congestion status of fishing spots and suggest the best fishing spot for the user. For example, locations with lower congestion levels can be displayed preferentially. Furthermore, the congestion status of fishing spots can be updated in real time, allowing the user to select a location where they can enjoy fishing comfortably. For example, a notification can be sent if the congestion level changes. In this way, by displaying the congestion status of fishing spots in real time, the user can select a location where they can enjoy fishing comfortably.

[0037] The fishing result information collection unit can use the generation AI to evaluate the reliability of the fishing result information and display only highly reliable information. The fishing result information collection unit, for example, uses the generation AI to evaluate the reliability of the fishing result information and display only highly reliable information. For example, it prioritizes displaying information that matches past data. In addition, to evaluate the reliability of the fishing result information, it can also analyze the user's past posting history. For example, it can prioritize displaying information from highly reliable users. Furthermore, it can use the generation AI to evaluate the reliability of the fishing result information and filter out information with low reliability. For example, it can filter out contradictory or inaccurate information. This allows accurate information to be provided to the user by displaying only highly reliable fishing result information.

[0038] The catch information collection unit can add a voice input function to allow users to easily provide information. The catch information collection unit can add, for example, a voice input function to allow users to easily provide information. For example, the type and number of fish can be input by voice. In addition, using a generation AI, the voice-input catch information can be analyzed and converted into text data. For example, the information can be automatically analyzed using voice recognition technology. Furthermore, the voice input function can be used to allow users to easily provide catch information. For example, information can be input by voice command. This allows users to easily provide information.

[0039] The fishing result information collection unit can share fishing result information with other fishing enthusiasts in real time, promoting information exchange throughout the community. The fishing result information collection unit can, for example, share fishing result information with other fishing enthusiasts in real time, promoting information exchange throughout the community. For example, the fishing result information can be shared via social media or a messaging app. The generation AI can also be used to analyze fishing result information in real time and notify other fishing enthusiasts. For example, information that a specific fish has been caught can be shared instantly. Furthermore, fishing result information can be shared in real time, promoting information exchange throughout the community. For example, fishing result information can be displayed on a map so that other users can view it. In this way, by sharing fishing result information in real time, information exchange throughout the community can be promoted.

[0040] The fishing spot evaluation unit can use the generation AI to analyze environmental data of the fishing spot (e.g., water quality and ecosystem) and evaluate the optimal fishing spot. The fishing spot evaluation unit can, for example, use the generation AI to analyze the water quality data of the fishing spot and evaluate the optimal fishing spot. For example, it can prioritize and display locations with good water quality. It can also analyze the ecosystem data of the fishing spot and evaluate the optimal fishing spot. For example, it can display locations with good fish habitats. Furthermore, it can use the generation AI to comprehensively analyze the environmental data of the fishing spot and evaluate the optimal fishing spot. For example, it can integrate and evaluate water quality and ecosystem data. In this way, it is possible to evaluate the optimal fishing spot by analyzing the environmental data.

[0041] The fishing spot evaluation unit can dynamically update the evaluation by combining the user's past fishing data with current weather conditions. The fishing spot evaluation unit, for example, uses a generation AI to combine the user's past fishing data with current weather conditions to evaluate fishing spots. For example, the evaluation is updated based on locations where many fish have been caught in the past and the current weather. The unit can also analyze the user's fishing data and weather conditions in real time to dynamically update the fishing spot evaluation. For example, the evaluation can be automatically adjusted every time the weather changes. Furthermore, a system can be built that combines fishing data and weather conditions to evaluate fishing spots. For example, the fishing spot evaluation can be updated in real time based on weather data. This allows the fishing spot evaluation to be dynamically updated by combining past fishing data with current weather conditions.

[0042] The fishing spot evaluation unit can integrate data from other outdoor activities (e.g., camping and hiking) to provide a composite evaluation. The fishing spot evaluation unit, for example, integrates data from camping and hiking into the evaluation of the fishing spot to provide a composite evaluation. For example, fishing spots that are close to campsites or hiking trails are preferentially displayed. In addition, using a generation AI, data from other outdoor activities can be integrated into the evaluation of the fishing spot. For example, evaluations are made based on the popularity and number of users of the outdoor activity. Furthermore, by incorporating data from other outdoor activities into the evaluation of the fishing spot, a composite evaluation can be provided. For example, fishing spots that can be enjoyed in combination with camping or hiking are suggested. This allows a composite evaluation to be provided by integrating data from other outdoor activities.

[0043] The fishing spot evaluation unit can customize the fishing spot evaluation results according to the user's preferences and provide individually optimized recommendations. The fishing spot evaluation unit, for example, customizes the fishing spot evaluation results according to the user's preferences and provides individually optimized recommendations. For example, it can suggest places where a user who likes a particular species of fish can catch a lot of that fish. Generative AI can also be used to analyze the user's past behavioral data and recommend fishing spots based on preferences. For example, recommendations can be customized based on data on fishing spots visited in the past. Furthermore, it is possible to build a system that customizes the fishing spot evaluation results according to the user's preferences and provides individually optimized recommendations. For example, recommendations can be adjusted based on user feedback. This makes it possible to customize the fishing spot evaluation results according to the user's preferences and provide individually optimized recommendations.

[0044] The fishing hint providing unit can dynamically update advice by combining real-time weather data and tidal information. The fishing hint providing unit, for example, combines fishing hints with real-time weather data to dynamically update advice. For example, the unit suggests the best fishing gear and fishing spots every time the weather changes. The unit can also dynamically update advice by combining tidal information with fishing hints. For example, it can provide fishing tips according to the tides. Furthermore, a system can be built that combines real-time weather data and tidal information to dynamically update fishing hints. For example, it can suggest the best time of day for the weather and tidal conditions. This allows the unit to dynamically update advice by combining real-time weather data and tidal information.

[0045] The fishing hint providing unit can provide fishing hints in video or audio format to make them easier to understand visually and aurally. The fishing hint providing unit, for example, provides fishing hints in video format to make them easier to understand visually. For example, fishing tips and techniques are explained in video. Fishing hints can also be provided in audio format to make them easier to understand aurally. For example, fishing advice is provided as an audio guide. Furthermore, a system can be constructed that provides fishing hints in video or audio format to make them easier for users to understand visually and aurally. For example, a guide combining video and audio is provided. In this way, fishing hints can be provided in video or audio format to make them easier to understand visually and aurally.

[0046] The fishing hint provider can share fishing tips with other fishing enthusiasts to promote knowledge exchange throughout the community. For example, the fishing hint provider can share fishing tips with other fishing enthusiasts to promote knowledge exchange throughout the community. For example, fishing tips and techniques can be shared via social media or messaging apps. The generative AI can also be used to analyze fishing tips in real time and notify other fishing enthusiasts. For example, the time periods and locations when specific fish are most likely to be caught can be shared. Furthermore, a system can be built that shares fishing tips and promotes knowledge exchange throughout the community. For example, fishing advice can be displayed on a map so that other users can view it. In this way, sharing fishing tips can promote knowledge exchange throughout the community.

[0047] The information sharing unit can use the generation AI to automatically extract and share the most useful information when sharing information with fellow anglers. The information sharing unit, for example, uses the generation AI to automatically extract and share the most useful information when sharing information with fellow anglers. For example, useful information is selected based on catch information and fishing spot ratings. It is also possible to build a system in which the generation AI automatically extracts useful information when sharing information with fellow anglers. For example, useful information is selected based on past data. It is also possible to use the generation AI to automatically extract and share the most useful information when sharing information with fellow anglers. For example, useful information is selected based on fishing tips and techniques. This automatically extracts and shares the most useful information, thereby improving the quality of information sharing.

[0048] The information sharing unit can provide individually customized information based on the user's past fishing result data when sharing information. The information sharing unit, for example, can provide individually customized information based on the user's past fishing result data when sharing information. For example, it can suggest locations and times when a specific fish species is likely to be caught. It can also use generation AI to build a system that analyzes the user's fishing result data and provides customized information. For example, it can suggest optimal fishing gear and bait based on past data. Furthermore, it can also provide individually customized information based on the user's past fishing result data when sharing information. For example, it can suggest tips for fishing in specific weather conditions. This makes it possible to provide individually customized information based on the user's past fishing result data.

[0049] The information sharing unit can add a real-time chat function to the information sharing, enabling instantaneous information exchange. The information sharing unit can, for example, add a real-time chat function to the information sharing, enabling instantaneous information exchange. For example, information on catches and fishing spots can be shared in real time. It is also possible to use generative AI to analyze the real-time chat function and build a system that provides optimal information to users. For example, useful information can be suggested based on the chat content. Furthermore, the real-time chat function can be used to promote information exchange with fellow anglers. For example, fishing tips and techniques can be shared in real time. In this way, adding the real-time chat function enables instantaneous information exchange.

[0050] The information sharing unit can add a function that allows for easy uploading of images and videos when sharing information, thereby promoting visual information exchange. The information sharing unit can add a function that allows for easy uploading of images and videos when sharing information, thereby promoting visual information exchange. For example, photos of catches and videos of fishing spots can be shared. It is also possible to use generative AI to analyze uploaded images and videos and build a system that provides optimal information to users. For example, useful information can be suggested based on images and videos. Furthermore, the function that allows for easy uploading of images and videos can be used to promote information exchange with fellow anglers. For example, fishing tips and techniques can be shared visually. By adding a function that allows for easy uploading of images and videos, visual information exchange can be promoted.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The fishing result information acquisition system can further include a congestion status display unit that displays the congestion status of fishing spots in real time. For example, the congestion level of fishing spots can be displayed in different colors, allowing the user to select a location where they can enjoy fishing comfortably. The congestion status display unit can also suggest the nearest vacant fishing spot to the user's current location. Furthermore, the congestion status display unit can notify the user when the congestion level changes. This allows the user to avoid crowds and enjoy fishing comfortably.

[0053] The location information acquisition unit can also recommend fishing spots that are individually customized based on the user's past fishing results. For example, it can prioritize locations where many fish have been caught in the past. It can also recommend fishing spots where specific fish species are likely to be caught. It can also recommend fishing spots according to the season and weather conditions. This allows it to individually recommend the best fishing spot for each user.

[0054] The fishing result information collection unit can further add a voice input function, allowing users to easily provide information. For example, the type and number of fish can be input by voice. Also, using voice recognition technology, the voice-inputted fishing result information can be converted into text data. Furthermore, information can be input by voice command. This allows users to easily provide information.

[0055] The fishing result information display unit can also add a fishing spot rating function, allowing users to check fishing spot ratings. For example, it can display the rating scores of fishing spots rated by other fishing enthusiasts. It can also suggest the best fishing spot for the user based on the fishing spot ratings. It can also update fishing spot ratings in real time to provide the latest information. This allows users to select a fishing spot based on the fishing spot ratings.

[0056] The fishing information collection unit can further use the generation AI to evaluate the reliability of the fishing information and display only highly reliable information. For example, it can prioritize displaying information that matches past data. It can also analyze the user's past posting history to evaluate the reliability of the fishing information. It can also use the generation AI to evaluate the reliability of the fishing information and filter out information with low reliability. This allows it to display only highly reliable fishing information.

[0057] The fishing spot evaluation unit can further integrate data from other outdoor activities (e.g., camping and hiking) to provide a composite evaluation. For example, fishing spots that are close to campsites or hiking trails can be preferentially displayed. Generative AI can also be used to integrate data from other outdoor activities into the fishing spot evaluation. Furthermore, by incorporating data from other outdoor activities into the fishing spot evaluation, a composite evaluation can be provided. This allows for a composite evaluation to be provided by integrating data from other outdoor activities.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The location information acquisition unit acquires the location information of the user's current location or a specified location. For example, the user's current location is acquired using the GPS function of a smartphone. The user can also manually input the location information of a specified location. Step 2: The map display unit displays the location information acquired by the location information acquisition unit on a map. For example, the map display unit uses a map application to display the user's current location or a specified location on the map. Step 3: The fishing information collection unit collects fishing information provided by the fishing enthusiast. For example, the fishing enthusiast inputs the type and number of fish caught and fishing spot information through the app. Step 4: The fishing result information display unit displays the fishing result information collected by the fishing result information collection unit on a map. For example, the fishing result information may be displayed as a marker at a specific point on the map, and the user can click on it to view detailed information.

[0060] (Example 2) The fishing result information acquisition system according to the embodiment of the present invention is a system that provides information on the types and numbers of fish caught in the vicinity and fishing spots based on location information of the user's current location or a specified location. As a result, the fishing result information acquisition system provides information that allows the user to enjoy fishing efficiently and improve their fishing results.

[0061] A fishing result information acquisition system according to an embodiment includes a location information acquisition unit, a map display unit, a fishing result information collection unit, and a fishing result information display unit. The location information acquisition unit acquires location information of a user's current location or a specified location. For example, the location information of the user's current location is acquired using the GPS function of a smartphone. The user can also manually input location information of a specified location. The map display unit displays the location information acquired by the location information acquisition unit on a map. For example, a map app may be used to display the user's current location or a specified location on a map. The fishing result information collection unit collects fishing result information provided by fishing enthusiasts. For example, the fishing enthusiast may input the type and number of fish caught and fishing spot information through the app. The fishing result information display unit displays the fishing result information collected by the fishing result information collection unit on a map. For example, the fishing result information may be displayed as a marker at a specific point on the map, and the user can click to view detailed information. This allows the user to acquire information to enjoy fishing efficiently.

[0062] The location information acquisition unit can acquire the user's current location using the smartphone's GPS function. The location information acquisition unit acquires the user's current location, for example, using the smartphone's built-in GPS function. For example, it receives GPS signals and acquires latitude and longitude information. The location information acquisition unit can also identify the current location using Wi-Fi and cell tower information. For example, it estimates the current location based on the location information of a Wi-Fi access point. Furthermore, the location information acquisition unit can connect an external GPS device to acquire the current location. For example, it acquires location information from an external GPS device connected via Bluetooth. This allows the user's current location to be acquired accurately.

[0063] The catch information collection unit allows fishing enthusiasts to input information about the type and number of fish caught and fishing spots through the app. For example, fishing enthusiasts can use the app's input form to input information about the type and number of fish caught and fishing spots. For example, fishing enthusiasts can select from a list of fish species or input information manually. The catch information collection unit can also use image recognition technology to automatically identify fish species from photos uploaded by fishing enthusiasts. For example, it can analyze the characteristics of the fish and determine the species. Furthermore, the catch information collection unit can add a voice input function so that fishing enthusiasts can input information by voice. For example, it can use voice recognition technology to convert voice into text data. This allows for efficient collection of information from fishing enthusiasts.

[0064] The fishing result information display unit can display the fishing result information collected by the fishing result information collection unit on a map. The fishing result information display unit, for example, displays the fishing result information as markers at specific points on the map. For example, a marker can be placed at the location of a fishing spot to display the type and number of fish caught. The fishing result information display unit can also display the fishing result information as a pop-up window. For example, when a user clicks on a marker, detailed information is displayed. Furthermore, the fishing result information display unit can display the fishing result information in different colors. For example, the color of the marker can be changed depending on the number of fish caught. This allows the fishing result information to be visually confirmed.

[0065] The location information acquisition unit can analyze the user's movement patterns and suggest routes to fishing spots in real time. For example, the location information acquisition unit can use a generation AI to analyze the user's past movement data and suggest the optimal route to the fishing spot. For example, the location information acquisition unit can display the shortest route or a route that takes traffic conditions into account based on data from fishing spots visited in the past. The location information acquisition unit also updates the route from the user's current location to the desired fishing spot in real time, reflecting information such as traffic congestion and road construction. For example, the generation AI can analyze traffic data and suggest the optimal route. Furthermore, the location information acquisition unit can learn the user's movement patterns and customize the route to the fishing spot. For example, the optimal route can be suggested based on the user's preferences and past movement history. This allows the optimal route to be suggested in real time.

[0066] The location information acquisition unit can recommend fishing spots that are individually customized based on the user's past fishing data. For example, the location information acquisition unit analyzes the user's past fishing data and recommends the optimal fishing spot. For example, it can prioritize displaying locations where many fish have been caught in the past. In addition, using generation AI, it can recommend fishing spots where specific fish species are likely to be caught based on the user's fishing data. For example, it can suggest locations where many of the user's favorite fish species can be caught. Furthermore, the location information acquisition unit can recommend fishing spots according to season and weather conditions based on the user's fishing data. For example, it can display locations where many fish can be caught in a specific season. This allows it to individually recommend the optimal fishing spot to the user.

[0067] The location information acquisition unit can use the emotion estimation function to analyze the user's current emotional state and suggest relaxing fishing spots. The location information acquisition unit can, for example, use the emotion estimation function to analyze the user's current emotional state and suggest relaxing fishing spots. For example, if the user is highly stressed, a quiet fishing spot can be recommended. Furthermore, based on the user's emotional data, fishing spots with a high relaxing effect can be suggested. For example, places with abundant natural environments or places with few people can be displayed. Furthermore, the emotion estimation function can be used to recommend fishing spots according to the user's emotional state. For example, if the user wants to relax, a place with a beautiful view can be suggested. In this way, fishing spots can be suggested according to the user's emotional state.

[0068] The location information acquisition unit can integrate weather information and tidal data into the acquisition of location information and map display to provide information to increase the success rate of fishing. The location information acquisition unit, for example, integrates location information and weather information to provide information to increase the success rate of fishing. For example, sunny days and days with little wind are preferentially displayed. Tidal data can also be integrated with location information to provide information to increase the success rate of fishing. For example, the time periods when the tides are suitable for fishing can be displayed. Furthermore, weather information and tidal data can be integrated to provide information to increase the success rate of fishing. For example, fishing spots with optimal weather and tidal conditions can be suggested. In this way, by integrating weather information and tidal data, information to increase the success rate of fishing can be provided.

[0069] The location information acquisition unit can display the congestion status of fishing spots in real time, allowing the user to select a location where they can enjoy fishing comfortably. The location information acquisition unit can, for example, display the congestion status of fishing spots in real time, allowing the user to select a location where they can enjoy fishing comfortably. For example, the congestion level can be displayed in different colors. Furthermore, the generation AI can be used to analyze the congestion status of fishing spots and suggest the best fishing spot for the user. For example, locations with lower congestion levels can be displayed preferentially. Furthermore, the congestion status of fishing spots can be updated in real time, allowing the user to select a location where they can enjoy fishing comfortably. For example, a notification can be sent if the congestion level changes. In this way, by displaying the congestion status of fishing spots in real time, the user can select a location where they can enjoy fishing comfortably.

[0070] The location information acquisition unit can use the emotion estimation function to analyze the emotion a user has when selecting a fishing spot and suggest fishing spots that will elicit positive emotions. The location information acquisition unit can, for example, use the emotion estimation function to analyze the emotion a user has when selecting a fishing spot and suggest fishing spots that will elicit positive emotions. For example, it can recommend fishing spots where the user has had a pleasant experience in the past. It can also suggest fishing spots that will elicit positive emotions based on the user's emotion data. For example, it can display places with beautiful scenery or places with rich natural environments. Furthermore, it can use the emotion estimation function to recommend fishing spots based on the user's emotional state. For example, if the user wants to relax, it can suggest a quiet place. In this way, it is possible to provide a positive fishing experience by suggesting fishing spots that suit the user's emotions.

[0071] The fishing result information collection unit can use the generation AI to evaluate the reliability of the fishing result information and display only highly reliable information. The fishing result information collection unit, for example, uses the generation AI to evaluate the reliability of the fishing result information and display only highly reliable information. For example, it prioritizes displaying information that matches past data. In addition, to evaluate the reliability of the fishing result information, it can also analyze the user's past posting history. For example, it can prioritize displaying information from highly reliable users. Furthermore, it can use the generation AI to evaluate the reliability of the fishing result information and filter out information with low reliability. For example, it can filter out contradictory or inaccurate information. This allows accurate information to be provided to the user by displaying only highly reliable fishing result information.

[0072] The fishing result information collection unit can use the emotion estimation function to analyze the user's emotions when inputting fishing result information and promote positive feedback. The fishing result information collection unit can, for example, use the emotion estimation function to analyze the user's emotions when inputting fishing result information and promote positive feedback. For example, a positive message can be displayed when the user feels joy or satisfaction. Feedback to promote input of fishing result information can also be provided based on the user's emotion data. For example, a message that elicits positive emotions can be displayed. Furthermore, the emotion estimation function can be used to analyze the user's emotions when inputting fishing result information and promote positive feedback. For example, a reward can be provided if the emotion score is high. This can promote positive feedback according to the user's emotions.

[0073] The catch information collection unit can add a voice input function to allow users to easily provide information. The catch information collection unit can add, for example, a voice input function to allow users to easily provide information. For example, the type and number of fish can be input by voice. In addition, using a generation AI, the voice-input catch information can be analyzed and converted into text data. For example, the information can be automatically analyzed using voice recognition technology. Furthermore, the voice input function can be used to allow users to easily provide catch information. For example, information can be input by voice command. This allows users to easily provide information.

[0074] The fishing result information collection unit can share fishing result information with other fishing enthusiasts in real time, promoting information exchange throughout the community. The fishing result information collection unit can, for example, share fishing result information with other fishing enthusiasts in real time, promoting information exchange throughout the community. For example, the fishing result information can be shared via social media or a messaging app. The generation AI can also be used to analyze fishing result information in real time and notify other fishing enthusiasts. For example, information that a specific fish has been caught can be shared instantly. Furthermore, fishing result information can be shared in real time, promoting information exchange throughout the community. For example, fishing result information can be displayed on a map so that other users can view it. In this way, by sharing fishing result information in real time, information exchange throughout the community can be promoted.

[0075] The fishing result information collection unit can use the emotion estimation function to analyze other users' emotional reactions to the fishing result information and identify popular fishing spots and fish species. The fishing result information collection unit can, for example, use the emotion estimation function to analyze other users' emotional reactions to the fishing result information and identify popular fishing spots and fish species. For example, it can display fishing spots with a high number of positive emotional reactions. Popular fishing spots and fish species can also be identified based on the emotional data of other users. For example, it can preferentially display fishing spots and fish species with high emotional scores. Furthermore, it can use the emotion estimation function to analyze other users' emotional reactions to the fishing result information and identify popular fishing spots and fish species. For example, it can create rankings based on the emotional scores. In this way, popular fishing spots and fish species can be identified by analyzing the emotional reactions.

[0076] The fishing spot evaluation unit can use the generation AI to analyze environmental data of the fishing spot (e.g., water quality and ecosystem) and evaluate the optimal fishing spot. The fishing spot evaluation unit can, for example, use the generation AI to analyze the water quality data of the fishing spot and evaluate the optimal fishing spot. For example, it can prioritize and display locations with good water quality. It can also analyze the ecosystem data of the fishing spot and evaluate the optimal fishing spot. For example, it can display locations with good fish habitats. Furthermore, it can use the generation AI to comprehensively analyze the environmental data of the fishing spot and evaluate the optimal fishing spot. For example, it can integrate and evaluate water quality and ecosystem data. In this way, it is possible to evaluate the optimal fishing spot by analyzing the environmental data.

[0077] The fishing spot evaluation unit can dynamically update the evaluation by combining the user's past fishing data with current weather conditions. The fishing spot evaluation unit, for example, uses a generation AI to combine the user's past fishing data with current weather conditions to evaluate fishing spots. For example, the evaluation is updated based on locations where many fish have been caught in the past and the current weather. The unit can also analyze the user's fishing data and weather conditions in real time to dynamically update the fishing spot evaluation. For example, the evaluation can be automatically adjusted every time the weather changes. Furthermore, a system can be built that combines fishing data and weather conditions to evaluate fishing spots. For example, the fishing spot evaluation can be updated in real time based on weather data. This allows the fishing spot evaluation to be dynamically updated by combining past fishing data with current weather conditions.

[0078] The fishing spot evaluation unit uses the emotion estimation function to evaluate fishing spots taking into account the user's emotional state and can recommend relaxing spots. The fishing spot evaluation unit, for example, uses the emotion estimation function to analyze the user's emotional state and evaluate relaxing fishing spots. For example, if the user is highly stressed, it can recommend a quiet fishing spot. It can also evaluate fishing spots that have a high relaxing effect based on the user's emotional data. For example, it can display places with abundant natural environments or places with few people. Furthermore, it can use the emotion estimation function to evaluate fishing spots according to the user's emotional state. For example, if the user wants to relax, it can suggest a place with a beautiful view. In this way, it can evaluate fishing spots taking into account the user's emotional state and recommend a relaxing spot.

[0079] The fishing spot evaluation unit can integrate data from other outdoor activities (e.g., camping and hiking) to provide a composite evaluation. The fishing spot evaluation unit, for example, integrates data from camping and hiking into the evaluation of the fishing spot to provide a composite evaluation. For example, fishing spots that are close to campsites or hiking trails are preferentially displayed. In addition, using a generation AI, data from other outdoor activities can be integrated into the evaluation of the fishing spot. For example, evaluations are made based on the popularity and number of users of the outdoor activity. Furthermore, by incorporating data from other outdoor activities into the evaluation of the fishing spot, a composite evaluation can be provided. For example, fishing spots that can be enjoyed in combination with camping or hiking are suggested. This allows a composite evaluation to be provided by integrating data from other outdoor activities.

[0080] The fishing spot evaluation unit can customize the fishing spot evaluation results according to the user's preferences and provide individually optimized recommendations. The fishing spot evaluation unit, for example, customizes the fishing spot evaluation results according to the user's preferences and provides individually optimized recommendations. For example, it can suggest places where a user who likes a particular species of fish can catch a lot of that fish. Generative AI can also be used to analyze the user's past behavioral data and recommend fishing spots based on preferences. For example, recommendations can be customized based on data on fishing spots visited in the past. Furthermore, it is possible to build a system that customizes the fishing spot evaluation results according to the user's preferences and provides individually optimized recommendations. For example, recommendations can be adjusted based on user feedback. This makes it possible to customize the fishing spot evaluation results according to the user's preferences and provide individually optimized recommendations.

[0081] The fishing spot evaluation unit can use the emotion estimation function to collect users' emotional responses to fishing spot evaluations and continuously improve the evaluation criteria. The fishing spot evaluation unit, for example, uses the emotion estimation function to collect users' emotional responses to fishing spot evaluations and continuously improve the evaluation criteria. For example, it prioritizes the adoption of evaluation criteria with a high number of positive emotional responses. It is also possible to build a system that continuously improves fishing spot evaluation criteria based on user emotion data. For example, it adjusts the evaluation criteria based on the emotion score. Furthermore, it is also possible to use the emotion estimation function to collect users' emotional responses to fishing spot evaluations and improve the evaluation criteria. For example, it is possible to review evaluation criteria with a high number of negative emotional responses. In this way, the evaluation criteria can be continuously improved by collecting users' emotional responses.

[0082] The fishing hint providing unit can dynamically update advice by combining real-time weather data and tidal information. The fishing hint providing unit, for example, combines fishing hints with real-time weather data to dynamically update advice. For example, the unit suggests the best fishing gear and fishing spots every time the weather changes. The unit can also dynamically update advice by combining tidal information with fishing hints. For example, it can provide fishing tips according to the tides. Furthermore, a system can be built that combines real-time weather data and tidal information to dynamically update fishing hints. For example, it can suggest the best time of day for the weather and tidal conditions. This allows the unit to dynamically update advice by combining real-time weather data and tidal information.

[0083] The fishing hint providing unit can use the emotion estimation function to provide advice that takes into account the user's emotional state, thereby promoting a positive fishing experience. The fishing hint providing unit, for example, uses the emotion estimation function to analyze the user's emotional state and provide advice that promotes a positive fishing experience. For example, it can suggest relaxing fishing spots and fishing gear. It can also build a system that provides advice to promote a positive fishing experience based on the user's emotional data. For example, it can suggest optimal fishing tips based on the emotion score. Furthermore, the emotion estimation function can be used to provide advice that takes into account the user's emotional state, thereby promoting a positive fishing experience. For example, a reward can be provided if the emotion score is high. In this way, a positive fishing experience can be promoted by providing advice that takes into account the user's emotional state.

[0084] The fishing hint providing unit can provide fishing hints in video or audio format to make them easier to understand visually and aurally. The fishing hint providing unit, for example, provides fishing hints in video format to make them easier to understand visually. For example, fishing tips and techniques are explained in video. Fishing hints can also be provided in audio format to make them easier to understand aurally. For example, fishing advice is provided as an audio guide. Furthermore, a system can be constructed that provides fishing hints in video or audio format to make them easier for users to understand visually and aurally. For example, a guide combining video and audio is provided. In this way, fishing hints can be provided in video or audio format to make them easier to understand visually and aurally.

[0085] The fishing hint provider can share fishing tips with other fishing enthusiasts to promote knowledge exchange throughout the community. For example, the fishing hint provider can share fishing tips with other fishing enthusiasts to promote knowledge exchange throughout the community. For example, fishing tips and techniques can be shared via social media or messaging apps. The generative AI can also be used to analyze fishing tips in real time and notify other fishing enthusiasts. For example, the time periods and locations when specific fish are most likely to be caught can be shared. Furthermore, a system can be built that shares fishing tips and promotes knowledge exchange throughout the community. For example, fishing advice can be displayed on a map so that other users can view it. In this way, sharing fishing tips can promote knowledge exchange throughout the community.

[0086] The fishing hint providing unit can use the emotion estimation function to collect the user's emotional reactions to the fishing hints and continuously improve the quality of the advice. The fishing hint providing unit, for example, uses the emotion estimation function to collect the user's emotional reactions to the fishing hints and continuously improve the quality of the advice. For example, advice with a high number of positive emotional reactions is preferentially provided. Also, a system can be built that continuously improves the quality of fishing hints based on the user's emotional data. For example, advice can be adjusted based on an emotion score. Furthermore, the emotion estimation function can be used to collect the user's emotional reactions to the fishing hints and improve the quality of the advice. For example, advice with a high number of negative emotional reactions can be reviewed. In this way, the quality of advice can be continuously improved by collecting the user's emotional reactions.

[0087] The information sharing unit can use the generation AI to automatically extract and share the most useful information when sharing information with fellow anglers. The information sharing unit, for example, uses the generation AI to automatically extract and share the most useful information when sharing information with fellow anglers. For example, useful information is selected based on catch information and fishing spot ratings. It is also possible to build a system in which the generation AI automatically extracts useful information when sharing information with fellow anglers. For example, useful information is selected based on past data. It is also possible to use the generation AI to automatically extract and share the most useful information when sharing information with fellow anglers. For example, useful information is selected based on fishing tips and techniques. This automatically extracts and shares the most useful information, thereby improving the quality of information sharing.

[0088] The information sharing unit can provide individually customized information based on the user's past fishing result data when sharing information. The information sharing unit, for example, can provide individually customized information based on the user's past fishing result data when sharing information. For example, it can suggest locations and times when a specific fish species is likely to be caught. It can also use generation AI to build a system that analyzes the user's fishing result data and provides customized information. For example, it can suggest optimal fishing gear and bait based on past data. Furthermore, it can also provide individually customized information based on the user's past fishing result data when sharing information. For example, it can suggest tips for fishing in specific weather conditions. This makes it possible to provide individually customized information based on the user's past fishing result data.

[0089] The information sharing unit can use the emotion estimation function to analyze the user's emotions when sharing information and promote positive communication. The information sharing unit can, for example, use the emotion estimation function to analyze the user's emotions when sharing information and promote positive communication. For example, a positive message can be displayed when the user feels joy or satisfaction. Feedback can also be provided to promote communication when sharing information based on the user's emotion data. For example, a message that elicits positive emotions can be displayed. Furthermore, the emotion estimation function can be used to analyze the user's emotions when sharing information and promote positive communication. For example, a reward can be provided if the emotion score is high. In this way, positive communication can be promoted by analyzing the user's emotions.

[0090] The information sharing unit can add a real-time chat function to the information sharing, enabling instantaneous information exchange. The information sharing unit can, for example, add a real-time chat function to the information sharing, enabling instantaneous information exchange. For example, information on catches and fishing spots can be shared in real time. It is also possible to use generative AI to analyze the real-time chat function and build a system that provides optimal information to users. For example, useful information can be suggested based on the chat content. Furthermore, the real-time chat function can be used to promote information exchange with fellow anglers. For example, fishing tips and techniques can be shared in real time. In this way, adding the real-time chat function enables instantaneous information exchange.

[0091] The information sharing unit can add a function that allows for easy uploading of images and videos when sharing information, thereby promoting visual information exchange. The information sharing unit can add a function that allows for easy uploading of images and videos when sharing information, thereby promoting visual information exchange. For example, photos of catches and videos of fishing spots can be shared. It is also possible to use generative AI to analyze uploaded images and videos and build a system that provides optimal information to users. For example, useful information can be suggested based on images and videos. Furthermore, the function that allows for easy uploading of images and videos can be used to promote information exchange with fellow anglers. For example, fishing tips and techniques can be shared visually. By adding a function that allows for easy uploading of images and videos, visual information exchange can be promoted.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The fishing result information acquisition system can further include a congestion status display unit that displays the congestion status of fishing spots in real time. For example, the congestion level of fishing spots can be displayed in different colors, allowing the user to select a location where they can enjoy fishing comfortably. The congestion status display unit can also suggest the nearest vacant fishing spot to the user's current location. Furthermore, the congestion status display unit can notify the user when the congestion level changes. This allows the user to avoid crowds and enjoy fishing comfortably.

[0094] The location information acquisition unit can also recommend fishing spots that are individually customized based on the user's past fishing results. For example, it can prioritize locations where many fish have been caught in the past. It can also recommend fishing spots where specific fish species are likely to be caught. It can also recommend fishing spots according to the season and weather conditions. This allows it to individually recommend the best fishing spot for each user.

[0095] The fishing result information collection unit can further add a voice input function, allowing users to easily provide information. For example, the type and number of fish can be input by voice. Also, using voice recognition technology, the voice-inputted fishing result information can be converted into text data. Furthermore, information can be input by voice command. This allows users to easily provide information.

[0096] The fishing result information display unit can also add a fishing spot rating function, allowing users to check fishing spot ratings. For example, it can display the rating scores of fishing spots rated by other fishing enthusiasts. It can also suggest the best fishing spot for the user based on the fishing spot ratings. It can also update fishing spot ratings in real time to provide the latest information. This allows users to select a fishing spot based on the fishing spot ratings.

[0097] The location information acquisition unit can further use the emotion estimation function to analyze the user's current emotional state and suggest relaxing fishing spots. For example, if the user is highly stressed, a quiet fishing spot can be recommended. Also, based on the user's emotional data, it can suggest fishing spots with a high relaxing effect. Furthermore, the emotion estimation function can also be used to recommend fishing spots according to the user's emotional state. This makes it possible to suggest fishing spots according to the user's emotional state.

[0098] The fishing result information collection unit can further use an emotion estimation function to analyze the user's emotions when inputting fishing result information and promote positive feedback. For example, a positive message can be displayed when the user feels joy or satisfaction. Feedback can also be provided to promote input of fishing result information based on the user's emotion data. Furthermore, the emotion estimation function can also be used to analyze the user's emotions when inputting fishing result information and promote positive feedback. This makes it possible to promote positive feedback according to the user's emotions.

[0099] The fishing information collection unit can further use the generation AI to evaluate the reliability of the fishing information and display only highly reliable information. For example, it can prioritize displaying information that matches past data. It can also analyze the user's past posting history to evaluate the reliability of the fishing information. It can also use the generation AI to evaluate the reliability of the fishing information and filter out information with low reliability. This allows it to display only highly reliable fishing information.

[0100] The fishing spot evaluation unit can further use the emotion estimation function to evaluate fishing spots taking into account the user's emotional state and recommend places where the user can relax. For example, if the user is highly stressed, a quiet fishing spot can be recommended. Fishing spots with a high relaxation effect can also be evaluated based on the user's emotional data. Furthermore, the emotion estimation function can also be used to evaluate fishing spots according to the user's emotional state. This allows the evaluation of fishing spots taking into account the user's emotional state and recommend places where the user can relax.

[0101] The fishing spot evaluation unit can further integrate data from other outdoor activities (e.g., camping and hiking) to provide a composite evaluation. For example, fishing spots that are close to campsites or hiking trails can be preferentially displayed. Generative AI can also be used to integrate data from other outdoor activities into the fishing spot evaluation. Furthermore, by incorporating data from other outdoor activities into the fishing spot evaluation, a composite evaluation can be provided. This allows for a composite evaluation to be provided by integrating data from other outdoor activities.

[0102] The fishing hint providing unit can further use the emotion estimation function to provide advice that takes into account the user's emotional state, thereby promoting a positive fishing experience. For example, it can suggest relaxing fishing spots and fishing gear. It can also provide advice to promote a positive fishing experience based on the user's emotional data. It can also use the emotion estimation function to provide advice that takes into account the user's emotional state, thereby promoting a positive fishing experience. As a result, it is possible to promote a positive fishing experience by providing advice that takes into account the user's emotional state.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The location information acquisition unit acquires the location information of the user's current location or a specified location. For example, the user's current location is acquired using the GPS function of a smartphone. The user can also manually input the location information of a specified location. Step 2: The map display unit displays the location information acquired by the location information acquisition unit on a map. For example, the map display unit uses a map application to display the user's current location or a specified location on the map. Step 3: The fishing information collection unit collects fishing information provided by the fishing enthusiast. For example, the fishing enthusiast inputs the type and number of fish caught and fishing spot information through the app. Step 4: The fishing result information display unit displays the fishing result information collected by the fishing result information collection unit on a map. For example, the fishing result information may be displayed as a marker at a specific point on the map, and the user can click on it to view detailed information.

[0105] 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.

[0106] 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> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). 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 speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 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.

[0110] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0111] 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.

[0112] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0113] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0118] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0119] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] 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.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0126] 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.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0141] 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.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] 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 image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] 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.

[0145] The control object 443 includes a display device, LEDs in the eyes, and motors that drive 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.

[0146] 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.

[0147] 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.

[0148] 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0149] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] 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.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] 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.

[0155] FIG. 9 illustrates 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 behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions 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.

[0156] 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.

[0157] 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).

[0158] 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 expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, 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 expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0159] 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."

[0160] 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.

[0161] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, 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. A processor also includes 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.

[0166] The hardware resource that executes the specific process 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 process may be a single processor.

[0167] 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.

[0168] 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.

[0169] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0170] 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.

[0171] 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. [Explanation of symbols]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a location information acquisition unit that acquires location information of a user's current location or a location designated by the user; a map display unit that displays the location information acquired by the location information acquisition unit on a map; a fishing result information collection section that collects fishing result information provided by fishing enthusiasts; a fishing result information display unit that displays the fishing result information collected by the fishing result information collection unit on a map. A system characterized by:

2. The location information acquisition unit The location information and map display are integrated with weather information and tide data to provide information to increase the success rate of fishing.

2. The system of claim 1.

3. The fishing result information collection unit Using a generation AI to evaluate the reliability of the fishing information, and display only the highly reliable information 2. The system of claim 1.

4. The fishing spot evaluation unit Analyzing environmental data of the fishing spot using a generative AI and evaluating the optimal fishing spot 2. The system of claim 1.

5. The fishing hint providing unit Using generative AI to analyze the user's past fishing data and provide the user with individually customized fishing tips.

2. The system of claim 1.

6. The Information Sharing Department Analyzing the user's emotions when sharing information and promoting positive communication 2. The system of claim 1.

7. The location information acquisition unit Analyzing the user's current emotional state and suggesting the fishing spot where the user can relax 2. The system of claim 1.

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