System

Abstract

To provide a system according to an embodiment that predicts a catch in fishing and proposes an optimal way of fishing or lure.SOLUTION: A system comprises a collection unit, an analysis unit, and a proposal unit. The collection unit collects data on weather information, tide, and water temperature. The analysis unit analyzes the data collected by the collection unit and predicts a catch in fishing. The proposal unit proposes an optimal way of fishing or lure on the basis of a prediction result obtained by the analysis unit.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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology does not adequately analyze data to predict fishing results, and there is room for improvement.

[0005] The system according to the embodiment aims to predict fishing results and suggest appropriate fishing methods and lures. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a suggestion unit. The collection unit collects data on weather information, tides, and water temperature. The analysis unit analyzes the data collected by the collection unit and predicts fishing results. The suggestion unit suggests an appropriate fishing method or lure based on the prediction result obtained by the analysis unit. Effect of the Invention

[0007] The system according to the embodiment can predict fishing results and suggest appropriate fishing methods and lures. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

[0009] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the 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 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. 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 prediction system according to the embodiment of the present invention is a system that uses AI to predict fishing results and suggests optimal fishing methods and lures to users. This system starts when a user opens an app and selects a fishing spot and a target fish species. Next, the AI ​​analyzes data such as past fishing results, weather information, tides, and water temperature, and predicts optimal fishing results for the selected fishing spot and fish species. Furthermore, the AI ​​suggests optimal fishing methods and lures to use based on the fishing result prediction. This solves the problem of "where to catch fish" for anglers from family fishing to beginners and intermediate fishers who enjoy fishing as a hobby. For example, if a user selects "I want to catch bass in Lake A," that information is input into the AI. Next, the AI ​​analyzes data such as past fishing results, weather information, tides, and water temperature, and predicts optimal fishing results for the selected fishing spot and fish species. For example, a prediction such as "6:00 to 8:00 a.m. is optimal for bass fishing in Lake A" can be obtained from past data. Furthermore, the AI ​​suggests optimal fishing methods and lures to use based on the fishing result prediction. For example, specific advice such as "Minnow-type lures are effective for catching bass in Lake A" is provided. This allows users to increase their success rate in fishing. This app is suitable for a wide range of users, from family anglers to beginners and intermediate anglers who enjoy fishing as a hobby. For example, when enjoying fishing with the family, an interface that is easy for children to use is provided, and even beginners can get specific advice on how to increase their catch. In addition, for intermediate anglers, highly accurate predictions based on past data are provided, which helps improve fishing skills. In this way, "Anglsearch" is an app that uses AI to predict catches and suggests the best fishing methods and lures to users, thereby increasing the fun and success rate of fishing. As a result, the catch prediction system can predict catches for users and suggest the best fishing methods and lures.

[0029] The fishing result prediction system according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects data such as weather information, tides, and water temperature. The collection unit collects weather information such as air temperature, wind speed, and precipitation. The collection unit can also collect tide data such as the time and tide level of high tide and low tide. The collection unit can also collect water temperature data such as surface water temperature and deep water temperature. For example, the collection unit acquires weather information from a service that provides weather data, acquires tide information from a service that provides tide data, and acquires water temperature information from a sensor that provides water temperature data. The analysis unit analyzes the data collected by the collection unit and predicts the fishing result. The analysis unit analyzes the collected data using, for example, a machine learning algorithm, and predicts the fishing result. The analysis unit can also predict the fishing result by combining current weather information, tide data, and water temperature data based on past fishing result data. For example, the analysis unit compares past fishing result data with current weather information to predict the fishing result. The analysis unit can also compare past fishing result data with current tide data to predict fishing results. The analysis unit can also compare past fishing result data with current water temperature data to predict fishing results. The suggestion unit suggests optimal fishing methods and lures based on the prediction results obtained by the analysis unit. The suggestion unit suggests optimal fishing methods and lures for a specific fish species based on, for example, the fishing result prediction. The suggestion unit can also make suggestions based on a fishing spot selected by the user and a target fish species. For example, the suggestion unit suggests optimal fishing methods and lures for a specific fish species at a fishing spot selected by the user. In this way, the fishing result prediction system according to the embodiment can predict fishing results for the user and suggest optimal fishing methods and lures. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can make suggestions using an AI model that inputs the prediction results obtained by the analysis unit and outputs optimal fishing methods and lures.

[0030] The collection unit collects data such as weather information, tides, and water temperature. Specifically, to collect weather information such as temperature, wind speed, and precipitation, data is acquired in real time from a service that provides weather data. This includes data from weather satellites and ground weather observation stations. In addition, to collect tidal data such as the time and tide level of high and low tides, information is acquired from a service that provides tidal data. This includes data from marine observation buoys and coastal tidal observation stations. In addition, to collect water temperature data such as surface water temperature and deep water temperature, information is acquired from a sensor that provides water temperature data. This includes data from marine observation buoys and underwater drones. The collection unit centrally manages these data and stores them in a database that is updated in real time. The frequency and accuracy of data collection can be adjusted according to the system settings, allowing for flexible response according to specific situations and conditions. For example, when a sudden change in weather is predicted, more detailed information can be acquired by increasing the frequency of data collection. This allows the collection unit to collect a wide range of information from diverse data sources, improving the accuracy and reliability of the entire system.

[0031] The analysis unit analyzes the data collected by the collection unit and predicts the catch. Specifically, the analysis unit uses a machine learning algorithm to analyze the collected data and predict the catch. For example, the analysis unit predicts the catch by combining current weather information, tide data, and water temperature data based on past catch data. The analysis unit can use methods such as regression analysis and clustering to compare past catch data with current weather information and predict the catch. In addition, the analysis unit can use time series analysis and pattern recognition technology to compare past catch data with current tide data and predict the catch. Furthermore, the analysis unit can use advanced machine learning models such as neural networks and support vector machines to compare past catch data with current water temperature data and predict the catch. This allows the analysis unit to analyze the collected data from multiple angles and predict the catch with high accuracy. Furthermore, the analysis unit can continuously revise the prediction results based on data updated in real time to respond to the latest situation. For example, if the weather, tides, or water temperature change suddenly, the analysis unit immediately takes in new data and updates the prediction results. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings, allowing the analysis unit to not only grasp the situation in real time, but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The suggestion unit suggests the optimal fishing method and lure based on the prediction result obtained by the analysis unit. Specifically, the optimal fishing method and lure for a specific fish species is suggested based on the catch prediction. The suggestion unit can also make suggestions based on the fishing spot and target fish species selected by the user. For example, past fishing result data and expert knowledge can be used to suggest the optimal fishing method and lure for a specific fish species at the fishing spot selected by the user. The suggestion unit can make suggestions using an AI model that inputs the prediction result obtained by the analysis unit and outputs the optimal fishing method and lure. The AI ​​model can learn past fishing result data and environmental data and predict the optimal fishing method and lure under specific conditions. For example, the optimal fishing method and lure are suggested based on the water temperature and tidal conditions preferred by a specific fish species. In addition, the suggestion unit can collect user feedback and continuously improve the accuracy and effectiveness of the suggestions. For example, the accuracy of the suggestions can be improved by collecting the results of actual fishing by the user as feedback and using them as learning data for the AI ​​model. In addition, the suggestion unit can make individually customized suggestions taking into account the user's preferences and past fishing result history. This enables the suggestion unit to suggest optimal fishing methods and lures to the user, and provide support for maximizing catches.

[0033] The collection unit can collect past catch data. The collection unit, for example, acquires data from a service that provides past catch data. For example, the collection unit collects data such as the type of fish caught in the past, the place where it was caught, and the time it was caught. The collection unit can also collect past catch data input by the user. For example, the collection unit collects catch data input by the user into the app and uses it for analysis. By collecting past catch data, more accurate catch prediction is possible. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can acquire data from a service that provides past catch data, and input the data into an AI model to use it for analysis.

[0034] The suggestion unit can make suggestions based on the fishing spot and target fish species selected by the user. The suggestion unit suggests the optimal fishing method and lure based on the fishing spot and target fish species selected by the user, for example. For example, if the user selects "I want to fish for bass in Lake A," the suggestion unit suggests the optimal fishing method and lure based on that information. The suggestion unit can also suggest the optimal fishing method and lure by referring to past fishing result data based on the fishing spot and target fish species selected by the user. For example, the suggestion unit makes suggestions based on predictions such as "6:00 to 8:00 in the morning is optimal for bass fishing in Lake A" from past data. As a result, more appropriate fishing methods and lures can be suggested by making suggestions based on the fishing spot and target fish species selected by the user. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input information on the fishing spot and target fish species selected by the user into an AI model and output the optimal fishing method and lure.

[0035] The collection unit can analyze past catch data and select the optimal data collection method. For example, the collection unit collects data intensively during a specific time period based on past catch data. The collection unit can also analyze past catch data and prioritize data collection under specific weather conditions. Furthermore, the collection unit can strengthen data collection at a specific fishing spot by referring to past catch data. For example, the collection unit acquires data from a service that provides past catch data and selects the optimal data collection method based on the data. In this way, the optimal data collection method can be selected by analyzing the past catch data. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can acquire data from a service that provides past catch data and input the data into an AI model to select the optimal data collection method.

[0036] The collection unit can perform filtering based on the user's current fishing experience and skill level when collecting data. For example, for a beginner user, the collection unit can collect only basic data and provide simple feedback. For an intermediate user, the collection unit can also collect detailed data and provide specific advice. For an experienced user, the collection unit can also collect specialized data and provide advanced feedback. For example, the collection unit evaluates the user's fishing experience and skill level based on the user's past fishing result data and self-reporting, and collects data accordingly. This makes it possible to provide more appropriate feedback by filtering the data based on the user's fishing experience and skill level. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past fishing result data into an AI model, evaluate the user's fishing experience and skill level, and collect data accordingly.

[0037] The collection unit can expand the type of data collected to include topographical information of the fishing spot and water quality data. The collection unit, for example, collects topographical information of the fishing spot and reflects it in the catch prediction. The collection unit can also collect water quality data and suggest fishing spots suitable for a specific fish species. The collection unit can also collect vegetation information of the fishing spot and use it to predict the catch. For example, the collection unit collects topographical information such as water depth, bottom sediment, and obstacle positions and reflects it in the catch prediction. The collection unit also collects water quality data such as pH value, dissolved oxygen amount, and transparency and suggests fishing spots suitable for a specific fish species. This allows for more accurate catch prediction by expanding the type of data collected. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input topographical information and water quality data of the fishing spot into an AI model and use it to predict the catch.

[0038] The collection unit can dynamically adjust the frequency of data collection according to the season and the time period. For example, the collection unit adjusts the frequency of data collection for each season to perform optimal catch prediction. The collection unit can also adjust the frequency of data collection for each time period to provide a real-time catch prediction. Furthermore, the collection unit can increase the frequency of data collection in accordance with a specific event (e.g., spring tide). For example, the collection unit adjusts the frequency of data collection according to the season of spring, summer, autumn, and winter to perform optimal catch prediction. The collection unit also adjusts the frequency of data collection according to the time period of morning, afternoon, and night to provide a real-time catch prediction. This allows more appropriate data collection by dynamically adjusting the frequency of data collection. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can dynamically adjust the frequency of data collection according to the season and time period by inputting it into an AI model.

[0039] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the user when collecting data. For example, the collection unit preferentially collects data of the fishing spot where the user is currently located. The collection unit can also collect data of nearby fishing spots based on the user's location information. Furthermore, the collection unit can also collect highly relevant data by referring to the user's movement history. For example, the collection unit acquires the user's GPS data and preferentially collects data of the fishing spot where the user is currently located. The collection unit can also collect data of nearby fishing spots using a location information service. This allows more relevant data to be collected by collecting data in consideration of the user's geographical location information. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into an AI model and preferentially collect highly relevant data.

[0040] The collection unit can analyze the user's social media activity and collect related data when collecting data. The collection unit, for example, collects fishing result information shared by the user on social media. The collection unit can also collect data on popular fishing spots from the user's social media activity. The collection unit can also analyze the user's social media postings and collect related data. For example, the collection unit collects fishing result information shared by the user on social media and uses it for analysis. The collection unit can also analyze the user's followers and collect data on popular fishing spots. This allows more relevant data to be collected by analyzing the user's social media activity. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the user's social media activity into an AI model and collect related data.

[0041] The collection unit can expand the type of data collected to include the congestion status of the fishing spot and activity information of other anglers. The collection unit, for example, collects the congestion status of the fishing spot and reflects it in the catch prediction. The collection unit can also collect activity information of other anglers and suggest the optimal fishing spot. Furthermore, the collection unit can collect the usage status of the fishing spot and use it for the catch prediction. For example, the collection unit collects the congestion status of the fishing spot using a real-time camera and reflects it in the catch prediction. The collection unit can also collect activity information of other anglers based on the user's report and suggest the optimal fishing spot. This allows for more accurate catch prediction by expanding the type of data collected. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the congestion status of the fishing spot and activity information of other anglers into an AI model and use it for the catch prediction.

[0042] The collection unit can improve the accuracy of the collected data by combining different data sources. For example, the collection unit collects weather information from multiple weather data sources to improve the accuracy. The collection unit can also collect tide data from multiple ocean data sources to improve the accuracy. Furthermore, the collection unit can collect water temperature data from multiple sensor data sources to improve the accuracy. For example, the collection unit collects weather information from multiple weather data sources to improve the accuracy. The collection unit can also collect tide data from multiple ocean data sources to improve the accuracy. As a result, the accuracy of the collected data is improved by combining different data sources. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input weather information acquired from multiple weather data sources into an AI model to improve the accuracy.

[0043] The analysis unit can improve prediction accuracy by comparing past catch data with current environmental data during analysis. The analysis unit can, for example, compare past catch data with current weather data to improve prediction accuracy. The analysis unit can also compare past catch data with current tide data to improve prediction accuracy. The analysis unit can also compare past catch data with current water temperature data to improve prediction accuracy. For example, the analysis unit can compare past catch data with current weather data to predict catches. The analysis unit can also compare past catch data with current tide data to predict catches. In this way, prediction accuracy is improved by comparing past catch data with current environmental data. Part or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI or can be performed without using AI. For example, the analysis unit can input past catch data and current environmental data into an AI model to improve prediction accuracy.

[0044] The analysis unit can make predictions taking into account the topography and water quality data of the fishing spot during analysis. The analysis unit, for example, takes into account the topography data of the fishing spot to make a catch prediction. The analysis unit can also take into account water quality data to predict a fishing spot suitable for a specific fish species. The analysis unit can also take into account vegetation data of the fishing spot to make a catch prediction. For example, the analysis unit takes into account topography data such as water depth, bottom sediment, and obstacle positions to make a catch prediction. The analysis unit also takes into account water quality data such as pH value, dissolved oxygen amount, and transparency to predict a fishing spot suitable for a specific fish species. As a result, by taking into account the topography and water quality data of the fishing spot, more accurate predictions are possible. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input topography data and water quality data of the fishing spot into an AI model to make a catch prediction.

[0045] The analysis unit can customize the analysis algorithm according to different fish species and fishing spots. The analysis unit customizes the analysis algorithm for bass fishing, for example. The analysis unit can also customize the analysis algorithm for sea fishing. The analysis unit can also customize the analysis algorithm for mountain stream fishing. For example, the analysis unit customizes the analysis algorithm for bass fishing and performs a catch prediction specialized for bass fishing. The analysis unit can also customize the analysis algorithm for sea fishing and performs a catch prediction specialized for sea fishing. This allows for more appropriate prediction by customizing the analysis algorithm according to different fish species and fishing spots. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data corresponding to different fish species and fishing spots into an AI model to customize the analysis algorithm.

[0046] The analysis unit can adjust the analysis results according to the fishing experience and skill level of the user. For example, the analysis unit provides basic analysis results to a beginner user. The analysis unit can also provide detailed analysis results to an intermediate user. Furthermore, the analysis unit can provide specialized analysis results to an experienced user. For example, the analysis unit evaluates the fishing experience and skill level of the user based on the user's past fishing result data and self-reporting, and provides the corresponding analysis results. This allows the analysis results to be adjusted according to the user's fishing experience and skill level, thereby providing more appropriate feedback. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's past fishing result data into an AI model, evaluate the user's fishing experience and skill level, and provide the corresponding analysis results.

[0047] The analysis unit can make predictions taking into account the geographical location information of the user during analysis. The analysis unit makes predictions based on data of the fishing spot where the user is currently located, for example. The analysis unit can also make predictions of nearby fishing spots based on the user's location information. Furthermore, the analysis unit can make highly relevant predictions by referring to the user's movement history. For example, the analysis unit acquires the user's GPS data and makes predictions based on data of the fishing spot where the user is currently located. The analysis unit can also make predictions based on data of nearby fishing spots using a location information service. This makes it possible to make more appropriate predictions by making predictions taking into account the user's geographical location information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's geographical location information into an AI model and make predictions.

[0048] The analysis unit can analyze the user's social media activities during the analysis and incorporate relevant data into the analysis. The analysis unit, for example, incorporates fishing result information shared by the user on social media into the analysis. The analysis unit can also incorporate data on popular fishing spots from the user's social media activities into the analysis. The analysis unit can also analyze the content of the user's social media posts and incorporate relevant data into the analysis. For example, the analysis unit incorporates fishing result information shared by the user on social media into the analysis to help with fishing result prediction. The analysis unit can also analyze the user's followers and incorporate data on popular fishing spots into the analysis. In this way, more relevant data can be incorporated into the analysis by analyzing the user's social media activities. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input the user's social media activities into an AI model and incorporate related data into the analysis.

[0049] The analysis unit can customize the analysis results according to the user's purpose of fishing (e.g., leisure or competition). For example, the analysis unit can provide analysis results that emphasize fun to a user who is fishing for leisure. The analysis unit can also provide analysis results that emphasize efficiency to a user who is fishing for competition. Furthermore, the analysis unit can provide analysis results that emphasize safety to a user who enjoys fishing with his or her family. For example, the analysis unit customizes the analysis results according to the user's purpose of fishing and provides appropriate feedback. In this way, by customizing the analysis results according to the user's purpose of fishing, more appropriate feedback can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's purpose of fishing into an AI model to customize the analysis results.

[0050] The analysis unit can display the analysis results optimized for different devices (smartphone, tablet, PC). For example, the analysis unit displays concise analysis results adapted to the screen size for smartphones. Also, the analysis unit can display detailed analysis results on a large screen for tablets. Furthermore, the analysis unit can provide an interface capable of simultaneously displaying multiple data for PCs. For example, the analysis unit displays concise analysis results adapted to the screen size for smartphones, allowing the user to obtain information appropriately on any device. Also, the analysis unit displays detailed analysis results on a large screen for tablets, allowing the user to obtain more information. As a result, by displaying the analysis results optimized for different devices, the user can obtain information appropriately on any device. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the analysis results optimized for different devices into an AI model and display them.

[0051] The suggestion unit can adjust the level of detail of the proposal based on the accuracy of the catch prediction when making a proposal. For example, when the accuracy of the catch prediction is high, the suggestion unit makes a detailed proposal. Also, when the accuracy of the catch prediction is low, the suggestion unit can make a concise proposal. Furthermore, the suggestion unit can adjust the specificity of the proposal according to the accuracy of the catch prediction. For example, when the accuracy of the catch prediction is high, the suggestion unit makes a detailed proposal and provides specific advice to the user. Also, when the accuracy of the catch prediction is low, the suggestion unit makes a concise proposal and provides basic advice to the user. This allows for a more appropriate proposal by adjusting the level of detail of the proposal based on the accuracy of the catch prediction. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the accuracy of the catch prediction into an AI model and adjust the level of detail of the proposal.

[0052] The suggestion unit can suggest the optimal fishing method and lure in consideration of the topography and water quality data of the fishing spot when making a suggestion. The suggestion unit can suggest the optimal fishing method based on, for example, the topography data of the fishing spot. The suggestion unit can also suggest a lure suitable for a specific fish species based on the water quality data. The suggestion unit can also suggest the optimal fishing method based on the vegetation data of the fishing spot. For example, the suggestion unit can suggest the optimal fishing method based on topography data such as water depth, bottom sediment, and obstacle positions. The suggestion unit can also suggest a lure suitable for a specific fish species based on water quality data such as pH value, dissolved oxygen amount, and transparency. In this way, by considering the topography and water quality data of the fishing spot, a more appropriate fishing method and lure can be suggested. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the topography data and water quality data of the fishing spot into an AI model to suggest the optimal fishing method and lure.

[0053] The suggestion unit can customize the proposed algorithm according to different fish species and fishing spots. The suggestion unit customizes the proposed algorithm for bass fishing, for example. The suggestion unit can also customize the proposed algorithm for sea fishing. The suggestion unit can also customize the proposed algorithm for mountain stream fishing. For example, the suggestion unit customizes the proposed algorithm for bass fishing and makes a proposal specialized for bass fishing. The suggestion unit can also customize the proposed algorithm for sea fishing and makes a proposal specialized for sea fishing. This allows for more appropriate proposals by customizing the proposed algorithm according to different fish species and fishing spots. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input data corresponding to different fish species and fishing spots into an AI model to customize the proposed algorithm.

[0054] The suggestion unit can adjust the content of the suggestion according to the fishing experience and skill level of the user. For example, the suggestion unit makes basic suggestions to a beginner user. In addition, the suggestion unit can make detailed suggestions to an intermediate user. Furthermore, the suggestion unit can make specialized suggestions to an experienced user. For example, the suggestion unit evaluates the fishing experience and skill level of the user based on the user's past fishing result data and self-reporting, and makes suggestions accordingly. This allows more appropriate suggestions to be made by adjusting the content of the suggestion according to the user's fishing experience and skill level. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's past fishing result data into an AI model, evaluate the user's fishing experience and skill level, and make suggestions accordingly.

[0055] The suggestion unit can suggest the optimal fishing method and lure in consideration of the user's geographical location information when making a suggestion. The suggestion unit makes a suggestion based on, for example, data on the fishing spot where the user is currently located. The suggestion unit can also suggest nearby fishing spots based on the user's location information. Furthermore, the suggestion unit can make highly relevant suggestions by referring to the user's movement history. For example, the suggestion unit acquires the user's GPS data and makes a suggestion based on data on the fishing spot where the user is currently located. The suggestion unit can also use a location information service to make a suggestion based on data on nearby fishing spots. This makes it possible to suggest more appropriate fishing methods and lures by making suggestions in consideration of the user's geographical location information. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's geographical location information into an AI model and suggest the optimal fishing method and lure.

[0056] The suggestion unit can analyze the user's social media activity and make a related suggestion when making a suggestion. The suggestion unit makes a suggestion based on, for example, fishing result information shared by the user on social media. The suggestion unit can also suggest popular fishing spots from the user's social media activity. The suggestion unit can also analyze the user's social media posting content and make a related suggestion. For example, the suggestion unit makes a suggestion based on fishing result information shared by the user on social media, which is useful for fishing result prediction. The suggestion unit can also analyze the user's followers and make a suggestion of popular fishing spots. This makes it possible to make more relevant suggestions by analyzing the user's social media activity. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's social media activity into an AI model and make a related suggestion.

[0057] The suggestion unit can customize the suggested content according to the user's fishing purpose (e.g., leisure or competition). For example, the suggestion unit makes suggestions that emphasize fun to a user who is fishing for leisure. The suggestion unit can also make suggestions that emphasize efficiency to a user who is fishing for competition. Furthermore, the suggestion unit can make suggestions that emphasize safety to a user who enjoys fishing with his or her family. For example, the suggestion unit customizes the suggested content according to the user's fishing purpose and provides appropriate feedback. This allows for more appropriate suggestions by customizing the suggested content according to the user's fishing purpose. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the user's fishing purpose into an AI model to customize the suggested content.

[0058] The suggestion unit can display the proposed contents optimized for different devices (smartphone, tablet, PC). For example, the suggestion unit displays a concise proposal that matches the screen size for a smartphone. Also, the suggestion unit can display a detailed proposal on a large screen for a tablet. Furthermore, the suggestion unit can provide an interface that can simultaneously display multiple data for a PC. For example, the suggestion unit displays a concise proposal that matches the screen size for a smartphone, so that the user can appropriately obtain information on any device. Also, the suggestion unit displays a detailed proposal on a large screen for a tablet, so that the user can obtain more information. As a result, by displaying the proposed contents optimized for different devices, the user can appropriately obtain information on any device. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the proposed contents optimized for different devices into an AI model and display them.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The analysis unit can adjust the analysis algorithm according to the user's fishing purpose. For example, for a user who is fishing for leisure, the analysis can be performed with an emphasis on enjoyment, and suggestions can be made to maximize the enjoyment of fishing. For a user who is fishing for competitive purposes, the analysis can be performed with an emphasis on efficiency, and suggestions can be made to maximize the catch in the shortest time. Furthermore, for a user who enjoys fishing with his or her family, the analysis can be performed with an emphasis on safety, and suggestions can be made about fishing spots and fishing methods that are safe for children to enjoy. This makes it possible to perform an analysis according to the user's fishing purpose, and provide more appropriate feedback.

[0061] The suggestion unit can adjust the suggestions depending on the user's fishing experience and skill level. For example, for a novice user, the suggestion unit can suggest basic fishing techniques and how to use lures to help the user learn the basics of fishing. For an intermediate user, the suggestion unit can provide more advanced techniques and advice tailored to specific situations. Furthermore, for an experienced user, the suggestion unit can suggest specialized knowledge and the latest fishing techniques to help the user further improve their fishing skills. This allows suggestions to be made according to the user's fishing experience and skill level, providing more appropriate feedback.

[0062] The collection unit can prioritize collection of highly relevant data taking into account the user's geographical location information. For example, data on the fishing spot where the user is currently located can be prioritized and the best fishing method and lure for that spot can be suggested. Data on nearby fishing spots can also be collected based on the user's location information to help the user plan fishing trips at the destination. Furthermore, data on fishing spots visited in the past can be collected based on the user's movement history to predict catches when the user revisits the spot. This makes it possible to collect data taking into account the user's geographical location information and provide more appropriate feedback.

[0063] The analysis unit can analyze the user's social media activity and incorporate related data into the analysis. For example, fishing result information shared by the user on social media can be incorporated into the analysis to help predict fishing results. In addition, data on popular fishing spots from the user's social media activity can be incorporated into the analysis to provide useful information to other users. Furthermore, by analyzing the content of the user's social media posts and incorporating related data into the analysis, more accurate fishing result predictions can be made. This makes it possible to perform data analysis based on the user's social media activity, and provide more appropriate feedback.

[0064] The suggestion unit can display the proposal contents optimized for different devices (smartphone, tablet, PC). For example, for smartphones, concise proposals tailored to the screen size can be displayed, allowing the user to obtain information appropriately on any device. For tablets, detailed proposals can be displayed on a large screen, allowing the user to obtain more information. Furthermore, for PCs, an interface capable of simultaneously displaying multiple pieces of data can be provided, allowing the user to obtain information efficiently. This makes it possible to display proposal contents optimized for different devices, allowing the user to obtain information appropriately on any device.

[0065] The process flow of the first embodiment will be briefly described below.

[0066] Step 1: The collection unit collects data such as weather information, tides, and water temperature. Specifically, it collects weather information such as air temperature, wind speed, and precipitation, tidal data such as the times and tide levels of high and low tides, and water temperature data such as surface water temperature and deep water temperature. The collection unit obtains weather information from a service that provides weather data, obtains tidal information from a service that provides tidal data, and obtains water temperature information from a sensor that provides water temperature data. Step 2: The analysis unit analyzes the data collected by the collection unit and predicts the catch. The analysis unit uses a machine learning algorithm to analyze the collected data and predicts the catch. The analysis unit also predicts the catch based on past catch data, combining current weather information, tide data, and water temperature data. Specifically, the analysis unit predicts the catch by comparing past catch data with current weather information, tide data, and water temperature data. Step 3: The suggestion unit suggests optimal fishing methods and lures based on the prediction results obtained by the analysis unit. The suggestion unit suggests optimal fishing methods and lures for specific fish species based on the catch prediction. It can also make suggestions based on fishing spots and target fish species selected by the user. The suggestion unit can make suggestions using an AI model that takes the prediction results obtained by the analysis unit as input and outputs optimal fishing methods and lures.

[0067] (Example 2) The fishing result prediction system according to the embodiment of the present invention is a system that uses AI to predict fishing results and suggests optimal fishing methods and lures to users. This system starts when a user opens an app and selects a fishing spot and a target fish species. Next, the AI ​​analyzes data such as past fishing results, weather information, tides, and water temperature, and predicts optimal fishing results for the selected fishing spot and fish species. Furthermore, the AI ​​suggests optimal fishing methods and lures to use based on the fishing result prediction. This solves the problem of "where to catch fish" for anglers from family fishing to beginners and intermediate fishers who enjoy fishing as a hobby. For example, if a user selects "I want to catch bass in Lake A," that information is input into the AI. Next, the AI ​​analyzes data such as past fishing results, weather information, tides, and water temperature, and predicts optimal fishing results for the selected fishing spot and fish species. For example, a prediction such as "6:00 to 8:00 a.m. is optimal for bass fishing in Lake A" can be obtained from past data. Furthermore, the AI ​​suggests optimal fishing methods and lures to use based on the fishing result prediction. For example, specific advice such as "Minnow-type lures are effective for catching bass in Lake A" is provided. This allows users to increase their success rate in fishing. This app is suitable for a wide range of users, from family anglers to beginners and intermediate anglers who enjoy fishing as a hobby. For example, when enjoying fishing with the family, an interface that is easy for children to use is provided, and even beginners can get specific advice on how to increase their catch. In addition, for intermediate anglers, highly accurate predictions based on past data are provided, which helps improve fishing skills. In this way, "Anglsearch" is an app that uses AI to predict catches and suggests the best fishing methods and lures to users, thereby increasing the fun and success rate of fishing. As a result, the catch prediction system can predict catches for users and suggest the best fishing methods and lures.

[0068] The fishing result prediction system according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects data such as weather information, tides, and water temperature. The collection unit collects weather information such as air temperature, wind speed, and precipitation. The collection unit can also collect tide data such as the time and tide level of high tide and low tide. The collection unit can also collect water temperature data such as surface water temperature and deep water temperature. For example, the collection unit acquires weather information from a service that provides weather data, acquires tide information from a service that provides tide data, and acquires water temperature information from a sensor that provides water temperature data. The analysis unit analyzes the data collected by the collection unit and predicts the fishing result. The analysis unit analyzes the collected data using, for example, a machine learning algorithm, and predicts the fishing result. The analysis unit can also predict the fishing result by combining current weather information, tide data, and water temperature data based on past fishing result data. For example, the analysis unit compares past fishing result data with current weather information to predict the fishing result. The analysis unit can also compare past fishing result data with current tide data to predict fishing results. The analysis unit can also compare past fishing result data with current water temperature data to predict fishing results. The suggestion unit suggests optimal fishing methods and lures based on the prediction results obtained by the analysis unit. The suggestion unit suggests optimal fishing methods and lures for a specific fish species based on, for example, the fishing result prediction. The suggestion unit can also make suggestions based on a fishing spot selected by the user and a target fish species. For example, the suggestion unit suggests optimal fishing methods and lures for a specific fish species at a fishing spot selected by the user. In this way, the fishing result prediction system according to the embodiment can predict fishing results for the user and suggest optimal fishing methods and lures. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can make suggestions using an AI model that inputs the prediction results obtained by the analysis unit and outputs optimal fishing methods and lures.

[0069] The collection unit collects data such as weather information, tides, and water temperature. Specifically, to collect weather information such as temperature, wind speed, and precipitation, data is acquired in real time from a service that provides weather data. This includes data from weather satellites and ground weather observation stations. In addition, to collect tidal data such as the time and tide level of high and low tides, information is acquired from a service that provides tidal data. This includes data from marine observation buoys and coastal tidal observation stations. In addition, to collect water temperature data such as surface water temperature and deep water temperature, information is acquired from a sensor that provides water temperature data. This includes data from marine observation buoys and underwater drones. The collection unit centrally manages these data and stores them in a database that is updated in real time. The frequency and accuracy of data collection can be adjusted according to the system settings, allowing for flexible response according to specific situations and conditions. For example, when a sudden change in weather is predicted, more detailed information can be acquired by increasing the frequency of data collection. This allows the collection unit to collect a wide range of information from diverse data sources, improving the accuracy and reliability of the entire system.

[0070] The analysis unit analyzes the data collected by the collection unit and predicts the catch. Specifically, the analysis unit uses a machine learning algorithm to analyze the collected data and predict the catch. For example, the analysis unit predicts the catch by combining current weather information, tide data, and water temperature data based on past catch data. The analysis unit can use methods such as regression analysis and clustering to compare past catch data with current weather information and predict the catch. In addition, the analysis unit can use time series analysis and pattern recognition technology to compare past catch data with current tide data and predict the catch. Furthermore, the analysis unit can use advanced machine learning models such as neural networks and support vector machines to compare past catch data with current water temperature data and predict the catch. This allows the analysis unit to analyze the collected data from multiple angles and predict the catch with high accuracy. Furthermore, the analysis unit can continuously revise the prediction results based on data updated in real time to respond to the latest situation. For example, if the weather, tides, or water temperature change suddenly, the analysis unit immediately takes in new data and updates the prediction results. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings, allowing the analysis unit to not only grasp the situation in real time, but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0071] The suggestion unit suggests the optimal fishing method and lure based on the prediction result obtained by the analysis unit. Specifically, the optimal fishing method and lure for a specific fish species is suggested based on the catch prediction. The suggestion unit can also make suggestions based on the fishing spot and target fish species selected by the user. For example, past fishing result data and expert knowledge can be used to suggest the optimal fishing method and lure for a specific fish species at the fishing spot selected by the user. The suggestion unit can make suggestions using an AI model that inputs the prediction result obtained by the analysis unit and outputs the optimal fishing method and lure. The AI ​​model can learn past fishing result data and environmental data and predict the optimal fishing method and lure under specific conditions. For example, the optimal fishing method and lure are suggested based on the water temperature and tidal conditions preferred by a specific fish species. In addition, the suggestion unit can collect user feedback and continuously improve the accuracy and effectiveness of the suggestions. For example, the accuracy of the suggestions can be improved by collecting the results of actual fishing by the user as feedback and using them as learning data for the AI ​​model. In addition, the suggestion unit can make individually customized suggestions taking into account the user's preferences and past fishing result history. This enables the suggestion unit to suggest optimal fishing methods and lures to the user, and provide support for maximizing catches.

[0072] The collection unit can collect past catch data. The collection unit, for example, acquires data from a service that provides past catch data. For example, the collection unit collects data such as the type of fish caught in the past, the place where it was caught, and the time it was caught. The collection unit can also collect past catch data input by the user. For example, the collection unit collects catch data input by the user into the app and uses it for analysis. By collecting past catch data, more accurate catch prediction is possible. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can acquire data from a service that provides past catch data, and input the data into an AI model to use it for analysis.

[0073] The suggestion unit can make suggestions based on the fishing spot and target fish species selected by the user. The suggestion unit suggests the optimal fishing method and lure based on the fishing spot and target fish species selected by the user, for example. For example, if the user selects "I want to fish for bass in Lake A," the suggestion unit suggests the optimal fishing method and lure based on that information. The suggestion unit can also suggest the optimal fishing method and lure by referring to past fishing result data based on the fishing spot and target fish species selected by the user. For example, the suggestion unit makes suggestions based on predictions such as "6:00 to 8:00 in the morning is optimal for bass fishing in Lake A" from past data. As a result, more appropriate fishing methods and lures can be suggested by making suggestions based on the fishing spot and target fish species selected by the user. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input information on the fishing spot and target fish species selected by the user into an AI model and output the optimal fishing method and lure.

[0074] The collection unit can estimate the user's emotion and adjust the timing of data collection based on the estimated user's emotion. For example, when the user is excited, the collection unit collects data in real time and provides immediate feedback. Also, when the user is relaxed, the collection unit can collect data at regular intervals and provide relaxed feedback. Furthermore, when the user is stressed, the collection unit can reduce the frequency of data collection to reduce the burden on the user. For example, the collection unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on changes in facial expression. Also, the collection unit can record the user's voice and estimate the emotion using a voice analysis technique. For example, the collection unit analyzes the tone and speed of the voice and calculates the emotion score. Also, the collection unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on the fluctuations in heart rate. This allows more appropriate data collection by adjusting the timing of data collection based on the user's emotion. The estimation of emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input image data of a user captured by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0075] The collection unit can analyze past catch data and select the optimal data collection method. For example, the collection unit collects data intensively during a specific time period based on past catch data. The collection unit can also analyze past catch data and prioritize data collection under specific weather conditions. Furthermore, the collection unit can strengthen data collection at a specific fishing spot by referring to past catch data. For example, the collection unit acquires data from a service that provides past catch data and selects the optimal data collection method based on the data. In this way, the optimal data collection method can be selected by analyzing the past catch data. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can acquire data from a service that provides past catch data and input the data into an AI model to select the optimal data collection method.

[0076] The collection unit can perform filtering based on the user's current fishing experience and skill level when collecting data. For example, for a beginner user, the collection unit can collect only basic data and provide simple feedback. For an intermediate user, the collection unit can also collect detailed data and provide specific advice. For an experienced user, the collection unit can also collect specialized data and provide advanced feedback. For example, the collection unit evaluates the user's fishing experience and skill level based on the user's past fishing result data and self-reporting, and collects data accordingly. This makes it possible to provide more appropriate feedback by filtering the data based on the user's fishing experience and skill level. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past fishing result data into an AI model, evaluate the user's fishing experience and skill level, and collect data accordingly.

[0077] The collection unit can expand the type of data collected to include topographical information of the fishing spot and water quality data. The collection unit, for example, collects topographical information of the fishing spot and reflects it in the catch prediction. The collection unit can also collect water quality data and suggest fishing spots suitable for a specific fish species. The collection unit can also collect vegetation information of the fishing spot and use it to predict the catch. For example, the collection unit collects topographical information such as water depth, bottom sediment, and obstacle positions and reflects it in the catch prediction. The collection unit also collects water quality data such as pH value, dissolved oxygen amount, and transparency and suggests fishing spots suitable for a specific fish species. This allows for more accurate catch prediction by expanding the type of data collected. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input topographical information and water quality data of the fishing spot into an AI model and use it to predict the catch.

[0078] The collection unit can dynamically adjust the frequency of data collection according to the season and the time period. For example, the collection unit adjusts the frequency of data collection for each season to perform optimal catch prediction. The collection unit can also adjust the frequency of data collection for each time period to provide a real-time catch prediction. Furthermore, the collection unit can increase the frequency of data collection in accordance with a specific event (e.g., spring tide). For example, the collection unit adjusts the frequency of data collection according to the season of spring, summer, autumn, and winter to perform optimal catch prediction. The collection unit also adjusts the frequency of data collection according to the time period of morning, afternoon, and night to provide a real-time catch prediction. This allows more appropriate data collection by dynamically adjusting the frequency of data collection. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can dynamically adjust the frequency of data collection according to the season and time period by inputting it into an AI model.

[0079] The collection unit can estimate the user's emotion and determine the priority of data to be collected based on the estimated user's emotion. For example, when the user is excited, the collection unit prioritizes collecting real-time weather information. Furthermore, when the user is relaxed, the collection unit can also prioritize collecting past fishing result data. Furthermore, when the user is stressed, the collection unit can also prioritize collecting data that is easily understandable. For example, the collection unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on the change in facial expression. Furthermore, the collection unit can record the user's voice and estimate the emotion using a voice analysis technique. For example, the collection unit analyzes the tone and speed of the voice and calculates the emotion score. Furthermore, the collection unit can collect the user's biodata (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on the fluctuation of the heart rate. This allows more appropriate data collection by determining the priority of data to be collected based on the user's emotion. The estimation of emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input image data of a user captured by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0080] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the user when collecting data. For example, the collection unit preferentially collects data of the fishing spot where the user is currently located. The collection unit can also collect data of nearby fishing spots based on the user's location information. Furthermore, the collection unit can also collect highly relevant data by referring to the user's movement history. For example, the collection unit acquires the user's GPS data and preferentially collects data of the fishing spot where the user is currently located. The collection unit can also collect data of nearby fishing spots using a location information service. This allows more relevant data to be collected by collecting data in consideration of the user's geographical location information. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into an AI model and preferentially collect highly relevant data.

[0081] The collection unit can analyze the user's social media activity and collect related data when collecting data. The collection unit, for example, collects fishing result information shared by the user on social media. The collection unit can also collect data on popular fishing spots from the user's social media activity. The collection unit can also analyze the user's social media postings and collect related data. For example, the collection unit collects fishing result information shared by the user on social media and uses it for analysis. The collection unit can also analyze the user's followers and collect data on popular fishing spots. This allows more relevant data to be collected by analyzing the user's social media activity. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the user's social media activity into an AI model and collect related data.

[0082] The collection unit can expand the type of data collected to include the congestion status of the fishing spot and activity information of other anglers. The collection unit, for example, collects the congestion status of the fishing spot and reflects it in the catch prediction. The collection unit can also collect activity information of other anglers and suggest the optimal fishing spot. Furthermore, the collection unit can collect the usage status of the fishing spot and use it for the catch prediction. For example, the collection unit collects the congestion status of the fishing spot using a real-time camera and reflects it in the catch prediction. The collection unit can also collect activity information of other anglers based on the user's report and suggest the optimal fishing spot. This allows for more accurate catch prediction by expanding the type of data collected. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the congestion status of the fishing spot and activity information of other anglers into an AI model and use it for the catch prediction.

[0083] The collection unit can improve the accuracy of the collected data by combining different data sources. For example, the collection unit collects weather information from multiple weather data sources to improve the accuracy. The collection unit can also collect tide data from multiple ocean data sources to improve the accuracy. Furthermore, the collection unit can collect water temperature data from multiple sensor data sources to improve the accuracy. For example, the collection unit collects weather information from multiple weather data sources to improve the accuracy. The collection unit can also collect tide data from multiple ocean data sources to improve the accuracy. As a result, the accuracy of the collected data is improved by combining different data sources. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input weather information acquired from multiple weather data sources into an AI model to improve the accuracy.

[0084] The analysis unit can estimate the user's emotion and adjust the analysis algorithm based on the estimated user's emotion. For example, when the user is excited, the analysis unit prioritizes real-time analysis. Furthermore, when the user is relaxed, the analysis unit can perform detailed analysis. Furthermore, when the user is stressed, the analysis unit can perform simple and quick analysis. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on the change in facial expression. Furthermore, the analysis unit can record the user's voice and estimate the emotion using a voice analysis technique. For example, the analysis unit analyzes the tone and speed of the voice and calculates an emotion score. Furthermore, the analysis unit can collect the user's biodata (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on the fluctuation of the heart rate. This allows for more appropriate analysis by adjusting the analysis algorithm based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit may input image data of a user captured by a camera to the generation AI, and cause the generation AI to estimate the user's emotions.

[0085] The analysis unit can improve prediction accuracy by comparing past catch data with current environmental data during analysis. The analysis unit can, for example, compare past catch data with current weather data to improve prediction accuracy. The analysis unit can also compare past catch data with current tide data to improve prediction accuracy. The analysis unit can also compare past catch data with current water temperature data to improve prediction accuracy. For example, the analysis unit can compare past catch data with current weather data to predict catches. The analysis unit can also compare past catch data with current tide data to predict catches. In this way, prediction accuracy is improved by comparing past catch data with current environmental data. Part or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI or can be performed without using AI. For example, the analysis unit can input past catch data and current environmental data into an AI model to improve prediction accuracy.

[0086] The analysis unit can make predictions taking into account the topography and water quality data of the fishing spot during analysis. The analysis unit, for example, takes into account the topography data of the fishing spot to make a catch prediction. The analysis unit can also take into account water quality data to predict a fishing spot suitable for a specific fish species. The analysis unit can also take into account vegetation data of the fishing spot to make a catch prediction. For example, the analysis unit takes into account topography data such as water depth, bottom sediment, and obstacle positions to make a catch prediction. The analysis unit also takes into account water quality data such as pH value, dissolved oxygen amount, and transparency to predict a fishing spot suitable for a specific fish species. As a result, by taking into account the topography and water quality data of the fishing spot, more accurate predictions are possible. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input topography data and water quality data of the fishing spot into an AI model to make a catch prediction.

[0087] The analysis unit can customize the analysis algorithm according to different fish species and fishing spots. The analysis unit customizes the analysis algorithm for bass fishing, for example. The analysis unit can also customize the analysis algorithm for sea fishing. The analysis unit can also customize the analysis algorithm for mountain stream fishing. For example, the analysis unit customizes the analysis algorithm for bass fishing and performs a catch prediction specialized for bass fishing. The analysis unit can also customize the analysis algorithm for sea fishing and performs a catch prediction specialized for sea fishing. This allows for more appropriate prediction by customizing the analysis algorithm according to different fish species and fishing spots. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data corresponding to different fish species and fishing spots into an AI model to customize the analysis algorithm.

[0088] The analysis unit can adjust the analysis results according to the fishing experience and skill level of the user. For example, the analysis unit provides basic analysis results to a beginner user. The analysis unit can also provide detailed analysis results to an intermediate user. Furthermore, the analysis unit can provide specialized analysis results to an experienced user. For example, the analysis unit evaluates the fishing experience and skill level of the user based on the user's past fishing result data and self-reporting, and provides the corresponding analysis results. This allows the analysis results to be adjusted according to the user's fishing experience and skill level, thereby providing more appropriate feedback. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's past fishing result data into an AI model, evaluate the user's fishing experience and skill level, and provide the corresponding analysis results.

[0089] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated emotion of the user. For example, when the user is nervous, the analysis unit provides a simple and highly visible display method. Furthermore, when the user is relaxed, the analysis unit can also provide a display method including detailed information. Furthermore, when the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on the change in facial expression. Furthermore, the analysis unit can record the user's voice and estimate the emotion using a voice analysis technique. For example, the analysis unit analyzes the tone and speed of the voice and calculates the emotion score. Furthermore, the analysis unit can collect the user's biodata (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on the fluctuation of the heart rate. In this way, the display method of the analysis result can be adjusted based on the user's emotion, thereby providing more appropriate feedback. The estimation of emotions is realized, for example, by using an emotion estimation function using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using an AI or may be performed without using an AI. For example, the analysis unit may input image data of a user captured by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0090] The analysis unit can make predictions taking into account the geographical location information of the user during analysis. The analysis unit makes predictions based on data of the fishing spot where the user is currently located, for example. The analysis unit can also make predictions of nearby fishing spots based on the user's location information. Furthermore, the analysis unit can make highly relevant predictions by referring to the user's movement history. For example, the analysis unit acquires the user's GPS data and makes predictions based on data of the fishing spot where the user is currently located. The analysis unit can also make predictions based on data of nearby fishing spots using a location information service. This makes it possible to make more appropriate predictions by making predictions taking into account the user's geographical location information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's geographical location information into an AI model and make predictions.

[0091] The analysis unit can analyze the user's social media activities during the analysis and incorporate relevant data into the analysis. The analysis unit, for example, incorporates fishing result information shared by the user on social media into the analysis. The analysis unit can also incorporate data on popular fishing spots from the user's social media activities into the analysis. The analysis unit can also analyze the content of the user's social media posts and incorporate relevant data into the analysis. For example, the analysis unit incorporates fishing result information shared by the user on social media into the analysis to help with fishing result prediction. The analysis unit can also analyze the user's followers and incorporate data on popular fishing spots into the analysis. In this way, more relevant data can be incorporated into the analysis by analyzing the user's social media activities. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input the user's social media activities into an AI model and incorporate related data into the analysis.

[0092] The analysis unit can customize the analysis results according to the user's purpose of fishing (e.g., leisure or competition). For example, the analysis unit can provide analysis results that emphasize fun to a user who is fishing for leisure. The analysis unit can also provide analysis results that emphasize efficiency to a user who is fishing for competition. Furthermore, the analysis unit can provide analysis results that emphasize safety to a user who enjoys fishing with his or her family. For example, the analysis unit customizes the analysis results according to the user's purpose of fishing and provides appropriate feedback. In this way, by customizing the analysis results according to the user's purpose of fishing, more appropriate feedback can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's purpose of fishing into an AI model to customize the analysis results.

[0093] The analysis unit can display the analysis results optimized for different devices (smartphone, tablet, PC). For example, the analysis unit displays concise analysis results adapted to the screen size for smartphones. Also, the analysis unit can display detailed analysis results on a large screen for tablets. Furthermore, the analysis unit can provide an interface capable of simultaneously displaying multiple data for PCs. For example, the analysis unit displays concise analysis results adapted to the screen size for smartphones, allowing the user to obtain information appropriately on any device. Also, the analysis unit displays detailed analysis results on a large screen for tablets, allowing the user to obtain more information. As a result, by displaying the analysis results optimized for different devices, the user can obtain information appropriately on any device. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the analysis results optimized for different devices into an AI model and display them.

[0094] The suggestion unit can estimate the user's emotion and adjust the way of expressing the suggestion based on the estimated emotion of the user. For example, when the user is relaxed, the suggestion unit makes a detailed suggestion. Also, when the user is in a hurry, the suggestion unit can make a brief suggestion. Furthermore, when the user is excited, the suggestion unit can make a visually stimulating suggestion. For example, the suggestion unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the suggestion unit calculates an emotion score based on the change in the facial expression. Also, the suggestion unit can record the user's voice and estimate the emotion using a voice analysis technology. For example, the suggestion unit analyzes the tone and speed of the voice and calculates the emotion score. Also, the suggestion unit can collect the user's biodata (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the suggestion unit calculates an emotion score based on the fluctuation of the heart rate. As a result, by adjusting the way of expressing the suggestion based on the user's emotion, more appropriate suggestions can be made. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit may input image data of a user captured by a camera to the generation AI, and cause the generation AI to estimate the user's emotions.

[0095] The suggestion unit can adjust the level of detail of the proposal based on the accuracy of the catch prediction when making a proposal. For example, when the accuracy of the catch prediction is high, the suggestion unit makes a detailed proposal. Also, when the accuracy of the catch prediction is low, the suggestion unit can make a concise proposal. Furthermore, the suggestion unit can adjust the specificity of the proposal according to the accuracy of the catch prediction. For example, when the accuracy of the catch prediction is high, the suggestion unit makes a detailed proposal and provides specific advice to the user. Also, when the accuracy of the catch prediction is low, the suggestion unit makes a concise proposal and provides basic advice to the user. This allows for a more appropriate proposal by adjusting the level of detail of the proposal based on the accuracy of the catch prediction. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the accuracy of the catch prediction into an AI model and adjust the level of detail of the proposal.

[0096] The suggestion unit can suggest the optimal fishing method and lure in consideration of the topography and water quality data of the fishing spot when making a suggestion. The suggestion unit can suggest the optimal fishing method based on, for example, the topography data of the fishing spot. The suggestion unit can also suggest a lure suitable for a specific fish species based on the water quality data. The suggestion unit can also suggest the optimal fishing method based on the vegetation data of the fishing spot. For example, the suggestion unit can suggest the optimal fishing method based on topography data such as water depth, bottom sediment, and obstacle positions. The suggestion unit can also suggest a lure suitable for a specific fish species based on water quality data such as pH value, dissolved oxygen amount, and transparency. In this way, by considering the topography and water quality data of the fishing spot, a more appropriate fishing method and lure can be suggested. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the topography data and water quality data of the fishing spot into an AI model to suggest the optimal fishing method and lure.

[0097] The suggestion unit can customize the proposed algorithm according to different fish species and fishing spots. The suggestion unit customizes the proposed algorithm for bass fishing, for example. The suggestion unit can also customize the proposed algorithm for sea fishing. The suggestion unit can also customize the proposed algorithm for mountain stream fishing. For example, the suggestion unit customizes the proposed algorithm for bass fishing and makes a proposal specialized for bass fishing. The suggestion unit can also customize the proposed algorithm for sea fishing and makes a proposal specialized for sea fishing. This allows for more appropriate proposals by customizing the proposed algorithm according to different fish species and fishing spots. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input data corresponding to different fish species and fishing spots into an AI model to customize the proposed algorithm.

[0098] The suggestion unit can adjust the content of the suggestion according to the fishing experience and skill level of the user. For example, the suggestion unit makes basic suggestions to a beginner user. In addition, the suggestion unit can make detailed suggestions to an intermediate user. Furthermore, the suggestion unit can make specialized suggestions to an experienced user. For example, the suggestion unit evaluates the fishing experience and skill level of the user based on the user's past fishing result data and self-reporting, and makes suggestions accordingly. This allows more appropriate suggestions to be made by adjusting the content of the suggestion according to the user's fishing experience and skill level. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's past fishing result data into an AI model, evaluate the user's fishing experience and skill level, and make suggestions accordingly.

[0099] The suggestion unit can estimate the user's emotion and determine the priority of suggestions based on the estimated user's emotion. For example, when the user is excited, the suggestion unit prioritizes real-time suggestions. In addition, when the user is relaxed, the suggestion unit can prioritize detailed suggestions. In addition, when the user is stressed, the suggestion unit can prioritize simple and quick suggestions. For example, the suggestion unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the suggestion unit calculates an emotion score based on changes in facial expression. In addition, the suggestion unit can record the user's voice and estimate the emotion using a voice analysis technology. For example, the suggestion unit analyzes the tone and speed of the voice and calculates the emotion score. In addition, the suggestion unit can collect the user's biodata (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the suggestion unit calculates an emotion score based on the fluctuation of the heart rate. As a result, more appropriate suggestions can be made by determining the priority of suggestions based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit may input image data of a user captured by a camera to the generation AI, and cause the generation AI to estimate the user's emotions.

[0100] The suggestion unit can suggest the optimal fishing method and lure in consideration of the user's geographical location information when making a suggestion. The suggestion unit makes a suggestion based on, for example, data on the fishing spot where the user is currently located. The suggestion unit can also suggest nearby fishing spots based on the user's location information. Furthermore, the suggestion unit can make highly relevant suggestions by referring to the user's movement history. For example, the suggestion unit acquires the user's GPS data and makes a suggestion based on data on the fishing spot where the user is currently located. The suggestion unit can also use a location information service to make a suggestion based on data on nearby fishing spots. This makes it possible to suggest more appropriate fishing methods and lures by making suggestions in consideration of the user's geographical location information. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's geographical location information into an AI model and suggest the optimal fishing method and lure.

[0101] The suggestion unit can analyze the user's social media activity and make a related suggestion when making a suggestion. The suggestion unit makes a suggestion based on, for example, fishing result information shared by the user on social media. The suggestion unit can also suggest popular fishing spots from the user's social media activity. The suggestion unit can also analyze the user's social media posting content and make a related suggestion. For example, the suggestion unit makes a suggestion based on fishing result information shared by the user on social media, which is useful for fishing result prediction. The suggestion unit can also analyze the user's followers and make a suggestion of popular fishing spots. This makes it possible to make more relevant suggestions by analyzing the user's social media activity. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's social media activity into an AI model and make a related suggestion.

[0102] The suggestion unit can customize the suggested content according to the user's fishing purpose (e.g., leisure or competition). For example, the suggestion unit makes suggestions that emphasize fun to a user who is fishing for leisure. The suggestion unit can also make suggestions that emphasize efficiency to a user who is fishing for competition. Furthermore, the suggestion unit can make suggestions that emphasize safety to a user who enjoys fishing with his or her family. For example, the suggestion unit customizes the suggested content according to the user's fishing purpose and provides appropriate feedback. This allows for more appropriate suggestions by customizing the suggested content according to the user's fishing purpose. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the user's fishing purpose into an AI model to customize the suggested content.

[0103] The suggestion unit can display the proposed contents optimized for different devices (smartphone, tablet, PC). For example, the suggestion unit displays a concise proposal that matches the screen size for a smartphone. Also, the suggestion unit can display a detailed proposal on a large screen for a tablet. Furthermore, the suggestion unit can provide an interface that can simultaneously display multiple data for a PC. For example, the suggestion unit displays a concise proposal that matches the screen size for a smartphone, so that the user can appropriately obtain information on any device. Also, the suggestion unit displays a detailed proposal on a large screen for a tablet, so that the user can obtain more information. As a result, by displaying the proposed contents optimized for different devices, the user can appropriately obtain information on any device. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the proposed contents optimized for different devices into an AI model and display them.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The analysis unit can adjust the analysis algorithm according to the user's fishing purpose. For example, for a user who is fishing for leisure, the analysis can be performed with an emphasis on enjoyment, and suggestions can be made to maximize the enjoyment of fishing. For a user who is fishing for competitive purposes, the analysis can be performed with an emphasis on efficiency, and suggestions can be made to maximize the catch in the shortest time. Furthermore, for a user who enjoys fishing with his or her family, the analysis can be performed with an emphasis on safety, and suggestions can be made about fishing spots and fishing methods that are safe for children to enjoy. This makes it possible to perform an analysis according to the user's fishing purpose, and provide more appropriate feedback.

[0106] The suggestion unit can adjust the suggestions depending on the user's fishing experience and skill level. For example, for a novice user, the suggestion unit can suggest basic fishing techniques and how to use lures to help the user learn the basics of fishing. For an intermediate user, the suggestion unit can provide more advanced techniques and advice tailored to specific situations. Furthermore, for an experienced user, the suggestion unit can suggest specialized knowledge and the latest fishing techniques to help the user further improve their fishing skills. This allows suggestions to be made according to the user's fishing experience and skill level, providing more appropriate feedback.

[0107] The collection unit can prioritize collection of highly relevant data taking into account the user's geographical location information. For example, data on the fishing spot where the user is currently located can be prioritized and the best fishing method and lure for that spot can be suggested. Data on nearby fishing spots can also be collected based on the user's location information to help the user plan fishing trips at the destination. Furthermore, data on fishing spots visited in the past can be collected based on the user's movement history to predict catches when the user revisits the spot. This makes it possible to collect data taking into account the user's geographical location information and provide more appropriate feedback.

[0108] The analysis unit can analyze the user's social media activity and incorporate related data into the analysis. For example, fishing result information shared by the user on social media can be incorporated into the analysis to help predict fishing results. In addition, data on popular fishing spots from the user's social media activity can be incorporated into the analysis to provide useful information to other users. Furthermore, by analyzing the content of the user's social media posts and incorporating related data into the analysis, more accurate fishing result predictions can be made. This makes it possible to perform data analysis based on the user's social media activity, and provide more appropriate feedback.

[0109] The suggestion unit can display the proposal contents optimized for different devices (smartphone, tablet, PC). For example, for smartphones, concise proposals tailored to the screen size can be displayed, allowing the user to obtain information appropriately on any device. For tablets, detailed proposals can be displayed on a large screen, allowing the user to obtain more information. Furthermore, for PCs, an interface capable of simultaneously displaying multiple pieces of data can be provided, allowing the user to obtain information efficiently. This makes it possible to display proposal contents optimized for different devices, allowing the user to obtain information appropriately on any device.

[0110] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user's emotions. For example, if the user is excited, real-time analysis can be prioritized and feedback can be provided immediately. If the user is relaxed, detailed analysis can be performed and deep insight can be provided. Furthermore, if the user is stressed, simple and quick analysis can be performed to reduce the burden on the user. This makes it possible to adjust the analysis algorithm based on the user's emotions and provide more appropriate feedback.

[0111] The suggestion unit can estimate the user's emotions and adjust the way in which suggestions are expressed based on the estimated user's emotions. For example, when the user is relaxed, detailed suggestions can be provided to allow the user to understand deeply. When the user is in a hurry, concise suggestions can be provided to quickly provide information. Furthermore, when the user is excited, visually stimulating suggestions can be provided to keep the user interested. This makes it possible to adjust the way suggestions are expressed based on the user's emotions, and more appropriate feedback can be provided.

[0112] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user's emotions. For example, when the user is excited, data can be collected in real time to provide instant feedback. When the user is relaxed, data can be collected at regular intervals to provide relaxed feedback. Furthermore, when the user is stressed, the frequency of data collection can be reduced to reduce the burden on the user. This makes it possible to adjust the timing of data collection based on the user's emotions and provide more appropriate feedback.

[0113] The suggestion unit can estimate the user's emotions and prioritize suggestions based on the estimated user's emotions. For example, if the user is excited, real-time suggestions can be prioritized to provide instant feedback. Also, if the user is relaxed, detailed suggestions can be prioritized to provide deep insight. Furthermore, if the user is stressed, simple and quick suggestions can be prioritized to reduce the burden on the user. This makes it possible to prioritize suggestions based on the user's emotions and provide more appropriate feedback.

[0114] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This makes it possible to adjust the display method of the analysis results based on the user's emotions, and provide more appropriate feedback.

[0115] The process flow of the second embodiment will be briefly described below.

[0116] Step 1: The collection unit collects data such as weather information, tides, and water temperature. Specifically, it collects weather information such as air temperature, wind speed, and precipitation, tidal data such as the times and tide levels of high and low tides, and water temperature data such as surface water temperature and deep water temperature. The collection unit obtains weather information from a service that provides weather data, obtains tidal information from a service that provides tidal data, and obtains water temperature information from a sensor that provides water temperature data. Step 2: The analysis unit analyzes the data collected by the collection unit and predicts the catch. The analysis unit uses a machine learning algorithm to analyze the collected data and predicts the catch. The analysis unit also predicts the catch based on past catch data, combining current weather information, tide data, and water temperature data. Specifically, the analysis unit predicts the catch by comparing past catch data with current weather information, tide data, and water temperature data. Step 3: The suggestion unit suggests optimal fishing methods and lures based on the prediction results obtained by the analysis unit. The suggestion unit suggests optimal fishing methods and lures for specific fish species based on the catch prediction. It can also make suggestions based on fishing spots and target fish species selected by the user. The suggestion unit can make suggestions using an AI model that takes the prediction results obtained by the analysis unit as input and outputs optimal fishing methods and lures.

[0117] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0118] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0119] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0120] Each of the multiple elements including the above-mentioned collection unit, analysis unit, and suggestion unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects data such as weather information, tides, and water temperature using the sensor and communication I / F 44 of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to predict the fishing results. The suggestion unit is realized, for example, by the control unit 46A of the smart device 14, and suggests the optimal fishing method and lure to the user based on the analysis results. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 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.

[0123] 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, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] 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.

[0125] 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 the voice according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0127] 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 performed in a secure state.

[0128] Fig. 4 shows an example of 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.

[0129] 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.

[0130] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0131] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0132] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0133] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0134] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0135] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, 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, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements including the above-mentioned collection unit, analysis unit, and suggestion unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects data such as weather information, tides, and water temperature using the sensor and communication I / F 44 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to predict the fishing results. The suggestion unit is realized, for example, by the control unit 46A of the smart glasses 214, and suggests the optimal fishing method and lure to the user based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 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.

[0139] 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, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] 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.

[0141] 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 the voice according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0143] 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 performed in a secure state.

[0144] 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.

[0145] 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.

[0146] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0147] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific 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 program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0148] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0149] 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 voice indicating a user input for 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.

[0150] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0151] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements including the above-mentioned collection unit, analysis unit, and suggestion unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects data such as weather information, tides, and water temperature using the sensor and communication I / F 44 of the headset type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts the catch by analyzing the collected data. The suggestion unit is realized, for example, by the control unit 46A of the headset type terminal 314, and suggests the optimal fishing method and lure to the user based on the analysis result. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] 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, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] 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.

[0157] 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 the voice according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0159] 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 performed in a secure state.

[0160] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0161] 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.

[0162] 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.

[0163] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0164] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0165] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0166] 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 a voice indicating a user input for 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.

[0167] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0168] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, 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.

[0169] Each of the multiple elements including the above-mentioned collection unit, analysis unit, and suggestion unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects data such as weather information, tides, and water temperature using the sensors and communication I / F 44 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts the catch by analyzing the collected data. The suggestion unit is realized, for example, by the control unit 46A of the robot 414, and suggests the optimal fishing method and lure to the user based on the analysis result. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0170] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0171] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0172] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0173] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0174] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0175] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is 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 the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0176] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0177] 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.

[0178] 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 Universal Serial Bus (USB) 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.

[0179] In addition, 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 upon request from the data processing device 12.

[0180] 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.

[0181] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0182] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0183] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0184] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0185] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0186] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0187] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0188] (Appendix 1) a collection unit for collecting weather information, tides, and water temperature data; An analysis unit that analyzes the data collected by the collection unit and predicts a catch; A suggestion unit that suggests an appropriate fishing method or lure based on the prediction result obtained by the analysis unit. A system characterized by: (Appendix 2) The collecting unit includes: Collect past fishing data 2. The system of claim 1, (Appendix 3) The suggestion unit, Providing suggestions based on user-selected fishing spots and target fish species 2. The system of claim 1, (Appendix 4) The collecting unit includes: Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1, (Appendix 5) The collecting unit includes: Analyze past fishing data and select the most suitable data collection method 2. The system of claim 1, (Appendix 6) The collecting unit includes: As data is collected, it is filtered based on the user's current fishing experience and skill level. 2. The system of claim 1, (Appendix 7) The collecting unit includes: Expand the types of data collected to include topographical or water quality data for fishing spots 2. The system of claim 1, (Appendix 8) The collecting unit includes: Dynamically adjust the frequency of data collection based on season and time of day 2. The system of claim 1, (Appendix 9) The collecting unit includes: Infer user sentiment and prioritize data to be collected based on the inferred user sentiment 2. The system of claim 1, (Appendix 10) The collecting unit includes: When collecting data, prioritize collecting data that is more relevant by taking into account the user's geographic location. 2. The system of claim 1, (Appendix 11) The collecting unit includes: When collecting data, analyze your social media activity and collect relevant data 2. The system of claim 1, (Appendix 12) The collecting unit includes: Expand the types of data collected to include information on the crowding of fishing spots and the activities of other anglers. 2. The system of claim 1, (Appendix 13) The collecting unit includes: Improve the accuracy of the data you collect by combining different data sources 2. The system of claim 1, (Appendix 14) The analysis unit is Inferring user emotions and adjusting analysis algorithms based on the inferred user emotions 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, past fishing data is compared with current environmental data to improve prediction accuracy. 2. The system of claim 1, (Appendix 16) The analysis unit is During analysis, predictions are made taking into account the topography and water quality data of the fishing spot. 2. The system of claim 1, (Appendix 17) The analysis unit is Customize analysis algorithms for different fish species and fishing locations 2. The system of claim 1, (Appendix 18) The analysis unit is Adjust the analysis results according to the user's fishing experience and skill level. 2. The system of claim 1, (Appendix 19) The analysis unit is Infer user emotions and adjust the way analysis results are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 20) The analysis unit is During analysis, predictions are made taking into account the user's geographic location. 2. The system of claim 1, (Appendix 21) The analysis unit is During analytics, we analyze your social media activity and incorporate the relevant data into the analytics. 2. The system of claim 1, (Appendix 22) The analysis unit is Customize the analysis results according to the user's fishing purpose 2. The system of claim 1, (Appendix 23) The analysis unit is Display analysis results in a way that is optimized for different devices 2. The system of claim 1, (Appendix 24) The suggestion unit, Infer user emotions and adjust the presentation of suggestions based on the inferred user emotions 2. The system of claim 1, (Appendix 25) The suggestion unit, When making suggestions, the level of detail of the suggestions will be adjusted based on the accuracy of the catch prediction. 2. The system of claim 1, (Appendix 26) The suggestion unit, When making a proposal, we will consider the topography and water quality data of the fishing spot to suggest the best fishing method and lure. 2. The system of claim 1, (Appendix 27) The suggestion unit, Customize the proposed algorithm for different fish species and fishing locations. 2. The system of claim 1, (Appendix 28) The suggestion unit, Tailor the suggestions to suit the user's fishing experience and skill level 2. The system of claim 1, (Appendix 29) The suggestion unit, Estimate user sentiment and prioritize suggestions based on the estimated user sentiment 2. The system of claim 1, (Appendix 30) The suggestion unit, When making suggestions, the system takes into account the user's geographic location to suggest optimal fishing methods and lures. 2. The system of claim 1, (Appendix 31) The suggestion unit, When making a suggestion, analyze the user's social media activity and make relevant suggestions 2. The system of claim 1, (Appendix 32) The suggestion unit, Customize the suggestions based on the user's fishing goals 2. The system of claim 1, (Appendix 33) The suggestion unit, Optimize your suggestions for different devices 2. The system of claim 1, [Explanation of symbols]

[0189] 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 collection unit for collecting data on weather information, tides, and water temperature; An analysis unit that analyzes the data collected by the collection unit and predicts a catch; A suggestion unit that suggests an appropriate fishing method or lure based on the prediction result obtained by the analysis unit. A system characterized by:

2. The collecting unit includes: Collect past fishing data 2. The system of claim 1 .

3. The suggestion unit, Providing suggestions based on user-selected fishing spots and target fish species 2. The system of claim 1 .

4. The collecting unit includes: Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1 .

5. The collecting unit includes: Analyze past fishing data and select the most suitable data collection method 2. The system of claim 1 .

6. The collecting unit includes: As data is collected, it is filtered based on the user's current fishing experience and skill level.

2. The system of claim 1 .

7. The collecting unit includes: Expand the types of data collected to include topographical or water quality data for fishing spots 2. The system of claim 1 .

8. The collecting unit includes: Dynamically adjust the frequency of data collection based on season and time of day 2. The system of claim 1 .

9. The collecting unit includes: Infer user sentiment and prioritize data to be collected based on the inferred user sentiment 2. The system of claim 1 .

10. The collecting unit includes: When collecting data, prioritize collecting data that is more relevant by taking into account the user's geographic location.

2. The system of claim 1 .

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