system

Abstract

The system according to the embodiment aims to suggest optimal fishing spots and timings to users in real time. [Solution] A system according to an embodiment includes a reception unit, a collection unit, an analysis unit, a suggestion unit, and a provision unit. The reception unit accepts input of fishing locations or times from a user. The collection unit collects past fishing results or weather information based on the information accepted by the reception unit. The analysis unit analyzes the data collected by the collection unit and predicts fish movements or the probability of successful fishing. The suggestion unit suggests fishing spots or timing based on the prediction results obtained by the analysis unit. The provision unit provides the information suggested by the suggestion unit in real time.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has difficulty providing real-time information to increase the chances of successful fishing, and there is room for improvement.

[0005] The system according to the embodiment aims to suggest optimal fishing spots and timings to users in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a collection unit, an analysis unit, a suggestion unit, and a provision unit. The reception unit accepts input of fishing locations or times from a user. The collection unit collects past fishing results data or weather information based on the information accepted by the reception unit. The analysis unit analyzes the data collected by the collection unit and predicts fish movements or the probability of successful fishing. The suggestion unit suggests fishing spots or timing based on the prediction results obtained by the analysis unit. The provision unit provides the information suggested by the suggestion unit in real time. [Effects of the Invention]

[0007] The system according to the embodiment can suggest optimal fishing spots and timings to the user in real time. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

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

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

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 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. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The AI ​​virtual fishing guide system according to an embodiment of the present invention analyzes fishing data and weather information in real time to suggest optimal fishing spots and timings to users. This system allows users to input the location and time they wish to fish. Based on this information, the AI ​​collects and analyzes past fishing results and current weather information to predict fish movements and the probability of successful fishing. For example, the system can analyze past fishing results and weather data to suggest optimal fishing spots and timings to users. It also provides real-time information. For example, information on current weather, water temperature, and tide flow is collected in real time and analyzed by the AI. This allows users to always fish based on the latest information, maximizing their catches. This service is extremely convenient for fishing enthusiasts and can significantly improve their fishing success rate. For example, even beginners can efficiently increase their fishing results by knowing the optimal fishing spots and timings. Furthermore, even experienced anglers can enjoy more advanced fishing thanks to the real-time information provided. This allows the AI ​​virtual fishing guide system to suggest optimal fishing spots and timings to users, thereby improving their fishing success rate.

[0029] An AI virtual fishing guide system according to an embodiment includes a reception unit, a collection unit, an analysis unit, a suggestion unit, and a provision unit. The reception unit receives input of a fishing location or time from a user. When the user inputs the location and time they wish to fish, the reception unit performs the following processing based on the information. For example, the reception unit stores the user-specified fishing location and time in a database for use in subsequent processing. The collection unit collects past fishing results data and weather information based on the information received by the reception unit. For example, the collection unit obtains necessary information from a past fishing results database and a weather database. For example, the collection unit collects past fishing results data and obtains information such as the type, quantity, location, and time of the fish caught. The collection unit also collects weather information and obtains information such as temperature, wind speed, and precipitation. The analysis unit analyzes the data collected by the collection unit and predicts fish movement and the probability of successful fishing. For example, the analysis unit executes an algorithm that predicts fish movement patterns and the probability of successful fishing based on the collected data. The analysis unit predicts fish movements using, for example, data from a fish finder or analysis results of past fishing results. The analysis unit also calculates the probability of successful fishing using statistical analysis results of past fishing results. The suggestion unit suggests fishing spots and timings to the user based on the prediction results obtained by the analysis unit. For example, the suggestion unit suggests optimal fishing spots and timings to the user based on the prediction results. The suggestion unit selects fishing spots based on criteria such as locations where many fish gather and ease of access. The suggestion unit also suggests fishing timings based on criteria such as tide levels and sunrise and sunset times. The provision unit provides the information suggested by the suggestion unit in real time. For example, the provision unit provides the user with information such as weather, water temperature, and tide flow in real time. The provision unit provides the information in real time based on criteria such as data update frequency and delay time. As a result, the AI ​​virtual fishing guide system according to the embodiment can suggest optimal fishing spots and timings to the user, thereby improving the probability of successful fishing.

[0030] The collection unit can collect past fishing result data and weather information. For example, the collection unit collects past fishing result data and acquires information such as the type, quantity, location, and time of the fish caught. For example, the collection unit acquires necessary information from a past fishing result database. The collection unit also collects weather information and acquires information such as temperature, wind speed, and precipitation. For example, the collection unit acquires necessary information from a weather database. In this way, by collecting past fishing result data and weather information, the accuracy of analysis can be improved.

[0031] The analysis unit analyzes the collected data and can predict fish movements and the probability of successful fishing. The analysis unit, for example, executes an algorithm that predicts fish movement patterns and the probability of successful fishing based on the collected data. For example, the analysis unit predicts fish movements using data from a fish finder and the results of analysis of past fishing data. The analysis unit also calculates the probability of successful fishing using the results of statistical analysis of past fishing data. In this way, the probability of successful fishing can be predicted by analyzing the collected data.

[0032] The suggestion unit can suggest fishing spots and timings based on the prediction results. For example, the suggestion unit suggests optimal fishing spots and timings to the user based on the prediction results. For example, the suggestion unit selects fishing spots based on criteria such as locations where many fish gather and ease of access. The suggestion unit also suggests fishing timings based on criteria such as tide levels and sunrise and sunset times. This allows the probability of successful fishing to be improved by suggesting optimal fishing spots and timings based on the prediction results.

[0033] The providing unit can provide information on weather, water temperature, and tide flow in real time. The providing unit provides, for example, information on weather, water temperature, tide flow, etc. to a user in real time. For example, the providing unit provides information in real time based on criteria such as data update frequency and delay time. By providing information in real time, the user can fish based on the latest information.

[0034] The reception unit can analyze the user's past fishing history and suggest the optimal input method. For example, the reception unit can automatically display fishing locations that the user has frequently visited in the past as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest fishing locations and times to be used during specific times based on the user's past fishing history. In this way, the optimal input method can be suggested by analyzing the user's past fishing history.

[0035] The reception unit can filter the fishing location and time input based on the user's current fishing experience and skill level. For example, the reception unit can prioritize displaying easy and accessible fishing locations for a beginner user. The reception unit can also suggest challenging fishing spots for an experienced user. The reception unit can also suggest appropriate fishing times based on the user's skill level. This allows filtering based on the user's fishing experience and skill level to suggest appropriate fishing locations and times.

[0036] When inputting a fishing location and time, the reception unit can prioritize inputting highly relevant locations and times, taking into account the user's geographical location information. For example, the reception unit can prioritize displaying fishing spots close to the user's current location. The reception unit can also suggest optimal fishing times based on the user's geographical location information. The reception unit can also suggest fishing spots that are easy to access, taking into account the user's geographical location information. In this way, highly relevant fishing locations and times can be suggested by taking into account the user's geographical location information.

[0037] When a fishing location and time are entered, the reception unit can analyze the user's social media activity and suggest related locations and times. For example, the reception unit can suggest optimal fishing locations based on fishing spots shared by the user on social media. The reception unit can also suggest popular fishing times based on the user's social media activity. The reception unit can also analyze the user's social media activity and suggest related fishing events and spots. In this way, related fishing locations and times can be suggested by analyzing the user's social media activity.

[0038] The collection unit can evaluate the reliability of past fishing result data and weather information during collection and select the most suitable data. For example, the collection unit evaluates the reliability of past fishing result data and prioritizes collection of highly reliable data. The collection unit can also evaluate the reliability of weather information and prioritize collection of highly reliable information. The collection unit can also comprehensively evaluate the reliability of the collected data and select the most suitable data. In this way, the most suitable data can be collected by evaluating the reliability of the data.

[0039] The collection unit can customize the data collection range based on the user's current fishing plan during collection. For example, the collection unit prioritizes collection of data related to the fishing location the user is planning. The collection unit can also collect data for a specific time period based on the user's fishing plan. The collection unit can also customize the collection range of required data according to the user's fishing plan. This allows for efficient collection of required data by customizing the data collection range based on the user's fishing plan.

[0040] The collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, the collection unit can prioritize collecting data on fishing spots close to the user's current location. The collection unit can also collect data on optimal fishing times based on the user's geographical location information. The collection unit can also collect data on fishing spots that are easily accessible by taking into account the user's geographical location information. In this way, highly relevant data can be prioritized by taking into account the user's geographical location information.

[0041] During collection, the collection unit can analyze the user's social media activity and collect related data. For example, the collection unit collects data on fishing spots shared by the user on social media. The collection unit can also collect data on popular fishing times from the user's social media activity. The collection unit can also analyze the user's social media activity and collect data on related fishing events and spots. In this way, related data can be collected by analyzing the user's social media activity.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by taking into account the correlation between past fishing result data and weather information. The analysis unit, for example, analyzes the correlation between past fishing result data and weather information to make highly accurate predictions. The analysis unit can also improve the accuracy of the analysis by taking into account the correlation between fishing result data and weather information. The analysis unit can also improve the accuracy by analyzing the impact of weather conditions on fishing results based on past data. In this way, the accuracy of the analysis can be improved by taking into account the correlation between past fishing result data and weather information.

[0043] During analysis, the analysis unit can customize the analysis results taking into account the user's fishing experience and skill level. For example, the analysis unit can provide a beginner user with simple and easy-to-understand analysis results. The analysis unit can also provide an experienced user with detailed and advanced analysis results. The analysis unit can also customize appropriate analysis results according to the user's skill level. This allows the analysis results to be customized according to the user's fishing experience and skill level, thereby providing more appropriate analysis results.

[0044] The analysis unit can improve the accuracy of the analysis by taking into account the user's geographical location information during the analysis. For example, the analysis unit performs the analysis based on data on fishing spots close to the user's current location. The analysis unit can also analyze the optimal fishing time based on the user's geographical location information. The analysis unit can also analyze fishing spots that are easy to access by taking into account the user's geographical location information. In this way, the accuracy of the analysis can be improved by taking into account the user's geographical location information.

[0045] During the analysis, the analysis unit can improve the accuracy of the analysis by referring to related fishing literature. For example, the analysis unit can improve the accuracy of the analysis by referring to the latest research papers on fishing. The analysis unit can also improve the accuracy of the analysis by referring to specialized books on fishing. The analysis unit can also improve the accuracy of the analysis by referring to online articles on fishing. In this way, by referring to related fishing literature, the accuracy of the analysis can be improved.

[0046] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the fishing spot and timing when making a suggestion. For example, the suggestion unit can provide detailed information for fishing spots with high importance. The suggestion unit can also provide concise information for fishing spots with low importance. The suggestion unit can also make detailed suggestions depending on the importance of the fishing timing. This allows for more appropriate suggestions to be provided by adjusting the level of detail of the suggestion based on the importance of the fishing spot and timing.

[0047] When making suggestions, the suggestion unit can apply different suggestion algorithms depending on the category of fishing spot and timing. For example, for sea fishing spots, the suggestion unit may make suggestions that emphasize tide flow and water temperature. For river fishing spots, the suggestion unit may also make suggestions that emphasize water flow and water quality. For lake fishing spots, the suggestion unit may also make suggestions that emphasize wind direction and water depth. This allows for more appropriate suggestions to be provided by applying different suggestion algorithms depending on the category of fishing spot and timing.

[0048] When making suggestions, the suggestion unit can determine the priority of the suggestions based on the time of submission of the fishing spot and timing. For example, the suggestion unit can prioritize suggestions when the time of submission of the fishing spot is approaching. The suggestion unit can also prioritize suggestions when the time of submission of the fishing timing is approaching. The suggestion unit can also determine the optimal order of suggestions based on the time of submission. In this way, by prioritizing the suggestions based on the time of submission of the fishing spot and timing, more appropriate suggestions can be provided.

[0049] The suggestion unit can adjust the order of suggestions based on the relevance of fishing spots and timings when making suggestions. For example, the suggestion unit prioritizes suggesting highly relevant fishing spots. The suggestion unit can also prioritize suggesting highly relevant fishing timings. The suggestion unit can also determine the optimal order of suggestions based on the relevance of fishing spots and timings. This allows for more appropriate suggestions to be provided by adjusting the order of suggestions based on the relevance of fishing spots and timings.

[0050] When providing information, the providing unit can select the optimal information providing method by referring to the user's past fishing history. For example, the providing unit preferentially selects the information providing method that the user has used favorably in the past. The providing unit can also select the optimal information providing method based on the user's past fishing history. The providing unit can also provide highly relevant information by referring to the user's past fishing history. In this way, the optimal information providing method can be selected by referring to the user's past fishing history.

[0051] The providing unit can customize the means of providing information based on the user's current fishing plan when providing information. The providing unit, for example, selects the optimal means of providing information based on the user's fishing plan. The providing unit can also customize and provide necessary information according to the user's fishing plan. The providing unit can also provide information in real time based on the user's fishing plan. In this way, by customizing the means of providing information based on the user's fishing plan, more appropriate information can be provided.

[0052] The providing unit can select the optimal information providing method by taking into consideration the user's geographical location information when providing information. For example, the providing unit can provide information on fishing spots close to the user's current location with priority. The providing unit can also provide information on optimal fishing times based on the user's geographical location information. The providing unit can also provide information on fishing spots that are easy to access by taking into consideration the user's geographical location information. In this way, the optimal information providing method can be selected by taking into consideration the user's geographical location information.

[0053] When providing information, the providing unit can analyze the user's social media activity and suggest a means of providing the information. For example, the providing unit can provide information about fishing spots shared by the user on social media. The providing unit can also provide information about popular fishing times based on the user's social media activity. The providing unit can also analyze the user's social media activity and provide information about related fishing events and spots. In this way, related information can be provided by analyzing the user's social media activity.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The reception unit can learn the user's fishing preferences and past behavioral patterns and make individually customized suggestions for fishing spots and timings. For example, the reception unit can record the user's favorite fishing spots and fishing times in the past and reflect them in the next suggestions. Also, if the user prefers a specific fish species, the reception unit can prioritize suggestions for spots and times when that fish species is often caught. Furthermore, the reception unit can suggest optimal fishing spots and timings depending on the user's fishing style (e.g., catch and release or tournament fishing). This allows for more personalized suggestions based on the user's individual preferences and behavioral patterns.

[0056] The collection unit can analyze the user's fishing-related social media posts to identify popular fishing spots and time periods. For example, the collection unit can collect information about fishing spots shared by the user on social media and compare it with posts from other users to identify popular spots. The collection unit can also collect information about fishing spots shared by the user's followers and friends to suggest relevant spots. Furthermore, the collection unit can analyze fishing-related trends on social media to identify the latest popular spots and time periods. This makes it possible to utilize the user's social media activity to suggest more relevant fishing spots and time periods.

[0057] The analysis unit can analyze the user's past successful fishing experiences and predict the probability of successful fishing under similar conditions. For example, the analysis unit collects data on the fishing spots and time periods where the user has had past success and identifies days with similar weather and water temperature conditions. The analysis unit can also predict the probability of success under similar conditions by taking into account the type of fishing equipment and bait used by the user. Furthermore, the analysis unit can also suggest optimal fishing spots and time periods by taking into account the user's fishing style and skill level. This allows the analysis unit to suggest fishing spots and time periods with a higher probability of success based on the user's past successful experiences.

[0058] The suggestion unit can suggest optimal fishing spots and times based on the user's fishing goals and desires. For example, if the user wants to catch a big fish, the suggestion unit can suggest spots and times when that fish species is often caught. Also, if the user wants to relax, the suggestion unit can suggest fishing spots that are quiet and less crowded. Furthermore, if the user wants to enjoy fishing with family or friends, the suggestion unit can suggest fishing spots that are easily accessible and well-equipped. In this way, the suggestion unit can suggest optimal fishing spots and times based on the user's goals and desires.

[0059] The providing unit can collect real-time feedback on the user's fishing and instantly adjust the suggestions. For example, the providing unit can collect feedback on the user's catch and weather changes while the user is fishing and update the suggestions. The providing unit can also collect feedback on the user's catch and experience after the user has finished fishing and reflect this in the next suggestions. Furthermore, the providing unit can analyze how the user reacts to the information provided in real time and optimize the suggestions. This makes it possible to utilize the user's real-time feedback to provide more appropriate suggestions.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The reception unit receives input from the user about the fishing location and time. When the user inputs the location and time they want to fish, the reception unit performs the following process based on that information. For example, the reception unit stores the fishing location and time specified by the user in a database and uses them for subsequent processing. Step 2: The collection unit collects past fishing result data and weather information based on the information received by the reception unit. For example, the collection unit obtains necessary information from a past fishing result database and a weather database. For example, the collection unit collects past fishing result data and obtains information such as the type, quantity, location, and time of the fish caught. The collection unit also collects weather information and obtains information such as temperature, wind speed, and precipitation. Step 3: The analysis unit analyzes the data collected by the collection unit and predicts fish movements and the probability of successful fishing. For example, the analysis unit executes an algorithm that predicts fish movement patterns and the probability of successful fishing based on the collected data. The analysis unit predicts fish movements using, for example, data from a fish finder and the results of analysis of past fishing data. The analysis unit also calculates the probability of successful fishing using the results of statistical analysis of past fishing data. Step 4: The suggestion unit suggests fishing spots and timing based on the prediction results obtained by the analysis unit. For example, the suggestion unit suggests optimal fishing spots and timing to the user based on the prediction results. The suggestion unit selects fishing spots based on criteria such as locations where many fish gather and ease of access. The suggestion unit also suggests fishing timing based on criteria such as tide levels and sunrise and sunset times. Step 5: The providing unit provides the information proposed by the proposing unit in real time. For example, the providing unit provides the user with information such as weather, water temperature, and tide flow in real time. The providing unit provides the information in real time based on criteria such as the data update frequency and delay time.

[0062] (Example 2) The AI ​​virtual fishing guide system according to an embodiment of the present invention analyzes fishing data and weather information in real time to suggest optimal fishing spots and timings to users. This system allows users to input the location and time they wish to fish. Based on this information, the AI ​​collects and analyzes past fishing results and current weather information to predict fish movements and the probability of successful fishing. For example, the system can analyze past fishing results and weather data to suggest optimal fishing spots and timings to users. It also provides real-time information. For example, information on current weather, water temperature, and tide flow is collected in real time and analyzed by the AI. This allows users to always fish based on the latest information, maximizing their catches. This service is extremely convenient for fishing enthusiasts and can significantly improve their fishing success rate. For example, even beginners can efficiently increase their fishing results by knowing the optimal fishing spots and timings. Furthermore, even experienced anglers can enjoy more advanced fishing thanks to the real-time information provided. This allows the AI ​​virtual fishing guide system to suggest optimal fishing spots and timings to users, thereby improving their fishing success rate.

[0063] An AI virtual fishing guide system according to an embodiment includes a reception unit, a collection unit, an analysis unit, a suggestion unit, and a provision unit. The reception unit receives input of a fishing location or time from a user. When the user inputs the location and time they wish to fish, the reception unit performs the following processing based on the information. For example, the reception unit stores the user-specified fishing location and time in a database for use in subsequent processing. The collection unit collects past fishing results data and weather information based on the information received by the reception unit. For example, the collection unit obtains necessary information from a past fishing results database and a weather database. For example, the collection unit collects past fishing results data and obtains information such as the type, quantity, location, and time of the fish caught. The collection unit also collects weather information and obtains information such as temperature, wind speed, and precipitation. The analysis unit analyzes the data collected by the collection unit and predicts fish movement and the probability of successful fishing. For example, the analysis unit executes an algorithm that predicts fish movement patterns and the probability of successful fishing based on the collected data. The analysis unit predicts fish movements using, for example, data from a fish finder or analysis results of past fishing results. The analysis unit also calculates the probability of successful fishing using statistical analysis results of past fishing results. The suggestion unit suggests fishing spots and timings to the user based on the prediction results obtained by the analysis unit. For example, the suggestion unit suggests optimal fishing spots and timings to the user based on the prediction results. The suggestion unit selects fishing spots based on criteria such as locations where many fish gather and ease of access. The suggestion unit also suggests fishing timings based on criteria such as tide levels and sunrise and sunset times. The provision unit provides the information suggested by the suggestion unit in real time. For example, the provision unit provides the user with information such as weather, water temperature, and tide flow in real time. The provision unit provides the information in real time based on criteria such as data update frequency and delay time. As a result, the AI ​​virtual fishing guide system according to the embodiment can suggest optimal fishing spots and timings to the user, thereby improving the probability of successful fishing.

[0064] The collection unit can collect past fishing result data and weather information. For example, the collection unit collects past fishing result data and acquires information such as the type, quantity, location, and time of the fish caught. For example, the collection unit acquires necessary information from a past fishing result database. The collection unit also collects weather information and acquires information such as temperature, wind speed, and precipitation. For example, the collection unit acquires necessary information from a weather database. In this way, by collecting past fishing result data and weather information, the accuracy of analysis can be improved.

[0065] The analysis unit analyzes the collected data and can predict fish movements and the probability of successful fishing. The analysis unit, for example, executes an algorithm that predicts fish movement patterns and the probability of successful fishing based on the collected data. For example, the analysis unit predicts fish movements using data from a fish finder and the results of analysis of past fishing data. The analysis unit also calculates the probability of successful fishing using the results of statistical analysis of past fishing data. In this way, the probability of successful fishing can be predicted by analyzing the collected data.

[0066] The suggestion unit can suggest fishing spots and timings based on the prediction results. For example, the suggestion unit suggests optimal fishing spots and timings to the user based on the prediction results. For example, the suggestion unit selects fishing spots based on criteria such as locations where many fish gather and ease of access. The suggestion unit also suggests fishing timings based on criteria such as tide levels and sunrise and sunset times. This allows the probability of successful fishing to be improved by suggesting optimal fishing spots and timings based on the prediction results.

[0067] The providing unit can provide information on weather, water temperature, and tide flow in real time. The providing unit provides, for example, information on weather, water temperature, tide flow, etc. to a user in real time. For example, the providing unit provides information in real time based on criteria such as data update frequency and delay time. By providing information in real time, the user can fish based on the latest information.

[0068] The reception unit can estimate the user's emotions and customize the input interface for the fishing location and time based on the estimated user emotions. For example, if the user is excited, the reception unit can provide a simple and intuitive interface to allow the user to quickly input the fishing location and time. If the user is relaxed, the reception unit can provide detailed options and suggest a customizable input method. If the user is stressed, the reception unit can minimize the input steps to allow the user to easily set the fishing location and time. This improves user convenience by customizing the input interface according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0069] The reception unit can analyze the user's past fishing history and suggest the optimal input method. For example, the reception unit can automatically display fishing locations that the user has frequently visited in the past as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest fishing locations and times to be used during specific times based on the user's past fishing history. In this way, the optimal input method can be suggested by analyzing the user's past fishing history.

[0070] The reception unit can filter the fishing location and time input based on the user's current fishing experience and skill level. For example, the reception unit can prioritize displaying easy and accessible fishing locations for a beginner user. The reception unit can also suggest challenging fishing spots for an experienced user. The reception unit can also suggest appropriate fishing times based on the user's skill level. This allows filtering based on the user's fishing experience and skill level to suggest appropriate fishing locations and times.

[0071] The reception unit can estimate the user's emotions and determine the priority of fishing locations and times to be input based on the estimated user emotions. For example, if the user is excited, the reception unit can prioritize displaying popular fishing spots. Also, if the user is relaxed, the reception unit can prioritize displaying quiet fishing spots. Also, if the user is stressed, the reception unit can prioritize displaying easily accessible fishing spots. This can improve user satisfaction by prioritizing fishing locations and times according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. Generation AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0072] When inputting a fishing location and time, the reception unit can prioritize inputting highly relevant locations and times, taking into account the user's geographical location information. For example, the reception unit can prioritize displaying fishing spots close to the user's current location. The reception unit can also suggest optimal fishing times based on the user's geographical location information. The reception unit can also suggest fishing spots that are easy to access, taking into account the user's geographical location information. In this way, highly relevant fishing locations and times can be suggested by taking into account the user's geographical location information.

[0073] When a fishing location and time are entered, the reception unit can analyze the user's social media activity and suggest related locations and times. For example, the reception unit can suggest optimal fishing locations based on fishing spots shared by the user on social media. The reception unit can also suggest popular fishing times based on the user's social media activity. The reception unit can also analyze the user's social media activity and suggest related fishing events and spots. In this way, related fishing locations and times can be suggested by analyzing the user's social media activity.

[0074] The collection unit can estimate the user's emotions and adjust the type of data to be collected based on the estimated user emotions. For example, if the user is excited, the collection unit prioritizes collecting the latest fishing result data. Furthermore, if the user is relaxed, the collection unit can also collect detailed data on past fishing results. Furthermore, if the user is stressed, the collection unit can also prioritize collecting reliable weather information. In this way, by adjusting the type of data to be collected according to the user's emotions, more appropriate data can be collected. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0075] The collection unit can evaluate the reliability of past fishing result data and weather information during collection and select the most suitable data. For example, the collection unit evaluates the reliability of past fishing result data and prioritizes collection of highly reliable data. The collection unit can also evaluate the reliability of weather information and prioritize collection of highly reliable information. The collection unit can also comprehensively evaluate the reliability of the collected data and select the most suitable data. In this way, the most suitable data can be collected by evaluating the reliability of the data.

[0076] The collection unit can customize the data collection range based on the user's current fishing plan during collection. For example, the collection unit prioritizes collection of data related to the fishing location the user is planning. The collection unit can also collect data for a specific time period based on the user's fishing plan. The collection unit can also customize the collection range of required data according to the user's fishing plan. This allows for efficient collection of required data by customizing the data collection range based on the user's fishing plan.

[0077] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, if the user is excited, the collection unit prioritizes collecting the latest fishing result data. Furthermore, if the user is relaxed, the collection unit can also collect detailed data on past fishing results. Furthermore, if the user is stressed, the collection unit can prioritize collecting reliable weather information. In this way, by determining the priority of data according to the user's emotions, important data can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0078] The collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, the collection unit can prioritize collecting data on fishing spots close to the user's current location. The collection unit can also collect data on optimal fishing times based on the user's geographical location information. The collection unit can also collect data on fishing spots that are easily accessible by taking into account the user's geographical location information. In this way, highly relevant data can be prioritized by taking into account the user's geographical location information.

[0079] During collection, the collection unit can analyze the user's social media activity and collect related data. For example, the collection unit collects data on fishing spots shared by the user on social media. The collection unit can also collect data on popular fishing times from the user's social media activity. The collection unit can also analyze the user's social media activity and collect data on related fishing events and spots. In this way, related data can be collected by analyzing the user's social media activity.

[0080] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, when the user is excited, the analysis unit can quickly perform an analysis and provide results. When the user is relaxed, the analysis unit can also perform a detailed analysis and provide highly accurate results. When the user is stressed, the analysis unit can also provide concise and easy-to-understand analysis results. This allows for adjusting the analysis algorithm according to the user's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0081] During analysis, the analysis unit can improve the accuracy of the analysis by taking into account the correlation between past fishing result data and weather information. The analysis unit, for example, analyzes the correlation between past fishing result data and weather information to make highly accurate predictions. The analysis unit can also improve the accuracy of the analysis by taking into account the correlation between fishing result data and weather information. The analysis unit can also improve the accuracy by analyzing the impact of weather conditions on fishing results based on past data. In this way, the accuracy of the analysis can be improved by taking into account the correlation between past fishing result data and weather information.

[0082] During analysis, the analysis unit can customize the analysis results taking into account the user's fishing experience and skill level. For example, the analysis unit can provide a beginner user with simple and easy-to-understand analysis results. The analysis unit can also provide an experienced user with detailed and advanced analysis results. The analysis unit can also customize appropriate analysis results according to the user's skill level. This allows the analysis results to be customized according to the user's fishing experience and skill level, thereby providing more appropriate analysis results.

[0083] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is excited, the analysis unit provides a visually stimulating display method. If the user is relaxed, the analysis unit can also provide a display method including detailed information. If the user is stressed, the analysis unit can also provide a simple, highly visible display method. This allows the display method of the analysis results to be adjusted according to the user's emotions, thereby providing a more appropriate display method. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0084] The analysis unit can improve the accuracy of the analysis by taking into account the user's geographical location information during the analysis. For example, the analysis unit performs the analysis based on data on fishing spots close to the user's current location. The analysis unit can also analyze the optimal fishing time based on the user's geographical location information. The analysis unit can also analyze fishing spots that are easy to access by taking into account the user's geographical location information. In this way, the accuracy of the analysis can be improved by taking into account the user's geographical location information.

[0085] During the analysis, the analysis unit can improve the accuracy of the analysis by referring to related fishing literature. For example, the analysis unit can improve the accuracy of the analysis by referring to the latest research papers on fishing. The analysis unit can also improve the accuracy of the analysis by referring to specialized books on fishing. The analysis unit can also improve the accuracy of the analysis by referring to online articles on fishing. In this way, by referring to related fishing literature, the accuracy of the analysis can be improved.

[0086] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. For example, if the user is excited, the suggestion unit can make visually stimulating suggestions. If the user is relaxed, the suggestion unit can also make suggestions that include detailed information. If the user is stressed, the suggestion unit can also make simple, highly visible suggestions. This allows for more appropriate suggestions to be provided by adjusting the way suggestions are expressed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0087] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the fishing spot and timing when making a suggestion. For example, the suggestion unit can provide detailed information for fishing spots with high importance. The suggestion unit can also provide concise information for fishing spots with low importance. The suggestion unit can also make detailed suggestions depending on the importance of the fishing timing. This allows for more appropriate suggestions to be provided by adjusting the level of detail of the suggestion based on the importance of the fishing spot and timing.

[0088] When making suggestions, the suggestion unit can apply different suggestion algorithms depending on the category of fishing spot and timing. For example, for sea fishing spots, the suggestion unit may make suggestions that emphasize tide flow and water temperature. For river fishing spots, the suggestion unit may also make suggestions that emphasize water flow and water quality. For lake fishing spots, the suggestion unit may also make suggestions that emphasize wind direction and water depth. This allows for more appropriate suggestions to be provided by applying different suggestion algorithms depending on the category of fishing spot and timing.

[0089] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is excited, the suggestion unit can make short, to-the-point suggestions. If the user is relaxed, the suggestion unit can also make longer suggestions with detailed explanations. If the user is stressed, the suggestion unit can also make concise, easy-to-understand suggestions. This allows the length of the suggestions to be adjusted according to the user's emotions, making it possible to provide more appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0090] When making suggestions, the suggestion unit can determine the priority of the suggestions based on the time of submission of the fishing spot and timing. For example, the suggestion unit can prioritize suggestions when the time of submission of the fishing spot is approaching. The suggestion unit can also prioritize suggestions when the time of submission of the fishing timing is approaching. The suggestion unit can also determine the optimal order of suggestions based on the time of submission. In this way, by prioritizing the suggestions based on the time of submission of the fishing spot and timing, more appropriate suggestions can be provided.

[0091] The suggestion unit can adjust the order of suggestions based on the relevance of fishing spots and timings when making suggestions. For example, the suggestion unit prioritizes suggesting highly relevant fishing spots. The suggestion unit can also prioritize suggesting highly relevant fishing timings. The suggestion unit can also determine the optimal order of suggestions based on the relevance of fishing spots and timings. This allows for more appropriate suggestions to be provided by adjusting the order of suggestions based on the relevance of fishing spots and timings.

[0092] The providing unit can estimate the user's emotions and adjust the method of providing information based on the estimated user's emotions. For example, if the user is excited, the providing unit can provide information in a visually stimulating manner. Furthermore, if the user is relaxed, the providing unit can provide information in a manner including detailed information. Furthermore, if the user is feeling stressed, the providing unit can provide information in a simple and highly visible manner. In this way, by adjusting the method of providing information according to the user's emotions, more appropriate information can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0093] When providing information, the providing unit can select the optimal information providing method by referring to the user's past fishing history. For example, the providing unit preferentially selects the information providing method that the user has used favorably in the past. The providing unit can also select the optimal information providing method based on the user's past fishing history. The providing unit can also provide highly relevant information by referring to the user's past fishing history. In this way, the optimal information providing method can be selected by referring to the user's past fishing history.

[0094] The providing unit can customize the means of providing information based on the user's current fishing plan when providing information. The providing unit, for example, selects the optimal means of providing information based on the user's fishing plan. The providing unit can also customize and provide necessary information according to the user's fishing plan. The providing unit can also provide information in real time based on the user's fishing plan. In this way, by customizing the means of providing information based on the user's fishing plan, more appropriate information can be provided.

[0095] The providing unit can estimate the user's emotions and determine the priority of information provision based on the estimated user's emotions. For example, when the user is excited, the providing unit can prioritize providing important information. Furthermore, when the user is relaxed, the providing unit can prioritize providing detailed information. Furthermore, when the user is stressed, the providing unit can prioritize providing concise and important information. In this way, by determining the priority of information provision according to the user's emotions, important information can be prioritized. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0096] The providing unit can select the optimal information providing method by taking into consideration the user's geographical location information when providing information. For example, the providing unit can provide information on fishing spots close to the user's current location with priority. The providing unit can also provide information on optimal fishing times based on the user's geographical location information. The providing unit can also provide information on fishing spots that are easy to access by taking into consideration the user's geographical location information. In this way, the optimal information providing method can be selected by taking into consideration the user's geographical location information.

[0097] When providing information, the providing unit can analyze the user's social media activity and suggest a means of providing the information. For example, the providing unit can provide information about fishing spots shared by the user on social media. The providing unit can also provide information about popular fishing times based on the user's social media activity. The providing unit can also analyze the user's social media activity and provide information about related fishing events and spots. In this way, related information can be provided by analyzing the user's social media activity. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, collection unit, analysis unit, suggestion unit, and provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and accepts input of fishing locations and times from the user. The collection unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and collects past fishing results and weather information. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to predict fish movements and the probability of successful fishing. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and suggests fishing spots and timings based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the smart device 14 and provides information to the user in real time. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, collection unit, analysis unit, suggestion unit, and provision unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and accepts input of fishing locations and times from the user. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects past fishing results data and weather information. 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 fish movements and the probability of fishing success. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests fishing spots and timings based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides information to the user in real time. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, collection unit, analysis unit, suggestion unit, and provision unit, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset terminal 314 and accepts input of fishing locations and times from the user. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects past fishing results data and weather information. 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 fish movements and the probability of fishing success. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests fishing spots and timings based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the headset terminal 314 and provides information to the user in real time. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, collection unit, analysis unit, suggestion unit, and provision unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and accepts input of fishing locations and times from the user. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects past fishing results data and weather information. 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 fish movements and the probability of fishing success. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests fishing spots and timings based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides information to the user in real time.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The reception unit can learn the user's fishing preferences and past behavioral patterns and make individually customized suggestions for fishing spots and timings. For example, the reception unit can record the user's favorite fishing spots and fishing times in the past and reflect them in the next suggestions. Also, if the user prefers a specific fish species, the reception unit can prioritize suggestions for spots and times when that fish species is often caught. Furthermore, the reception unit can suggest optimal fishing spots and timings depending on the user's fishing style (e.g., catch and release or tournament fishing). This allows for more personalized suggestions based on the user's individual preferences and behavioral patterns.

[0100] The collection unit can analyze the user's fishing-related social media posts to identify popular fishing spots and time periods. For example, the collection unit can collect information about fishing spots shared by the user on social media and compare it with posts from other users to identify popular spots. The collection unit can also collect information about fishing spots shared by the user's followers and friends to suggest relevant spots. Furthermore, the collection unit can analyze fishing-related trends on social media to identify the latest popular spots and time periods. This makes it possible to utilize the user's social media activity to suggest more relevant fishing spots and time periods.

[0101] The analysis unit can analyze the user's past successful fishing experiences and predict the probability of successful fishing under similar conditions. For example, the analysis unit collects data on the fishing spots and time periods where the user has had past success and identifies days with similar weather and water temperature conditions. The analysis unit can also predict the probability of success under similar conditions by taking into account the type of fishing equipment and bait used by the user. Furthermore, the analysis unit can also suggest optimal fishing spots and time periods by taking into account the user's fishing style and skill level. This allows the analysis unit to suggest fishing spots and time periods with a higher probability of success based on the user's past successful experiences.

[0102] The suggestion unit can suggest optimal fishing spots and times based on the user's fishing goals and desires. For example, if the user wants to catch a big fish, the suggestion unit can suggest spots and times when that fish species is often caught. Also, if the user wants to relax, the suggestion unit can suggest fishing spots that are quiet and less crowded. Furthermore, if the user wants to enjoy fishing with family or friends, the suggestion unit can suggest fishing spots that are easily accessible and well-equipped. In this way, the suggestion unit can suggest optimal fishing spots and times based on the user's goals and desires.

[0103] The providing unit can collect real-time feedback on the user's fishing and instantly adjust the suggestions. For example, the providing unit can collect feedback on the user's catch and weather changes while the user is fishing and update the suggestions. The providing unit can also collect feedback on the user's catch and experience after the user has finished fishing and reflect this in the next suggestions. Furthermore, the providing unit can analyze how the user reacts to the information provided in real time and optimize the suggestions. This makes it possible to utilize the user's real-time feedback to provide more appropriate suggestions.

[0104] The reception unit can estimate the user's emotions and customize the input interface for fishing locations and time based on the estimated user emotions. For example, if the user is excited, a simple and intuitive interface can be provided to allow the user to quickly input fishing locations and time. Alternatively, if the user is relaxed, detailed options can be provided and a customizable input method can be suggested. Alternatively, if the user is stressed, the input steps can be minimized to allow the user to easily set fishing locations and time. In this way, by customizing the input interface according to the user's emotions, user convenience can be improved.

[0105] The collection unit can estimate the user's emotions and adjust the type of data to be collected based on the estimated user emotions. For example, if the user is excited, the latest fishing result data can be collected with priority. Alternatively, if the user is relaxed, detailed data on past fishing results can be collected. Alternatively, if the user is stressed, more reliable weather information can be collected with priority. In this way, by adjusting the type of data to be collected according to the user's emotions, more appropriate data can be collected.

[0106] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, if the user is excited, the analysis unit can quickly perform an analysis and provide results. If the user is relaxed, the analysis unit can perform a detailed analysis and provide highly accurate results. If the user is stressed, the analysis unit can provide concise and easy-to-understand analysis results. By adjusting the analysis algorithm according to the user's emotions, more appropriate analysis results can be provided.

[0107] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user's emotions. For example, if the user is excited, a visually stimulating suggestion can be made. If the user is relaxed, a suggestion including detailed information can be made. If the user is stressed, a simple and highly visible suggestion can be made. In this way, by adjusting the way suggestions are expressed according to the user's emotions, more appropriate suggestions can be provided.

[0108] The providing unit can estimate the user's emotions and adjust the method of providing information based on the estimated user's emotions. For example, if the user is excited, information can be provided in a visually stimulating manner. If the user is relaxed, information can be provided in a manner including detailed information. If the user is stressed, information can be provided in a simple, highly visible manner. In this way, by adjusting the method of providing information according to the user's emotions, more appropriate information can be provided.

[0109] The processing flow of the second embodiment will be briefly explained below.

[0110] Step 1: The reception unit receives input from the user about the fishing location and time. When the user inputs the location and time they want to fish, the reception unit performs the following process based on that information. For example, the reception unit stores the fishing location and time specified by the user in a database and uses them for subsequent processing. Step 2: The collection unit collects past fishing result data and weather information based on the information received by the reception unit. For example, the collection unit obtains necessary information from a past fishing result database and a weather database. For example, the collection unit collects past fishing result data and obtains information such as the type, quantity, location, and time of the fish caught. The collection unit also collects weather information and obtains information such as temperature, wind speed, and precipitation. Step 3: The analysis unit analyzes the data collected by the collection unit and predicts fish movements and the probability of successful fishing. For example, the analysis unit executes an algorithm that predicts fish movement patterns and the probability of successful fishing based on the collected data. The analysis unit predicts fish movements using, for example, data from a fish finder and the results of analysis of past fishing data. The analysis unit also calculates the probability of successful fishing using the results of statistical analysis of past fishing data. Step 4: The suggestion unit suggests fishing spots and timing based on the prediction results obtained by the analysis unit. For example, the suggestion unit suggests optimal fishing spots and timing to the user based on the prediction results. The suggestion unit selects fishing spots based on criteria such as locations where many fish gather and ease of access. The suggestion unit also suggests fishing timing based on criteria such as tide levels and sunrise and sunset times. Step 5: The providing unit provides the information proposed by the proposing unit in real time. For example, the providing unit provides the user with information such as weather, water temperature, and tide flow in real time. The providing unit provides the information in real time based on criteria such as the data update frequency and delay time.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] 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 engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0113] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] 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.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0122] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0123] 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.

[0124] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0125] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also 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 perform processing similar to that of the specific processing unit 290 using these models.

[0126] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0130] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 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.

[0133] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] 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.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0138] 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.

[0139] 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.

[0140] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0141] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 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.

[0142] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0150] 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.

[0151] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0153] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0154] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0155] 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.

[0156] 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.

[0157] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0158] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0159] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0161] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0163] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0164] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0166] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0167] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0168] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0171] 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.

[0172] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0173] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0174] 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.

[0175] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0176] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0177] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0179] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0180] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0181] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0182] [Explanation of symbols]

[0183] 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 reception unit that receives input of a fishing location or time from a user; a collection unit that collects past fishing result data or weather information based on the information received by the reception unit; an analysis unit that analyzes the data collected by the collection unit and predicts the movement of fish or the probability of successful fishing; A suggestion unit that suggests fishing spots or timings based on the prediction results obtained by the analysis unit; a providing unit that provides the information proposed by the proposing unit in real time. A system characterized by:

2. The collecting unit Collect past fishing data and weather information 2. The system of claim 1.

3. The analysis unit Analyzing the collected data to predict fish movements and the probability of successful fishing 2. The system of claim 1.

4. The proposal unit Suggest fishing spots or timing based on the forecast results 2. The system of claim 1.

5. The providing unit Providing real-time information on weather, water temperature, and tide flow 2. The system of claim 1.

6. The reception unit Estimate the user's emotions and customize the input interface for fishing locations and times based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit Analyzes the user's past fishing history and suggests the optimal input method 2. The system of claim 1.

8. The reception unit Filtering based on the user's current fishing experience and skill level when entering fishing locations and times 2. The system of claim 1.

Images