System

Abstract

An object of the system according to the embodiment is for a fishing enthusiast to efficiently find a specific spot of a fishing spot.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects fishing results. The analysis unit analyzes the information collected by the collection unit and specifies a specific spot of the fishing spot. The providing unit provides the user with the specific spot of the fishing spot identified by the analyzing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem of making it difficult for fishing enthusiasts to efficiently find specific fishing spots.

[0005] The system according to the embodiment aims to enable fishing enthusiasts to efficiently find specific fishing spots. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects fishing results. The analysis unit analyzes the information collected by the collection unit and identifies specific fishing spots. The provision unit provides the specific fishing spots identified by the analysis unit to a user. [Effects of the Invention]

[0007] The system according to the embodiment allows fishing enthusiasts to efficiently find specific fishing spots. [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) A fishing information system according to an embodiment of the present invention collects fishing results, analyzes them using a generation AI, and identifies and provides great fishing spots. The fishing information system collects fishing results, analyzes them using a generation AI, identifies great fishing spots, and provides them to users. For example, the fishing information system collects information uploaded by users to apps or social media. Next, the fishing information system collects various information, including from social media, and the generation AI analyzes that information. The generation AI analyzes fishing spot information and catch data to identify great fishing spots. For example, if a large number of fish are caught at a particular fishing spot, the generation AI identifies great fishing spots based on that information. Finally, the fishing information system informs users of the identified great fishing spots. Users can receive information about great fishing spots through apps or social media. This allows fishing enthusiasts to enjoy fishing efficiently, creating a wonderful system where supply and demand are balanced. This allows fishing enthusiasts to share their fishing results and exchange information with other users. Furthermore, fishing can be enjoyed efficiently based on the information analyzed by the generation AI, further expanding the enjoyment of fishing.

[0029] The fishing information providing system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects fishing results. Fishing results include, but are not limited to, the type and size of the fish caught, the fishing location, and the time of fishing. For example, the collection unit collects information uploaded by users to an app or social networking site (SNS). The collection unit can also collect information by manual input, automatic collection using a sensor, or data acquisition from an app. For example, the collection unit collects information entered by users into an app. The collection unit can also automatically collect fishing results using a sensor. The collection unit can also acquire data from an app. The analysis unit uses a generation AI to analyze the information collected by the collection unit and identify specific fishing spots. Analysis can be performed using, for example, data mining, statistical analysis, machine learning algorithms, or other methods, but is not limited to these examples. For example, the analysis unit uses a generation AI to analyze fishing spot information and catch data to identify great fishing spots. The analysis unit can also analyze multiple pieces of information, including social networking sites, to identify specific fishing spots. The analysis unit can also use the generation AI to identify specific fishing spots based on fishing spot information and catch data. The provision unit provides the specific fishing spots identified by the analysis unit to the user. This provision can be performed, for example, by app notification, email, dashboard display, or other methods, but is not limited to these examples. For example, the provision unit can provide the user with information about the identified great fishing spots via app notification. The provision unit can also provide the user with information about the identified great fishing spots via email. The provision unit can also provide the user with information about the identified great fishing spots via dashboard display. This allows the fishing information provision system according to the embodiment to efficiently collect, analyze, and provide fishing results.

[0030] The collection unit can collect information that users upload about their fishing catches to an app or social networking site. For example, the collection unit collects information that users upload about their fishing catches to an app. The collection unit can also collect information that users upload about their fishing catches to social networking sites. For example, the collection unit collects information from a specific fishing app. The collection unit can also collect information from social networking sites such as Facebook (registered trademark) and Instagram (registered trademark). This allows for efficient collection of fishing catches uploaded by users. Some or all of the above-described processing in the collection unit may be performed using, or without, AI, for example. For example, the collection unit can input information obtained from an app or social networking site into a generation AI and cause the generation AI to collect information.

[0031] The analysis unit can analyze multiple pieces of information, including social media, and identify specific fishing spots based on fishing spot information and catch data. The analysis unit, for example, analyzes social media posts and identifies specific fishing spots based on fishing spot information and catch data. The analysis unit can also analyze weather data and identify specific fishing spots. For example, the analysis unit can identify specific fishing spots based on weather data. The analysis unit can also analyze water temperature data and identify specific fishing spots. For example, the analysis unit can identify specific fishing spots based on water temperature data. In this way, by analyzing various information, including social media, it is possible to identify hot spots. Some or all of the above-mentioned processing by the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input social media posts, weather data, and water temperature data into the generation AI and have the generation AI identify specific fishing spots.

[0032] The providing unit can provide the user with information about the identified smash-fishing spots. For example, the providing unit can provide the user with information about the identified smash-fishing spots via an app notification. The providing unit can also provide the user with information about the identified smash-fishing spots via email. For example, the providing unit can provide the user with information about the identified smash-fishing spots via a dashboard display. The providing unit can also provide the user with information about the identified smash-fishing spots via social media. By providing the user with information about the identified smash-fishing spots, the user can enjoy fishing efficiently. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input information about the identified smash-fishing spots into a generation AI and have the generation AI provide the information.

[0033] The collection unit can analyze the user's past fishing results and select an appropriate collection method. For example, if the user has uploaded many photos in the past, the collection unit can prioritize photo collection. Furthermore, if the user has previously reported their fishing results in text, the collection unit can also prompt the user to enter text. Furthermore, if the user has previously uploaded videos, the collection unit can also prioritize video collection. This allows the optimal collection method to be selected by analyzing the user's past fishing results. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past fishing results data into the generation AI and have the generation AI select the optimal collection method.

[0034] When collecting fishing results, the collection unit can filter the results based on the user's current fishing spot or area of ​​interest. For example, the collection unit prioritizes collecting information about the fishing spot where the user is currently located. The collection unit can also prioritize collecting information about fish species in which the user is interested. The collection unit can also prioritize collecting information about fishing techniques in which the user is interested. This allows for filtering based on the user's current fishing spot or area of ​​interest, thereby collecting highly relevant fishing results. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input data about the user's current fishing spot or area of ​​interest into the generation AI and have the generation AI perform the filtering.

[0035] When collecting fishing results, the collection unit can select an appropriate collection means depending on the user's input method. For example, if the user prefers voice input, the collection unit can enable the user to report fishing results by voice. Furthermore, if the user prefers text input, the collection unit can enable the user to report fishing results by text. Furthermore, if the user prefers image input, the collection unit can enable the user to report fishing results by photo. This allows fishing results to be collected efficiently by selecting the optimal collection means depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input method data into the generation AI and have the generation AI select the optimal collection means.

[0036] When collecting fishing results, the collection unit can prioritize collecting highly relevant results based on the user's geographical location information. For example, the collection unit prioritizes collecting fishing results near the fishing spot where the user is currently located. The collection unit can also prioritize collecting information about fishing spots that the user has visited in the past. The collection unit can also prioritize collecting information about fishing spots that the user is planning to visit. This allows highly relevant fishing results to be collected preferentially by taking the user's geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information data into the generation AI and cause the generation AI to collect highly relevant results.

[0037] When collecting fishing results, the collection unit can analyze the user's social media usage and collect related results. For example, the collection unit collects fishing results shared by the user on social media. The collection unit can also collect fishing results shared by the user's friends. The collection unit can also collect fishing results that the user has "liked." This makes it possible to collect related fishing results by analyzing the user's social media activity. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data into the generation AI and cause the generation AI to collect related results.

[0038] When collecting fishing results, the collection unit can adjust the collection method by reflecting the user's past feedback. For example, the collection unit prioritizes collection methods that the user has previously rated highly. The collection unit can also avoid collection methods that the user has previously rated poorly. The collection unit can also suggest new collection methods based on the user's feedback. This allows the collection method to be customized by reflecting the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and have the generation AI adjust the collection method.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the fishing spot. For example, the analysis unit provides detailed analysis results for popular fishing spots. The analysis unit can also provide basic analysis results for new fishing spots. The analysis unit can also provide detailed analysis results including past data for fishing spots frequently visited by the user. This allows appropriate analysis results to be provided by adjusting the level of detail of the analysis based on the importance of the fishing spot. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input fishing spot importance data into the generation AI and have the generation AI adjust the level of detail of the analysis.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the fishing spot category. For example, in the case of sea fishing, the analysis unit applies an analysis algorithm that takes into account tide flow and water temperature. In addition, in the case of river fishing, the analysis unit can apply an analysis algorithm that takes into account water flow and water quality. In addition, in the case of lake fishing, the analysis unit can apply an analysis algorithm that takes into account the lake's depth and transparency. In this way, by applying different analysis algorithms depending on the fishing spot category, more accurate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input fishing spot category data into the generation AI and have the generation AI apply different analysis algorithms.

[0041] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on analysis results that the user has previously rated highly. The analysis unit can also avoid analysis results that the user has previously rated poorly. The analysis unit can also propose a new analysis method based on the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0042] During analysis, the analysis unit can determine the analysis priority based on the time of submission of the fishing catch. For example, the analysis unit prioritizes analysis of the most recent fishing catch. The analysis unit can also prioritize analysis of fishing catches within a period specified by the user. The analysis unit can also prioritize analysis of fishing catches during a specific event period. In this way, by determining the analysis priority based on the time of submission of the fishing catch, the most recent information can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input data on the time of submission of the fishing catch into the generation AI and have the generation AI determine the analysis priority.

[0043] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the fishing results. For example, the analysis unit prioritizes analysis of fishing results related to fish species in which the user is interested. The analysis unit can also prioritize analysis of fishing results at fishing spots frequently visited by the user. The analysis unit can also prioritize analysis of fishing results related to fishing techniques in which the user is interested. In this way, by adjusting the order of analysis based on the relevance of the fishing results, highly relevant information can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input relevance data of the fishing results into the generation AI and have the generation AI adjust the order of analysis.

[0044] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, the analysis unit can provide analysis results in simple language to a novice user. The analysis unit can also provide analysis results using appropriate technical terminology to an intermediate user. The analysis unit can also provide analysis results using detailed technical terminology to an advanced user. In this way, by adjusting the use of technical terminology in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI adjust the use of technical terminology.

[0045] The providing unit can adjust the level of detail of the information to be provided based on the importance of the fishing spot when providing the information. For example, the providing unit provides detailed information for popular fishing spots. The providing unit can also provide basic information for new fishing spots. The providing unit can also provide detailed information, including past data, for fishing spots frequently visited by the user. This allows appropriate information to be provided by adjusting the level of detail of the information based on the importance of the fishing spot. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input fishing spot importance data into the generating AI and have the generating AI adjust the level of detail of the information.

[0046] The providing unit can apply different providing algorithms depending on the fishing spot category when providing information. For example, in the case of sea fishing, the providing unit provides information that takes into account the tide and water temperature. In addition, in the case of river fishing, the providing unit can also provide information that takes into account the water current and water quality. In addition, in the case of lake fishing, the providing unit can also provide information that takes into account the lake's depth and transparency. In this way, by applying different providing algorithms depending on the fishing spot category, more accurate information can be provided. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input fishing spot category data into the generating AI and cause the generating AI to apply different providing algorithms.

[0047] When providing information, the providing unit can improve the accuracy of the information provided by referring to the user's past providing results. For example, the providing unit prioritizes providing methods that the user has previously rated highly. The providing unit can also avoid providing methods that the user has previously rated poorly. The providing unit can also suggest new providing methods based on the user's past providing results. This makes it possible to improve the accuracy of the information provided by referring to the user's past providing results. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's past providing result data into the generation AI and cause the generation AI to improve the accuracy of the information provided.

[0048] The providing unit can determine the priority of the information to be provided based on the time of submission of the fishing catch when the information is provided. The providing unit, for example, provides information based on the most recent fishing catch. The providing unit can also provide information based on fishing catch within a period specified by the user. The providing unit can also provide information based on fishing catch during a specific event period. In this way, by determining the priority of information based on the time of submission of the fishing catch, the most recent information can be provided preferentially. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input fishing catch submission time data into the generating AI and have the generating AI determine the priority of the information.

[0049] The providing unit can adjust the order of information to be provided based on the relevance of fishing results when providing the information. For example, the providing unit can prioritize providing information about fish species in which the user is interested. The providing unit can also prioritize providing information about fishing spots that the user frequently visits. The providing unit can also prioritize providing information about fishing techniques in which the user is interested. In this way, by adjusting the order of information based on the relevance of fishing results, highly relevant information can be provided preferentially. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input fishing result relevance data into a generating AI and cause the generating AI to adjust the order of the information.

[0050] The providing unit can adjust the use of technical terminology in the information to be provided according to the user's level of expertise when providing the information. For example, the providing unit can provide information in simple language to a novice user. The providing unit can also provide information using appropriate technical terminology to an intermediate user. The providing unit can also provide information using detailed technical terminology to an advanced user. This allows for the provision of information that is easier to understand by adjusting the use of technical terminology in the information according to the user's level of expertise. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input the user's level of expertise data into the generating AI and cause the generating AI to adjust the use of technical terminology.

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

[0052] The collection unit collects information on how the user uses fishing gear, and the analysis unit can suggest optimal ways to use the fishing gear based on that information. For example, the collection unit collects information such as the type of fishing rod and reel used by the user, the thickness of the fishing line, and the type of lure. The collection unit can also collect the success and failure rates of the user's fishing gear. Based on the collected information, the analysis unit can suggest optimal combinations of fishing gear for specific fishing spots and fish species. This allows the user to optimize the way they use their fishing gear and improve their catches.

[0053] The analysis unit can estimate the user's fishing experience level and adjust the level of detail of the analysis results based on the estimated experience level. For example, the analysis unit can provide basic information to beginner users and more detailed information to intermediate users. It can also provide specialized analysis results to advanced users. This allows the user to deepen their understanding by providing appropriate analysis results according to the user's experience level.

[0054] The providing unit can customize the information to be provided based on the user's fishing goal. For example, if the user is aiming for a big fish, the providing unit can provide information on fishing spots where there is a high possibility of catching a big fish. If the user wants to enjoy catching a lot of fish, the providing unit can provide information on fishing spots where many fish can be caught. If the user is aiming for a specific fish species, the providing unit can provide information on fishing spots where that fish species is often caught. In this way, the user's fishing experience can be improved by providing information according to the user's fishing goal.

[0055] The collection unit can adjust the method of collecting information based on the user's fishing frequency. For example, for a user who frequently goes fishing, information can be collected in real time, and fishing results can be reflected immediately. For a user who only occasionally goes fishing, past fishing results can be collected all at once. The collection unit can also adjust the timing of information collection based on the user's fishing frequency. This allows for efficient collection of fishing results by collecting appropriate information according to the user's fishing frequency.

[0056] The analysis unit can analyze environmental data of the fishing spot and provide fishing advice that corresponds to environmental changes at the fishing spot. For example, the analysis unit can analyze environmental data such as water temperature, water quality, and tidal currents to provide fishing advice that corresponds to environmental changes at the fishing spot. The analysis unit can also analyze weather data to provide fishing advice that corresponds to weather changes. The analysis unit can also analyze ecosystem data of the fishing spot and provide fishing advice based on fish behavior patterns. This can improve the user's catch by providing appropriate fishing advice that corresponds to environmental changes at the fishing spot.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The collection unit collects fishing results. Fishing results include the type and size of the fish caught, the fishing location, and the time. The collection unit collects information that users upload to an app or social media about their fishing results. Information can also be collected by manual input, automatic collection using sensors, or data acquisition from an app. Step 2: The analysis unit uses the generation AI to analyze the information collected by the collection unit and identify specific fishing spots. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. For example, multiple pieces of information, including social media, can be analyzed to identify specific fishing spots. Step 3: The providing unit provides the user with the specific fishing spots identified by the analysis unit. The information is provided by means of an app notification, email, dashboard display, or other methods. For example, information about the identified hot fishing spots can be provided to the user by app notification or email.

[0059] (Example 2) A fishing information system according to an embodiment of the present invention collects fishing results, analyzes them using a generation AI, and identifies and provides great fishing spots. The fishing information system collects fishing results, analyzes them using a generation AI, identifies great fishing spots, and provides them to users. For example, the fishing information system collects information uploaded by users to apps or social media. Next, the fishing information system collects various information, including from social media, and the generation AI analyzes that information. The generation AI analyzes fishing spot information and catch data to identify great fishing spots. For example, if a large number of fish are caught at a particular fishing spot, the generation AI identifies great fishing spots based on that information. Finally, the fishing information system informs users of the identified great fishing spots. Users can receive information about great fishing spots through apps or social media. This allows fishing enthusiasts to enjoy fishing efficiently, creating a wonderful system where supply and demand are balanced. This allows fishing enthusiasts to share their fishing results and exchange information with other users. Furthermore, fishing can be enjoyed efficiently based on the information analyzed by the generation AI, further expanding the enjoyment of fishing.

[0060] The fishing information providing system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects fishing results. Fishing results include, but are not limited to, the type and size of the fish caught, the fishing location, and the time of fishing. For example, the collection unit collects information uploaded by users to an app or social networking site (SNS). The collection unit can also collect information by manual input, automatic collection using a sensor, or data acquisition from an app. For example, the collection unit collects information entered by users into an app. The collection unit can also automatically collect fishing results using a sensor. The collection unit can also acquire data from an app. The analysis unit uses a generation AI to analyze the information collected by the collection unit and identify specific fishing spots. Analysis can be performed using, for example, data mining, statistical analysis, machine learning algorithms, or other methods, but is not limited to these examples. For example, the analysis unit uses a generation AI to analyze fishing spot information and catch data to identify great fishing spots. The analysis unit can also analyze multiple pieces of information, including social networking sites, to identify specific fishing spots. The analysis unit can also use the generation AI to identify specific fishing spots based on fishing spot information and catch data. The provision unit provides the specific fishing spots identified by the analysis unit to the user. This provision can be performed, for example, by app notification, email, dashboard display, or other methods, but is not limited to these examples. For example, the provision unit can provide the user with information about the identified great fishing spots via app notification. The provision unit can also provide the user with information about the identified great fishing spots via email. The provision unit can also provide the user with information about the identified great fishing spots via dashboard display. This allows the fishing information provision system according to the embodiment to efficiently collect, analyze, and provide fishing results.

[0061] The collection unit can collect information that users upload about their fishing catches to an app or social media. For example, the collection unit collects information that users upload about their fishing catches to an app. The collection unit can also collect information that users upload about their fishing catches to social media. For example, the collection unit collects information from a specific fishing app. The collection unit can also collect information from social media such as Facebook and Instagram. This allows for efficient collection of fishing catches uploaded by users. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input information obtained from an app or social media into a generation AI and cause the generation AI to collect information.

[0062] The analysis unit can analyze multiple pieces of information, including social media, and identify specific fishing spots based on fishing spot information and catch data. The analysis unit, for example, analyzes social media posts and identifies specific fishing spots based on fishing spot information and catch data. The analysis unit can also analyze weather data and identify specific fishing spots. For example, the analysis unit can identify specific fishing spots based on weather data. The analysis unit can also analyze water temperature data and identify specific fishing spots. For example, the analysis unit can identify specific fishing spots based on water temperature data. In this way, by analyzing various information, including social media, it is possible to identify hot spots. Some or all of the above-mentioned processing by the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input social media posts, weather data, and water temperature data into the generation AI and have the generation AI identify specific fishing spots.

[0063] The providing unit can provide the user with information about the identified smash-fishing spots. For example, the providing unit can provide the user with information about the identified smash-fishing spots via an app notification. The providing unit can also provide the user with information about the identified smash-fishing spots via email. For example, the providing unit can provide the user with information about the identified smash-fishing spots via a dashboard display. The providing unit can also provide the user with information about the identified smash-fishing spots via social media. By providing the user with information about the identified smash-fishing spots, the user can enjoy fishing efficiently. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input information about the identified smash-fishing spots into a generation AI and have the generation AI provide the information.

[0064] The collection unit can estimate the user's emotions and adjust the timing of collecting fishing catches based on the estimated user emotions. For example, if the user is excited, the collection unit can immediately collect the fishing catches and prompt them to upload them in real time. Furthermore, if the user is relaxed, the collection unit can collect the fishing catches all at once after the fishing is finished. Furthermore, if the user is tired, the collection unit can postpone collecting the fishing catches until the next day so the user can rest. This allows the fishing catches to be collected at a more appropriate time by adjusting the timing of collecting fishing catches according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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. Some or all of the above-described processing in the collection unit may be performed using AI, or may be performed without AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0065] The collection unit can analyze the user's past fishing results and select an appropriate collection method. For example, if the user has uploaded many photos in the past, the collection unit can prioritize photo collection. Furthermore, if the user has previously reported their fishing results in text, the collection unit can also prompt the user to enter text. Furthermore, if the user has previously uploaded videos, the collection unit can also prioritize video collection. This allows the optimal collection method to be selected by analyzing the user's past fishing results. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past fishing results data into the generation AI and have the generation AI select the optimal collection method.

[0066] When collecting fishing results, the collection unit can filter the results based on the user's current fishing spot or area of ​​interest. For example, the collection unit prioritizes collecting information about the fishing spot where the user is currently located. The collection unit can also prioritize collecting information about fish species in which the user is interested. The collection unit can also prioritize collecting information about fishing techniques in which the user is interested. This allows for filtering based on the user's current fishing spot or area of ​​interest, thereby collecting highly relevant fishing results. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input data about the user's current fishing spot or area of ​​interest into the generation AI and have the generation AI perform the filtering.

[0067] When collecting fishing results, the collection unit can select an appropriate collection means depending on the user's input method. For example, if the user prefers voice input, the collection unit can enable the user to report fishing results by voice. Furthermore, if the user prefers text input, the collection unit can enable the user to report fishing results by text. Furthermore, if the user prefers image input, the collection unit can enable the user to report fishing results by photo. This allows fishing results to be collected efficiently by selecting the optimal collection means depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input method data into the generation AI and have the generation AI select the optimal collection means.

[0068] The collection unit can estimate the user's emotions and determine the priority of fishing results to be collected based on the estimated user emotions. For example, when the user is excited, the collection unit prioritizes collecting the most recent fishing results. Furthermore, when the user is relaxed, the collection unit can also collect past fishing results. Furthermore, when the user is tired, the collection unit can prioritize collecting only important fishing results. Thus, by determining the priority of fishing results according to the user's emotions, important fishing results can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, 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. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0069] When collecting fishing results, the collection unit can prioritize collecting highly relevant results based on the user's geographical location information. For example, the collection unit prioritizes collecting fishing results near the fishing spot where the user is currently located. The collection unit can also prioritize collecting information about fishing spots that the user has visited in the past. The collection unit can also prioritize collecting information about fishing spots that the user is planning to visit. This allows highly relevant fishing results to be collected preferentially by taking the user's geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information data into the generation AI and cause the generation AI to collect highly relevant results.

[0070] When collecting fishing results, the collection unit can analyze the user's social media usage and collect related results. For example, the collection unit collects fishing results shared by the user on social media. The collection unit can also collect fishing results shared by the user's friends. The collection unit can also collect fishing results that the user has "liked." This makes it possible to collect related fishing results by analyzing the user's social media activity. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data into the generation AI and cause the generation AI to collect related results.

[0071] When collecting fishing results, the collection unit can adjust the collection method by reflecting the user's past feedback. For example, the collection unit prioritizes collection methods that the user has previously rated highly. The collection unit can also avoid collection methods that the user has previously rated poorly. The collection unit can also suggest new collection methods based on the user's feedback. This allows the collection method to be customized by reflecting the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and have the generation AI adjust the collection method.

[0072] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is excited, the analysis unit can generate visually stimulating analysis results. Furthermore, if the user is relaxed, the analysis unit can display the analysis results in a calm tone. Furthermore, if the user is tired, the analysis unit can provide concise and to-the-point analysis results. By adjusting the presentation method of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is realized using an emotion estimation function, for example, 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. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0073] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the fishing spot. For example, the analysis unit provides detailed analysis results for popular fishing spots. The analysis unit can also provide basic analysis results for new fishing spots. The analysis unit can also provide detailed analysis results including past data for fishing spots frequently visited by the user. This allows appropriate analysis results to be provided by adjusting the level of detail of the analysis based on the importance of the fishing spot. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input fishing spot importance data into the generation AI and have the generation AI adjust the level of detail of the analysis.

[0074] During analysis, the analysis unit can apply different analysis algorithms depending on the fishing spot category. For example, in the case of sea fishing, the analysis unit applies an analysis algorithm that takes into account tide flow and water temperature. In addition, in the case of river fishing, the analysis unit can apply an analysis algorithm that takes into account water flow and water quality. In addition, in the case of lake fishing, the analysis unit can apply an analysis algorithm that takes into account the lake's depth and transparency. In this way, by applying different analysis algorithms depending on the fishing spot category, more accurate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input fishing spot category data into the generation AI and have the generation AI apply different analysis algorithms.

[0075] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on analysis results that the user has previously rated highly. The analysis unit can also avoid analysis results that the user has previously rated poorly. The analysis unit can also propose a new analysis method based on the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0076] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is excited, the analysis unit can also provide a visually stimulating analysis result. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0077] During analysis, the analysis unit can determine the analysis priority based on the time of submission of the fishing catch. For example, the analysis unit prioritizes analysis of the most recent fishing catch. The analysis unit can also prioritize analysis of fishing catches within a period specified by the user. The analysis unit can also prioritize analysis of fishing catches during a specific event period. In this way, by determining the analysis priority based on the time of submission of the fishing catch, the most recent information can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input data on the time of submission of the fishing catch into the generation AI and have the generation AI determine the analysis priority.

[0078] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the fishing results. For example, the analysis unit prioritizes analysis of fishing results related to fish species in which the user is interested. The analysis unit can also prioritize analysis of fishing results at fishing spots frequently visited by the user. The analysis unit can also prioritize analysis of fishing results related to fishing techniques in which the user is interested. In this way, by adjusting the order of analysis based on the relevance of the fishing results, highly relevant information can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input relevance data of the fishing results into the generation AI and have the generation AI adjust the order of analysis.

[0079] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, the analysis unit can provide analysis results in simple language to a novice user. The analysis unit can also provide analysis results using appropriate technical terminology to an intermediate user. The analysis unit can also provide analysis results using detailed technical terminology to an advanced user. In this way, by adjusting the use of technical terminology in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI adjust the use of technical terminology.

[0080] The providing unit can estimate the user's emotions and adjust the way the information is presented based on the estimated user's emotions. For example, if the user is excited, the providing unit can provide visually stimulating information. Furthermore, if the user is relaxed, the providing unit can provide information in a calm tone. Furthermore, if the user is tired, the providing unit can provide concise, to-the-point information. This allows for more appropriate information to be provided by adjusting the way the information is presented according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, 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. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI, or without an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0081] The providing unit can adjust the level of detail of the information to be provided based on the importance of the fishing spot when providing the information. For example, the providing unit provides detailed information for popular fishing spots. The providing unit can also provide basic information for new fishing spots. The providing unit can also provide detailed information, including past data, for fishing spots frequently visited by the user. This allows appropriate information to be provided by adjusting the level of detail of the information based on the importance of the fishing spot. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input fishing spot importance data into the generating AI and have the generating AI adjust the level of detail of the information.

[0082] The providing unit can apply different providing algorithms depending on the fishing spot category when providing information. For example, in the case of sea fishing, the providing unit provides information that takes into account the tide and water temperature. In addition, in the case of river fishing, the providing unit can also provide information that takes into account the water current and water quality. In addition, in the case of lake fishing, the providing unit can also provide information that takes into account the lake's depth and transparency. In this way, by applying different providing algorithms depending on the fishing spot category, more accurate information can be provided. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input fishing spot category data into the generating AI and cause the generating AI to apply different providing algorithms.

[0083] When providing information, the providing unit can improve the accuracy of the information provided by referring to the user's past providing results. For example, the providing unit prioritizes providing methods that the user has previously rated highly. The providing unit can also avoid providing methods that the user has previously rated poorly. The providing unit can also suggest new providing methods based on the user's past providing results. This makes it possible to improve the accuracy of the information provided by referring to the user's past providing results. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's past providing result data into the generation AI and cause the generation AI to improve the accuracy of the information provided.

[0084] The providing unit can estimate the user's emotions and adjust the length of the information to be provided based on the estimated user's emotions. For example, if the user is in a hurry, the providing unit can provide short, to-the-point information. The providing unit can also provide detailed information if the user is relaxed. The providing unit can also provide visually stimulating information if the user is excited. By adjusting the length of the information according to the user's emotions, more appropriate information can be provided. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a 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. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0085] The providing unit can determine the priority of the information to be provided based on the time of submission of the fishing catch when the information is provided. The providing unit, for example, provides information based on the most recent fishing catch. The providing unit can also provide information based on fishing catch within a period specified by the user. The providing unit can also provide information based on fishing catch during a specific event period. In this way, by determining the priority of information based on the time of submission of the fishing catch, the most recent information can be provided preferentially. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input fishing catch submission time data into the generating AI and have the generating AI determine the priority of the information.

[0086] The providing unit can adjust the order of information to be provided based on the relevance of fishing results when providing the information. For example, the providing unit can prioritize providing information about fish species in which the user is interested. The providing unit can also prioritize providing information about fishing spots that the user frequently visits. The providing unit can also prioritize providing information about fishing techniques in which the user is interested. In this way, by adjusting the order of information based on the relevance of fishing results, highly relevant information can be provided preferentially. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input fishing result relevance data into a generating AI and cause the generating AI to adjust the order of the information.

[0087] The providing unit can adjust the use of technical terminology in the information to be provided according to the user's level of expertise when providing the information. For example, the providing unit can provide information in simple language to a novice user. The providing unit can also provide information using appropriate technical terminology to an intermediate user. The providing unit can also provide information using detailed technical terminology to an advanced user. This allows for the provision of information that is easier to understand by adjusting the use of technical terminology in the information according to the user's level of expertise. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input the user's level of expertise data into the generating AI and cause the generating AI to adjust the use of technical terminology. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, and provision unit, described above, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can automatically collect fishing results using the camera 42 or sensors of the smart device 14. The collection unit can also collect information entered by the user into the app using the control unit 46A of the smart device 14. The analysis unit, implemented, for example, by the identification processing unit 290 of the data processing device 12, analyzes the collected information using a generation AI and identifies great fishing spots. The provision unit, implemented, for example, by the control unit 46A of the smart device 14, provides the user with information about the identified great fishing spots via app notifications or dashboard displays. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can automatically collect fishing results using the camera 42 or sensors of the smart glasses 214. The collection unit can also collect information entered by the user into the app using the control unit 46A of the smart glasses 214. The analysis unit, for example, is realized by the identification processing unit 290 of the data processing device 12 and analyzes the collected information using a generation AI to identify great fishing spots. The provision unit, for example, is realized by the control unit 46A of the smart glasses 214 and provides the user with information about the identified great fishing spots via app notifications or dashboard displays. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, and provision unit, described above, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit can automatically collect fishing results using the camera 42 or sensors of the headset terminal 314. The collection unit can also collect information that users enter into the app about their fishing results using the control unit 46A of the headset terminal 314. The analysis unit, implemented, for example, by the identification processing unit 290 of the data processing device 12, analyzes the collected information using a generation AI and identifies great fishing spots. The provision unit, implemented, for example, by the control unit 46A of the headset terminal 314, provides the user with information about the identified great fishing spots via app notifications or dashboard displays. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, and provision unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can automatically collect fishing results using the camera 42 and sensors of the robot 414. The collection unit can also collect information that users enter into an app about their fishing results using the control unit 46A of the robot 414. The analysis unit, realized, for example, by the identification processing unit 290 of the data processing device 12, analyzes the collected information using a generation AI and identifies great fishing spots. The provision unit, realized, for example, by the control unit 46A of the robot 414, provides the user with information about the identified great fishing spots via app notifications or dashboard displays.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The collection unit collects information on how the user uses fishing gear, and the analysis unit can suggest optimal ways to use the fishing gear based on that information. For example, the collection unit collects information such as the type of fishing rod and reel used by the user, the thickness of the fishing line, and the type of lure. The collection unit can also collect the success and failure rates of the user's fishing gear. Based on the collected information, the analysis unit can suggest optimal combinations of fishing gear for specific fishing spots and fish species. This allows the user to optimize the way they use their fishing gear and improve their catches.

[0090] The analysis unit can estimate the user's fishing experience level and adjust the level of detail of the analysis results based on the estimated experience level. For example, the analysis unit can provide basic information to beginner users and more detailed information to intermediate users. It can also provide specialized analysis results to advanced users. This allows the user to deepen their understanding by providing appropriate analysis results according to the user's experience level.

[0091] The providing unit can customize the information to be provided based on the user's fishing goal. For example, if the user is aiming for a big fish, the providing unit can provide information on fishing spots where there is a high possibility of catching a big fish. If the user wants to enjoy catching a lot of fish, the providing unit can provide information on fishing spots where many fish can be caught. If the user is aiming for a specific fish species, the providing unit can provide information on fishing spots where that fish species is often caught. In this way, the user's fishing experience can be improved by providing information according to the user's fishing goal.

[0092] The collection unit can adjust the method of collecting information based on the user's fishing frequency. For example, for a user who frequently goes fishing, information can be collected in real time, and fishing results can be reflected immediately. For a user who only occasionally goes fishing, past fishing results can be collected all at once. The collection unit can also adjust the timing of information collection based on the user's fishing frequency. This allows for efficient collection of fishing results by collecting appropriate information according to the user's fishing frequency.

[0093] The analysis unit can analyze environmental data of the fishing spot and provide fishing advice that corresponds to environmental changes at the fishing spot. For example, the analysis unit can analyze environmental data such as water temperature, water quality, and tidal currents to provide fishing advice that corresponds to environmental changes at the fishing spot. The analysis unit can also analyze weather data to provide fishing advice that corresponds to weather changes. The analysis unit can also analyze ecosystem data of the fishing spot and provide fishing advice based on fish behavior patterns. This can improve the user's catch by providing appropriate fishing advice that corresponds to environmental changes at the fishing spot.

[0094] The collection unit can estimate the user's emotions and adjust the fishing catch collection method based on the estimated user emotions. For example, if the user is excited, the collection unit can collect information in real time and immediately reflect the fishing catch. If the user is relaxed, the collection unit can collect the fishing catch all at once after fishing. If the user is tired, the collection unit can postpone collecting the fishing catch until the next day so that the user can rest. This can improve the user's fishing experience by providing an appropriate fishing catch collection method according to the user's emotions.

[0095] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. For example, if the user is excited, the analysis unit can display visually stimulating analysis results. If the user is relaxed, the analysis unit can display the analysis results in a calm tone. If the user is tired, the analysis unit can provide concise and to-the-point analysis results. This allows the user to deepen their understanding by providing an appropriate display method of the analysis results according to the user's emotions.

[0096] The providing unit can estimate the user's emotions and adjust the timing of providing information based on the estimated user's emotions. For example, if the user is excited, the providing unit can provide information immediately to maintain the user's excitement. If the user is relaxed, the providing unit can provide information after the fishing is over. If the user is tired, the providing unit can provide information the next day to allow the user to rest. This can improve the user's fishing experience by providing information at an appropriate timing according to the user's emotions.

[0097] The providing unit can estimate the user's emotions and adjust the format of the information to be provided based on the estimated user's emotions. For example, if the user is excited, the providing unit can provide information in a visually stimulating format. If the user is relaxed, the providing unit can provide information in a calm tone. If the user is tired, the providing unit can provide information in a concise and to-the-point format. This allows the user's understanding to be deepened by providing information in an appropriate format according to the user's emotions.

[0098] The providing unit can estimate the user's emotions and adjust the content of the information to be provided based on the estimated user's emotions. For example, if the user is excited, the providing unit can provide detailed information to maintain the user's excitement. If the user is relaxed, the providing unit can also provide basic information. If the user is tired, the providing unit can also provide concise, to-the-point information. This allows the user's understanding to be deepened by providing appropriate information content according to the user's emotions.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The collection unit collects fishing results. Fishing results include the type and size of the fish caught, the fishing location, and the time. The collection unit collects information that users upload to an app or social media about their fishing results. Information can also be collected by manual input, automatic collection using sensors, or data acquisition from an app. Step 2: The analysis unit uses the generation AI to analyze the information collected by the collection unit and identify specific fishing spots. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. For example, multiple pieces of information, including social media, can be analyzed to identify specific fishing spots. Step 3: The providing unit provides the user with the specific fishing spots identified by the analysis unit. The information is provided by means of an app notification, email, dashboard display, or other methods. For example, information about the identified hot fishing spots can be provided to the user by app notification or email.

[0101] 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.

[0102] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0103] 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.

[0104] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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.

[0124] 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.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] 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).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] 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.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0131] 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.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 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.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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.

[0140] 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.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] 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).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0156] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0157] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

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

[0159] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

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

[0162] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0163] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0164] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0165] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0166] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0167] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

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

[0170] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0171] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0172] [Explanation of symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection department that collects fishing results; an analysis unit that analyzes the information collected by the collection unit and identifies a specific spot in the fishing spot; a providing unit that provides a user with the specific spot of the fishing spot identified by the analysis unit. A system characterized by:

2. The collecting unit Collect information when users upload their fishing results to the app or social media 2. The system of claim 1.

3. The analysis unit Analyze multiple sources of information, including social media, and identify specific fishing spots based on fishing spot information and catch data.

2. The system of claim 1.

4. The providing unit Providing users with information about identified hot fishing spots 2. The system of claim 1.

5. The collecting unit The user's emotions are estimated, and the timing of collecting fishing results is adjusted based on the estimated user's emotions.

2. The system of claim 1.

6. The collecting unit Analyze the user's past fishing results and select the appropriate collection method 2. The system of claim 1.

7. The collecting unit As fishing results are collected, they can be filtered based on the user's current fishing location or area of ​​interest.

2. The system of claim 1.

8. The collecting unit When collecting fishing results, select the appropriate collection method according to the user's input method.

2. The system of claim 1.

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