System

Abstract

An object of a system according to an embodiment is to recommend a hobby suitable for a user.SOLUTION: A system includes a collection unit, an analysis unit, and a provision unit. The collection unit collects information on a personality characteristic, a life rhythm, an annual income, and a residence of a user. The analysis unit analyzes the information collected by the collection unit and recommends a hobby suitable for the user. The provision unit provides the detailed information of the hobby recommended by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that it is difficult for users to find hobbies that suit them, and many people have no hobbies.

[0005] The system according to the embodiment aims to recommend hobbies suitable for a user. [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 information on a user's personality traits, lifestyle, annual income, and place of residence. The analysis unit analyzes the information collected by the collection unit and recommends hobbies suitable for the user. The provision unit provides detailed information on the hobbies recommended by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can recommend hobbies suitable for the user. [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 hobby recommendation system according to an embodiment of the present invention is a system that recommends optimal hobbies to users by having them answer questions about their personality traits, lifestyle, annual income, place of residence, etc. In this system, users access an app and answer questions about their personality traits, lifestyle, annual income, place of residence, etc. The hobby recommendation system then analyzes the user's answers and recommends optimal hobbies to the user. This recommendation is made by AI based on the user's answers. For example, a user who enjoys the outdoors and has free time on weekends may be recommended hiking or camping, while an indoor user who prefers free time on weekdays may be recommended reading or watching movies. By using this app, even people with no hobbies can find new hobbies, improving their quality of life and contributing to economic revitalization. For example, a hobby recommendation system allows users to access an app and answer questions about their personality traits, lifestyle, annual income, place of residence, etc. The user must provide detailed answers about their personality and lifestyle. These questions include whether the user is extroverted or introverted, whether they have free time on weekends, their annual income, and whether they live in a city or the countryside. The hobby recommendation system then analyzes the user's responses. Based on the user's responses, the AI ​​recommends the most suitable hobby for the user. For example, an extroverted user with free time on weekends might be recommended outdoor activities such as hiking or camping. On the other hand, an introverted user with free time during the week might be recommended indoor activities such as reading or watching movies. Furthermore, the hobby recommendation system provides the user with detailed information about the recommended hobby. For example, if hiking is recommended, the system provides information such as nearby hiking trails, necessary equipment, and beginner's guides. This allows users to learn specific steps to start a new hobby. This allows the hobby recommendation system to help even those without hobbies find a new one. Having a hobby can improve quality of life, reduce stress, and build new relationships. Furthermore, increased hobby-related consumption can also lead to economic revitalization.For example, buying the equipment needed to start hiking or going to the cinema to watch a movie will stimulate related industries. This allows hobby recommendation systems to improve the quality of users' lives. For example, even people with no hobbies can find a new hobby, which can improve their quality of life, reduce stress, and help build new relationships. In addition, an increase in hobby-related consumption activity can also stimulate the economy. For example, buying the equipment needed to start hiking or going to the cinema to watch a movie will stimulate related industries.

[0029] A hobby recommendation system according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects information on a user's personality traits, daily routine, annual income, and place of residence. For example, the collection unit collects information by having the user access an app and answer questions about the user's personality traits, daily routine, annual income, place of residence, and the like. The collection unit may also include questions about the user's personality and lifestyle. For example, questions may include whether the user is extroverted or introverted, whether they have free time on weekends, how much their annual income is, and whether they live in an urban or rural area. The collection unit may also encrypt and securely protect the user's personal information. For example, the collection unit may protect the user's personal information using encryption technology such as AES or RSA. The analysis unit analyzes the information collected by the collection unit and recommends hobbies suitable for the user. The analysis unit may analyze the user's responses using, for example, a machine learning algorithm. For example, the analysis unit may perform the analysis using a machine learning algorithm such as a neural network or a support vector machine. The analysis unit can also estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results. The provision unit provides detailed information about the hobby recommended by the analysis unit. For example, the provision unit provides information on how to start the recommended hobby, necessary tools, related events, and the like. The provision unit can also provide information on consumption activities related to the hobby. For example, the provision unit provides information on available products and services, related store information, and the like. The provision unit can also estimate the user's emotions and adjust the way the information is presented based on the estimated user emotions. For example, the provision unit provides detailed information when the user is relaxed. This allows the hobby recommendation system according to the embodiment to improve the quality of life of users. For example, even people with no hobbies can find a new hobby, which can improve their quality of life, reduce stress, and help build new relationships. In addition, an increase in hobby-related consumption activity can also lead to economic revitalization.For example, buying the equipment needed to start hiking or going to the cinema to watch a movie stimulates related industries.

[0030] The collection unit may include questions about the user's personality or lifestyle. The collection unit may include, for example, questions about the user's personality or lifestyle. For example, the collection unit may include questions such as whether the user is extroverted or introverted, whether they have free time on weekends, their annual income, and whether they live in an urban or rural area. The collection unit may also collect detailed information about the user's personality and lifestyle. For example, the collection unit may evaluate the user's personality using a personality diagnostic test or a psychological assessment. Furthermore, the collection unit may also collect information such as daily habits and consumption behavior. By collecting detailed information about the user's personality and lifestyle, the collection unit may be able to recommend hobbies with greater accuracy. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit may input the results of the user's personality diagnostic test into the generation AI and cause the generation AI to perform a personality evaluation.

[0031] The analysis unit can analyze the user's answers using a machine learning algorithm and recommend suitable hobbies. The analysis unit, for example, analyzes the user's answers using a machine learning algorithm. For example, the analysis unit performs analysis using a machine learning algorithm such as a neural network or a support vector machine. The analysis unit can also recommend optimal hobbies based on the user's answers. For example, the analysis unit analyzes the user's answers using data mining or statistical analysis and recommends optimal hobbies. Furthermore, the analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, the analysis unit provides detailed analysis results when the user is relaxed. As a result, the analysis unit can use a machine learning algorithm to recommend optimal hobbies for the user with high accuracy. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's answer data into a generation AI and cause the generation AI to recommend hobbies.

[0032] The providing unit can provide detailed information about the recommended hobby. For example, the providing unit provides detailed information about the recommended hobby. For example, the providing unit provides information such as how to start the hobby, necessary tools, and related events. The providing unit can also provide information about consumption activities related to the hobby. For example, the providing unit provides information about purchasable products and services, related store information, and the like. Furthermore, 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, the providing unit provides detailed information when the user is relaxed. This allows the providing unit to know specific steps for the user to start a new hobby. Some or all of the above-described processing in the providing unit may be performed using, or without, AI. For example, the providing unit can input detailed information about the hobby into a generation AI and cause the generation AI to provide the information.

[0033] The providing unit can provide information on consumption activities related to hobbies. The providing unit, for example, provides information on consumption activities related to hobbies. For example, the providing unit provides information on purchasable products and services, related store information, and the like. Furthermore, by providing information on consumption activities related to hobbies, the providing unit can provide specific assistance to help the user start a hobby. For example, by providing information on consumption activities related to hobbies, the providing unit provides information for the user to purchase necessary tools and services. In this way, by providing information on consumption activities related to hobbies, the providing unit can provide specific assistance to help the user start a hobby. 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 information on consumption activities to a generation AI and cause the generation AI to provide the information.

[0034] The collection unit can encrypt and protect the user's personal information. For example, the collection unit encrypts and securely protects the user's personal information. For example, the collection unit protects the user's personal information using encryption technology such as AES or RSA. The collection unit can also ensure privacy by encrypting the user's personal information. For example, the collection unit encrypts and securely protects the user's personal information to prevent unauthorized access by third parties. In this way, the collection unit can ensure privacy by securely protecting the user's personal information. Some or all of the above-mentioned 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 personal information into a generation AI and have the generation AI perform encryption.

[0035] The collection unit can analyze the user's past answer history and select an appropriate question format. The collection unit, for example, analyzes the user's past answer history and selects the optimal question format. For example, if the user has previously preferred multiple-choice questions, the collection unit can preferentially present multiple-choice questions. Furthermore, if the user has previously preferred free-form questions, the collection unit can also preferentially present free-form questions. Furthermore, the collection unit can analyze the accuracy of answers to specific question formats from the user's past answer history and select the optimal format. This allows the collection unit to select the optimal question format based on the user's past answer history, making it easier for the user to answer. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the user's past answer data into a generation AI and cause the generation AI to select a question format.

[0036] The collection unit may filter questions based on the user's current lifestyle and areas of interest when collecting questions. For example, when collecting questions, the collection unit may filter based on the user's current lifestyle and areas of interest. For example, if the user is currently busy, the collection unit may prioritize questions that can be answered in a short time. Furthermore, if the user has a specific area of ​​interest, the collection unit may prioritize questions related to that area. Furthermore, the collection unit may select appropriate questions based on the user's lifestyle and eliminate unnecessary questions. Thus, the collection unit can eliminate unnecessary questions by filtering questions based on the user's lifestyle and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit may input the user's lifestyle data into a generation AI and cause the generation AI to filter questions.

[0037] The collection unit can select an appropriate collection means according to the user's input method when collecting questions. For example, when collecting questions, the collection unit selects the optimal collection means according to the user's input method (voice, text, image, etc.). For example, if the user prefers voice input, the collection unit can provide a question format that supports voice input. Furthermore, if the user prefers text input, the collection unit can also provide a question format that supports text input. Furthermore, if the user prefers image input, the collection unit can also provide a question format that uses images. In this way, the collection unit can select the optimal collection means according to the user's input method, making it easier for the user to answer. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input the user's input data to a generation AI and cause the generation AI to select a collection means.

[0038] When collecting questions, the collection unit can prioritize collecting highly relevant questions by taking into account the user's geographical location information. For example, when collecting questions, the collection unit prioritizes collecting highly relevant questions by taking into account the user's geographical location information. For example, if the user lives in an urban area, the collection unit can prioritize collecting questions related to hobbies that can be enjoyed in the city. Also, if the user lives in a rural area, the collection unit can prioritize collecting questions related to hobbies that allow the user to enjoy nature. Also, the collection unit can prioritize collecting questions related to hobbies that are unique to the region based on the user's geographical location information. In this way, the collection unit can obtain useful information for the user by collecting highly relevant questions based on the user's geographical location information. Some or all of the above-described processing by 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 geographical location data into the generation AI and cause the generation AI to collect questions.

[0039] The collection unit can analyze the user's social media activity and collect related questions when collecting questions. For example, the collection unit analyzes the user's social media activity and collects related questions when collecting questions. For example, the collection unit collects related questions based on content frequently posted by the user on social media. The collection unit can also collect related questions by referring to the activities of the user's friends on social media. The collection unit can also analyze the user's interests on social media and collect related questions. In this way, the collection unit can collect questions based on the user's interests by analyzing the user's social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to collect questions.

[0040] The collection unit can customize the collection method by reflecting the user's past feedback when collecting questions. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting questions. For example, the collection unit customizes the content and format of questions based on feedback provided by the user in the past. The collection unit can also prioritize and provide preferred question formats based on the user's past feedback. The collection unit can also optimize the order and content of questions by referring to the user's past feedback. In this way, the collection unit can provide the optimal question format for the user by reflecting the user's past feedback. Some or all of the above-described processing by 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 feedback data into a generation AI and cause the generation AI to customize the collection method.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the user's answer during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the user's answer during analysis. For example, the analysis unit performs a detailed analysis when the user's answer includes important information. The analysis unit can also perform a concise analysis when the user's answer includes general information. The analysis unit can also adjust the depth of the analysis depending on the importance of the user's answer. As a result, the analysis unit can analyze information important to the user in detail by adjusting the level of detail of the analysis based on the importance of the user's answer. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's answer data to a generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the user category during analysis. For example, the analysis unit can apply different analysis algorithms depending on the user category during analysis. For example, if the user is an outdoorsy person, the analysis unit can apply an analysis algorithm specialized for outdoor activities. Also, if the user is an indoor person, the analysis unit can apply an analysis algorithm specialized for indoor activities. The analysis unit can also select the optimal analysis algorithm depending on the user category. In this way, the analysis unit can provide more accurate analysis results by applying the optimal analysis algorithm depending on the user category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user category data into the generation AI and have the generation AI select the analysis algorithm.

[0043] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can adjust the analysis algorithm based on the user's past analysis results. The analysis unit can also extract patterns for performing highly accurate analysis from the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. In this way, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's past analysis data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0044] The analysis unit can determine the analysis priority based on the time of the user's response during analysis. The analysis unit, for example, determines the analysis priority based on the time of the user's response during analysis. For example, the analysis unit prioritizes analysis of questions that the user has recently answered. The analysis unit can also prioritize analysis of questions that the user has answered during a specific time period. The analysis unit can also determine the analysis priority based on the time of the user's response. In this way, the analysis unit can prioritize analysis of the latest information by determining the analysis priority based on the time of the user's response. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input user response time data into the generation AI and have the generation AI determine the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the user's relevance during analysis. The analysis unit, for example, adjusts the order of analysis based on the user's relevance during analysis. For example, the analysis unit analyzes questions in order of relevance to the user's answers. The analysis unit can also analyze questions in order of relevance to the user's answers later. The analysis unit can also adjust the order of analysis based on the user's relevance. In this way, the analysis unit can prioritize analysis of important information by adjusting the order of analysis based on the user's relevance. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input user relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0046] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can also provide analysis results in simple language. Furthermore, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. This allows the analysis unit to provide analysis results that are easy for the user to understand by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI use technical terms.

[0047] The providing unit can adjust the level of detail of the information to be provided based on the importance of the hobby at the time of providing. The providing unit, for example, adjusts the level of detail of the information to be provided based on the importance of the hobby at the time of providing. For example, the providing unit provides detailed information for a hobby that the user feels is important. The providing unit can also provide concise information for a hobby that the user does not feel is very important. The providing unit can also adjust the level of detail of the information to be provided according to the importance of the user's hobby. In this way, the providing unit can provide information that is important to the user in detail by adjusting the level of detail of the information based on the importance of the hobby. 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 importance data of the user's hobby to a generating AI and cause the generating AI to adjust the level of detail of the information.

[0048] The providing unit can apply different providing algorithms depending on the hobby category when providing the information. For example, the providing unit can apply different providing algorithms depending on the hobby category when providing the information. For example, the providing unit can apply a providing algorithm specialized for outdoor activities to an outdoor hobby. The providing unit can also apply a providing algorithm specialized for indoor activities to an indoor hobby. The providing unit can also select an optimal providing algorithm depending on the hobby category. In this way, the providing unit can provide useful information to the user by applying an optimal providing algorithm depending on the hobby category. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's hobby category data into the generation AI and cause the generation AI to select a providing algorithm.

[0049] The providing unit can improve the accuracy of the data provided by referring to the user's past provision results when providing the data. For example, the providing unit can improve the accuracy of the data provided by referring to the user's past provision results when providing the data. For example, the providing unit can adjust the providing algorithm based on the user's past provision results. The providing unit can also extract patterns for providing data with high accuracy from the user's past provision results. The providing unit can also improve the accuracy of the data provided by referring to the user's past provision results. In this way, the providing unit can improve the accuracy of the data provided by referring to the user's past provision results. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's past provision data into the generation AI and cause the generation AI to improve the accuracy of the data provided.

[0050] The providing unit can adjust the order of information to be provided based on the relevance of the hobby at the time of providing. The providing unit, for example, adjusts the order of information to be provided based on the relevance of the hobby at the time of providing. For example, the providing unit prioritizes providing information related to hobbies in which the user is interested. The providing unit can also postpone providing information related to hobbies in which the user is not very interested. The providing unit can also adjust the order of information to be provided based on the relevance of the hobby of the user. In this way, the providing unit can prioritize providing information that is useful to the user by adjusting the order of information based on the relevance of the hobby. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the relevance of the user's hobby to the generation AI and cause the generation AI to adjust the order of the information.

[0051] The providing unit can determine the priority of information to be provided based on the detailed hobby information at the time of providing. The providing unit, for example, determines the priority of information to be provided based on the detailed hobby information at the time of providing. For example, the providing unit preferentially provides information for hobbies for which the user requires detailed information. The providing unit can also provide concise information for hobbies for which the user does not require much detailed information. The providing unit can also determine the priority of information to be provided based on the detailed hobby information of the user. In this way, the providing unit can prioritize information that is important to the user by determining the priority of information based on the detailed hobby information. 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 detailed hobby information of the user to a generation AI and cause the generation AI to determine the priority of information.

[0052] The providing unit can adjust the use of technical terms in the information to be provided according to the user's level of expertise when providing the information. For example, the providing unit can adjust the use of technical terms in the information to be provided according to the user's level of expertise when providing the information. For example, if the user has technical knowledge, the providing unit can provide information that uses a lot of technical terms. Also, if the user does not have technical knowledge, the providing unit can provide information in simple language. The providing unit can also adjust the use of technical terms in the information to be provided according to the user's level of expertise. In this way, the providing unit can provide information that is easy for the user to understand by adjusting the use of technical terms in the information according to the user's level of expertise. Some or all of the above-mentioned 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 level of expertise data into the generation AI and cause the generation AI to use technical terms.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The analysis unit can also estimate the user's health condition based on the user's answers and recommend hobbies based on the estimated health condition. For example, if the analysis unit estimates that the user is not getting enough exercise, it can recommend hobbies that include light exercise such as walking or yoga. If the analysis unit estimates that the user is feeling stressed, it can recommend hobbies that help reduce stress, such as relaxation or meditation. Furthermore, if the user wants to eat a healthy diet, the analysis unit can recommend health-related hobbies such as cooking or gardening. In this way, the analysis unit can contribute to maintaining and improving the user's health by recommending appropriate hobbies based on the user's health condition.

[0055] The providing unit may also provide community information related to the user's hobbies. For example, the providing unit may provide information on online forums and social media groups related to hobbies in which the user is interested. The providing unit may also provide information on local clubs and circles that the user can join. Furthermore, the providing unit may provide event information that allows the user to make new friends through their hobbies. In this way, the providing unit can help the user build new relationships through their hobbies.

[0056] The collection unit can also analyze the user's past hobby history and reflect it in current hobby recommendations. For example, the collection unit collects data on hobbies that the user enjoyed in the past and uses it in current recommendations. The collection unit can also analyze reasons why the user tried but did not continue with a hobby in the past, so as to prevent similar problems from occurring. Furthermore, the collection unit can collect data on hobby-related events and activities that the user participated in in the past and reflect it in current recommendations. This allows the collection unit to provide more personalized hobby recommendations based on the user's past hobby history.

[0057] The analysis unit can also estimate the user's learning style based on the user's answers and recommend hobbies based on the estimated learning style. For example, if the analysis unit estimates that the user is a visual learner, it can recommend visual hobbies such as painting or photography. If the analysis unit estimates that the user is an auditory learner, it can recommend auditory hobbies such as listening to music or playing an instrument. Furthermore, if the analysis unit estimates that the user is an experiential learner, it can recommend practical hobbies such as cooking or DIY. In this way, the analysis unit can recommend appropriate hobbies based on the user's learning style, allowing the user to enjoy their hobbies more.

[0058] The provider may also provide educational resources related to the user's hobbies. For example, the provider may provide information on online courses or workshops related to the hobbies in which the user is interested. The provider may also provide a list of books or videos to help the user deepen their hobbies. Furthermore, the provider may provide tutorials or guides to help the user learn skills related to the hobbies. In this way, the provider can help the user acquire new knowledge and skills through their hobbies.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The collection unit collects information about the user's personality traits, lifestyle, annual income, and place of residence. For example, the user accesses the app and answers these questions to collect information. The collection unit may also include questions about the user's personality and lifestyle. Furthermore, the collection unit may encrypt and securely protect the user's personal information. For example, the collection unit protects the user's personal information using encryption technologies such as AES and RSA. Step 2: The analysis unit analyzes the information collected by the collection unit and recommends hobbies suitable for the user. For example, the analysis unit analyzes the user's responses using a machine learning algorithm. The analysis unit performs the analysis using a machine learning algorithm such as a neural network or a support vector machine. The analysis unit can also estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. Step 3: The providing unit provides detailed information about the hobby recommended by the analyzing unit. For example, the providing unit provides information about how to start the recommended hobby, the necessary tools, related events, etc. The providing unit can also provide information about consumption activities related to the hobby. Furthermore, the providing unit can estimate the user's emotions and adjust the way the information is presented based on the estimated user emotions.

[0061] (Example 2) A hobby recommendation system according to an embodiment of the present invention is a system that recommends optimal hobbies to users by having them answer questions about their personality traits, lifestyle, annual income, place of residence, etc. In this system, users access an app and answer questions about their personality traits, lifestyle, annual income, place of residence, etc. The hobby recommendation system then analyzes the user's answers and recommends optimal hobbies to the user. This recommendation is made by AI based on the user's answers. For example, a user who enjoys the outdoors and has free time on weekends may be recommended hiking or camping, while an indoor user who prefers free time on weekdays may be recommended reading or watching movies. By using this app, even people with no hobbies can find new hobbies, improving their quality of life and contributing to economic revitalization. For example, a hobby recommendation system allows users to access an app and answer questions about their personality traits, lifestyle, annual income, place of residence, etc. The user must provide detailed answers about their personality and lifestyle. These questions include whether the user is extroverted or introverted, whether they have free time on weekends, their annual income, and whether they live in a city or the countryside. The hobby recommendation system then analyzes the user's responses. Based on the user's responses, the AI ​​recommends the most suitable hobby for the user. For example, an extroverted user with free time on weekends might be recommended outdoor activities such as hiking or camping. On the other hand, an introverted user with free time during the week might be recommended indoor activities such as reading or watching movies. Furthermore, the hobby recommendation system provides the user with detailed information about the recommended hobby. For example, if hiking is recommended, the system provides information such as nearby hiking trails, necessary equipment, and beginner's guides. This allows users to learn specific steps to start a new hobby. This allows the hobby recommendation system to help even those without hobbies find a new one. Having a hobby can improve quality of life, reduce stress, and build new relationships. Furthermore, increased hobby-related consumption can also lead to economic revitalization.For example, buying the equipment needed to start hiking or going to the cinema to watch a movie will stimulate related industries. This allows hobby recommendation systems to improve the quality of users' lives. For example, even people with no hobbies can find a new hobby, which can improve their quality of life, reduce stress, and help build new relationships. In addition, an increase in hobby-related consumption activity can also stimulate the economy. For example, buying the equipment needed to start hiking or going to the cinema to watch a movie will stimulate related industries.

[0062] A hobby recommendation system according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects information on a user's personality traits, daily routine, annual income, and place of residence. For example, the collection unit collects information by having the user access an app and answer questions about the user's personality traits, daily routine, annual income, place of residence, and the like. The collection unit may also include questions about the user's personality and lifestyle. For example, questions may include whether the user is extroverted or introverted, whether they have free time on weekends, how much their annual income is, and whether they live in an urban or rural area. The collection unit may also encrypt and securely protect the user's personal information. For example, the collection unit may protect the user's personal information using encryption technology such as AES or RSA. The analysis unit analyzes the information collected by the collection unit and recommends hobbies suitable for the user. The analysis unit may analyze the user's responses using, for example, a machine learning algorithm. For example, the analysis unit may perform the analysis using a machine learning algorithm such as a neural network or a support vector machine. The analysis unit can also estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results. The provision unit provides detailed information about the hobby recommended by the analysis unit. For example, the provision unit provides information on how to start the recommended hobby, necessary tools, related events, and the like. The provision unit can also provide information on consumption activities related to the hobby. For example, the provision unit provides information on available products and services, related store information, and the like. The provision unit can also estimate the user's emotions and adjust the way the information is presented based on the estimated user emotions. For example, the provision unit provides detailed information when the user is relaxed. This allows the hobby recommendation system according to the embodiment to improve the quality of life of users. For example, even people with no hobbies can find a new hobby, which can improve their quality of life, reduce stress, and help build new relationships. In addition, an increase in hobby-related consumption activity can also lead to economic revitalization.For example, buying the equipment needed to start hiking or going to the cinema to watch a movie stimulates related industries.

[0063] The collection unit may include questions about the user's personality or lifestyle. The collection unit may include, for example, questions about the user's personality or lifestyle. For example, the collection unit may include questions such as whether the user is extroverted or introverted, whether they have free time on weekends, their annual income, and whether they live in an urban or rural area. The collection unit may also collect detailed information about the user's personality and lifestyle. For example, the collection unit may evaluate the user's personality using a personality diagnostic test or a psychological assessment. Furthermore, the collection unit may also collect information such as daily habits and consumption behavior. By collecting detailed information about the user's personality and lifestyle, the collection unit may be able to recommend hobbies with greater accuracy. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit may input the results of the user's personality diagnostic test into the generation AI and cause the generation AI to perform a personality evaluation.

[0064] The analysis unit can analyze the user's answers using a machine learning algorithm and recommend suitable hobbies. The analysis unit, for example, analyzes the user's answers using a machine learning algorithm. For example, the analysis unit performs analysis using a machine learning algorithm such as a neural network or a support vector machine. The analysis unit can also recommend optimal hobbies based on the user's answers. For example, the analysis unit analyzes the user's answers using data mining or statistical analysis and recommends optimal hobbies. Furthermore, the analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, the analysis unit provides detailed analysis results when the user is relaxed. As a result, the analysis unit can use a machine learning algorithm to recommend optimal hobbies for the user with high accuracy. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's answer data into a generation AI and cause the generation AI to recommend hobbies.

[0065] The providing unit can provide detailed information about the recommended hobby. For example, the providing unit provides detailed information about the recommended hobby. For example, the providing unit provides information such as how to start the hobby, necessary tools, and related events. The providing unit can also provide information about consumption activities related to the hobby. For example, the providing unit provides information about purchasable products and services, related store information, and the like. Furthermore, 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, the providing unit provides detailed information when the user is relaxed. This allows the providing unit to know specific steps for the user to start a new hobby. Some or all of the above-described processing in the providing unit may be performed using, or without, AI. For example, the providing unit can input detailed information about the hobby into a generation AI and cause the generation AI to provide the information.

[0066] The providing unit can provide information on consumption activities related to hobbies. The providing unit, for example, provides information on consumption activities related to hobbies. For example, the providing unit provides information on purchasable products and services, related store information, and the like. Furthermore, by providing information on consumption activities related to hobbies, the providing unit can provide specific assistance to help the user start a hobby. For example, by providing information on consumption activities related to hobbies, the providing unit provides information for the user to purchase necessary tools and services. In this way, by providing information on consumption activities related to hobbies, the providing unit can provide specific assistance to help the user start a hobby. 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 information on consumption activities to a generation AI and cause the generation AI to provide the information.

[0067] The collection unit can encrypt and protect the user's personal information. For example, the collection unit encrypts and securely protects the user's personal information. For example, the collection unit protects the user's personal information using encryption technology such as AES or RSA. The collection unit can also ensure privacy by encrypting the user's personal information. For example, the collection unit encrypts and securely protects the user's personal information to prevent unauthorized access by third parties. In this way, the collection unit can ensure privacy by securely protecting the user's personal information. Some or all of the above-mentioned 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 personal information into a generation AI and have the generation AI perform encryption.

[0068] The collection unit can estimate the user's emotions and adjust the order or content of questions based on the estimated user emotions. For example, the collection unit estimates the user's emotions and adjusts the order or content of questions based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit starts with simple, easy-to-answer questions and gradually moves to more detailed questions. If the user is relaxed, the collection unit can present detailed questions first to collect in-depth information. If the user is in a hurry, the collection unit can prioritize important questions to collect necessary information in a short amount of time. This allows the collection unit to adjust the order or content of questions according to the user's emotions, making it easier for the user to answer. Emotion estimation is achieved using an emotion estimation function, such as 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 may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotional data into the generation AI and have the generation AI adjust the order and content of the questions.

[0069] The collection unit can analyze the user's past answer history and select an appropriate question format. The collection unit, for example, analyzes the user's past answer history and selects the optimal question format. For example, if the user has previously preferred multiple-choice questions, the collection unit can preferentially present multiple-choice questions. Furthermore, if the user has previously preferred free-form questions, the collection unit can also preferentially present free-form questions. Furthermore, the collection unit can analyze the accuracy of answers to specific question formats from the user's past answer history and select the optimal format. This allows the collection unit to select the optimal question format based on the user's past answer history, making it easier for the user to answer. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the user's past answer data into a generation AI and cause the generation AI to select a question format.

[0070] The collection unit may filter questions based on the user's current lifestyle and areas of interest when collecting questions. For example, when collecting questions, the collection unit may filter based on the user's current lifestyle and areas of interest. For example, if the user is currently busy, the collection unit may prioritize questions that can be answered in a short time. Furthermore, if the user has a specific area of ​​interest, the collection unit may prioritize questions related to that area. Furthermore, the collection unit may select appropriate questions based on the user's lifestyle and eliminate unnecessary questions. Thus, the collection unit can eliminate unnecessary questions by filtering questions based on the user's lifestyle and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit may input the user's lifestyle data into a generation AI and cause the generation AI to filter questions.

[0071] The collection unit can select an appropriate collection means according to the user's input method when collecting questions. For example, when collecting questions, the collection unit selects the optimal collection means according to the user's input method (voice, text, image, etc.). For example, if the user prefers voice input, the collection unit can provide a question format that supports voice input. Furthermore, if the user prefers text input, the collection unit can also provide a question format that supports text input. Furthermore, if the user prefers image input, the collection unit can also provide a question format that uses images. In this way, the collection unit can select the optimal collection means according to the user's input method, making it easier for the user to answer. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input the user's input data to a generation AI and cause the generation AI to select a collection means.

[0072] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and determines the priority of information to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting information related to relaxing hobbies. Also, if the user is excited, the collection unit can prioritize collecting information related to active hobbies. Also, if the user is tired, the collection unit can prioritize collecting information related to refreshing hobbies. In this way, the collection unit can prioritize information to be important to the user by determining the priority of information based on the user's emotions. Emotion estimation is realized 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-mentioned processing in the collection unit may be performed using an AI, for example, 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 determine the priority of information.

[0073] When collecting questions, the collection unit can prioritize collecting highly relevant questions by taking into account the user's geographical location information. For example, when collecting questions, the collection unit prioritizes collecting highly relevant questions by taking into account the user's geographical location information. For example, if the user lives in an urban area, the collection unit can prioritize collecting questions related to hobbies that can be enjoyed in the city. Also, if the user lives in a rural area, the collection unit can prioritize collecting questions related to hobbies that allow the user to enjoy nature. Also, the collection unit can prioritize collecting questions related to hobbies that are unique to the region based on the user's geographical location information. In this way, the collection unit can obtain useful information for the user by collecting highly relevant questions based on the user's geographical location information. Some or all of the above-described processing by 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 geographical location data into the generation AI and cause the generation AI to collect questions.

[0074] The collection unit can analyze the user's social media activity and collect related questions when collecting questions. For example, the collection unit analyzes the user's social media activity and collects related questions when collecting questions. For example, the collection unit collects related questions based on content frequently posted by the user on social media. The collection unit can also collect related questions by referring to the activities of the user's friends on social media. The collection unit can also analyze the user's interests on social media and collect related questions. In this way, the collection unit can collect questions based on the user's interests by analyzing the user's social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to collect questions.

[0075] The collection unit can customize the collection method by reflecting the user's past feedback when collecting questions. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting questions. For example, the collection unit customizes the content and format of questions based on feedback provided by the user in the past. The collection unit can also prioritize and provide preferred question formats based on the user's past feedback. The collection unit can also optimize the order and content of questions by referring to the user's past feedback. In this way, the collection unit can provide the optimal question format for the user by reflecting the user's past feedback. Some or all of the above-described processing by 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 feedback data into a generation AI and cause the generation AI to customize the collection method.

[0076] 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, 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, the analysis unit can provide detailed analysis results when the user is relaxed. Furthermore, the analysis unit can provide concise analysis results that focus on the main points when the user is in a hurry. Furthermore, the analysis unit can provide visually appealing analysis results when the user is excited. By adjusting the presentation method of the analysis based on the user's emotions, the analysis unit can provide analysis results that are easy for the user to understand. 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 adjust the presentation method of the analysis.

[0077] The analysis unit can adjust the level of detail of the analysis based on the importance of the user's answer during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the user's answer during analysis. For example, the analysis unit performs a detailed analysis when the user's answer includes important information. The analysis unit can also perform a concise analysis when the user's answer includes general information. The analysis unit can also adjust the depth of the analysis depending on the importance of the user's answer. As a result, the analysis unit can analyze information important to the user in detail by adjusting the level of detail of the analysis based on the importance of the user's answer. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's answer data to a generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0078] The analysis unit can apply different analysis algorithms depending on the user category during analysis. For example, the analysis unit can apply different analysis algorithms depending on the user category during analysis. For example, if the user is an outdoorsy person, the analysis unit can apply an analysis algorithm specialized for outdoor activities. Also, if the user is an indoor person, the analysis unit can apply an analysis algorithm specialized for indoor activities. The analysis unit can also select the optimal analysis algorithm depending on the user category. In this way, the analysis unit can provide more accurate analysis results by applying the optimal analysis algorithm depending on the user category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user category data into the generation AI and have the generation AI select the analysis algorithm.

[0079] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can adjust the analysis algorithm based on the user's past analysis results. The analysis unit can also extract patterns for performing highly accurate analysis from the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. In this way, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's past analysis data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0080] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, 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 provide a detailed analysis result. If the user is excited, the analysis unit can provide a visually appealing analysis result. By adjusting the length of the analysis based on the user's emotions, the analysis unit can provide an analysis result of an appropriate length for the user. 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 these 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 adjust the length of the analysis.

[0081] The analysis unit can determine the analysis priority based on the time of the user's response during analysis. The analysis unit, for example, determines the analysis priority based on the time of the user's response during analysis. For example, the analysis unit prioritizes analysis of questions that the user has recently answered. The analysis unit can also prioritize analysis of questions that the user has answered during a specific time period. The analysis unit can also determine the analysis priority based on the time of the user's response. In this way, the analysis unit can prioritize analysis of the latest information by determining the analysis priority based on the time of the user's response. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input user response time data into the generation AI and have the generation AI determine the analysis priority.

[0082] The analysis unit can adjust the order of analysis based on the user's relevance during analysis. The analysis unit, for example, adjusts the order of analysis based on the user's relevance during analysis. For example, the analysis unit analyzes questions in order of relevance to the user's answers. The analysis unit can also analyze questions in order of relevance to the user's answers later. The analysis unit can also adjust the order of analysis based on the user's relevance. In this way, the analysis unit can prioritize analysis of important information by adjusting the order of analysis based on the user's relevance. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input user relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0083] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can also provide analysis results in simple language. Furthermore, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. This allows the analysis unit to provide analysis results that are easy for the user to understand by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI use technical terms.

[0084] The providing unit can estimate the user's emotions and adjust the presentation method of the information to be provided based on the estimated user's emotions. For example, the providing unit can estimate the user's emotions and adjust the presentation method of the information to be provided based on the estimated user's emotions. For example, the providing unit can provide detailed information when the user is relaxed. Furthermore, the providing unit can provide concise information that focuses on the main points when the user is in a hurry. Furthermore, the providing unit can provide visually appealing information when the user is excited. In this way, the providing unit can provide information that is easy for the user to understand by adjusting the presentation method of the information based on the user's emotions. 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-mentioned 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 adjust the presentation method of the information.

[0085] The providing unit can adjust the level of detail of the information to be provided based on the importance of the hobby at the time of providing. The providing unit, for example, adjusts the level of detail of the information to be provided based on the importance of the hobby at the time of providing. For example, the providing unit provides detailed information for a hobby that the user feels is important. The providing unit can also provide concise information for a hobby that the user does not feel is very important. The providing unit can also adjust the level of detail of the information to be provided according to the importance of the user's hobby. In this way, the providing unit can provide information that is important to the user in detail by adjusting the level of detail of the information based on the importance of the hobby. 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 importance data of the user's hobby to a generating AI and cause the generating AI to adjust the level of detail of the information.

[0086] The providing unit can apply different providing algorithms depending on the hobby category when providing the information. For example, the providing unit can apply different providing algorithms depending on the hobby category when providing the information. For example, the providing unit can apply a providing algorithm specialized for outdoor activities to an outdoor hobby. The providing unit can also apply a providing algorithm specialized for indoor activities to an indoor hobby. The providing unit can also select an optimal providing algorithm depending on the hobby category. In this way, the providing unit can provide useful information to the user by applying an optimal providing algorithm depending on the hobby category. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's hobby category data into the generation AI and cause the generation AI to select a providing algorithm.

[0087] The providing unit can improve the accuracy of the data provided by referring to the user's past provision results when providing the data. For example, the providing unit can improve the accuracy of the data provided by referring to the user's past provision results when providing the data. For example, the providing unit can adjust the providing algorithm based on the user's past provision results. The providing unit can also extract patterns for providing data with high accuracy from the user's past provision results. The providing unit can also improve the accuracy of the data provided by referring to the user's past provision results. In this way, the providing unit can improve the accuracy of the data provided by referring to the user's past provision results. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's past provision data into the generation AI and cause the generation AI to improve the accuracy of the data provided.

[0088] The providing unit can estimate the user's emotions and adjust the length of information to be provided based on the estimated user emotions. For example, the providing unit can estimate the user's emotions and adjust the length of information to be provided based on the estimated user emotions. For example, if the user is in a hurry, the providing unit can provide short, concise information. Furthermore, if the user is relaxed, the providing unit can provide detailed information. Furthermore, if the user is excited, the providing unit can provide visually appealing information. Thus, the providing unit can adjust the length of information based on the user's emotions, thereby providing information of an appropriate length for the user. 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. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the information.

[0089] The providing unit can adjust the order of information to be provided based on the relevance of the hobby at the time of providing. The providing unit, for example, adjusts the order of information to be provided based on the relevance of the hobby at the time of providing. For example, the providing unit prioritizes providing information related to hobbies in which the user is interested. The providing unit can also postpone providing information related to hobbies in which the user is not very interested. The providing unit can also adjust the order of information to be provided based on the relevance of the hobby of the user. In this way, the providing unit can prioritize providing information that is useful to the user by adjusting the order of information based on the relevance of the hobby. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the relevance of the user's hobby to the generation AI and cause the generation AI to adjust the order of the information.

[0090] The providing unit can determine the priority of information to be provided based on the detailed hobby information at the time of providing. The providing unit, for example, determines the priority of information to be provided based on the detailed hobby information at the time of providing. For example, the providing unit preferentially provides information for hobbies for which the user requires detailed information. The providing unit can also provide concise information for hobbies for which the user does not require much detailed information. The providing unit can also determine the priority of information to be provided based on the detailed hobby information of the user. In this way, the providing unit can prioritize information that is important to the user by determining the priority of information based on the detailed hobby information. 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 detailed hobby information of the user to a generation AI and cause the generation AI to determine the priority of information.

[0091] The providing unit can adjust the use of technical terms in the information to be provided according to the user's level of expertise when providing the information. For example, the providing unit can adjust the use of technical terms in the information to be provided according to the user's level of expertise when providing the information. For example, if the user has technical knowledge, the providing unit can provide information that uses a lot of technical terms. Also, if the user does not have technical knowledge, the providing unit can provide information in simple language. The providing unit can also adjust the use of technical terms in the information to be provided according to the user's level of expertise. In this way, the providing unit can provide information that is easy for the user to understand by adjusting the use of technical terms in the information according to the user's level of expertise. Some or all of the above-mentioned 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 level of expertise data into the generation AI and cause the generation AI to use technical terms. === Hard Collateral 1-1 === 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 device 14 and the data processing device 12. For example, the collection unit is realized by the computer 36 of the smart device 14 and the specific processing unit 290 of the data processing device 12. For example, the collection unit uses the reception device 38 of the smart device 14 to collect information on the user's personality traits, lifestyle, annual income, and place of residence. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the information collected by the collection unit, and recommends hobbies suitable for the user. The provision unit is realized, for example, by the output device 40 of the smart device 14 and the specific processing unit 290 of the data processing device 12, and provides detailed information on the hobbies recommended by the analysis unit. === 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 is realized by the computer 36 of the smart glasses 214 and the specific processing unit 290 of the data processing device 12. For example, the collection unit uses the microphone 238 of the smart glasses 214 to collect information on the user's personality traits, lifestyle rhythm, annual income, and place of residence. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the information collected by the collection unit, and recommends hobbies suitable for the user. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214 and the specific processing unit 290 of the data processing device 12, and provides detailed information on the hobbies recommended by the analysis unit. === Hard Collateral 1-3 === 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 headset-type terminal 314 and the data processing device 12. For example, the collection unit is realized by the computer 36 of the headset-type terminal 314 and the specific processing unit 290 of the data processing device 12. For example, the collection unit uses the microphone 238 of the headset-type terminal 314 to collect information on the user's personality traits, lifestyle rhythm, annual income, and place of residence. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the information collected by the collection unit, and recommends hobbies suitable for the user. The provision unit is realized, for example, by the speaker 240 of the headset-type terminal 314 and the specific processing unit 290 of the data processing device 12, and provides detailed information on the hobbies recommended by the analysis unit. === 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 is realized by the computer 36 of the robot 414 and the specific processing unit 290 of the data processing device 12. For example, the collection unit uses the microphone 238 of the robot 414 to collect information on the user's personality traits, lifestyle rhythm, annual income, and place of residence. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the information collected by the collection unit, and recommends hobbies suitable for the user. The provision unit is realized, for example, by the speaker 240 of the robot 414 and the specific processing unit 290 of the data processing device 12, and provides detailed information on the hobbies recommended by the analysis unit.

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

[0093] The analysis unit can also estimate the user's health condition based on the user's answers and recommend hobbies based on the estimated health condition. For example, if the analysis unit estimates that the user is not getting enough exercise, it can recommend hobbies that include light exercise such as walking or yoga. If the analysis unit estimates that the user is feeling stressed, it can recommend hobbies that help reduce stress, such as relaxation or meditation. Furthermore, if the user wants to eat a healthy diet, the analysis unit can recommend health-related hobbies such as cooking or gardening. In this way, the analysis unit can contribute to maintaining and improving the user's health by recommending appropriate hobbies based on the user's health condition.

[0094] The providing unit may also provide community information related to the user's hobbies. For example, the providing unit may provide information on online forums and social media groups related to hobbies in which the user is interested. The providing unit may also provide information on local clubs and circles that the user can join. Furthermore, the providing unit may provide event information that allows the user to make new friends through their hobbies. In this way, the providing unit can help the user build new relationships through their hobbies.

[0095] The collection unit can also analyze the user's past hobby history and reflect it in current hobby recommendations. For example, the collection unit collects data on hobbies that the user enjoyed in the past and uses it in current recommendations. The collection unit can also analyze reasons why the user tried but did not continue with a hobby in the past, so as to prevent similar problems from occurring. Furthermore, the collection unit can collect data on hobby-related events and activities that the user participated in in the past and reflect it in current recommendations. This allows the collection unit to provide more personalized hobby recommendations based on the user's past hobby history.

[0096] The analysis unit can also estimate the user's learning style based on the user's answers and recommend hobbies based on the estimated learning style. For example, if the analysis unit estimates that the user is a visual learner, it can recommend visual hobbies such as painting or photography. If the analysis unit estimates that the user is an auditory learner, it can recommend auditory hobbies such as listening to music or playing an instrument. Furthermore, if the analysis unit estimates that the user is an experiential learner, it can recommend practical hobbies such as cooking or DIY. In this way, the analysis unit can recommend appropriate hobbies based on the user's learning style, allowing the user to enjoy their hobbies more.

[0097] The provider may also provide educational resources related to the user's hobbies. For example, the provider may provide information on online courses or workshops related to the hobbies in which the user is interested. The provider may also provide a list of books or videos to help the user deepen their hobbies. Furthermore, the provider may provide tutorials or guides to help the user learn skills related to the hobbies. In this way, the provider can help the user acquire new knowledge and skills through their hobbies.

[0098] The analysis unit can also estimate the user's emotions and adjust the hobby recommendations based on the estimated user emotions. For example, if the analysis unit estimates that the user is tired, it can recommend a hobby that allows the user to relax. If the analysis unit estimates that the user is excited, it can recommend an active hobby. Furthermore, if the analysis unit estimates that the user is sad, it can recommend a hobby that can lift the user's spirits. In this way, the analysis unit can help improve the user's emotional state by recommending an appropriate hobby based on the user's emotions.

[0099] The providing unit can also estimate the user's emotions and track the progress of a hobby based on the estimated user emotions. For example, if the providing unit estimates that the user is enjoying a hobby, it can suggest a new challenge or goal related to the hobby. If the providing unit estimates that the user is bored, it can also suggest a new hobby. Furthermore, if the providing unit estimates that the user is experiencing difficulty, it can also provide support or advice. In this way, the providing unit can track the progress of a hobby based on the user's emotions and provide appropriate support.

[0100] The collection unit can also estimate the user's emotions and adjust the content of the questions based on the estimated user's emotions. For example, if the collection unit estimates that the user is relaxed, it can present detailed questions. Furthermore, if the collection unit estimates that the user is feeling stressed, it can present simple questions. Furthermore, if the collection unit estimates that the user is excited, it can present interesting questions. In this way, the collection unit can adjust the content of the questions based on the user's emotions, making it easier for the user to answer.

[0101] The analysis unit can also estimate the user's emotions and adjust the timing of the analysis based on the estimated user's emotions. For example, if the analysis unit estimates that the user is relaxed, it can perform a detailed analysis. If the analysis unit estimates that the user is in a hurry, it can perform a concise analysis. Furthermore, if the analysis unit estimates that the user is excited, it can perform a visually appealing analysis. In this way, the analysis unit can adjust the timing of the analysis based on the user's emotions and provide analysis results at the optimal timing for the user.

[0102] The providing unit can also estimate the user's emotions and provide hobby feedback based on the estimated user emotions. For example, if the providing unit estimates that the user is satisfied, it can provide positive feedback. If the providing unit estimates that the user is dissatisfied, it can also provide improvements or new suggestions. Furthermore, if the providing unit estimates that the user is experiencing difficulties, it can also provide support or advice. In this way, the providing unit can support the user's hobby activities by providing appropriate feedback based on the user's emotions.

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

[0104] Step 1: The collection unit collects information about the user's personality traits, lifestyle, annual income, and place of residence. For example, the user accesses the app and answers these questions to collect information. The collection unit may also include questions about the user's personality and lifestyle. Furthermore, the collection unit may encrypt and securely protect the user's personal information. For example, the collection unit protects the user's personal information using encryption technologies such as AES and RSA. Step 2: The analysis unit analyzes the information collected by the collection unit and recommends hobbies suitable for the user. For example, the analysis unit analyzes the user's responses using a machine learning algorithm. The analysis unit performs the analysis using a machine learning algorithm such as a neural network or a support vector machine. The analysis unit can also estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. Step 3: The providing unit provides detailed information about the hobby recommended by the analyzing unit. For example, the providing unit provides information about how to start the recommended hobby, the necessary tools, related events, etc. The providing unit can also provide information about consumption activities related to the hobby. Furthermore, the providing unit can estimate the user's emotions and adjust the way the information is presented based on the estimated user emotions.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (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.

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

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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 AI 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.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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 AI 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.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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 AI 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.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] [Explanation of symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects information on the user's personality traits, lifestyle, annual income, and place of residence; an analysis unit that analyzes the information collected by the collection unit and recommends hobbies suitable for the user; a providing unit that provides detailed information about the hobby recommended by the analysis unit. A system characterized by:

2. The collecting unit Contains questions about the user's personality or lifestyle 2. The system of claim 1.

3. The analysis unit It uses machine learning algorithms to analyze user responses and recommend suitable hobbies.

2. The system of claim 1.

4. The providing unit Providing detailed information about recommended hobbies 2. The system of claim 1.

5. The providing unit Providing information on hobby-related consumption activities 2. The system of claim 1.

6. The collecting unit Encrypt and protect your personal information 2. The system of claim 1.

7. The collecting unit Inferring user sentiment and adjusting the order or content of questions based on the estimated user sentiment 2. The system of claim 1.

8. The collecting unit Analyze the user's past answer history and select the most appropriate question format 2. The system of claim 1.

Images