System

Abstract

A system according to an embodiment aims to provide information and guidance for users to find and practice new hobbies.SOLUTION: A system includes an information collection unit, an analysis unit, a proposal unit, and a guide provision unit. The information collection unit collects information on the user's interest, preference, and lifestyle. The analysis unit analyzes the information collected by the information collection unit. The proposal unit proposes a hobby on the basis of the information analyzed by the analysis unit. The guide providing unit provides a method of practicing the hobby proposed by the proposal unit, necessary tools, and a step-by-step guide.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 technologies do not adequately provide users with appropriate information and guidance when discovering new hobbies, and there is room for improvement.

[0005] The system according to the embodiment aims to provide information and guidance for users to find and practice new hobbies. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, an analysis unit, a suggestion unit, and a guide provision unit. The information collection unit collects information related to a user's interests, preferences, and lifestyle. The analysis unit analyzes the information collected by the information collection unit. The suggestion unit suggests hobbies based on the information analyzed by the analysis unit. The guide provision unit provides methods for practicing the hobbies suggested by the suggestion unit, necessary tools, and step-by-step guides. [Effects of the Invention]

[0007] The system according to the embodiment can provide information and guidance for users to find and practice new hobbies. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) The hobby suggestion system according to an embodiment of the present invention is a system that provides information and alleviates anxiety to people who have no hobbies and want to start one but don't know what or how to start, and encourages them to find a hobby. As a result, the hobby suggestion system encourages people to find a hobby, allowing them to continue with their hobby and maintain their motivation.

[0029] The hobby suggestion system according to the embodiment includes an information collection unit, an analysis unit, a suggestion unit, and a guide provision unit. The information collection unit collects information about a user's interests, preferences, and lifestyle. For example, the information collection unit collects information such as the user's hobbies, food preferences, and daily activities through a questionnaire. The information collection unit can also collect user activity data using a sensor. For example, the information collection unit can collect the user's exercise amount and heart rate using a wearable device. The information collection unit can also collect online data. For example, the information collection unit can collect the user's social media posts and browsing history. The analysis unit analyzes the information collected by the information collection unit. For example, the analysis unit can analyze the user's interests and preferences using data mining technology. The analysis unit can also analyze the user's lifestyle using a machine learning algorithm. For example, the analysis unit can analyze the user's behavioral patterns using clustering technology. The suggestion unit suggests hobbies based on the information analyzed by the analysis unit. For example, the suggestion unit can suggest hobbies based on the user's past behavioral data and interest trends. The suggestion unit can also suggest hobbies that match the user's lifestyle. For example, if the user prefers outdoor activities, the suggestion unit can suggest hiking or camping. The guide providing unit provides step-by-step guides and necessary tools for practicing the hobby suggested by the suggestion unit. For example, the guide providing unit may provide a gardening kit for beginners, instructions on selecting seasonal plants, and basic care methods. The guide providing unit may also provide video tutorials and interactive step-by-step guides. For example, the guide providing unit may explain cooking recipes through videos. In this way, the hobby suggestion system according to the embodiment can suggest appropriate hobbies based on the user's interests and preferences and provide instructions on how to practice them, thereby helping the user continue with the hobby and maintaining their motivation.

[0030] The information collection unit can analyze a user's past social media posts and online activity history to discover potential interests and suggest hobbies. For example, the generation AI in the information collection unit analyzes a user's past social media posts and extracts frequently mentioned keywords and hashtags. For example, if a user frequently uses keywords such as "travel" or "cooking," the information collection unit can suggest hobbies related to travel and cooking based on these keywords. The information collection unit can also analyze a user's online activity history and suggest hobbies based on the websites visited and the content of articles read. For example, if a user spends a lot of time on gardening-related websites, the information collection unit can suggest gardening as a hobby. The information collection unit can also analyze a user's social media photo posts and identify interests using image recognition technology. For example, if a user posts many outdoor photos, the information collection unit can suggest outdoor activities such as hiking and camping as hobbies. This allows the information collection unit to discover potential interests and suggest appropriate hobbies based on the user's past activity history.

[0031] The information collection unit analyzes the hobbies of the user's friends and family and suggests common hobbies, thereby strengthening social connections. For example, the information collection unit uses a generation AI to analyze social media posts from the user's friends and family to identify common hobbies. For example, if the user's friends spend a lot of time cooking, the generation AI suggests cooking as a common hobby. The information collection unit also analyzes the online activity history of the user's family and friends to find common interests. For example, if all family members frequently visit fitness-related sites, the generation AI suggests fitness as a common hobby. The information collection unit also collects data on the hobbies of the user's friends and family and suggests common hobbies. For example, if a friend spends a lot of time gardening, the generation AI suggests gardening as a common hobby. This allows the generation AI to analyze the hobbies of the user's friends and family and suggest common hobbies, thereby strengthening social connections.

[0032] The information collection unit can analyze the user's health data and suggest hobbies suitable for the user's health condition. For example, the generation AI analyzes data from the user's fitness tracker and suggests hobbies suitable for the user's health condition based on their heart rate and exercise volume. For example, if the user maintains a high level of exercise, the generation AI can suggest running or cycling as hobbies. The information collection unit can also analyze the user's health data and suggest hobbies that have a relaxing effect based on their stress level and sleep patterns. For example, if the user shows a high stress level, the generation AI can suggest yoga or meditation as hobbies. The information collection unit can also suggest hobbies that improve physical strength and flexibility based on the user's health data. For example, if the user wants to improve their flexibility, the generation AI can suggest Pilates or dancing as hobbies. This makes it possible to suggest hobbies suitable for the user's health condition based on the user's health data.

[0033] The information gathering unit can suggest hobbies that will lead to career advancement, taking into account the user's occupation and skill set. For example, the generation AI analyzes the user's occupational data and suggests hobbies that will lead to career advancement. For example, if the user works in the IT industry, it suggests programming and data science as hobbies. The information gathering unit also analyzes the user's skill set and suggests hobbies that will improve those skills. For example, if the user has design skills, it suggests graphic design and photography as hobbies. The information gathering unit also suggests hobbies that will improve networking and business skills, based on the user's occupation and skill set. For example, if the user wants to improve their business skills, it suggests business seminars and entrepreneurial activities as hobbies. This makes it possible to suggest hobbies that will lead to career advancement based on the user's occupation and skill set.

[0034] The analysis unit can analyze a user's purchasing history and identify interests and preferences. For example, the generation AI analyzes a user's online shopping purchase history to identify interests and preferences. For example, if a user frequently purchases outdoor equipment, the analysis unit suggests hobbies related to outdoor activities. The analysis unit also analyzes a user's purchasing history and, if the user purchases many products in a specific category, suggests hobbies related to that category. For example, if the user purchases many cookbooks, the analysis unit suggests cooking as a hobby. The analysis unit also identifies interests and preferences based on the user's purchasing history and suggests related hobbies. For example, if the user purchases many art supplies, the analysis unit suggests painting and sculpting as hobbies. In this way, interests and preferences can be identified based on the user's purchasing history and appropriate hobbies can be suggested.

[0035] The analysis unit can analyze a user's music and movie viewing history and suggest hobbies based on their entertainment preferences. For example, the analysis unit uses a generation AI to analyze a user's music viewing history and suggest hobbies based on their favorite genres and artists. For example, if a user likes jazz, the analysis unit can suggest jazz dance or playing musical instruments as hobbies. The analysis unit can also analyze a user's movie viewing history and suggest hobbies based on their favorite genres and directors. For example, if a user likes science fiction movies, the analysis unit can suggest hobbies such as writing science fiction novels and filmmaking as hobbies. The analysis unit can also use a generation AI to suggest hobbies that match the user's entertainment preferences based on the user's music and movie viewing history. For example, if a user spends a lot of time using music streaming services, the analysis unit can suggest hobbies such as DJing and music production as hobbies. This allows the analysis unit to suggest hobbies that match the user's entertainment preferences based on the user's music and movie viewing history.

[0036] The analysis unit can analyze the user's food preferences and suggest hobbies related to cooking and gourmet food. For example, the generation AI in the analysis unit analyzes the user's food records and suggests hobbies based on the user's favorite dishes and ingredients. For example, if the user likes Italian food, the analysis unit suggests Italian cooking classes as a hobby. The analysis unit can also analyze the user's food preferences and suggest hobbies related to cooking and gourmet food. For example, if the user likes sweets, the analysis unit suggests the hobby of learning pastry chef techniques. The analysis unit can also use the generation AI to suggest hobbies related to cooking and gourmet food based on the user's food preferences. For example, if the user spends a lot of time on restaurant review sites, the analysis unit suggests writing a gourmet blog as a hobby. This makes it possible to suggest hobbies related to cooking and gourmet food based on the user's food preferences.

[0037] The guide providing unit can analyze the user's learning style and provide a practical hobby guide using the optimal learning method. For example, the generation AI in the guide providing unit analyzes the user's learning style and provides a visual guide to users who prefer visual learning. For example, a guide using videos or illustrations is provided. The guide providing unit also provides a practical hobby guide using the optimal learning method based on the user's learning style. For example, an audio guide is provided to users who prefer auditory learning. The guide providing unit also analyzes the user's learning style and provides a hands-on guide to users who prefer practical learning. For example, a workshop-style guide is provided where users can learn by actually doing their hands. In this way, a practical hobby guide can be provided using the optimal learning method based on the user's learning style.

[0038] The guide providing unit can monitor the user's progress in real time and adjust the guide content as needed. For example, the generation AI of the guide providing unit monitors the user's progress in real time and adjusts the guide content according to the progress. For example, if the user stumbles in the early stages of gardening, the guide providing unit re-explains basic maintenance methods. The guide providing unit also adjusts the guide content in real time based on the user's progress. For example, if the user is progressing smoothly through the cooking steps, the guide providing unit provides advice on how to proceed to the next step. The generation AI of the guide providing unit also monitors the user's progress and adjusts the guide content as needed. For example, if the user is falling behind in sports training, the guide providing unit provides advice on changing the training method. This makes it possible to adjust the guide content in real time according to the user's progress.

[0039] The guide providing unit can analyze the user's visual and auditory information and provide a multimodal guide. For example, the generation AI in the guide providing unit analyzes the user's visual information and provides a guide that is easy to understand visually. For example, if the user prefers visual information, the guide providing unit provides a guide using illustrations or videos. The guide providing unit can also analyze the user's auditory information and provide a guide that is easy to understand auditorily. For example, if the user prefers audio information, the guide providing unit provides an audio guide or podcast-style guide. The guide providing unit can also integrate the user's visual and auditory information and provide a multimodal guide. For example, if the user prefers to learn using both visual and auditory means, the guide providing unit provides a guide that combines video and audio. This makes it possible to provide a multimodal guide based on the user's visual and auditory information.

[0040] The guide providing unit can collect user feedback and continuously improve the guide content. For example, the guide providing unit constructs a system in which a generation AI collects user feedback and continuously improves the guide content. For example, a user can provide their opinion on the guide content, and the guide is updated based on that opinion. The guide providing unit also collects user feedback in real time and immediately improves the guide content. For example, if a user finds a particular part of the guide difficult to understand, that part is improved. The guide providing unit also has the generation AI analyze user feedback and continuously improve the guide content. For example, if a user provides feedback on the guide's progress speed, the speed is adjusted. This allows the guide content to be continuously improved based on user feedback.

[0041] The analysis unit can analyze the user's past successful experiences and present a future vision based on them. For example, the generation AI of the analysis unit analyzes the user's past successful experiences and presents a future vision based on those successful experiences. For example, if the user has had success in gardening in the past, the analysis unit presents the relaxation effects and health benefits that can be obtained by continuing to garden in the future. The analysis unit also presents a future vision based on the user's past successful experiences. For example, if the user has had success in cooking in the past, the analysis unit presents the deepening of communication with family and friends that can be obtained by continuing to cook in the future. The analysis unit can also present a future vision based on the user's past successful experiences. For example, if the user has had success in sports in the past, the analysis unit presents the physical strength improvement and stress relief that can be obtained by continuing to play sports in the future. In this way, a future vision can be presented based on the user's past successful experiences.

[0042] The analysis unit can analyze the user's long-term goals and present a future vision that matches those goals. For example, the generation AI analyzes the user's long-term goals and presents a future vision that matches those goals. For example, if the user's long-term goal is to maintain health, the analysis unit presents a future vision that can be achieved by continuing a health-related hobby. The analysis unit also presents a future vision based on the user's long-term goals. For example, if the user is aiming to advance their career, the analysis unit presents a future vision that can be achieved by continuing a career-related hobby. The analysis unit also presents a future vision that matches those goals. For example, if the user's goal is to deepen social connections, the analysis unit presents a future vision that can be achieved by continuing a hobby related to community activities. In this way, a future vision can be presented based on the user's long-term goals.

[0043] The analysis unit can analyze the user's health data and present a future vision of health. In the analysis unit, for example, a generation AI analyzes the user's health data and presents a future vision of health. For example, it presents a future vision of health promotion and disease prevention that the user will achieve by continuing to exercise regularly. The analysis unit also presents a future vision of health based on the user's health data. For example, it presents a future vision of weight management and improved nutritional balance that the user will achieve by continuing to eat a balanced diet. In addition, the analysis unit can analyze the user's health data and present a future vision of health. For example, it presents a future vision of improved mental health and relaxation effects that the user will achieve by continuing to manage stress. In this way, a future vision of health can be presented based on the user's health data.

[0044] The analysis unit can analyze the user's career data and present a future vision for their career. In the analysis unit, for example, the generation AI analyzes the user's career data and presents a future vision for their career. For example, it presents a future vision of career advancement and promotion that the user can achieve by honing specific skills. The analysis unit also presents a future vision for their career based on the user's career data. For example, it presents a future vision of expanded career options and improved expertise that the user can achieve by obtaining new qualifications. In addition, the analysis unit can analyze the user's career data and present a future vision for their career. For example, it presents a future vision of business opportunities and expanded personal connections that the user can achieve by continuing to network. In this way, it is possible to present a future vision for their career based on the user's career data.

[0045] The analysis unit can analyze the user's past motivation data and suggest optimal methods for maintaining motivation. For example, the generation AI analyzes the user's past motivation data and suggests optimal methods for maintaining motivation. For example, if the user has maintained motivation in the past by setting goals, the analysis unit suggests specific goal setting. The analysis unit also suggests optimal methods for maintaining motivation based on the user's motivation data. For example, if the user has maintained motivation in the past by using a reward system, the analysis unit suggests a reward system. The analysis unit also suggests specific methods for maintaining motivation based on the user's past motivation data. For example, if the user has maintained motivation in the past by participating in activities with friends, the analysis unit suggests community activities. This makes it possible to suggest optimal methods for maintaining motivation based on the user's past motivation data.

[0046] The guide providing unit can monitor the user's progress in real time and provide feedback at appropriate times. For example, the generation AI of the guide providing unit monitors the user's progress in real time and provides feedback at appropriate times. For example, if the user is recording their gardening progress, the guide providing unit provides feedback praising the growth process. The guide providing unit also provides feedback at appropriate times based on the user's progress. For example, if the user has completed a cooking recipe, the guide providing unit provides feedback praising the result. The guide providing unit also monitors the user's progress in real time and provides feedback at appropriate times. For example, if the user has completed sports training, the guide providing unit provides feedback acknowledging the effort. This makes it possible to provide feedback at appropriate times according to the user's progress.

[0047] The guide providing unit can analyze data related to the user's hobbies and periodically provide new information and advice. For example, the generation AI of the guide providing unit analyzes data related to the user's hobbies and periodically provides new information and advice. For example, if the user's hobby is gardening, the guide providing unit provides information on how to select and care for seasonal plants. The guide providing unit also periodically provides new information and advice based on data related to the user's hobbies. For example, if the user's hobby is cooking, the guide providing unit provides new recipes and cooking techniques. The generation AI of the guide providing unit also analyzes data related to the user's hobbies and periodically provides new information and advice in real time. For example, if the user's hobby is sports, the guide providing unit provides new training methods and competition information. In this way, the guide providing unit can analyze data related to the user's hobbies and periodically provide new information and advice.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The hobby suggestion system can also analyze a user's past travel history to suggest travel-related hobbies. For example, based on data on countries and regions the user has visited in the past, it can suggest hobbies related to the culture and cuisine of those regions. It can also analyze data on travel events and activities the user has participated in in the past to suggest similar activities as hobbies. It can also analyze photos and videos taken by the user during travel and suggest photography and video editing as hobbies. This makes it possible to suggest travel-related hobbies based on the user's travel history.

[0050] The hobby suggestion system can also analyze a user's reading history to suggest hobbies related to reading. For example, based on data on the genres and authors of books the user has read in the past, it can suggest books of similar genres or authors. It can also analyze data on reviews and impressions posted by users about books and suggest book clubs or writing book review blogs as hobbies. It can also analyze data on notes and highlights taken by users while reading and suggest creating reading notebooks or running a book club as hobbies. In this way, it is possible to suggest hobbies related to reading based on a user's reading history.

[0051] The hobby suggestion system can also analyze a user's music production history to suggest music-related hobbies. For example, it can suggest similar instruments and music genres based on data on songs the user has composed in the past and the instruments they have used. It can also analyze data on the user's participation in music production tutorials and workshops to suggest similar tutorials and workshops as hobbies. It can also analyze data on the software and tools the user has used in music production to suggest hobbies such as how to use music production software or learning new tools. This makes it possible to suggest music-related hobbies based on the user's music production history.

[0052] The hobby suggestion system can also analyze a user's sports viewing history to suggest sports-related hobbies. For example, similar sports and teams can be suggested based on data on sports events the user has watched in the past and teams the user has supported. The system can also analyze a user's blogs and social media posts about sports viewing to suggest hobbies such as writing sports spectator reports or participating in sports fan communities. Furthermore, the system can analyze photos and videos the user has taken while watching sports to suggest sports photography and video editing as hobbies. This allows the system to suggest sports-related hobbies based on the user's sports viewing history.

[0053] The hobby suggestion system can also analyze a user's movie-watching history to suggest movie-related hobbies. For example, based on data on the genres and directors of movies the user has watched in the past, it can suggest movies of similar genres or directors. It can also analyze data on movie reviews and impressions posted by users and suggest writing movie reviews or joining a movie club as hobbies. It can also analyze photos and videos taken by users while watching movies and suggest movie production or video editing as hobbies. In this way, it can suggest movie-related hobbies based on a user's movie-watching history.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The information collection unit collects information about the user's interests, preferences, and lifestyle. For example, it collects information such as the user's hobbies, food preferences, and daily activities through questionnaires. It can also collect user activity data using sensors. For example, it can collect the user's exercise volume and heart rate using a wearable device. It can also collect online data. For example, it can collect the user's social media posts and browsing history. Step 2: The analysis unit analyzes the information collected by the information collection unit. For example, it uses data mining technology to analyze the user's interests and preferences. It can also analyze the user's lifestyle using machine learning algorithms. For example, it uses clustering technology to analyze the user's behavioral patterns. Step 3: The suggestion unit suggests hobbies based on the information analyzed by the analysis unit. For example, it can suggest hobbies based on the user's past behavioral data and tendencies of interest. It can also suggest hobbies that fit the user's lifestyle. For example, if the user likes outdoor activities, it can suggest hiking and camping. Step 4: The guide provider provides step-by-step instructions, tools, and guides for the hobby suggested by the suggestion provider. For example, it can provide gardening kits for beginners, instructions on how to select seasonal plants, and basic care methods. It can also provide video tutorials and interactive step-by-step guides. For example, it can explain cooking recipes through video.

[0056] (Example 2) The hobby suggestion system according to an embodiment of the present invention is a system that provides information and alleviates anxiety to people who have no hobbies and want to start one but don't know what or how to start, and encourages them to find a hobby. As a result, the hobby suggestion system encourages people to find a hobby, allowing them to continue with their hobby and maintain their motivation.

[0057] The hobby suggestion system according to the embodiment includes an information collection unit, an analysis unit, a suggestion unit, and a guide provision unit. The information collection unit collects information about a user's interests, preferences, and lifestyle. For example, the information collection unit collects information such as the user's hobbies, food preferences, and daily activities through a questionnaire. The information collection unit can also collect user activity data using a sensor. For example, the information collection unit can collect the user's exercise amount and heart rate using a wearable device. The information collection unit can also collect online data. For example, the information collection unit can collect the user's social media posts and browsing history. The analysis unit analyzes the information collected by the information collection unit. For example, the analysis unit can analyze the user's interests and preferences using data mining technology. The analysis unit can also analyze the user's lifestyle using a machine learning algorithm. For example, the analysis unit can analyze the user's behavioral patterns using clustering technology. The suggestion unit suggests hobbies based on the information analyzed by the analysis unit. For example, the suggestion unit can suggest hobbies based on the user's past behavioral data and interest trends. The suggestion unit can also suggest hobbies that match the user's lifestyle. For example, if the user prefers outdoor activities, the suggestion unit can suggest hiking or camping. The guide providing unit provides step-by-step guides and necessary tools for practicing the hobby suggested by the suggestion unit. For example, the guide providing unit may provide a gardening kit for beginners, instructions on selecting seasonal plants, and basic care methods. The guide providing unit may also provide video tutorials and interactive step-by-step guides. For example, the guide providing unit may explain cooking recipes through videos. In this way, the hobby suggestion system according to the embodiment can suggest appropriate hobbies based on the user's interests and preferences and provide instructions on how to practice them, thereby helping the user continue with the hobby and maintaining their motivation.

[0058] The information collection unit can analyze a user's past social media posts and online activity history to discover potential interests and suggest hobbies. For example, the generation AI in the information collection unit analyzes a user's past social media posts and extracts frequently mentioned keywords and hashtags. For example, if a user frequently uses keywords such as "travel" or "cooking," the information collection unit can suggest hobbies related to travel and cooking based on these keywords. The information collection unit can also analyze a user's online activity history and suggest hobbies based on the websites visited and the content of articles read. For example, if a user spends a lot of time on gardening-related websites, the information collection unit can suggest gardening as a hobby. The information collection unit can also analyze a user's social media photo posts and identify interests using image recognition technology. For example, if a user posts many outdoor photos, the information collection unit can suggest outdoor activities such as hiking and camping as hobbies. This allows the information collection unit to discover potential interests and suggest appropriate hobbies based on the user's past activity history.

[0059] The information collection unit analyzes the hobbies of the user's friends and family and suggests common hobbies, thereby strengthening social connections. For example, the information collection unit uses a generation AI to analyze social media posts from the user's friends and family to identify common hobbies. For example, if the user's friends spend a lot of time cooking, the generation AI suggests cooking as a common hobby. The information collection unit also analyzes the online activity history of the user's family and friends to find common interests. For example, if all family members frequently visit fitness-related sites, the generation AI suggests fitness as a common hobby. The information collection unit also collects data on the hobbies of the user's friends and family and suggests common hobbies. For example, if a friend spends a lot of time gardening, the generation AI suggests gardening as a common hobby. This allows the generation AI to analyze the hobbies of the user's friends and family and suggest common hobbies, thereby strengthening social connections.

[0060] The information collection unit can use the emotion estimation function to suggest hobbies based on the activity in which the user expressed the most positive emotions in the past. For example, the information collection unit uses the generation AI to analyze the user's past social media posts and online activity history to identify posts that show positive emotions. For example, if a user's posts about travel have received many likes and positive comments, the information collection unit can suggest traveling as a hobby. The information collection unit can also use the emotion estimation function to identify the activity in which the user expressed the most positive emotions in the past and suggest hobbies based on that. For example, if a user's posts about cooking have received many positive responses, the information collection unit can suggest cooking as a hobby. The information collection unit can also use the generation AI to analyze the user's past activity data to identify moments in which the user expressed positive emotions. For example, if the user expressed many positive emotions when participating in a sporting event, the information collection unit can suggest sports as a hobby. This allows the information collection unit to suggest appropriate hobbies based on the activity in which the user expressed the most positive emotions in the past.

[0061] The information collection unit can analyze the user's health data and suggest hobbies suitable for the user's health condition. For example, the generation AI analyzes data from the user's fitness tracker and suggests hobbies suitable for the user's health condition based on their heart rate and exercise volume. For example, if the user maintains a high level of exercise, the generation AI can suggest running or cycling as hobbies. The information collection unit can also analyze the user's health data and suggest hobbies that have a relaxing effect based on their stress level and sleep patterns. For example, if the user shows a high stress level, the generation AI can suggest yoga or meditation as hobbies. The information collection unit can also suggest hobbies that improve physical strength and flexibility based on the user's health data. For example, if the user wants to improve their flexibility, the generation AI can suggest Pilates or dancing as hobbies. This makes it possible to suggest hobbies suitable for the user's health condition based on the user's health data.

[0062] The information gathering unit can suggest hobbies that will lead to career advancement, taking into account the user's occupation and skill set. For example, the generation AI analyzes the user's occupational data and suggests hobbies that will lead to career advancement. For example, if the user works in the IT industry, it suggests programming and data science as hobbies. The information gathering unit also analyzes the user's skill set and suggests hobbies that will improve those skills. For example, if the user has design skills, it suggests graphic design and photography as hobbies. The information gathering unit also suggests hobbies that will improve networking and business skills, based on the user's occupation and skill set. For example, if the user wants to improve their business skills, it suggests business seminars and entrepreneurial activities as hobbies. This makes it possible to suggest hobbies that will lead to career advancement based on the user's occupation and skill set.

[0063] The information collection unit uses the emotion estimation function to monitor the user's emotions in real time when choosing a hobby and make optimal suggestions. For example, the information collection unit uses the emotion estimation function to monitor the user's emotions in real time when choosing a hobby and suggest hobbies that indicate positive emotions. For example, if the user smiles while looking at hobby options, that hobby is preferentially suggested. The information collection unit also uses the generation AI to analyze the user's emotion data and suggest optimal hobbies in real time. For example, if the user shows excitement or joy while looking at hobby options, that hobby is suggested. The information collection unit also uses the emotion estimation function to monitor the user's emotions when choosing a hobby and eliminate hobbies that indicate negative emotions. For example, hobbies that make the user feel anxious or stressed are excluded from suggestions. This allows the information collection unit to monitor the user's emotions in real time when choosing a hobby and make optimal suggestions.

[0064] The analysis unit can analyze a user's purchasing history and identify interests and preferences. For example, the generation AI analyzes a user's online shopping purchase history to identify interests and preferences. For example, if a user frequently purchases outdoor equipment, the analysis unit suggests hobbies related to outdoor activities. The analysis unit also analyzes a user's purchasing history and, if the user purchases many products in a specific category, suggests hobbies related to that category. For example, if the user purchases many cookbooks, the analysis unit suggests cooking as a hobby. The analysis unit also identifies interests and preferences based on the user's purchasing history and suggests related hobbies. For example, if the user purchases many art supplies, the analysis unit suggests painting and sculpting as hobbies. In this way, interests and preferences can be identified based on the user's purchasing history and appropriate hobbies can be suggested.

[0065] The analysis unit can use the emotion estimation function to identify moments in daily life when the user shows the most positive emotions and suggest hobbies based on those moments. For example, the analysis unit can use the emotion estimation function to identify moments in daily life when the user shows the most positive emotions and suggest hobbies based on those moments. For example, if the user shows the most positive emotions when spending time with family, the analysis unit can suggest hobbies that can be enjoyed with family. The analysis unit also uses the generative AI to analyze the user's daily life emotional data and identify activities that show positive emotions. For example, if the user shows the most positive emotions when listening to music, the analysis unit can suggest playing musical instruments or music production as hobbies. The analysis unit also uses the emotion estimation function to identify moments in daily life when the user shows the most positive emotions and suggest hobbies based on those moments. For example, if the user shows the most positive emotions when spending time with pets, the analysis unit can suggest pet care and training as hobbies. This makes it possible to suggest appropriate hobbies based on the moments in daily life when the user shows the most positive emotions.

[0066] The analysis unit can analyze a user's music and movie viewing history and suggest hobbies based on their entertainment preferences. For example, the analysis unit uses a generation AI to analyze a user's music viewing history and suggest hobbies based on their favorite genres and artists. For example, if a user likes jazz, the analysis unit can suggest jazz dance or playing musical instruments as hobbies. The analysis unit can also analyze a user's movie viewing history and suggest hobbies based on their favorite genres and directors. For example, if a user likes science fiction movies, the analysis unit can suggest hobbies such as writing science fiction novels and filmmaking as hobbies. The analysis unit can also use a generation AI to suggest hobbies that match the user's entertainment preferences based on the user's music and movie viewing history. For example, if a user spends a lot of time using music streaming services, the analysis unit can suggest hobbies such as DJing and music production as hobbies. This allows the analysis unit to suggest hobbies that match the user's entertainment preferences based on the user's music and movie viewing history.

[0067] The analysis unit can analyze the user's food preferences and suggest hobbies related to cooking and gourmet food. For example, the generation AI in the analysis unit analyzes the user's food records and suggests hobbies based on the user's favorite dishes and ingredients. For example, if the user likes Italian food, the analysis unit suggests Italian cooking classes as a hobby. The analysis unit can also analyze the user's food preferences and suggest hobbies related to cooking and gourmet food. For example, if the user likes sweets, the analysis unit suggests the hobby of learning pastry chef techniques. The analysis unit can also use the generation AI to suggest hobbies related to cooking and gourmet food based on the user's food preferences. For example, if the user spends a lot of time on restaurant review sites, the analysis unit suggests writing a gourmet blog as a hobby. This makes it possible to suggest hobbies related to cooking and gourmet food based on the user's food preferences.

[0068] The analysis unit can use the emotion estimation function to analyze the emotions the user shows toward specific activities and suggest optimal hobbies. For example, the analysis unit can use the emotion estimation function to analyze the emotions the user shows toward specific activities and suggest hobbies based on activities that show positive emotions. For example, if the user shows positive emotions toward watching sports, the analysis unit can suggest sports as a hobby. The analysis unit also uses the generation AI to analyze the user's emotion data and suggest hobbies based on the user's emotional reactions to specific activities. For example, if the user shows positive emotions toward visiting an art gallery, the analysis unit can suggest painting and sculpting as hobbies. The analysis unit also uses the emotion estimation function to analyze the emotions the user shows toward specific activities in real time and suggest optimal hobbies. For example, if the user shows positive emotions toward music festivals, the analysis unit can suggest playing musical instruments and music production as hobbies. This makes it possible to suggest optimal hobbies based on the emotions the user shows toward specific activities.

[0069] The guide providing unit can analyze the user's learning style and provide a practical hobby guide using the optimal learning method. For example, the generation AI in the guide providing unit analyzes the user's learning style and provides a visual guide to users who prefer visual learning. For example, a guide using videos or illustrations is provided. The guide providing unit also provides a practical hobby guide using the optimal learning method based on the user's learning style. For example, an audio guide is provided to users who prefer auditory learning. The guide providing unit also analyzes the user's learning style and provides a hands-on guide to users who prefer practical learning. For example, a workshop-style guide is provided where users can learn by actually doing their hands. In this way, a practical hobby guide can be provided using the optimal learning method based on the user's learning style.

[0070] The guide providing unit can monitor the user's progress in real time and adjust the guide content as needed. For example, the generation AI of the guide providing unit monitors the user's progress in real time and adjusts the guide content according to the progress. For example, if the user stumbles in the early stages of gardening, the guide providing unit re-explains basic maintenance methods. The guide providing unit also adjusts the guide content in real time based on the user's progress. For example, if the user is progressing smoothly through the cooking steps, the guide providing unit provides advice on how to proceed to the next step. The generation AI of the guide providing unit also monitors the user's progress and adjusts the guide content as needed. For example, if the user is falling behind in sports training, the guide providing unit provides advice on changing the training method. This makes it possible to adjust the guide content in real time according to the user's progress.

[0071] The guide providing unit can use the emotion estimation function to identify the learning method that shows the user the most positive emotion and provide a guide based on that. For example, the guide providing unit can use the emotion estimation function to identify the learning method that shows the user the most positive emotion and provide a guide based on that. For example, if the user shows the most positive emotion while watching a video, a video guide is provided. The guide providing unit can also use the generation AI to analyze the user's emotion data and identify a learning method that shows positive emotion. For example, if the user shows the most positive emotion when learning by actually using their hands, a hands-on guide is provided. The guide providing unit can also use the emotion estimation function to identify in real time the learning method that shows the user the most positive emotion and provide a guide based on that. For example, if the user shows the most positive emotion while listening to an audio guide, an audio guide is provided. This makes it possible to provide a guide based on the learning method that shows the user the most positive emotion.

[0072] The guide providing unit can analyze the user's visual and auditory information and provide a multimodal guide. For example, the generation AI in the guide providing unit analyzes the user's visual information and provides a guide that is easy to understand visually. For example, if the user prefers visual information, the guide providing unit provides a guide using illustrations or videos. The guide providing unit can also analyze the user's auditory information and provide a guide that is easy to understand auditorily. For example, if the user prefers audio information, the guide providing unit provides an audio guide or podcast-style guide. The guide providing unit can also integrate the user's visual and auditory information and provide a multimodal guide. For example, if the user prefers to learn using both visual and auditory means, the guide providing unit provides a guide that combines video and audio. This makes it possible to provide a multimodal guide based on the user's visual and auditory information.

[0073] The guide providing unit can collect user feedback and continuously improve the guide content. For example, the guide providing unit constructs a system in which a generation AI collects user feedback and continuously improves the guide content. For example, a user can provide their opinion on the guide content, and the guide is updated based on that opinion. The guide providing unit also collects user feedback in real time and immediately improves the guide content. For example, if a user finds a particular part of the guide difficult to understand, that part is improved. The guide providing unit also has the generation AI analyze user feedback and continuously improve the guide content. For example, if a user provides feedback on the guide's progress speed, the speed is adjusted. This allows the guide content to be continuously improved based on user feedback.

[0074] The analysis unit can analyze the user's past successful experiences and present a future vision based on them. For example, the generation AI of the analysis unit analyzes the user's past successful experiences and presents a future vision based on those successful experiences. For example, if the user has had success in gardening in the past, the analysis unit presents the relaxation effects and health benefits that can be obtained by continuing to garden in the future. The analysis unit also presents a future vision based on the user's past successful experiences. For example, if the user has had success in cooking in the past, the analysis unit presents the deepening of communication with family and friends that can be obtained by continuing to cook in the future. The analysis unit can also present a future vision based on the user's past successful experiences. For example, if the user has had success in sports in the past, the analysis unit presents the physical strength improvement and stress relief that can be obtained by continuing to play sports in the future. In this way, a future vision can be presented based on the user's past successful experiences.

[0075] The analysis unit can analyze the user's long-term goals and present a future vision that matches those goals. For example, the generation AI analyzes the user's long-term goals and presents a future vision that matches those goals. For example, if the user's long-term goal is to maintain health, the analysis unit presents a future vision that can be achieved by continuing a health-related hobby. The analysis unit also presents a future vision based on the user's long-term goals. For example, if the user is aiming to advance their career, the analysis unit presents a future vision that can be achieved by continuing a career-related hobby. The analysis unit also presents a future vision that matches those goals. For example, if the user's goal is to deepen social connections, the analysis unit presents a future vision that can be achieved by continuing a hobby related to community activities. In this way, a future vision can be presented based on the user's long-term goals.

[0076] The analysis unit can use the emotion estimation function to identify the future vision in which the user expresses the most positive emotions and present it. For example, the analysis unit uses the emotion estimation function to identify the future vision in which the user expresses the most positive emotions and present it. For example, if the user expresses positive emotions about improving their health, a future vision related to health is presented. The analysis unit also uses the generation AI to analyze the user's emotion data and identify a future vision in which they express positive emotions. For example, if the user expresses positive emotions about spending time with family, a future vision of deepening time with family is presented. The analysis unit also uses the emotion estimation function to identify the future vision in which the user expresses the most positive emotions in real time and present it. For example, if the user expresses positive emotions about career advancement, a future vision related to career is presented. This makes it possible to identify the future vision in which the user expresses the most positive emotions and present it.

[0077] The analysis unit can analyze the user's health data and present a future vision of health. In the analysis unit, for example, a generation AI analyzes the user's health data and presents a future vision of health. For example, it presents a future vision of health promotion and disease prevention that the user will achieve by continuing to exercise regularly. The analysis unit also presents a future vision of health based on the user's health data. For example, it presents a future vision of weight management and improved nutritional balance that the user will achieve by continuing to eat a balanced diet. In addition, the analysis unit can analyze the user's health data and present a future vision of health. For example, it presents a future vision of improved mental health and relaxation effects that the user will achieve by continuing to manage stress. In this way, a future vision of health can be presented based on the user's health data.

[0078] The analysis unit can analyze the user's career data and present a future vision for their career. In the analysis unit, for example, the generation AI analyzes the user's career data and presents a future vision for their career. For example, it presents a future vision of career advancement and promotion that the user can achieve by honing specific skills. The analysis unit also presents a future vision for their career based on the user's career data. For example, it presents a future vision of expanded career options and improved expertise that the user can achieve by obtaining new qualifications. In addition, the analysis unit can analyze the user's career data and present a future vision for their career. For example, it presents a future vision of business opportunities and expanded personal connections that the user can achieve by continuing to network. In this way, it is possible to present a future vision for their career based on the user's career data.

[0079] The analysis unit can use the emotion estimation function to monitor the emotions of the user when viewing a future vision in real time and present an optimal vision. For example, the analysis unit can use the emotion estimation function to monitor the emotions of the user when viewing a future vision in real time and present a future vision that elicits positive emotions. For example, if the user expresses positive emotions when viewing a future vision of health improvement, the analysis unit emphasizes that vision. The analysis unit also uses the generation AI to analyze the user's emotion data in real time and presents an optimal future vision. For example, if the user expresses positive emotions when viewing a future vision of career advancement, the analysis unit emphasizes that vision. The analysis unit also uses the emotion estimation function to monitor the emotions of the user when viewing a future vision in real time and present a future vision that reduces negative emotions. For example, if the user feels anxious when viewing a future vision of stress management, the analysis unit emphasizes a future vision with a relaxing effect. In this way, the analysis unit can monitor the emotions of the user when viewing a future vision in real time and present an optimal vision.

[0080] The analysis unit can analyze the user's past motivation data and suggest optimal methods for maintaining motivation. For example, the generation AI analyzes the user's past motivation data and suggests optimal methods for maintaining motivation. For example, if the user has maintained motivation in the past by setting goals, the analysis unit suggests specific goal setting. The analysis unit also suggests optimal methods for maintaining motivation based on the user's motivation data. For example, if the user has maintained motivation in the past by using a reward system, the analysis unit suggests a reward system. The analysis unit also suggests specific methods for maintaining motivation based on the user's past motivation data. For example, if the user has maintained motivation in the past by participating in activities with friends, the analysis unit suggests community activities. This makes it possible to suggest optimal methods for maintaining motivation based on the user's past motivation data.

[0081] The guide providing unit can monitor the user's progress in real time and provide feedback at appropriate times. For example, the generation AI of the guide providing unit monitors the user's progress in real time and provides feedback at appropriate times. For example, if the user is recording their gardening progress, the guide providing unit provides feedback praising the growth process. The guide providing unit also provides feedback at appropriate times based on the user's progress. For example, if the user has completed a cooking recipe, the guide providing unit provides feedback praising the result. The guide providing unit also monitors the user's progress in real time and provides feedback at appropriate times. For example, if the user has completed sports training, the guide providing unit provides feedback acknowledging the effort. This makes it possible to provide feedback at appropriate times according to the user's progress.

[0082] The guide providing unit can use the emotion estimation function to identify and suggest a method of maintaining motivation that indicates the user's most positive emotions. For example, the guide providing unit can use the emotion estimation function to identify and suggest a method of maintaining motivation that indicates the user's most positive emotions. For example, if the user indicates the most positive emotions when achieving a goal, the guide providing unit can suggest specific goal setting. The guide providing unit also uses the generation AI to analyze the user's emotion data and identify a method of maintaining motivation that indicates positive emotions. For example, if the user indicates the most positive emotions when receiving a reward, the guide providing unit can suggest a reward system. The guide providing unit also uses the emotion estimation function to identify in real time a method of maintaining motivation that indicates the user's most positive emotions and suggest it. For example, if the user indicates the most positive emotions when doing activities with friends, the guide providing unit can suggest community activities. This makes it possible to identify and suggest a method of maintaining motivation that indicates the user's most positive emotions.

[0083] The guide providing unit can analyze data related to the user's hobbies and periodically provide new information and advice. For example, the generation AI of the guide providing unit analyzes data related to the user's hobbies and periodically provides new information and advice. For example, if the user's hobby is gardening, the guide providing unit provides information on how to select and care for seasonal plants. The guide providing unit also periodically provides new information and advice based on data related to the user's hobbies. For example, if the user's hobby is cooking, the guide providing unit provides new recipes and cooking techniques. The generation AI of the guide providing unit also analyzes data related to the user's hobbies and periodically provides new information and advice in real time. For example, if the user's hobby is sports, the guide providing unit provides new training methods and competition information. In this way, the guide providing unit can analyze data related to the user's hobbies and periodically provide new information and advice.

[0084] The guide providing unit can use the emotion estimation function to monitor the user's emotions in real time when practicing a hobby and provide optimal support. For example, the guide providing unit can use the emotion estimation function to monitor the user's emotions in real time when practicing a hobby and provide support to bring out positive emotions. For example, if the user shows positive emotions while gardening, the guide providing unit can provide advice on maintaining those emotions. The guide providing unit can also use the generation AI to analyze the user's emotion data in real time and provide optimal support. For example, if the user feels anxious while cooking, the guide providing unit can provide simple recipes and cooking methods to alleviate that anxiety. The guide providing unit can also use the emotion estimation function to monitor the user's emotions in real time when practicing a hobby and provide support to alleviate negative emotions. For example, if the user feels stressed while playing sports, the guide providing unit can provide relaxation and stretching techniques. This allows the user's emotions in real time when practicing a hobby to be monitored and optimal support to be provided.

[0085] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0086] The hobby suggestion system can also analyze a user's past travel history to suggest travel-related hobbies. For example, based on data on countries and regions the user has visited in the past, it can suggest hobbies related to the culture and cuisine of those regions. It can also analyze data on travel events and activities the user has participated in in the past to suggest similar activities as hobbies. It can also analyze photos and videos taken by the user during travel and suggest photography and video editing as hobbies. This makes it possible to suggest travel-related hobbies based on the user's travel history.

[0087] The hobby suggestion system can also analyze a user's reading history to suggest hobbies related to reading. For example, based on data on the genres and authors of books the user has read in the past, it can suggest books of similar genres or authors. It can also analyze data on reviews and impressions posted by users about books and suggest book clubs or writing book review blogs as hobbies. It can also analyze data on notes and highlights taken by users while reading and suggest creating reading notebooks or running a book club as hobbies. In this way, it is possible to suggest hobbies related to reading based on a user's reading history.

[0088] The hobby suggestion system can also analyze a user's music production history to suggest music-related hobbies. For example, it can suggest similar instruments and music genres based on data on songs the user has composed in the past and the instruments they have used. It can also analyze data on the user's participation in music production tutorials and workshops to suggest similar tutorials and workshops as hobbies. It can also analyze data on the software and tools the user has used in music production to suggest hobbies such as how to use music production software or learning new tools. This makes it possible to suggest music-related hobbies based on the user's music production history.

[0089] The hobby suggestion system can also analyze a user's sports viewing history to suggest sports-related hobbies. For example, similar sports and teams can be suggested based on data on sports events the user has watched in the past and teams the user has supported. The system can also analyze a user's blogs and social media posts about sports viewing to suggest hobbies such as writing sports spectator reports or participating in sports fan communities. Furthermore, the system can analyze photos and videos the user has taken while watching sports to suggest sports photography and video editing as hobbies. This allows the system to suggest sports-related hobbies based on the user's sports viewing history.

[0090] The hobby suggestion system can also analyze a user's movie-watching history to suggest movie-related hobbies. For example, based on data on the genres and directors of movies the user has watched in the past, it can suggest movies of similar genres or directors. It can also analyze data on movie reviews and impressions posted by users and suggest writing movie reviews or joining a movie club as hobbies. It can also analyze photos and videos taken by users while watching movies and suggest movie production or video editing as hobbies. In this way, it can suggest movie-related hobbies based on a user's movie-watching history.

[0091] The hobby suggestion system can further use the user's emotion estimation function to identify the music genre that the user feels most positively and suggest hobbies based on that. For example, if the user feels most positively when listening to jazz, jazz dancing or playing jazz instruments can be suggested as hobbies. If the user feels most positively when listening to classical music, listening to classical music or playing an instrument can be suggested as hobbies. Furthermore, if the user feels most positively when listening to rock music, forming a rock band or performing live can be suggested as hobbies. In this way, music-related hobbies can be suggested based on the music genre that the user feels most positively.

[0092] The hobby suggestion system can further use the user's emotion estimation function to identify travel destinations for which the user expresses the most positive emotions and suggest hobbies based on that. For example, if the user expresses the most positive emotions while staying at a beach resort, beach activities and marine sports can be suggested as hobbies. If the user expresses the most positive emotions while staying in a mountainous area, hiking and camping can be suggested as hobbies. Furthermore, if the user expresses the most positive emotions while sightseeing in a city, urban exploration and visiting historical buildings can be suggested as hobbies. In this way, travel-related hobbies can be suggested based on the travel destinations for which the user expresses the most positive emotions.

[0093] The hobby suggestion system can further use the user's emotion estimation function to identify the sport for which the user expresses the most positive emotion and suggest hobbies based on that. For example, if the user expresses the most positive emotion when playing soccer, playing or watching soccer can be suggested as a hobby. If the user expresses the most positive emotion when playing basketball, playing or watching basketball can be suggested as a hobby. If the user expresses the most positive emotion when playing tennis, playing or watching tennis can be suggested as a hobby. In this way, sports-related hobbies can be suggested based on the sport for which the user expresses the most positive emotion.

[0094] The hobby suggestion system can further use the user's emotion estimation function to identify the cooking genre for which the user expresses the most positive emotion and suggest hobbies based on that. For example, if the user expresses the most positive emotion when cooking Italian food, the system can suggest Italian cooking classes and studying recipes as hobbies. If the user expresses the most positive emotion when cooking Japanese food, the system can suggest Japanese cooking classes and studying recipes as hobbies. If the user expresses the most positive emotion when cooking French food, the system can suggest French cooking classes and studying recipes as hobbies. This makes it possible to suggest cooking-related hobbies based on the cooking genre for which the user expresses the most positive emotion.

[0095] The hobby suggestion system can further use the user's emotion estimation function to identify the art activity that the user feels most positive about and suggest hobbies based on that. For example, if the user feels most positive about painting, painting classes or visiting art galleries can be suggested as hobbies. If the user feels most positive about sculpting, sculpture classes or visiting art galleries can be suggested as hobbies. If the user feels most positive about taking photos, photography classes or visiting photography exhibitions can be suggested as hobbies. In this way, art-related hobbies can be suggested based on the art activity that the user feels most positive about.

[0096] The processing flow of the second embodiment will be briefly explained below.

[0097] Step 1: The information collection unit collects information about the user's interests, preferences, and lifestyle. For example, it collects information such as the user's hobbies, food preferences, and daily activities through questionnaires. It can also collect user activity data using sensors. For example, it can collect the user's exercise volume and heart rate using a wearable device. It can also collect online data. For example, it can collect the user's social media posts and browsing history. Step 2: The analysis unit analyzes the information collected by the information collection unit. For example, it uses data mining technology to analyze the user's interests and preferences. It can also analyze the user's lifestyle using machine learning algorithms. For example, it uses clustering technology to analyze the user's behavioral patterns. Step 3: The suggestion unit suggests hobbies based on the information analyzed by the analysis unit. For example, it can suggest hobbies based on the user's past behavioral data and tendencies of interest. It can also suggest hobbies that fit the user's lifestyle. For example, if the user likes outdoor activities, it can suggest hiking and camping. Step 4: The guide provider provides step-by-step instructions, tools, and guides for the hobby suggested by the suggestion provider. For example, it can provide gardening kits for beginners, instructions on how to select seasonal plants, and basic care methods. It can also provide video tutorials and interactive step-by-step guides. For example, it can explain cooking recipes through video.

[0098] 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.

[0099] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] 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.

[0101] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] 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.

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0117] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the 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 specific processing unit 290 using these models.

[0127] 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.

[0128] 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.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] 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.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 7, the 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.

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

[0134] The 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.

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

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

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

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] In the robot 414, 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 robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0143] 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.

[0144] 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.

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0152] 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."

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0165] 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. an information gathering unit that gathers information about the user's interests, preferences, and lifestyle; an analysis unit that analyzes the information collected by the information collection unit; a suggestion unit that suggests hobbies based on the information analyzed by the analysis unit; a guide providing unit that provides a method for practicing the hobby, necessary tools, and a step-by-step guide for the hobby suggested by the suggestion unit; A system characterized by:

2. The information collecting unit Analyzes users' past social media posts and online activity history to discover potential interests and suggest hobbies 2. The system of claim 1.

3. The information collecting unit Strengthen social connections by analyzing the interests of users' friends and family and suggesting common interests 2. The system of claim 1.

4. The information collecting unit Suggest hobbies based on the activities that users have most positively felt in the past 2. The system of claim 1.

5. The information collecting unit Analyzing the user's health data and suggesting hobbies suited to their health condition 2. The system of claim 1.

Images