System

Abstract

An object of a system according to an embodiment is to enable a user to easily start a new hobby or sport at his / her own pace.SOLUTION: A system according to an embodiment includes an interest analyzer, a plan provider, and a guide generator. The interest analysis unit analyzes the user's interest and level. The plan providing unit provides a customized plan based on the information analyzed by the interest analysis unit. The guide generator generates a HowTo guide based on the plan provided by the plan provider.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] With conventional technology, users had difficulty finding appropriate guides and plans when starting a new hobby or sport.

[0005] The system according to the embodiment aims to enable users to easily start a new hobby or sport at their own pace. [Means for solving the problem]

[0006] The system according to the embodiment includes an interest analysis unit, a plan providing unit, and a guide generation unit. The interest analysis unit analyzes the user's interests and level. The plan providing unit provides a customized plan based on the information analyzed by the interest analysis unit. The guide generation unit generates a HowTo guide based on the plan provided by the plan providing unit. [Effects of the Invention]

[0007] The system according to the embodiment can enable users to easily start a new hobby or sport at their own pace. [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 total support system according to an embodiment of the present invention is a system that uses AI to provide total support for designing how-to and to-do lists for hobbies and sports that people want to try. This allows people to easily start a new hobby or sport at their own pace without relying on anyone else.

[0029] A total support system according to an embodiment includes an interest analysis unit, a plan providing unit, and a guide generation unit. The interest analysis unit analyzes a user's interests and level. For example, the interest analysis unit identifies the user's interest and level in hobbies and sports based on information input by the user. The interest analysis unit can also analyze the user's past history of hobbies and sports to understand changes and patterns in interests. For example, the interest analysis unit collects a history of the user's past participation in hobbies and sports and analyzes that data. The interest analysis unit can also analyze the user's lifestyle habits and time usage to suggest optimal hobbies and sports. For example, the interest analysis unit can investigate the user's weekday schedule and how they spend their weekends. The plan providing unit provides a customized plan based on the information analyzed by the interest analysis unit. For example, the plan providing unit creates a hobby or sports plan based on the user's interests and level. The plan providing unit can also analyze the user's progress data in real time and dynamically adjust the plan. For example, as the user practices guitar, the plan providing unit can change the practice content according to the user's level of improvement. The plan providing unit can also collect user feedback and continuously improve the plan based on that feedback. For example, a questionnaire is used to collect information about the user's satisfaction with the plan and areas for improvement. The guide generation unit generates a How-To guide based on the plan provided by the plan provision unit. For example, the guide generation unit provides a guide that explains in detail how to hold chords and strokes to a user who wants to learn how to play the guitar. The guide generation unit can also incorporate videos and animations into the How-To guide to provide it in a visually easy-to-understand format. For example, a video explaining how to hold guitar chords can be used. This allows the total support system according to the embodiment to provide plans and How-To guides customized according to the user's interests and level.

[0030] The interest analysis unit analyzes the user's past hobby and sports history to understand changes and patterns in interests. For example, the interest analysis unit collects the user's past hobby and sports history and analyzes that data. For example, it investigates what sports clubs the user belonged to and what hobbies the user engaged in in the past. The interest analysis unit also conducts a questionnaire about the user's past hobbies and sports and analyzes changes and patterns in interests based on the results. For example, it identifies which hobbies the user was interested in and at what time. The interest analysis unit can also analyze the user's social media posts and online activity to understand the user's past hobby and sports history. For example, it analyzes photos and comments about hobbies and sports that the user posted in the past. This allows the user's past hobby and sports history to be analyzed and changes and patterns in interests to be understood.

[0031] The interest analysis unit analyzes the user's lifestyle habits and how they spend their time, and can suggest optimal hobbies and sports. The interest analysis unit, for example, analyzes the user's lifestyle habits and identifies the amount of time they can allocate to hobbies and sports. For example, it investigates the user's weekday schedule and how they spend their weekends. The interest analysis unit also analyzes how the user spends their time and suggests time periods that are suitable for hobbies and sports. For example, it identifies time periods when the user is most relaxed or when their energy level is highest. The interest analysis unit also suggests hobbies and sports that the user can continue without difficulty, based on the user's lifestyle habits. For example, it suggests effective training methods that can be done in a short amount of time, or hobbies that are easy to incorporate into daily life. In this way, it is possible to analyze the user's lifestyle habits and how they spend their time, and suggest optimal hobbies and sports.

[0032] The interest analysis unit can collect more diverse information when analyzing a user's interests and level by using voice input and image input. The interest analysis unit, for example, allows a user to input information about hobbies or sports by voice and analyzes the voice data. For example, the interest and level can be determined from what the user says. The interest analysis unit can also allow a user to upload images related to hobbies or sports and analyze the image data. For example, the interest and level can be determined from photos or screenshots taken by the user. The interest analysis unit can also combine voice input and image input to more accurately analyze a user's interests and level. For example, what the user says and the images uploaded can be integrated and analyzed. This allows a user's interests and level to be more accurately analyzed by using voice input and image input.

[0033] The interest analysis unit can compare the interests and levels of users of different age groups and genders and make optimal suggestions for specific groups. For example, the interest analysis unit can compare the interests and levels of users of different age groups and suggest optimal hobbies and sports according to age. For example, it can suggest active sports for young people and relaxing hobbies for older people. The interest analysis unit can also analyze differences in interests and levels based on gender and suggest optimal hobbies and sports according to gender. For example, it can suggest outdoor sports for men and craft activities for women. The interest analysis unit can also make customized suggestions for specific groups based on age group and gender. For example, it can suggest hobbies and sports that users of the same age group and gender are interested in in common. This allows optimal suggestions to be made for users of different age groups and genders.

[0034] The plan providing unit can analyze the user's progress data in real time and dynamically adjust the plan. For example, the plan providing unit collects the user's progress data in real time and dynamically adjusts the plan based on that data. For example, as the user practices guitar, the practice content is changed according to the user's level of improvement. The plan providing unit also analyzes the user's progress data and suggests effective training and practice methods. For example, if the user is struggling with a particular technique, it provides a practice plan specialized for that technique. The plan providing unit also provides a plan to maintain the user's motivation based on the progress data collected in real time. For example, it sets goals that give the user a sense of accomplishment. This allows the user's progress data to be analyzed in real time and the plan to be dynamically adjusted.

[0035] The plan providing unit can collect user feedback and continuously improve the plan based on that feedback. For example, the plan providing unit periodically collects user feedback and continuously improves the plan based on that data. For example, the plan providing unit collects the user's level of satisfaction with the plan and areas for improvement through a questionnaire. The plan providing unit also analyzes the user's feedback and adjusts the content and progress of the plan. For example, it simplifies parts that the user finds difficult. The plan providing unit also provides a customized plan based on the user's needs based on the feedback. For example, it creates a plan that matches the user's desired practice frequency and time period. This allows the plan to be collected and continuously improved based on the user's feedback.

[0036] The plan providing unit can enable the customized plan to be used seamlessly across different devices. For example, the plan providing unit enables the customized plan to be used seamlessly across different devices, such as smartphones, tablets, and PCs. For example, the plan providing unit may build a cloud-based platform. The plan providing unit may also provide an interface optimized for each device, allowing users to comfortably use the plan on any device. For example, the plan providing unit may provide an app for smartphones and a web app for PCs. The plan providing unit may also synchronize data between devices, allowing users to check the latest plan on any device. For example, data entered on a smartphone may be instantly reflected on a PC. This allows the customized plan to be used seamlessly across different devices.

[0037] The plan providing unit can combine plans for different hobbies and sports to provide a plan that satisfies multiple interests simultaneously. For example, the plan providing unit combines plans for different hobbies and sports to provide a plan that satisfies multiple interests of the user simultaneously. For example, a plan that combines guitar practice and running training is created. The plan providing unit also customizes plans for different hobbies and sports according to the user's interests. For example, if the user is interested in music and fitness, a plan that incorporates both is provided. The plan providing unit also provides plans that combine multiple hobbies and sports to enable the user to work on them in a balanced manner. For example, a plan is created that involves guitar practice in the first half of the week and running training in the second half. In this way, plans for different hobbies and sports can be combined to provide a plan that satisfies multiple interests simultaneously.

[0038] The guide generation unit can incorporate videos and animations into the HowTo guide and provide it in a format that is visually easy to understand. For example, the guide generation unit can incorporate videos into the HowTo guide and provide it in a format that is visually easy for the user to understand. For example, a video can be used to explain how to play guitar chords. The guide generation unit can also incorporate animations into the HowTo guide and provide it in a format that is visually easy for the user to understand the steps. For example, basic sports movements can be explained using animations. The guide generation unit can also use videos and animations to visually enhance the content of the HowTo guide. For example, it can provide a guide that the user can actually watch and learn from. This allows videos and animations to be incorporated into the HowTo guide and provide it in a format that is visually easy to understand.

[0039] The guide generation unit can analyze the user's level of understanding and provide additional explanations or practice questions for parts that are insufficient in understanding. The guide generation unit, for example, analyzes the user's level of understanding and provides additional explanations for parts that are insufficient in understanding. For example, it provides detailed explanations for parts that the user found difficult to understand. The guide generation unit also provides practice questions for parts that are insufficient in understanding, allowing the user to deepen their understanding. For example, it provides practice questions on how to play guitar chords. The guide generation unit also analyzes the user's level of understanding in real time and provides additional explanations or practice questions as needed. For example, it immediately supplements parts that the user found difficult to understand. In this way, the user's level of understanding can be analyzed and additional explanations or practice questions can be provided for parts that are insufficient in understanding.

[0040] The guide generation unit can automatically translate HowTo guides into different languages ​​to accommodate international users. The guide generation unit, for example, automatically translates HowTo guides into different languages ​​to accommodate international users. For example, it translates into multiple languages ​​such as English, French, and Chinese. The guide generation unit also provides the automatically translated HowTo guides to a multilingual platform so that they can be used by users around the world. For example, it posts the translated guides on a website or app. The guide generation unit also collects feedback from an international perspective based on the HowTo guides translated into different languages ​​to improve the quality of the guides. For example, it reflects feedback that takes cultural and market differences into account. This allows the HowTo guides to be automatically translated into different languages ​​to accommodate international users.

[0041] The guide generation unit can dynamically change the content of the guide according to the user's interests and level, and provide optimal information. The guide generation unit dynamically changes the content of the HowTo guide according to the user's interests and level, for example. For example, it provides a guide for beginners and a guide for advanced users. The guide generation unit also dynamically adjusts the content of the guide based on the user's progress data. For example, it provides more advanced content as the user improves. The guide generation unit also customizes the content of the guide based on user feedback and provides optimal information. For example, it adjusts the guide to match the content and format desired by the user. This makes it possible to dynamically change the content of the guide according to the user's interests and level, and provide optimal information.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The total support system may further include a health management unit that monitors the user's health condition. For example, the health management unit may analyze the user's heart rate and sleep patterns and suggest optimal hobbies and sports based on the user's health condition. The health management unit may also collect the user's dietary and exercise data and provide advice for maintaining health. For example, the health management unit may suggest recipes for the user to eat a balanced diet. The health management unit may also provide a plan for the user to enjoy hobbies and sports within reasonable limits based on the user's health data. For example, the health management unit may provide guidelines for the user to avoid excessive exercise. This allows the total support system to make suggestions that take the user's health condition into consideration.

[0044] The total support system can further include a community collaboration unit to improve the user's sociability. For example, the community collaboration unit provides a platform for the user to connect with other users who share the same hobbies or sports interests. The community collaboration unit can also suggest events and workshops that the user can participate in. For example, it can introduce local sports clubs or hobby circles. The community collaboration unit can also make suggestions to improve the quality of community activities based on user feedback. For example, it can survey the user's satisfaction with an event they participated in and reflect the results in the next event. In this way, the total support system can provide support to improve the user's sociability.

[0045] The total support system may further include an education support unit to increase the user's motivation to learn. For example, the education support unit may provide an online course for the user to learn a new hobby or sport. The education support unit may also monitor the user's learning progress and provide appropriate feedback. For example, the education support unit may provide advice for the user to master a specific skill. The education support unit may also provide learning materials customized according to the user's learning style. For example, the education support unit may provide video learning materials for users who prefer visual learning and e-books for users who prefer text-based learning. In this way, the total support system can provide support to increase the user's motivation to learn.

[0046] The total support system may further include a progress recording unit that records the user's progress in hobbies or sports, allowing the user to feel a sense of accomplishment. For example, the progress recording unit may record and visually display the goals and progress achieved by the user. The progress recording unit may also provide rewards or badges for the goals achieved by the user. For example, a digital badge may be awarded when the user achieves a specific goal. The progress recording unit may also suggest the next goal based on the user's progress data. For example, it may suggest the next goal the user should attempt. In this way, the total support system may record the user's progress, allowing the user to feel a sense of accomplishment.

[0047] The total support system may further include a social sharing unit for sharing the user's progress in hobbies or sports. For example, the social sharing unit may enable the user to share the goals and progress achieved on social media. The social sharing unit may also provide a function for the user to compare their progress with other users and increase their competitive spirit. For example, the social sharing unit may provide a ranking function for the user to compete with their friends on progress. The social sharing unit may also make suggestions to promote interaction with other users based on the user's progress data. For example, the social sharing unit may connect users who are interested in the same hobbies or sports. In this way, the total support system may enable the user to share their progress and promote interaction with other users.

[0048] The total support system may further include a performance analysis unit that analyzes the user's progress in hobbies or sports and provides advice for improving performance. For example, the performance analysis unit may analyze the user's practice data and match results and provide specific advice for improving performance. The performance analysis unit may also identify the user's weaknesses and propose a training plan to overcome them. For example, it may provide a training menu to improve techniques in which the user is weak. The performance analysis unit may also set long-term goals based on the user's performance data. For example, it may set goals that the user should achieve in stages. This allows the total support system to provide advice for improving the user's performance.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The interest analysis unit analyzes the user's interests and level. For example, it identifies the user's interest and level in hobbies and sports based on the information entered by the user. It can also analyze the user's past history of hobbies and sports to understand changes and patterns in interests. It can also analyze the user's lifestyle habits and how they spend their time to suggest the most suitable hobbies and sports. Step 2: The plan provider provides a customized plan based on the information analyzed by the interest analyzer. For example, it creates a hobby or sports plan based on the user's interests and level. It can also analyze the user's progress data in real time and dynamically adjust the plan. It can also collect user feedback and continuously improve the plan based on that feedback. Step 3: The guide generation unit generates a HowTo guide based on the plan provided by the plan provision unit. For example, a user who wants to learn how to play the guitar can be provided with a guide that explains in detail how to hold chords and strokes. It is also possible to incorporate videos and animations into the HowTo guide and provide it in a visually easy-to-understand format.

[0051] (Example 2) The total support system according to an embodiment of the present invention is a system that uses AI to provide total support for designing how-to and to-do lists for hobbies and sports that people want to try. This allows people to easily start a new hobby or sport at their own pace without relying on anyone else.

[0052] A total support system according to an embodiment includes an interest analysis unit, a plan providing unit, and a guide generation unit. The interest analysis unit analyzes a user's interests and level. For example, the interest analysis unit identifies the user's interest and level in hobbies and sports based on information input by the user. The interest analysis unit can also analyze the user's past history of hobbies and sports to understand changes and patterns in interests. For example, the interest analysis unit collects a history of the user's past participation in hobbies and sports and analyzes that data. The interest analysis unit can also analyze the user's lifestyle habits and time usage to suggest optimal hobbies and sports. For example, the interest analysis unit can investigate the user's weekday schedule and how they spend their weekends. The plan providing unit provides a customized plan based on the information analyzed by the interest analysis unit. For example, the plan providing unit creates a hobby or sports plan based on the user's interests and level. The plan providing unit can also analyze the user's progress data in real time and dynamically adjust the plan. For example, as the user practices guitar, the plan providing unit can change the practice content according to the user's level of improvement. The plan providing unit can also collect user feedback and continuously improve the plan based on that feedback. For example, a questionnaire is used to collect information about the user's satisfaction with the plan and areas for improvement. The guide generation unit generates a How-To guide based on the plan provided by the plan provision unit. For example, the guide generation unit provides a guide that explains in detail how to hold chords and strokes to a user who wants to learn how to play the guitar. The guide generation unit can also incorporate videos and animations into the How-To guide to provide it in a visually easy-to-understand format. For example, a video explaining how to hold guitar chords can be used. This allows the total support system according to the embodiment to provide plans and How-To guides customized according to the user's interests and level.

[0053] The interest analysis unit analyzes the user's past hobby and sports history to understand changes and patterns in interests. For example, the interest analysis unit collects the user's past hobby and sports history and analyzes that data. For example, it investigates what sports clubs the user belonged to and what hobbies the user engaged in in the past. The interest analysis unit also conducts a questionnaire about the user's past hobbies and sports and analyzes changes and patterns in interests based on the results. For example, it identifies which hobbies the user was interested in and at what time. The interest analysis unit can also analyze the user's social media posts and online activity to understand the user's past hobby and sports history. For example, it analyzes photos and comments about hobbies and sports that the user posted in the past. This allows the user's past hobby and sports history to be analyzed and changes and patterns in interests to be understood.

[0054] The interest analysis unit analyzes the user's lifestyle habits and how they spend their time, and can suggest optimal hobbies and sports. The interest analysis unit, for example, analyzes the user's lifestyle habits and identifies the amount of time they can allocate to hobbies and sports. For example, it investigates the user's weekday schedule and how they spend their weekends. The interest analysis unit also analyzes how the user spends their time and suggests time periods that are suitable for hobbies and sports. For example, it identifies time periods when the user is most relaxed or when their energy level is highest. The interest analysis unit also suggests hobbies and sports that the user can continue without difficulty, based on the user's lifestyle habits. For example, it suggests effective training methods that can be done in a short amount of time, or hobbies that are easy to incorporate into daily life. In this way, it is possible to analyze the user's lifestyle habits and how they spend their time, and suggest optimal hobbies and sports.

[0055] The interest analysis unit uses the emotion estimation function to analyze which hobbies or sports the user has felt positively about in the past, and can make new suggestions based on that. The interest analysis unit, for example, analyzes the user's past emotions toward hobbies or sports and identifies activities for which the user felt positively. For example, it lists hobbies or sports that the user has enjoyed in the past. The interest analysis unit also uses the emotion estimation function to analyze which hobbies or sports the user has felt positively about in the past. For example, it estimates emotions based on the user's past survey results and feedback. The interest analysis unit also suggests new hobbies or sports based on the user's past emotion data regarding hobbies or sports. For example, it suggests hobbies or sports similar to activities the user has enjoyed in the past. This makes it possible to make new suggestions based on hobbies or sports for which the user has felt positively in the past.

[0056] The interest analysis unit can collect more diverse information when analyzing a user's interests and level by using voice input and image input. The interest analysis unit, for example, allows a user to input information about hobbies or sports by voice and analyzes the voice data. For example, the interest and level can be determined from what the user says. The interest analysis unit can also allow a user to upload images related to hobbies or sports and analyze the image data. For example, the interest and level can be determined from photos or screenshots taken by the user. The interest analysis unit can also combine voice input and image input to more accurately analyze a user's interests and level. For example, what the user says and the images uploaded can be integrated and analyzed. This allows a user's interests and level to be more accurately analyzed by using voice input and image input.

[0057] The interest analysis unit can compare the interests and levels of users of different age groups and genders and make optimal suggestions for specific groups. For example, the interest analysis unit can compare the interests and levels of users of different age groups and suggest optimal hobbies and sports according to age. For example, it can suggest active sports for young people and relaxing hobbies for older people. The interest analysis unit can also analyze differences in interests and levels based on gender and suggest optimal hobbies and sports according to gender. For example, it can suggest outdoor sports for men and craft activities for women. The interest analysis unit can also make customized suggestions for specific groups based on age group and gender. For example, it can suggest hobbies and sports that users of the same age group and gender are interested in in common. This allows optimal suggestions to be made for users of different age groups and genders.

[0058] The interest analysis unit uses the emotion estimation function to analyze the emotions of the user when entering information in real time, and can provide an interface that draws out positive emotions. The interest analysis unit uses the emotion estimation function to analyze the emotions of the user when entering information about hobbies or sports in real time. For example, it analyzes the user's facial expressions and tone of voice. The interest analysis unit also uses the emotion estimation function to analyze the emotions of the user when entering information in real time, and can provide an interface that draws out positive emotions. For example, it designs an interface that allows the user to enjoy entering information. The interest analysis unit also dynamically adjusts the input interface based on the user's emotion data to draw out positive emotions. For example, it employs colors and designs that help the user relax. In this way, the emotions of the user when entering information in real time can be analyzed, and an interface that draws out positive emotions can be provided.

[0059] The plan providing unit can analyze the user's progress data in real time and dynamically adjust the plan. For example, the plan providing unit collects the user's progress data in real time and dynamically adjusts the plan based on that data. For example, as the user practices guitar, the practice content is changed according to the user's level of improvement. The plan providing unit also analyzes the user's progress data and suggests effective training and practice methods. For example, if the user is struggling with a particular technique, it provides a practice plan specialized for that technique. The plan providing unit also provides a plan to maintain the user's motivation based on the progress data collected in real time. For example, it sets goals that give the user a sense of accomplishment. This allows the user's progress data to be analyzed in real time and the plan to be dynamically adjusted.

[0060] The plan providing unit can collect user feedback and continuously improve the plan based on that feedback. For example, the plan providing unit periodically collects user feedback and continuously improves the plan based on that data. For example, the plan providing unit collects the user's level of satisfaction with the plan and areas for improvement through a questionnaire. The plan providing unit also analyzes the user's feedback and adjusts the content and progress of the plan. For example, it simplifies parts that the user finds difficult. The plan providing unit also provides a customized plan based on the user's needs based on the feedback. For example, it creates a plan that matches the user's desired practice frequency and time period. This allows the plan to be collected and continuously improved based on the user's feedback.

[0061] The plan providing unit can use the emotion estimation function to analyze the user's emotions toward the plan and provide a plan to elicit positive emotions. The plan providing unit, for example, uses the emotion estimation function to analyze the user's emotions toward the plan in real time. For example, it analyzes the user's facial expressions and tone of voice. The plan providing unit also provides a plan to elicit positive emotions based on the user's emotion data. For example, it suggests practice content and training methods that the user can enjoy. The plan providing unit also uses the emotion estimation function to dynamically adjust the plan according to the user's emotions. For example, if the user is feeling stressed, it provides a plan that will help the user relax. In this way, it is possible to analyze the user's emotions toward the plan and provide a plan to elicit positive emotions.

[0062] The plan providing unit can enable the customized plan to be used seamlessly across different devices. For example, the plan providing unit enables the customized plan to be used seamlessly across different devices, such as smartphones, tablets, and PCs. For example, the plan providing unit may build a cloud-based platform. The plan providing unit may also provide an interface optimized for each device, allowing users to comfortably use the plan on any device. For example, the plan providing unit may provide an app for smartphones and a web app for PCs. The plan providing unit may also synchronize data between devices, allowing users to check the latest plan on any device. For example, data entered on a smartphone may be instantly reflected on a PC. This allows the customized plan to be used seamlessly across different devices.

[0063] The plan providing unit can combine plans for different hobbies and sports to provide a plan that satisfies multiple interests simultaneously. For example, the plan providing unit combines plans for different hobbies and sports to provide a plan that satisfies multiple interests of the user simultaneously. For example, a plan that combines guitar practice and running training is created. The plan providing unit also customizes plans for different hobbies and sports according to the user's interests. For example, if the user is interested in music and fitness, a plan that incorporates both is provided. The plan providing unit also provides plans that combine multiple hobbies and sports to enable the user to work on them in a balanced manner. For example, a plan is created that involves guitar practice in the first half of the week and running training in the second half. In this way, plans for different hobbies and sports can be combined to provide a plan that satisfies multiple interests simultaneously.

[0064] The plan providing unit can use the emotion estimation function to monitor the emotions of the user when executing the plan in real time and adjust the plan as needed. The plan providing unit, for example, uses the emotion estimation function to monitor the emotions of the user when executing the plan in real time. For example, it analyzes the user's facial expressions and tone of voice. The plan providing unit also dynamically adjusts the plan based on the user's emotion data. For example, if the user is feeling stressed, it suggests practice content and training methods that will help the user relax. The plan providing unit also uses the emotion estimation function to change the plan in real time according to the user's emotions. For example, it provides a plan that the user can enjoy. This allows the plan to be monitored in real time when the user executes the plan and adjust the plan as needed.

[0065] The guide generation unit can incorporate videos and animations into the HowTo guide and provide it in a format that is visually easy to understand. For example, the guide generation unit can incorporate videos into the HowTo guide and provide it in a format that is visually easy for the user to understand. For example, a video can be used to explain how to play guitar chords. The guide generation unit can also incorporate animations into the HowTo guide and provide it in a format that is visually easy for the user to understand the steps. For example, basic sports movements can be explained using animations. The guide generation unit can also use videos and animations to visually enhance the content of the HowTo guide. For example, it can provide a guide that the user can actually watch and learn from. This allows videos and animations to be incorporated into the HowTo guide and provide it in a format that is visually easy to understand.

[0066] The guide generation unit can analyze the user's level of understanding and provide additional explanations or practice questions for parts that are insufficient in understanding. The guide generation unit, for example, analyzes the user's level of understanding and provides additional explanations for parts that are insufficient in understanding. For example, it provides detailed explanations for parts that the user found difficult to understand. The guide generation unit also provides practice questions for parts that are insufficient in understanding, allowing the user to deepen their understanding. For example, it provides practice questions on how to play guitar chords. The guide generation unit also analyzes the user's level of understanding in real time and provides additional explanations or practice questions as needed. For example, it immediately supplements parts that the user found difficult to understand. In this way, the user's level of understanding can be analyzed and additional explanations or practice questions can be provided for parts that are insufficient in understanding.

[0067] The guide generation unit can use the emotion estimation function to analyze the emotions of the user when reading the guide and provide a guide that elicits positive emotions. The guide generation unit, for example, uses the emotion estimation function to analyze the emotions of the user when reading the guide in real time. For example, it analyzes the user's facial expressions and voice tone. The guide generation unit also provides a guide that elicits positive emotions based on the user's emotion data. For example, it uses content and expressions that allow the user to enjoy learning. The guide generation unit also uses the emotion estimation function to dynamically adjust the content of the guide according to the user's emotions. For example, it provides a guide that helps the user relax. In this way, it is possible to analyze the emotions of the user when reading the guide and provide a guide that elicits positive emotions.

[0068] The guide generation unit can automatically translate HowTo guides into different languages ​​to accommodate international users. The guide generation unit, for example, automatically translates HowTo guides into different languages ​​to accommodate international users. For example, it translates into multiple languages ​​such as English, French, and Chinese. The guide generation unit also provides the automatically translated HowTo guides to a multilingual platform so that they can be used by users around the world. For example, it posts the translated guides on a website or app. The guide generation unit also collects feedback from an international perspective based on the HowTo guides translated into different languages ​​to improve the quality of the guides. For example, it reflects feedback that takes cultural and market differences into account. This allows the HowTo guides to be automatically translated into different languages ​​to accommodate international users.

[0069] The guide generation unit can dynamically change the content of the guide according to the user's interests and level, and provide optimal information. The guide generation unit dynamically changes the content of the HowTo guide according to the user's interests and level, for example. For example, it provides a guide for beginners and a guide for advanced users. The guide generation unit also dynamically adjusts the content of the guide based on the user's progress data. For example, it provides more advanced content as the user improves. The guide generation unit also customizes the content of the guide based on user feedback and provides optimal information. For example, it adjusts the guide to match the content and format desired by the user. This makes it possible to dynamically change the content of the guide according to the user's interests and level, and provide optimal information.

[0070] The guide generation unit uses the emotion estimation function to monitor the emotions of the user when reading the guide in real time, and can adjust the content of the guide as needed. The guide generation unit, for example, uses the emotion estimation function to monitor the emotions of the user when reading the guide in real time. For example, it analyzes the user's facial expressions and tone of voice. The guide generation unit also dynamically adjusts the content of the guide based on the user's emotion data. For example, if the user is feeling stressed, it provides content that helps the user to relax. The guide generation unit also uses the emotion estimation function to change the content of the guide in real time according to the user's emotions. For example, it provides a guide that the user can enjoy and learn from. This makes it possible to monitor the emotions of the user when reading the guide in real time, and adjust the content of the guide as needed.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The total support system may further include a health management unit that monitors the user's health condition. For example, the health management unit may analyze the user's heart rate and sleep patterns and suggest optimal hobbies and sports based on the user's health condition. The health management unit may also collect the user's dietary and exercise data and provide advice for maintaining health. For example, the health management unit may suggest recipes for the user to eat a balanced diet. The health management unit may also provide a plan for the user to enjoy hobbies and sports within reasonable limits based on the user's health data. For example, the health management unit may provide guidelines for the user to avoid excessive exercise. This allows the total support system to make suggestions that take the user's health condition into consideration.

[0073] The total support system can further include a community collaboration unit to improve the user's sociability. For example, the community collaboration unit provides a platform for the user to connect with other users who share the same hobbies or sports interests. The community collaboration unit can also suggest events and workshops that the user can participate in. For example, it can introduce local sports clubs or hobby circles. The community collaboration unit can also make suggestions to improve the quality of community activities based on user feedback. For example, it can survey the user's satisfaction with an event they participated in and reflect the results in the next event. In this way, the total support system can provide support to improve the user's sociability.

[0074] The total support system may further include an education support unit to increase the user's motivation to learn. For example, the education support unit may provide an online course for the user to learn a new hobby or sport. The education support unit may also monitor the user's learning progress and provide appropriate feedback. For example, the education support unit may provide advice for the user to master a specific skill. The education support unit may also provide learning materials customized according to the user's learning style. For example, the education support unit may provide video learning materials for users who prefer visual learning and e-books for users who prefer text-based learning. In this way, the total support system can provide support to increase the user's motivation to learn.

[0075] The total support system may further include a relaxation unit that estimates the user's emotions and provides relaxation content based on the estimated emotions. For example, the relaxation unit may provide relaxing music or guided meditation when the user is feeling stressed. The relaxation unit may also suggest relaxation activities based on the user's emotional data. For example, the relaxation unit may suggest a yoga session that will help the user relax. The relaxation unit may also dynamically adjust the relaxation content according to the user's emotional state. For example, the relaxation unit may change the tempo of the music or the content of the guided meditation to help the user relax. In this way, the total support system can provide relaxation content based on the user's emotions.

[0076] The total support system may further include a motivation unit that estimates the user's emotions and provides content to increase motivation based on the estimated emotions. For example, the motivation unit provides an encouraging message or a success story when the user is feeling unmotivated. The motivation unit may also suggest an activity to increase motivation based on the user's emotional data. For example, the motivation unit may set a short-term goal to motivate the user. The motivation unit may also dynamically adjust motivational content according to the user's emotional state. For example, the motivation unit may change the content and timing of an encouraging message to motivate the user. In this way, the total support system can provide content to increase motivation based on the user's emotions.

[0077] The total support system may further include an entertainment unit that estimates the user's emotions and provides entertainment content based on the estimated emotions. For example, the entertainment unit may suggest interesting movies or games when the user is bored. The entertainment unit may also suggest entertainment activities based on the user's emotional data. For example, it may introduce online events that the user can enjoy. The entertainment unit may also dynamically adjust entertainment content according to the user's emotional state. For example, it may change the genre of a movie or the type of a game to suit the user's enjoyment. In this way, the total support system can provide entertainment content based on the user's emotions.

[0078] The total support system may further include a learning support unit that estimates the user's emotions and provides content to increase motivation to learn based on the estimated emotions. For example, the learning support unit may provide encouraging messages or success stories if the user has negative emotions toward learning. The learning support unit may also suggest activities to increase motivation to learn based on the user's emotional data. For example, it may set short-term goals that will help the user have positive emotions toward learning. The learning support unit may also dynamically adjust learning content according to the user's emotional state. For example, it may change the content or format of learning materials so that the user has positive emotions toward learning. In this way, the total support system can provide content to increase motivation to learn based on the user's emotions.

[0079] The total support system may further include a progress recording unit that records the user's progress in hobbies or sports, allowing the user to feel a sense of accomplishment. For example, the progress recording unit may record and visually display the goals and progress achieved by the user. The progress recording unit may also provide rewards or badges for the goals achieved by the user. For example, a digital badge may be awarded when the user achieves a specific goal. The progress recording unit may also suggest the next goal based on the user's progress data. For example, it may suggest the next goal the user should attempt. In this way, the total support system may record the user's progress, allowing the user to feel a sense of accomplishment.

[0080] The total support system may further include a social sharing unit for sharing the user's progress in hobbies or sports. For example, the social sharing unit may enable the user to share the goals and progress achieved on social media. The social sharing unit may also provide a function for the user to compare their progress with other users and increase their competitive spirit. For example, the social sharing unit may provide a ranking function for the user to compete with their friends on progress. The social sharing unit may also make suggestions to promote interaction with other users based on the user's progress data. For example, the social sharing unit may connect users who are interested in the same hobbies or sports. In this way, the total support system may enable the user to share their progress and promote interaction with other users.

[0081] The total support system may further include a performance analysis unit that analyzes the user's progress in hobbies or sports and provides advice for improving performance. For example, the performance analysis unit may analyze the user's practice data and match results and provide specific advice for improving performance. The performance analysis unit may also identify the user's weaknesses and propose a training plan to overcome them. For example, it may provide a training menu to improve techniques in which the user is weak. The performance analysis unit may also set long-term goals based on the user's performance data. For example, it may set goals that the user should achieve in stages. This allows the total support system to provide advice for improving the user's performance.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The interest analysis unit analyzes the user's interests and level. For example, it identifies the user's interest and level in hobbies and sports based on the information entered by the user. It can also analyze the user's past history of hobbies and sports to understand changes and patterns in interests. It can also analyze the user's lifestyle habits and how they spend their time to suggest the most suitable hobbies and sports. Step 2: The plan provider provides a customized plan based on the information analyzed by the interest analyzer. For example, it creates a hobby or sports plan based on the user's interests and level. It can also analyze the user's progress data in real time and dynamically adjust the plan. It can also collect user feedback and continuously improve the plan based on that feedback. Step 3: The guide generation unit generates a HowTo guide based on the plan provided by the plan provision unit. For example, a user who wants to learn how to play the guitar can be provided with a guide that explains in detail how to hold chords and strokes. It is also possible to incorporate videos and animations into the HowTo guide and provide it in a visually easy-to-understand format.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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]

[0151] 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 interest analysis unit that analyzes the user's interest and level; a plan providing unit that provides a customized plan based on the information analyzed by the interest analysis unit; a guide generation unit that generates a HowTo guide based on the plan provided by the plan providing unit; A system characterized by:

2. The interest analysis unit Analyze the user's past hobbies and sports history to understand changes and patterns in their interests 2. The system of claim 1.

3. The interest analysis unit Analyze the user's lifestyle and time usage habits to suggest the most suitable hobbies and sports 2. The system of claim 1.

4. The interest analysis unit Analyze which hobbies and sports the user has had positive feelings about in the past and make new suggestions based on that.

2. The system of claim 1.

5. The interest analysis unit When analyzing the user's interests and level, more diverse information is collected using voice and image input.

2. The system of claim 1.

Images