System

Abstract

An object of a system according to an embodiment is to provide a specific action guideline and mental attitude for a user to approach an ideal person image.SOLUTION: A system includes a character selection part, a feature analysis part, and an action guide provision part. The character selection component accepts a character selected by the user. The characteristic analyzer analyzes the characteristics of the character received by the character selector. The action guideline providing unit provides a specific action guideline and mental attitude based on the characteristics of the character analyzed by the characteristic analyzing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of lacking a means to provide users with specific guidelines for behavior and mental preparation to help them become the person they desire.

[0005] The system according to the embodiment aims to provide a user with specific guidelines for action and a mindset to help them become closer to their ideal image of the person. [Means for solving the problem]

[0006] The system according to the embodiment includes a character selection unit, a characteristic analysis unit, and a behavioral guideline provision unit. The character selection unit accepts a character selected by a user. The characteristic analysis unit analyzes the characteristics of the character accepted by the character selection unit. The behavioral guideline provision unit provides specific behavioral guidelines and mental attitudes based on the character characteristics analyzed by the characteristic analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide a user with specific guidelines for action and a mindset to help them become closer to their ideal image. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 Manga Hero Trainer according to an embodiment of the present invention is a system that allows users to explore the ideal person they aspire to be through manga characters and learn the characters' thoughts and behavior patterns. As a result, the Manga Hero Trainer can provide specific guidelines for behavior and mental preparations that will help users become closer to their ideal persona.

[0029] A manga hero trainer according to an embodiment includes a character selection unit, a characteristic analysis unit, and a behavioral guideline provision unit. The character selection unit accepts a character selected by a user. For example, the user can select a character such as Goku from Dragon Ball or Luffy from One Piece. The characteristic analysis unit analyzes the characteristics of the character accepted by the character selection unit. For example, the generation AI analyzes the character's episodes and famous quotes to extract the character's thinking and behavior patterns. The behavioral guideline provision unit provides specific behavioral guidelines and mental preparation based on the character's characteristics analyzed by the characteristic analysis unit. For example, the generation AI provides the user with advice such as, "To overcome challenges like Luffy, it is important to first clarify your goals and work toward them step by step." This allows the manga hero trainer according to an embodiment to provide specific behavioral guidelines and mental preparation to help the user become closer to their ideal character.

[0030] The character selection unit can analyze the user's past selection history and behavioral patterns to recommend the most suitable character. For example, the character selection unit uses a generation AI to analyze the user's past selection history and understand the user's tendency toward preferred characters. For example, it can recommend the most suitable character to the user based on the characteristics and episodes of characters selected in the past. In this way, the user can gain inspiration more effectively by analyzing the user's past selection history and behavioral patterns and recommending the most suitable character.

[0031] The feature analysis unit can customize the character's episodes and quotes to match the user's current psychological state and goals. For example, the generation AI analyzes the user's current psychological state and customizes the character's episodes and quotes based on that. For example, if the user is feeling down, it can provide an episode containing encouraging words. This allows the character's episodes and quotes to be customized to match the user's current psychological state and goals, thereby providing more effective inspiration.

[0032] The character selection unit can provide a wider variety of sources of inspiration by adding not only manga characters but also characters from movies and novels as options. The character selection unit builds a system that adds, for example, characters from movies and novels as options in addition to manga characters. For example, it can allow users to get inspiration from movie heroes and novel protagonists. By adding not only manga characters but also movie and novel characters as options, users can learn from a wider variety of sources of inspiration.

[0033] The character selection unit can provide a comprehensive experience by also providing merchandise and event information related to the character selected by the user. The character selection unit, for example, builds a system that provides merchandise and event information related to the character selected by the user. For example, it displays information about character figures and event tickets. This allows a comprehensive experience to be provided by also providing merchandise and event information related to the character selected by the user.

[0034] When analyzing a character's behavioral patterns, the feature analysis unit also takes into account the character's growth process and background story, allowing for deeper learning. For example, when the generation AI analyzes a character's behavioral patterns, the feature analysis unit takes into account the character's growth process and background story. For example, it analyzes the character's past experiences and growth trajectory to learn behavioral patterns. This allows for deeper learning for the user by taking into account the character's growth process and background story.

[0035] The feature analysis unit can reconstruct the character's behavioral patterns in a form that is easy to apply to specific scenarios in the user's daily life. The feature analysis unit, for example, builds a system that reconstructs the character's behavioral patterns in a form that is easy to apply to the user's daily life. For example, the feature analysis unit converts the character's behavior into a specific scenario and provides it to the user. This makes it easier for the user to put the character's behavioral patterns into practice by reconstructing them in a form that is easy to apply to the user's daily life.

[0036] The feature analysis unit can reinterpret the behavioral patterns of a character based on different cultures and historical backgrounds, thereby providing a new perspective to the user. The feature analysis unit, for example, builds a system that reinterprets the behavioral patterns of a character based on different cultures and historical backgrounds. For example, the feature analysis unit analyzes the behavior of a character from the perspective of a different culture and provides a new perspective. This makes it possible to provide a new perspective to the user by reinterpreting the behavioral patterns of a character based on different cultures and historical backgrounds.

[0037] The feature analysis unit can provide the character's behavior patterns as interactive content that can be learned in a game format, allowing users to learn while having fun. The feature analysis unit, for example, builds a system that provides the character's behavior patterns as interactive content that can be learned in a game format. For example, the character's behavior is provided as a simulation game. In this way, by providing the character's behavior patterns as interactive content that can be learned in a game format, users can learn while having fun.

[0038] The action guideline providing unit can personalize the action guideline and mindset based on the user's past action history and feedback. For example, the action guideline providing unit uses a generation AI to analyze the user's past action history and personalize the action guideline and mindset based on that. For example, advice is provided taking into account past experiences of success and failure. In this way, the action guideline and mindset can be personalized based on the user's past action history and feedback, making it possible to provide optimal advice to the user.

[0039] The action guideline providing unit can support gradual growth by providing action guidelines and mindsets separately for the user's short-term and long-term goals. For example, the action guideline providing unit constructs a system in which a generation AI analyzes the user's short-term and long-term goals and provides action guidelines and mindsets based on the results. For example, it provides a specific action plan for the short-term goal. In this way, by providing action guidelines and mindsets separately for the user's short-term and long-term goals, gradual growth can be supported.

[0040] The action guideline providing unit can provide the action guideline and mindset as visual notes or infographics to make them easier to understand visually. The action guideline providing unit, for example, builds a system that provides the action guideline and mindset provided by the generation AI as visual notes or infographics. For example, the action guideline is shown using diagrams or icons. In this way, providing the action guideline and mindset as visual notes or infographics makes it easier for users to understand visually.

[0041] The action guideline providing unit can customize the action guideline and mindset based on specific scenarios in daily life so that the user can easily put them into practice. For example, the action guideline providing unit builds a system in which a generation AI customizes the action guideline and mindset based on specific scenarios in the user's daily life. For example, it suggests methods for applying them to daily tasks. In this way, the action guideline and mindset can be customized based on specific scenarios in daily life so that the user can easily put them into practice, making it easier for the user to put them into practice.

[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 character selection unit can also provide historical and cultural backgrounds related to the character selected by the user. For example, if the character selected by the user is set in the Sengoku period of Japan, information about the history and culture of that period can be provided. This allows the user to gain a deeper understanding of the character's actions and thoughts. The unit can also recommend documentaries and books related to the character's background. This allows the user to understand the character from a more multifaceted perspective while learning about the character's background.

[0044] The character selection unit can also provide music and soundtracks related to the character selected by the user. For example, it can provide soundtracks from anime or movies in which the character selected by the user appears. This allows the user to experience the character's world more deeply through music. It can also recommend the character's theme song or music by related artists. This allows the user to feel the character's atmosphere through music.

[0045] The character selection unit may also provide artwork and fan art related to the character selected by the user. For example, it may display official artwork and fan-drawn illustrations of the character selected by the user. This allows users to enjoy the visual aspects of the character. It may also provide a function for users to share fan art they have drawn themselves. This allows users to enjoy interacting within the community.

[0046] The character selection unit can also provide dishes and recipes related to the character selected by the user. For example, it can provide recipes for dishes that the character selected by the user likes or dishes that appear in works in which the character appears. This allows the user to experience the character's worldview through food. Furthermore, by enjoying the process of cooking, the user can deepen their understanding of the character. This allows the user to enjoy the character's world from more diverse angles.

[0047] The character selection unit can also provide travel destinations and tourist spots related to the character selected by the user. For example, it can introduce the locations where the work in which the character selected by the user appears and tourist spots related to that character. This allows the user to experience the character's world more deeply by actually visiting it. It can also provide information on activities and events at the travel destination. This allows the user to enjoy the character's world as if it were real.

[0048] The character selection unit can also provide sports and fitness programs related to the character selected by the user. For example, it can introduce the training and fitness programs that the character selected by the user is undergoing. This allows the user to acquire the same physical strength and skills as the character. In addition, by actually trying out the character's training methods, the user can deepen their understanding of the character. This allows the user to live a healthy life while experiencing the character's world.

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

[0050] Step 1: The character selection unit accepts the character selected by the user. For example, the user can select a character such as Goku from Dragon Ball or Luffy from One Piece. Step 2: The feature analysis unit analyzes the features of the character accepted by the character selection unit. For example, the generation AI analyzes the character's episodes and famous quotes to extract the character's thinking and behavior patterns. Step 3: The action guideline provider provides specific action guidelines and mental preparation based on the character's characteristics analyzed by the feature analysis unit. For example, the generation AI might provide the user with advice such as, "To face challenges like Luffy, it's important to first clarify your goals and then move toward them step by step."

[0051] (Example 2) The Manga Hero Trainer according to an embodiment of the present invention is a system that allows users to explore the ideal person they aspire to be through manga characters and learn the characters' thoughts and behavior patterns. As a result, the Manga Hero Trainer can provide specific guidelines for behavior and mental preparations that will help users become closer to their ideal persona.

[0052] A manga hero trainer according to an embodiment includes a character selection unit, a characteristic analysis unit, and a behavioral guideline provision unit. The character selection unit accepts a character selected by a user. For example, the user can select a character such as Goku from Dragon Ball or Luffy from One Piece. The characteristic analysis unit analyzes the characteristics of the character accepted by the character selection unit. For example, the generation AI analyzes the character's episodes and famous quotes to extract the character's thinking and behavior patterns. The behavioral guideline provision unit provides specific behavioral guidelines and mental preparation based on the character's characteristics analyzed by the characteristic analysis unit. For example, the generation AI provides the user with advice such as, "To overcome challenges like Luffy, it is important to first clarify your goals and work toward them step by step." This allows the manga hero trainer according to an embodiment to provide specific behavioral guidelines and mental preparation to help the user become closer to their ideal character.

[0053] The character selection unit can analyze the user's past selection history and behavioral patterns to recommend the most suitable character. For example, the character selection unit uses a generation AI to analyze the user's past selection history and understand the user's tendency toward preferred characters. For example, it can recommend the most suitable character to the user based on the characteristics and episodes of characters selected in the past. In this way, the user can gain inspiration more effectively by analyzing the user's past selection history and behavioral patterns and recommending the most suitable character.

[0054] The feature analysis unit can customize the character's episodes and quotes to match the user's current psychological state and goals. For example, the generation AI analyzes the user's current psychological state and customizes the character's episodes and quotes based on that. For example, if the user is feeling down, it can provide an episode containing encouraging words. This allows the character's episodes and quotes to be customized to match the user's current psychological state and goals, thereby providing more effective inspiration.

[0055] The feature analysis unit can use the emotion estimation function to analyze in real time the emotions felt by the user when selecting a character and recommend characters that elicit positive emotions. The feature analysis unit, for example, uses the emotion estimation function to analyze in real time the emotions felt by the user when selecting a character. For example, it analyzes the user's facial expressions and tone of voice using a camera or microphone and calculates an emotion score. In this way, the emotion estimation function can be used to analyze in real time the emotions felt by the user when selecting a character and recommend characters that elicit positive emotions, thereby improving user satisfaction.

[0056] The character selection unit can provide a wider variety of sources of inspiration by adding not only manga characters but also characters from movies and novels as options. The character selection unit builds a system that adds, for example, characters from movies and novels as options in addition to manga characters. For example, it can allow users to get inspiration from movie heroes and novel protagonists. By adding not only manga characters but also movie and novel characters as options, users can learn from a wider variety of sources of inspiration.

[0057] The character selection unit can provide a comprehensive experience by also providing merchandise and event information related to the character selected by the user. The character selection unit, for example, builds a system that provides merchandise and event information related to the character selected by the user. For example, it displays information about character figures and event tickets. This allows a comprehensive experience to be provided by also providing merchandise and event information related to the character selected by the user.

[0058] The feature analysis unit can use the emotion estimation function to share other users' emotional reactions to a character selected by a user, thereby promoting empathy within the community. The feature analysis unit, for example, uses the emotion estimation function to build a system that collects and shares other users' emotional reactions to a character selected by a user. For example, the feature analysis unit displays other users' emotional scores and comments. This allows other users' emotional reactions to a character selected by a user to be shared using the emotion estimation function, promoting empathy within the community and improving user satisfaction.

[0059] When analyzing a character's behavioral patterns, the feature analysis unit also takes into account the character's growth process and background story, allowing for deeper learning. For example, when the generation AI analyzes a character's behavioral patterns, the feature analysis unit takes into account the character's growth process and background story. For example, it analyzes the character's past experiences and growth trajectory to learn behavioral patterns. This allows for deeper learning for the user by taking into account the character's growth process and background story.

[0060] The feature analysis unit can reconstruct the character's behavioral patterns in a form that is easy to apply to specific scenarios in the user's daily life. The feature analysis unit, for example, builds a system that reconstructs the character's behavioral patterns in a form that is easy to apply to the user's daily life. For example, the feature analysis unit converts the character's behavior into a specific scenario and provides it to the user. This makes it easier for the user to put the character's behavioral patterns into practice by reconstructing them in a form that is easy to apply to the user's daily life.

[0061] The feature analysis unit can use the emotion estimation function to analyze the emotional reactions of the user when learning the character's behavior patterns and provide feedback to maximize the learning effect. The feature analysis unit, for example, uses the emotion estimation function to build a system that analyzes the emotional reactions of the user when learning the character's behavior patterns. For example, the feature analysis unit analyzes the user's facial expressions and tone of voice to calculate an emotion score. This improves the user's learning effect by analyzing the emotional reactions of the user when learning the character's behavior patterns using the emotion estimation function and providing feedback to maximize the learning effect.

[0062] The feature analysis unit can reinterpret the behavioral patterns of a character based on different cultures and historical backgrounds, thereby providing a new perspective to the user. The feature analysis unit, for example, builds a system that reinterprets the behavioral patterns of a character based on different cultures and historical backgrounds. For example, the feature analysis unit analyzes the behavior of a character from the perspective of a different culture and provides a new perspective. This makes it possible to provide a new perspective to the user by reinterpreting the behavioral patterns of a character based on different cultures and historical backgrounds.

[0063] The feature analysis unit can provide the character's behavior patterns as interactive content that can be learned in a game format, allowing users to learn while having fun. The feature analysis unit, for example, builds a system that provides the character's behavior patterns as interactive content that can be learned in a game format. For example, the character's behavior is provided as a simulation game. In this way, by providing the character's behavior patterns as interactive content that can be learned in a game format, users can learn while having fun.

[0064] The feature analysis unit uses the emotion estimation function to allow a user to share the behavioral patterns he or she has learned with other users, thereby forming a community where he or she can receive sympathy and encouragement. The feature analysis unit, for example, uses the emotion estimation function to build a system where a user can share the behavioral patterns he or she has learned with other users. For example, emotional reactions to the learned behavioral patterns can be shared. This allows a user to share the behavioral patterns he or she has learned with other users using the emotion estimation function, thereby forming a community where he or she can receive sympathy and encouragement, thereby improving the user's learning effectiveness.

[0065] The action guideline providing unit can personalize the action guideline and mindset based on the user's past action history and feedback. For example, the action guideline providing unit uses a generation AI to analyze the user's past action history and personalize the action guideline and mindset based on that. For example, advice is provided taking into account past experiences of success and failure. In this way, the action guideline and mindset can be personalized based on the user's past action history and feedback, making it possible to provide optimal advice to the user.

[0066] The action guideline providing unit can support gradual growth by providing action guidelines and mindsets separately for the user's short-term and long-term goals. For example, the action guideline providing unit constructs a system in which a generation AI analyzes the user's short-term and long-term goals and provides action guidelines and mindsets based on the results. For example, it provides a specific action plan for the short-term goal. In this way, by providing action guidelines and mindsets separately for the user's short-term and long-term goals, gradual growth can be supported.

[0067] The action guideline providing unit can use the emotion estimation function to analyze the emotional reactions of the user when he or she puts the action guideline or mental attitude into practice, and provide additional advice to elicit positive emotions. The action guideline providing unit, for example, uses the emotion estimation function to build a system that analyzes the emotional reactions of the user when he or she puts the action guideline or mental attitude into practice. For example, the action guideline providing unit analyzes the user's facial expressions and tone of voice to calculate an emotion score. This allows the emotion estimation function to analyze the emotional reactions of the user when he or she puts the action guideline or mental attitude into practice, and provides additional advice to elicit positive emotions, thereby improving user satisfaction.

[0068] The action guideline providing unit can provide the action guideline and mindset as visual notes or infographics to make them easier to understand visually. The action guideline providing unit, for example, builds a system that provides the action guideline and mindset provided by the generation AI as visual notes or infographics. For example, the action guideline is shown using diagrams or icons. In this way, providing the action guideline and mindset as visual notes or infographics makes it easier for users to understand visually.

[0069] The action guideline providing unit can customize the action guideline and mindset based on specific scenarios in daily life so that the user can easily put them into practice. For example, the action guideline providing unit builds a system in which a generation AI customizes the action guideline and mindset based on specific scenarios in the user's daily life. For example, it suggests methods for applying them to daily tasks. In this way, the action guideline and mindset can be customized based on specific scenarios in daily life so that the user can easily put them into practice, making it easier for the user to put them into practice.

[0070] The action guideline providing unit can use the emotion estimation function to share the emotional reactions of other users to the action guideline or mindset practiced by the user, thereby promoting empathy within the community. The action guideline providing unit, for example, uses the emotion estimation function to build a system that collects and shares the emotional reactions of other users to the action guideline or mindset practiced by the user. For example, it displays emotion scores and comments. In this way, the emotion estimation function can be used to share the emotional reactions of other users to the action guideline or mindset practiced by the user, promoting empathy within the community and improving user satisfaction.

[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 character selection unit can also provide historical and cultural backgrounds related to the character selected by the user. For example, if the character selected by the user is set in the Sengoku period of Japan, information about the history and culture of that period can be provided. This allows the user to gain a deeper understanding of the character's actions and thoughts. The unit can also recommend documentaries and books related to the character's background. This allows the user to understand the character from a more multifaceted perspective while learning about the character's background.

[0073] The character selection unit can also provide music and soundtracks related to the character selected by the user. For example, it can provide soundtracks from anime or movies in which the character selected by the user appears. This allows the user to experience the character's world more deeply through music. It can also recommend the character's theme song or music by related artists. This allows the user to feel the character's atmosphere through music.

[0074] The feature analysis unit can estimate the user's emotions and adjust the learning content based on the emotions felt when learning the behavioral patterns of the character selected by the user. For example, if the user is feeling stressed, it can provide relaxing anecdotes and famous quotes. This allows the user to have an optimal learning experience according to their emotions. Also, if the user is feeling positive emotions, it can provide challenging guidelines for action. This maximizes the user's learning effectiveness.

[0075] The character selection unit may also provide artwork and fan art related to the character selected by the user. For example, it may display official artwork and fan-drawn illustrations of the character selected by the user. This allows users to enjoy the visual aspects of the character. It may also provide a function for users to share fan art they have drawn themselves. This allows users to enjoy interacting within the community.

[0076] The feature analysis unit can use the emotion estimation function to analyze the user's motivation when learning the character's behavior patterns and provide feedback to maintain motivation. For example, if the user loses motivation to study, it can provide encouraging messages or rewards. This helps the user maintain their motivation to continue studying. Also, if the user is highly motivated, it can provide further challenges. This can improve the user's learning effectiveness.

[0077] The character selection unit can also provide dishes and recipes related to the character selected by the user. For example, it can provide recipes for dishes that the character selected by the user likes or dishes that appear in works in which the character appears. This allows the user to experience the character's worldview through food. Furthermore, by enjoying the process of cooking, the user can deepen their understanding of the character. This allows the user to enjoy the character's world from more diverse angles.

[0078] The feature analysis unit uses the emotion estimation function to analyze the emotional reactions of the user when learning the character's behavior patterns, and can provide advice to reduce the negative emotions felt by the user. For example, if the user feels anxious, the unit can provide advice on how to relax or encourage positive thinking. This allows the user to reduce negative emotions and concentrate on their studies. Also, if the user feels angry, the unit can provide advice on how to stay calm. This can improve the user's learning effectiveness.

[0079] The character selection unit can also provide travel destinations and tourist spots related to the character selected by the user. For example, it can introduce the locations where the work in which the character selected by the user appears and tourist spots related to that character. This allows the user to experience the character's world more deeply by actually visiting it. It can also provide information on activities and events at the travel destination. This allows the user to enjoy the character's world as if it were real.

[0080] The feature analysis unit uses the emotion estimation function to analyze the user's emotional reactions as they learn the character's behavior patterns, and can provide advice to reinforce the positive emotions felt by the user. For example, if the user feels joy, the unit can provide activities or exercises to further reinforce those emotions, allowing the user to continue learning while maintaining positive emotions. Also, if the user feels a sense of accomplishment, the unit can provide rewards or praise to further enhance those emotions, maximizing the user's learning effectiveness.

[0081] The character selection unit can also provide sports and fitness programs related to the character selected by the user. For example, it can introduce the training and fitness programs that the character selected by the user is undergoing. This allows the user to acquire the same physical strength and skills as the character. In addition, by actually trying out the character's training methods, the user can deepen their understanding of the character. This allows the user to live a healthy life while experiencing the character's world.

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

[0083] Step 1: The character selection unit accepts the character selected by the user. For example, the user can select a character such as Goku from Dragon Ball or Luffy from One Piece. Step 2: The feature analysis unit analyzes the features of the character accepted by the character selection unit. For example, the generation AI analyzes the character's episodes and famous quotes to extract the character's thinking and behavior patterns. Step 3: The action guideline provider provides specific action guidelines and mental preparation based on the character's characteristics analyzed by the feature analysis unit. For example, the generation AI might provide the user with advice such as, "To face challenges like Luffy, it's important to first clarify your goals and then move toward them step by step."

[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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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. a character selection unit that accepts a character selected by a user; a feature analysis unit that analyzes the features of the character accepted by the character selection unit; and a behavioral guideline providing unit that provides specific behavioral guidelines and mental attitudes based on the characteristics of the character analyzed by the characteristic analysis unit. A system characterized by:

2. The character selection unit Analyze the user's past selection history and behavioral patterns to recommend the most suitable character.

2. The system of claim 1.

3. The character selection unit In addition to manga characters, movie and novel characters will also be added as options, providing a wider variety of inspiration sources.

2. The system of claim 1.

4. The feature analysis unit When analyzing the behavioral patterns of the character, the character's growth process and background story are also taken into consideration, providing deeper learning.

2. The system of claim 1.

5. The feature analysis unit Using an emotion estimation function, the emotions felt by the user when selecting a character are analyzed in real time, and characters that evoke positive emotions are recommended.

2. The system of claim 1.

6. The feature analysis unit Using an emotion estimation function, the emotional response of the user when learning the behavioral patterns of the character is analyzed, and feedback is provided to maximize the learning effect.

2. The system of claim 1.

7. The behavioral guideline providing unit Using an emotion estimation function, the emotional response of the user when practicing the behavioral guidelines or the mindset is analyzed, and additional advice is provided to elicit positive emotions.

2. The system of claim 1.

8. The behavioral guideline providing unit Using an emotion estimation function, the emotional reactions of other users to the behavioral guidelines or mindset practiced by the user are shared, promoting empathy within the community.

2. The system of claim 1.

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