System

Abstract

An object of a system according to an embodiment is to generate and provide an appropriate response on the basis of an emotion of a user.SOLUTION: A system according to an embodiment includes an emotion analysis unit, a response generation unit, and an information providing unit. The emotion analysis unit analyzes an emotion of a user. The response generation unit generates an appropriate response on the basis of the emotion analyzed by the emotion analysis unit. The information providing unit provides the response generated by the response generation unit to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques do not adequately generate appropriate responses based on the user's emotions, and there is room for improvement.

[0005] The system according to the embodiment aims to generate and provide an appropriate response based on the user's emotions. [Means for solving the problem]

[0006] The system according to the embodiment includes an emotion analysis unit, a response generation unit, and an information provision unit. The emotion analysis unit analyzes the emotion of a user. The response generation unit generates an appropriate response based on the emotion analyzed by the emotion analysis unit. The information provision unit provides the response generated by the response generation unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can generate and provide an appropriate response based on the user's emotions. [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) A communication system according to an embodiment of the present invention is an AI that acts like a personal best friend to the user. This AI understands the user's thoughts, emotions, hobbies, and interests, and provides relevant information. This allows the communication system to enrich the user's life and provide experiences that open up a new future.

[0029] The response generation unit can analyze the user's past conversation history and generate responses optimized for each individual user. For example, the response generation unit can analyze the user's past conversation history to identify the user's preferences and interests. For example, the response generation unit can identify from past conversations that the user likes music and generate a response related to music. The response generation unit can also analyze changes in the user's emotions based on the past conversation history and generate an appropriate response. For example, the response generation unit can identify from past conversations that the user is feeling stressed and generate a response to help them relax. The response generation unit can also identify the user's goals and interests based on the past conversation history and generate a response based on them. In this way, by analyzing the past conversation history, it is possible to provide the user with an optimized response.

[0030] The response generation unit can generate culturally appropriate responses for users with different languages ​​and cultural backgrounds. For example, the response generation unit generates responses in an appropriate language for users who speak different languages. For example, it responds in Japanese to a Japanese-speaking user and in English to an English-speaking user. The response generation unit also generates culturally appropriate responses for users with different cultural backgrounds. For example, it can generate responses based on Japanese culture and responses based on American culture. The response generation unit can also generate responses using appropriate expressions and etiquette, taking into account the user's cultural background. This makes it possible to provide culturally appropriate responses to users with different languages ​​and cultural backgrounds.

[0031] Furthermore, the communication system includes a growth support unit that tracks the user's learning progress in real time and provides feedback at appropriate times. The growth support unit, for example, tracks the user's learning progress in real time and grasps the progress status. For example, it tracks study time and achievement level and evaluates the user's progress. The growth support unit also provides feedback at appropriate times. For example, it provides feedback such as "Your progress this week is good. Let's keep it up." The growth support unit can also suggest the next step based on the user's progress. In this way, by tracking the user's learning progress in real time and providing feedback at appropriate times, it is possible to improve learning effectiveness.

[0032] The communication system further includes a learning plan generation unit that automatically generates a customized learning plan based on the user's interests and goals. The learning plan generation unit, for example, identifies the user's interests and goals and generates a learning plan based on them. For example, if the user is interested in programming, the learning plan generation unit generates a programming learning plan. The learning plan generation unit also adjusts the learning plan based on the user's goals. For example, the content and progress speed of the learning plan can be adjusted according to short-term and long-term goals. The learning plan generation unit can also update the learning plan according to the user's progress. This allows for improved learning effectiveness by providing a learning plan customized based on the user's interests and goals.

[0033] Furthermore, the communication system includes an expert collaboration unit that collaborates with experts in different fields to provide multifaceted learning support to the user. The expert collaboration unit, for example, collaborates with experts in different fields to provide multifaceted learning support to the user. For example, it collaborates with education experts to provide education-related advice to the user. It can also collaborate with technology experts to provide technical support to the user. It can also collaborate with psychology experts to support the user's mental health. In this way, by collaborating with experts in different fields, it is possible to provide multifaceted learning support to the user.

[0034] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0035] The communication system may also include a travel suggestion unit that proposes customized travel plans based on the user's hobbies and interests. For example, if the user likes nature, the travel suggestion unit may propose travel plans for enjoying nature. For example, it may provide a response such as, "Since you like nature, I recommend a trip to this national park." If the user is interested in history, the travel suggestion unit may propose travel plans for visiting historical places. For example, it may provide a response such as, "Since you are interested in history, I recommend a trip to this historical city." If the user likes active activities, the travel suggestion unit may propose travel plans that include active activities. This makes it possible to enrich the user's travel experience by providing customized travel plans based on the user's hobbies and interests.

[0036] The communication system may also include a learning style suggestion unit that suggests an optimal learning method based on the user's learning style. For example, if the user prefers visual learning, the learning style suggestion unit suggests visual learning materials. For example, it provides a response such as, "If you prefer visual learning, we recommend this video learning material." If the user prefers auditory learning, the learning style suggestion unit may suggest audio learning materials. For example, it provides a response such as, "If you prefer auditory learning, we recommend this podcast." If the user prefers hands-on learning, the learning style suggestion unit may suggest hands-on activities. This makes it possible to improve learning effectiveness by suggesting an optimal learning method based on the user's learning style.

[0037] The communication system may also include a schedule suggestion unit that suggests an optimal schedule based on the user's lifestyle. For example, if the user has a morning-type lifestyle, the schedule suggestion unit suggests tasks that will improve concentration in the morning. For example, it may provide a response such as, "Since you're a morning person, I recommend doing important tasks in the morning." If the user has a night-type lifestyle, the schedule suggestion unit may suggest tasks that will improve concentration in the evening. For example, it may provide a response such as, "Since you're a night-type, I recommend doing creative work in the evening." If the user has an irregular lifestyle, the schedule suggestion unit may also suggest a flexible schedule. This makes it possible to improve the user's quality of life by suggesting an optimal schedule based on the user's lifestyle.

[0038] The communication system may also include a career support unit that proposes a customized career plan based on the user's interests and goals. For example, if the user is interested in the technical field, the career support unit proposes a career plan in the technical field. For example, it may provide a response such as, "Since you are interested in the technical field, I recommend this programming course." If the user is interested in a creative field, the career support unit may also propose a career plan in the creative field. For example, it may provide a response such as, "Since you are interested in the creative field, I recommend this design course." If the user is interested in the business field, the career support unit may also propose a career plan in the business field. This makes it possible to support the user's career development by providing a customized career plan based on the user's interests and goals.

[0039] The communication system may also include a behavior prediction unit that predicts future behavior based on the user's past behavioral data and provides appropriate advice. For example, if the user has exercised during a specific time period in the past, the behavior prediction unit may suggest exercising during that time period. For example, it may provide a response such as, "You've exercised during this time period in the past. Why don't you try exercising today too?" If the user has rested on a specific day of the week in the past, the behavior prediction unit may suggest resting on that day. For example, it may provide a response such as, "You've rested on this day in the past. Let's relax today." If the user has participated in a specific event in the past, the behavior prediction unit may suggest activities related to that event. This allows the system to predict future behavior based on the user's past behavioral data and provide appropriate advice, thereby supporting the user's daily life.

[0040] The processing flow of the first embodiment will be briefly explained below.

[0041] Step 1: The emotion analysis unit analyzes the user's emotions. For example, the emotion analysis unit analyzes the user's words and facial expressions to identify emotions. It can also analyze voice tone to estimate emotions. Furthermore, it is possible to analyze the user's emotions using text analysis. Step 2: The response generator generates an appropriate response based on the emotions analyzed by the emotion analyzer. For example, if the user is analyzed to be tired, the response generator generates a response such as, "Thank you for your hard work. How about listening to some music to relax?" If the user is analyzed to want to learn a new skill, the response generator can generate a response such as, "What skills are you interested in? There are many options, such as programming or cooking." If the user is analyzed to be feeling anxious, the response generator can generate a response such as, "What topics do you like to talk about? How about talking about your hobbies or the latest news?" Step 3: The information providing unit provides the response generated by the response generating unit to the user. For example, the generated response may be displayed to the user as a text message. Alternatively, the generated response may be provided to the user as a voice message. Furthermore, the generated response may be provided to the user as a video message.

[0042] (Example 2) A communication system according to an embodiment of the present invention is an AI that acts like a personal best friend to the user. This AI understands the user's thoughts, emotions, hobbies, and interests, and provides relevant information. This allows the communication system to enrich the user's life and provide experiences that open up a new future.

[0043] A communication system according to an embodiment includes an emotion analysis unit, a response generation unit, and an information provision unit. The emotion analysis unit analyzes a user's emotion. For example, the emotion analysis unit analyzes the user's utterances and facial expressions to identify the emotion. The emotion analysis unit can also analyze the user's voice tone to estimate the emotion. The emotion analysis unit can also analyze the user's emotion using text analysis. The response generation unit generates an appropriate response based on the emotion analyzed by the emotion analysis unit. For example, if the response generation unit analyzes that the user is tired, it can generate a response such as, "Thank you for your hard work. How about listening to music to relax?" If the response generation unit analyzes that the user wants to learn a new skill, it can generate a response such as, "What skills are you interested in? There are many options, such as programming and cooking." If the response generation unit analyzes that the user is feeling anxious, it can generate a response such as, "What topics do you like to talk about? How about talking about your hobbies or recent news?" The information provision unit provides the response generated by the response generation unit to the user. For example, the information provision unit displays the generated response to the user as a text message. The information providing unit may also provide the generated response to the user as a voice message. The information providing unit may also provide the generated response to the user as a video message. In this way, the communication system according to the embodiment can improve user satisfaction by providing an appropriate response based on the user's emotions.

[0044] The emotion analysis unit can analyze the user's voice tone and facial expression. The emotion analysis unit, for example, analyzes the user's voice tone to identify the emotion. For example, it analyzes the pitch and speed of the voice tone to determine whether the user is tired. The emotion analysis unit also analyzes the user's facial expression to identify the emotion. For example, it detects facial feature points and analyzes changes in facial expression to determine whether the user is happy. The emotion analysis unit can also analyze both the voice tone and facial expression to comprehensively identify the emotion. This enables more accurate emotion analysis by analyzing the user's voice tone and facial expression.

[0045] The response generation unit can analyze the user's past conversation history and generate responses optimized for each individual user. For example, the response generation unit can analyze the user's past conversation history to identify the user's preferences and interests. For example, the response generation unit can identify from past conversations that the user likes music and generate a response related to music. The response generation unit can also analyze changes in the user's emotions based on the past conversation history and generate an appropriate response. For example, the response generation unit can identify from past conversations that the user is feeling stressed and generate a response to help them relax. The response generation unit can also identify the user's goals and interests based on the past conversation history and generate a response based on them. In this way, by analyzing the past conversation history, it is possible to provide the user with an optimized response.

[0046] The information providing unit can provide relaxation techniques or encouraging words based on the user's emotions. For example, if the information providing unit determines that the user is tired, it can provide relaxation techniques. For example, it can provide a response such as, "Thank you for your hard work. Take a deep breath to relax. Afterwards, it might be a good idea to listen to your favorite music." If the information providing unit determines that the user is feeling anxious, it can provide encouraging words. For example, it can provide a response such as, "It's okay. Just proceed at your own pace." If the information providing unit determines that the user is happy, it can also provide a positive message. In this way, by providing relaxation techniques or encouraging words based on the user's emotions, it is possible to reduce the user's stress.

[0047] The emotion analysis unit can analyze the user's voice tone and facial expression to generate a response that also takes non-verbal communication into consideration. The emotion analysis unit, for example, analyzes the user's voice tone to consider non-verbal communication. For example, it analyzes changes in voice tone to determine whether the user is nervous. The emotion analysis unit can also analyze the user's facial expression to consider non-verbal communication. For example, it analyzes changes in facial expression to determine whether the user is surprised. The emotion analysis unit can also analyze both voice tone and facial expression to comprehensively consider non-verbal communication. This allows for more natural communication by generating a response that also takes non-verbal communication into consideration.

[0048] The response generation unit can generate culturally appropriate responses for users with different languages ​​and cultural backgrounds. For example, the response generation unit generates responses in an appropriate language for users who speak different languages. For example, it responds in Japanese to a Japanese-speaking user and in English to an English-speaking user. The response generation unit also generates culturally appropriate responses for users with different cultural backgrounds. For example, it can generate responses based on Japanese culture and responses based on American culture. The response generation unit can also generate responses using appropriate expressions and etiquette, taking into account the user's cultural background. This makes it possible to provide culturally appropriate responses to users with different languages ​​and cultural backgrounds.

[0049] The information providing unit can use the emotion estimation function to suggest related music or videos when the user feels a specific emotion. For example, if the information providing unit analyzes that the user is tired, it can suggest relaxing music. For example, it can provide a response such as, "Thank you for your hard work. Why don't you listen to this relaxing music to relax?". Also, if the information providing unit analyzes that the user feels anxious, it can suggest relaxing videos. For example, it can provide a response such as, "It seems you are feeling anxious. Try watching this relaxing video." Also, if the information providing unit analyzes that the user is happy, it can suggest positive music or videos. In this way, when the user feels a specific emotion, it can support the user's emotions by suggesting related music or videos.

[0050] Furthermore, the communication system includes a growth support unit that tracks the user's learning progress in real time and provides feedback at appropriate times. The growth support unit, for example, tracks the user's learning progress in real time and grasps the progress status. For example, it tracks study time and achievement level and evaluates the user's progress. The growth support unit also provides feedback at appropriate times. For example, it provides feedback such as "Your progress this week is good. Let's keep it up." The growth support unit can also suggest the next step based on the user's progress. In this way, by tracking the user's learning progress in real time and providing feedback at appropriate times, it is possible to improve learning effectiveness.

[0051] The communication system further includes a learning plan generation unit that automatically generates a customized learning plan based on the user's interests and goals. The learning plan generation unit, for example, identifies the user's interests and goals and generates a learning plan based on them. For example, if the user is interested in programming, the learning plan generation unit generates a programming learning plan. The learning plan generation unit also adjusts the learning plan based on the user's goals. For example, the content and progress speed of the learning plan can be adjusted according to short-term and long-term goals. The learning plan generation unit can also update the learning plan according to the user's progress. This allows for improved learning effectiveness by providing a learning plan customized based on the user's interests and goals.

[0052] Furthermore, the communication system includes a motivation support unit that uses an emotion estimation function to send an encouraging message or suggest a break when the user loses motivation to study. The motivation support unit uses the emotion estimation function to detect the state of the user when the user loses motivation to study. For example, it detects a decrease in motivation from the user's words or facial expression. The motivation support unit also provides an encouraging message. For example, it provides a message such as "Take a short break and refresh yourself. Then, try again." The motivation support unit can also suggest a break. For example, it can make a suggestion such as "If you feel tired, take a short walk to refresh yourself." In this way, when the user loses motivation to study, it can support the user in continuing to study by sending an encouraging message or suggesting a break.

[0053] Furthermore, the communication system includes an expert collaboration unit that collaborates with experts in different fields to provide multifaceted learning support to the user. The expert collaboration unit, for example, collaborates with experts in different fields to provide multifaceted learning support to the user. For example, it collaborates with education experts to provide education-related advice to the user. It can also collaborate with technology experts to provide technical support to the user. It can also collaborate with psychology experts to support the user's mental health. In this way, by collaborating with experts in different fields, it is possible to provide multifaceted learning support to the user.

[0054] Furthermore, the communication system includes a challenge promotion unit that uses an emotion estimation function to suggest content that encourages further challenges when the user feels positive emotions about learning. The challenge promotion unit uses the emotion estimation function to detect a state when the user feels positive emotions about learning. For example, it detects positive emotions from the user's words or facial expressions. The challenge promotion unit also suggests content that encourages further challenges. For example, it provides a message such as, "That's great! Let's move on to the next step." The challenge promotion unit can also suggest new challenges or tasks according to the user's progress. This allows the user to maintain motivation for learning by suggesting content that encourages further challenges when they feel positive emotions about learning.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The communication system may also include a health management unit that estimates the user's emotions and monitors the user's health state based on the estimated emotions. For example, if it is estimated that the user is feeling stressed, the health management unit may suggest ways to relax. For example, it may provide a response such as, "You seem stressed. Take a deep breath and relax." If it is estimated that the user is tired, the health management unit may also suggest resting. For example, it may provide a response such as, "You seem tired. Take a short break and refresh yourself." If it is estimated that the user is feeling anxious, the health management unit may also provide advice regarding mental health. In this way, it is possible to support the user's health by monitoring the user's health state based on the user's emotions and providing appropriate advice.

[0057] The communication system may also include an event suggestion unit that estimates a user's emotions and provides event information related to the user's hobbies and interests based on the estimated emotions. For example, if the user is estimated to be happy, the event suggestion unit may suggest events related to the user's interests. For example, the event suggestion unit may provide a response such as, "Sounds fun! There's a music festival nearby this weekend." If the user is estimated to be bored, the event suggestion unit may suggest events related to a new hobby. For example, the event suggestion unit may provide a response such as, "You seem bored. Why don't you try an art workshop to find a new hobby?" If the user is estimated to be excited, the event suggestion unit may suggest active events. This makes it possible to enrich the user's life by providing event information related to the user's hobbies and interests based on the user's emotions.

[0058] The communication system may also include an emotion recording unit that estimates a user's emotions, records the user's emotions based on the estimated emotions, and allows the user to review them later. For example, if the user is estimated to be sad, the emotion recording unit records the emotions so that the user can review them later. For example, it provides a response such as, "It seems you were feeling a little sad today. Let's review it later." If the user is estimated to be happy, the emotion recording unit may record the emotions so that the user can review positive events. For example, it provides a response such as, "It seems you had a very fun day today. Let's review it later." If the user is estimated to be stressed, the emotion recording unit may record the emotions and use the information to identify the cause of the stress. By recording the user's emotions and reviewing them later, the user can understand changes in their emotions and use them for self-improvement.

[0059] The communication system may also include a fitness support unit that estimates the user's emotions and suggests exercises that correspond to the user's emotions based on the estimated emotions. For example, if it is estimated that the user is feeling stressed, the fitness support unit may suggest exercises to relax. For example, it may provide a response such as, "You seem stressed. Try doing yoga or stretching to relax." If it is estimated that the user is tired, the fitness support unit may suggest light exercises. For example, it may provide a response such as, "You seem tired. Try some light walking to refresh yourself." If it is estimated that the user is feeling energetic, the fitness support unit may suggest active exercises. In this way, it is possible to support the user's health and fitness by suggesting exercises that correspond to the user's emotions.

[0060] The communication system may also include a nutrition support unit that estimates the user's emotions and suggests a meal plan according to the user's emotions based on the estimated emotions. For example, if it is estimated that the user is feeling stressed, the nutrition support unit may suggest a meal with a relaxing effect. For example, it may provide a response such as, "You seem to be stressed. Why don't you try some herbal tea, which has a relaxing effect?". If it is estimated that the user is tired, the nutrition support unit may suggest a meal that replenishes energy. For example, it may provide a response such as, "You seem tired. Why don't you try eating a banana or nuts to replenish your energy?". If it is estimated that the user is happy, the nutrition support unit may suggest a celebratory meal. In this way, it is possible to support the user's health and nutrition by suggesting a meal plan according to the user's emotions.

[0061] The communication system may also include a travel suggestion unit that proposes customized travel plans based on the user's hobbies and interests. For example, if the user likes nature, the travel suggestion unit may propose travel plans for enjoying nature. For example, it may provide a response such as, "Since you like nature, I recommend a trip to this national park." If the user is interested in history, the travel suggestion unit may propose travel plans for visiting historical places. For example, it may provide a response such as, "Since you are interested in history, I recommend a trip to this historical city." If the user likes active activities, the travel suggestion unit may propose travel plans that include active activities. This makes it possible to enrich the user's travel experience by providing customized travel plans based on the user's hobbies and interests.

[0062] The communication system may also include a learning style suggestion unit that suggests an optimal learning method based on the user's learning style. For example, if the user prefers visual learning, the learning style suggestion unit suggests visual learning materials. For example, it provides a response such as, "If you prefer visual learning, we recommend this video learning material." If the user prefers auditory learning, the learning style suggestion unit may suggest audio learning materials. For example, it provides a response such as, "If you prefer auditory learning, we recommend this podcast." If the user prefers hands-on learning, the learning style suggestion unit may suggest hands-on activities. This makes it possible to improve learning effectiveness by suggesting an optimal learning method based on the user's learning style.

[0063] The communication system may also include a schedule suggestion unit that suggests an optimal schedule based on the user's lifestyle. For example, if the user has a morning-type lifestyle, the schedule suggestion unit suggests tasks that will improve concentration in the morning. For example, it may provide a response such as, "Since you're a morning person, I recommend doing important tasks in the morning." If the user has a night-type lifestyle, the schedule suggestion unit may suggest tasks that will improve concentration in the evening. For example, it may provide a response such as, "Since you're a night-type, I recommend doing creative work in the evening." If the user has an irregular lifestyle, the schedule suggestion unit may also suggest a flexible schedule. This makes it possible to improve the user's quality of life by suggesting an optimal schedule based on the user's lifestyle.

[0064] The communication system may also include a career support unit that proposes a customized career plan based on the user's interests and goals. For example, if the user is interested in the technical field, the career support unit proposes a career plan in the technical field. For example, it may provide a response such as, "Since you are interested in the technical field, I recommend this programming course." If the user is interested in a creative field, the career support unit may also propose a career plan in the creative field. For example, it may provide a response such as, "Since you are interested in the creative field, I recommend this design course." If the user is interested in the business field, the career support unit may also propose a career plan in the business field. This makes it possible to support the user's career development by providing a customized career plan based on the user's interests and goals.

[0065] The communication system may also include a behavior prediction unit that predicts future behavior based on the user's past behavioral data and provides appropriate advice. For example, if the user has exercised during a specific time period in the past, the behavior prediction unit may suggest exercising during that time period. For example, it may provide a response such as, "You've exercised during this time period in the past. Why don't you try exercising today too?" If the user has rested on a specific day of the week in the past, the behavior prediction unit may suggest resting on that day. For example, it may provide a response such as, "You've rested on this day in the past. Let's relax today." If the user has participated in a specific event in the past, the behavior prediction unit may suggest activities related to that event. This allows the system to predict future behavior based on the user's past behavioral data and provide appropriate advice, thereby supporting the user's daily life.

[0066] The processing flow of the second embodiment will be briefly explained below.

[0067] Step 1: The emotion analysis unit analyzes the user's emotions. For example, the emotion analysis unit analyzes the user's words and facial expressions to identify emotions. It can also analyze voice tone to estimate emotions. Furthermore, it is possible to analyze the user's emotions using text analysis. Step 2: The response generator generates an appropriate response based on the emotions analyzed by the emotion analyzer. For example, if the user is analyzed to be tired, the response generator generates a response such as, "Thank you for your hard work. How about listening to some music to relax?" If the user is analyzed to want to learn a new skill, the response generator can generate a response such as, "What skills are you interested in? There are many options, such as programming or cooking." If the user is analyzed to be feeling anxious, the response generator can generate a response such as, "What topics do you like to talk about? How about talking about your hobbies or the latest news?" Step 3: The information providing unit provides the response generated by the response generating unit to the user. For example, the generated response may be displayed to the user as a text message. Alternatively, the generated response may be provided to the user as a voice message. Furthermore, the generated response may be provided to the user as a video message.

[0068] 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.

[0069] 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.

[0070] 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.

[0071] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] 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).

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0102] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0103] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0104] The 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.

[0105] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0107] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0108] 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.

[0109] 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.

[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 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.

[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 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.

[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 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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."

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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]

[0135] 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 emotion analysis unit that analyzes the emotion of a user; a response generation unit that generates an appropriate response based on the emotion analyzed by the emotion analysis unit; an information providing unit that provides the response generated by the response generating unit to a user; A system characterized by:

2. The emotion analysis unit Analyzing the user's tone of voice and facial expressions 2. The system of claim 1.

3. The response generation unit Analyzing the user's past conversation history and generating responses optimized for the individual user 2. The system of claim 1.

4. The information providing unit Offer relaxation techniques or words of encouragement based on the emotion 2. The system of claim 1.

5. The Growth Support Department Tracking the user's learning progress in real time and providing timely feedback 2. The system of claim 1.

6. The learning plan generation section Automatically generate a customized learning plan based on the user's interests and goals.

2. The system of claim 1.

7. The Learning Contents Provider Division Providing visual, auditory, and tactile learning content according to the user's learning style 2. The system of claim 1.

8. The Challenge Promotion Department When the user has positive feelings about learning, content that encourages further challenges is suggested.

2. The system of claim 1.

Images