System

Abstract

An object of the system according to the embodiment is to encourage a user who is lonely or isolated by making an utterance that makes the user feel familiar.SOLUTION: A system according to an embodiment includes an emotion generation unit, an emotion analysis unit, and a speech generation unit. The feeling generation unit analyzes the situation and feeling of the user. The emotion analysis unit generates an utterance having an emotion on the basis of a result analyzed by the emotion generation unit. The utterance generation unit provides the user with the utterance generated by the emotion analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that sentence-speech devices are unable to produce speech that makes users feel familiar, making it difficult to cheer up users who are feeling lonely or isolated.

[0005] The system according to the embodiment aims to cheer up users who are feeling lonely or isolated by speaking in a way that makes them feel close to the user. [Means for solving the problem]

[0006] The system according to the embodiment includes an emotion generation unit, an emotion analysis unit, and an utterance generation unit. The emotion generation unit analyzes the situation and emotions of a user. The emotion analysis unit generates an utterance having an emotion based on the analysis result by the emotion generation unit. The utterance generation unit provides the utterance generated by the emotion analysis unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can provide speech that makes the user feel familiar, and can cheer up users who are feeling lonely or isolated. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The sentence speech system according to the embodiment of the present invention is a system that analyzes the situation and emotions of a user and generates emotional speech, thereby reducing the user's feelings of loneliness and isolation.

[0029] A sentence speech system according to an embodiment includes an emotion generation unit, an emotion analysis unit, and an utterance generation unit. The emotion generation unit analyzes a user's situation and emotions. For example, the emotion generation unit analyzes the user's tone of voice and word choice to understand the user's emotions. The emotion generation unit can also analyze the user's behavioral data to estimate the user's emotions. For example, if a user speaks in a depressed voice, the emotion generation unit analyzes the tone of the voice and determines that the user is depressed. The emotion analysis unit generates an utterance with an emotion based on the analysis results by the emotion generation unit. For example, if a user feels lonely, the emotion analysis unit utters a friendly phrase such as "How was your day? Did anything fun happen?" If a user says "I'm tired today," the emotion analysis unit can also generate an utterance such as "Thank you for your hard work! Take a good rest today." The utterance generation unit provides the utterance generated by the emotion analysis unit to the user. For example, the utterance generation unit outputs the generated utterance as audio. The speech generation unit can also display the generated utterance as text. For example, the utterance generation unit outputs the generated utterance as a voice using speech synthesis technology. As a result, the text speech system according to the embodiment can reduce the user's sense of loneliness and isolation by providing utterances that correspond to the user's situation and emotions. For example, even if the user spends a lot of time alone, the emotion generation unit can talk to the user as if they were a friend, making the user feel less lonely. Furthermore, by generating utterances that correspond to the user's emotions, the user can feel that their feelings are understood and can gain a sense of security.

[0030] The emotion generation unit can learn the user's past interaction history and generate emotional expressions optimized for the user. For example, the emotion generation unit analyzes the user's past interaction history and learns the user's preferences and patterns. For example, the emotion generation unit generates more friendly utterances based on the user's frequently used words and phrases. The emotion generation unit can also track changes in the user's emotions based on the user's past interaction history and generate utterances accordingly. For example, the emotion generation unit can refer to past interactions when the user felt sad and generate encouraging utterances in similar situations. The emotion generation unit can also learn the user's interaction history and generate utterances related to specific events or anniversaries. For example, the emotion generation unit can remember the user's birthday or special day and utter congratulatory words on that day. In this way, by learning the user's past interaction history, it is possible to provide emotional expressions optimized for each individual user.

[0031] The emotion generation unit can provide timely emotional expressions by taking into account the user's lifestyle and daily events. The emotion generation unit, for example, analyzes the user's calendar and schedule and generates utterances that match important events and plans. For example, if the user is nervous before a meeting, the emotion generation unit generates utterances that relax the user. The emotion generation unit can also learn the user's lifestyle and generate utterances that match the time of day, such as morning or evening. For example, the emotion generation unit utters encouraging words in the morning and relaxing words in the evening. The emotion generation unit can also grasp the user's daily events and provide emotional expressions that correspond to them. For example, if the user is tired after exercising, the emotion generation unit utters encouraging words. This makes it possible to provide timely emotional expressions that match the user's lifestyle and daily events.

[0032] The emotion generation unit can estimate the user's emotion and suggest music or videos that match the user's emotion. The emotion generation unit, for example, analyzes the user's emotion and suggests music that matches that emotion. For example, if the user is feeling sad, the emotion generation unit plays uplifting music. The emotion generation unit can also suggest relaxing videos based on the user's emotion. For example, if the user is feeling stressed, the emotion generation unit plays natural scenery or relaxation videos. The emotion generation unit can also estimate the user's emotion and suggest podcasts or audiobooks that match that emotion. For example, the emotion generation unit selects and plays content that the user is likely to be interested in. In this way, the user's mood can be improved by suggesting music or videos that match the user's emotion.

[0033] The emotion generation unit can work with other smart devices in the home to achieve a unified emotional expression. The emotion generation unit, for example, works with smart lighting in the home to adjust the color and brightness of the lighting according to the user's emotion. For example, the emotion generation unit changes the lighting to a warm color when the user wants to relax. The emotion generation unit can also work with a smart refrigerator to suggest meals according to the user's emotion. For example, the emotion generation unit suggests a nutritious meal when the user is feeling down. The emotion generation unit can also work with a smart speaker to play music or audio content according to the user's emotion. For example, the emotion generation unit plays relaxation music when the user wants to relax. In this way, by working with smart devices in the home, a unified emotional expression can be provided.

[0034] The emotion generation unit can be introduced into an educational robot and provide emotional expressions to increase children's motivation to learn. For example, the emotion generation unit can be introduced into an educational robot and utter words of encouragement according to children's learning status. For example, when a child is working on a difficult problem, the emotion generation unit might say, "Do your best! You're almost there!" The emotion generation unit can also analyze children's emotions and suggest games or quizzes to increase their motivation to learn. For example, when a child is getting bored with studying, the emotion generation unit might suggest, "Let's take a break and play a game!" The emotion generation unit can also grasp children's learning progress and utter utterances to give them a sense of accomplishment. For example, when a child achieves a goal, the emotion generation unit might praise them by saying, "That's amazing! You did a great job!" In this way, when introduced into an educational robot, it can provide emotional expressions that increase children's motivation to learn.

[0035] The emotion generation unit can suggest cooking recipes that correspond to the user's emotions. The emotion generation unit, for example, analyzes the user's emotions and suggests cooking recipes that match those emotions. For example, the emotion generation unit suggests nutritious dishes when the user is feeling down. The emotion generation unit can also suggest relaxing cooking recipes based on the user's emotions. For example, the emotion generation unit suggests easy, relaxing dishes when the user is feeling stressed. The emotion generation unit can also estimate the user's emotions and suggest dessert or sweets recipes that correspond to those emotions. For example, the emotion generation unit suggests a dessert recipe when the user has a craving for something sweet. In this way, the user's mood can be improved by suggesting cooking recipes that correspond to the user's emotions.

[0036] The emotion generation unit analyzes a user's SNS posts and messages and can grasp changes in emotions in real time. The emotion generation unit, for example, analyzes a user's SNS posts and grasps changes in emotions in real time from the content of the posts. For example, the emotion generation unit determines that a user is in a positive state when there are many positive posts. The emotion generation unit can also analyze the content of the user's messaging app to detect changes in emotions. For example, the emotion generation unit estimates the user's emotions from the content of conversations with friends. The emotion generation unit can also analyze the user's SNS and message history to grasp trends in emotions. For example, the emotion generation unit learns patterns of changes in emotions from the content of past posts. This makes it possible to grasp changes in emotions in real time by analyzing a user's SNS posts and messages.

[0037] The emotion generation unit can analyze the user's biometric data and detect changes in emotions. The emotion generation unit, for example, analyzes the user's heart rate data and detects changes in emotions. For example, the emotion generation unit determines that the user is nervous when the heart rate is rising. The emotion generation unit can also grasp changes in emotions in real time based on the user's electrodermal activity data. For example, the emotion generation unit determines that the user is excited when the electrodermal activity is active. The emotion generation unit can also detect changes in emotions by comprehensively analyzing the user's biometric data. For example, the emotion generation unit infers emotions by combining heart rate and electrodermal activity data. In this way, changes in emotions can be detected by analyzing the user's biometric data.

[0038] The emotion generation unit is introduced into a medical device and can monitor the emotional state of a patient and notify medical staff. The emotion generation unit is introduced into, for example, a medical device and monitors the emotional state of a patient in real time. For example, the emotion generation unit analyzes heart rate and electrodermal activity to detect changes in emotions. The emotion generation unit can also be used to build a system that monitors the emotional state of a patient and notifies medical staff when an abnormality is detected. For example, the emotion generation unit issues an alert when stress or anxiety increases. The emotion generation unit can also analyze the emotional state of a patient and suggest appropriate ways to respond to the patient to medical staff. For example, the emotion generation unit suggests relaxing conversations or music. Thus, by introducing the emotion generation unit into a medical device, it is possible to monitor the emotional state of a patient in real time and notify medical staff.

[0039] The emotion generation unit is introduced into an in-vehicle system and can provide driving assistance according to the emotional state of the driver. The emotion generation unit is introduced into, for example, an in-vehicle system and monitors the emotional state of the driver in real time. For example, the emotion generation unit analyzes heart rate and facial expressions to detect changes in emotions. The emotion generation unit can also be used to build a system that provides driving assistance according to the emotional state of the driver. For example, the emotion generation unit plays relaxing music when stress levels rise. The emotion generation unit can also analyze the emotional state of the driver and produce utterances to encourage safe driving. For example, the emotion generation unit suggests taking a break when the driver is tired. In this way, by introducing the emotion generation unit into an in-vehicle system, driving assistance according to the emotional state of the driver can be provided.

[0040] The emotion generation unit can suggest travel plans that correspond to the user's emotions. The emotion generation unit, for example, analyzes the user's emotions and suggests travel plans that match those emotions. For example, the emotion generation unit suggests a hot spring trip when the user wants to relax. The emotion generation unit can also suggest active travel plans based on the user's emotions. For example, the emotion generation unit suggests an adventure tour when the user is feeling energetic. The emotion generation unit can also estimate the user's emotions and suggest tourist spots and activities that correspond to those emotions. For example, the emotion generation unit suggests quiet tourist spots when the user wants to relax. In this way, the user's mood can be improved by suggesting travel plans that correspond to the user's emotions.

[0041] The emotion generation unit can learn the user's preferences and hobbies and generate friendly utterances based on them. The emotion generation unit can, for example, learn the user's preferences and hobbies and generate utterances based on them. For example, the emotion generation unit can provide topics about the user's favorite movies and music. The emotion generation unit can also generate utterances that provide information related to the user's hobbies. For example, if the user likes gardening, the emotion generation unit can suggest new ways to grow plants. The emotion generation unit can also grasp the user's preferences and generate utterances that include jokes and humor based on them. For example, the emotion generation unit can quote lines from the user's favorite comedy movie. This makes it possible to provide friendly utterances based on the user's preferences and hobbies.

[0042] The emotion generation unit can generate consistent and friendly utterances by referring to the user's past dialogue history. The emotion generation unit can, for example, refer to the user's past dialogue history to generate consistent utterances. For example, the emotion generation unit revisits topics that were discussed in previous conversations. The emotion generation unit can also generate utterances that match the user's preferences and interests based on the user's dialogue history. For example, the emotion generation unit continues the discussion of a hobby that was previously discussed. The emotion generation unit can also learn the past dialogue history and predict and speak topics that the user wants to talk about. For example, the emotion generation unit naturally continues the previous conversation. In this way, consistent and friendly utterances can be provided by referring to the user's past dialogue history.

[0043] The emotion generation unit can estimate the user's emotions and generate jokes or humorous utterances in accordance with the user's emotions. The emotion generation unit, for example, analyzes the user's emotions and utters jokes in accordance with those emotions. For example, the emotion generation unit cheers up the user with a light joke when the user is feeling down. The emotion generation unit can also generate humorous utterances based on the user's emotions. For example, the emotion generation unit provides a fun topic when the user is relaxed. The emotion generation unit can also estimate the user's emotions and generate humorous utterances in accordance with those emotions. For example, the emotion generation unit tells a funny story that makes the user smile. In this way, jokes or humorous utterances in accordance with the user's emotions can be provided.

[0044] The emotion generation unit can be introduced into customer support systems to provide a friendly response that matches the customer's emotions. The emotion generation unit can be introduced into customer support systems, for example, to analyze the customer's emotions and provide a friendly response that matches those emotions. For example, the emotion generation unit provides a polite response when the customer is dissatisfied. The emotion generation unit can also generate friendly utterances based on the customer's emotions. For example, the emotion generation unit can offer words of encouragement when the customer is in trouble. The emotion generation unit can also estimate the customer's emotions and provide a humorous response that matches those emotions. For example, the emotion generation unit can use light-hearted jokes to help the customer relax. In this way, by introducing the emotion generation unit into customer support systems, a friendly response that matches the customer's emotions can be provided.

[0045] The emotion generation unit can be introduced into educational apps to provide friendly utterances that motivate children to learn. The emotion generation unit can be introduced into educational apps, for example, to provide friendly utterances that correspond to children's learning status. For example, when a child is working on a difficult problem, the emotion generation unit might say, "Good luck! You're almost there!" The emotion generation unit can also analyze children's emotions and generate utterances to motivate them to learn. For example, when a child is getting bored with studying, the emotion generation unit might suggest, "Let's take a break and play a game!" The emotion generation unit can also grasp children's learning progress and generate utterances that make them feel a sense of accomplishment. For example, when a child achieves a goal, the emotion generation unit might praise them by saying, "That's amazing! You did a great job!" In this way, when introduced into educational apps, friendly utterances that motivate children to learn can be provided.

[0046] The emotion generation unit can provide fitness coaching according to the user's emotions. The emotion generation unit, for example, analyzes the user's emotions and provides fitness coaching according to those emotions. For example, the emotion generation unit may offer words of encouragement when the user is feeling down. The emotion generation unit can also suggest a fitness program that will help the user relax based on the user's emotions. For example, the emotion generation unit may suggest yoga or stretching when the user is feeling stressed. The emotion generation unit can also estimate the user's emotions and provide fitness coaching according to those emotions. For example, the emotion generation unit may offer words of motivation to motivate the user. In this way, the user's health can be supported by providing fitness coaching according to the user's emotions.

[0047] The emotion generation unit can estimate the user's emotions and introduce online communities that correspond to the user's emotions. The emotion generation unit, for example, analyzes the user's emotions and introduces online communities that match those emotions. For example, when the user is feeling lonely, the emotion generation unit suggests communities where people with the same hobbies gather. The emotion generation unit can also introduce support groups and forums based on the user's emotions. For example, when the user is feeling stressed, the emotion generation unit suggests communities related to relaxation and mental health. The emotion generation unit can also estimate the user's emotions and introduce online events and workshops that correspond to those emotions. For example, when the user wants to relax, the emotion generation unit suggests online yoga or meditation classes. In this way, the user's sense of loneliness can be reduced by introducing online communities that correspond to the user's emotions.

[0048] The emotion generation unit can estimate the user's emotions and suggest hobbies and activities that correspond to the user's emotions. The emotion generation unit, for example, analyzes the user's emotions and suggests hobbies and activities that match those emotions. For example, the emotion generation unit suggests a relaxing hobby when the user is feeling down. The emotion generation unit can also suggest active activities based on the user's emotions. For example, the emotion generation unit suggests sports or outdoor activities when the user is feeling energetic. The emotion generation unit can also estimate the user's emotions and suggest creative activities that correspond to those emotions. For example, the emotion generation unit suggests arts and crafts activities when the user is feeling stressed. In this way, the user's sense of loneliness can be reduced by suggesting hobbies and activities that correspond to the user's emotions.

[0049] The emotion generation unit can be introduced into a care robot and provide utterances to reduce the elderly person's sense of loneliness. The emotion generation unit can be introduced into, for example, a care robot and analyze the elderly person's emotions and provide utterances in accordance with those emotions. For example, the emotion generation unit can speak encouraging words when the elderly person is feeling lonely. The emotion generation unit can also provide relaxing utterances based on the elderly person's emotions. For example, the emotion generation unit can speak relaxation words when the elderly person is feeling stressed. The emotion generation unit can also estimate the elderly person's emotions and provide humorous utterances in accordance with those emotions. For example, the emotion generation unit can tell funny stories that make the elderly person smile. In this way, by introducing the emotion generation unit into a care robot, it can provide utterances that reduce the elderly person's sense of loneliness.

[0050] The emotion generation unit can suggest volunteer activities that correspond to the user's emotions. The emotion generation unit, for example, analyzes the user's emotions and suggests volunteer activities that match those emotions. For example, the emotion generation unit suggests community activities when the user is feeling lonely. The emotion generation unit can also suggest volunteer activities that will help the user relax based on the user's emotions. For example, the emotion generation unit suggests volunteering at an animal shelter when the user is feeling stressed. The emotion generation unit can also estimate the user's emotions and suggest creative volunteer activities that correspond to those emotions. For example, the emotion generation unit suggests arts and crafts volunteer activities when the user is feeling down. In this way, the user's sense of loneliness can be alleviated by suggesting volunteer activities that correspond to the user's emotions.

[0051] The emotion generation unit can suggest art therapy that corresponds to the user's emotions. The emotion generation unit, for example, analyzes the user's emotions and suggests art therapy that matches those emotions. For example, the emotion generation unit suggests drawing when the user is feeling stressed. The emotion generation unit can also suggest relaxing art therapy based on the user's emotions. For example, the emotion generation unit suggests coloring or crafting when the user wants to relax. The emotion generation unit can also estimate the user's emotions and suggest creative art therapy that corresponds to those emotions. For example, the emotion generation unit suggests clay or sculpting activities when the user is feeling down. In this way, the user's stress can be reduced by suggesting art therapy that corresponds to the user's emotions.

[0052] The emotion generation unit can learn the user's dialogue history and generate utterances in the next dialogue that take into account the content of the previous dialogue. The emotion generation unit, for example, learns the user's dialogue history and generates utterances in the next dialogue that take into account the content of the previous dialogue. For example, the emotion generation unit revisits topics that came up in the previous conversation. The emotion generation unit can also generate utterances that match the user's preferences and interests based on the user's dialogue history. For example, the emotion generation unit continues the discussion of a hobby that was previously discussed. The emotion generation unit can also learn the past dialogue history and predict topics that the user wants to talk about and generate utterances. For example, the emotion generation unit naturally begins a continuation of the previous conversation. In this way, by learning the user's dialogue history, it is possible to provide utterances in the next dialogue that take into account the content of the previous conversation.

[0053] The emotion generation unit can estimate the user's emotions and support long-term goal setting according to the user's emotions. The emotion generation unit, for example, analyzes the user's emotions and supports long-term goal setting according to those emotions. For example, the emotion generation unit sets a small goal when the user is feeling down. The emotion generation unit can also suggest long-term goals that will help the user relax based on the user's emotions. For example, the emotion generation unit sets a goal to help the user relax when the user is feeling stressed. The emotion generation unit can also estimate the user's emotions and suggest creative long-term goals that correspond to those emotions. For example, the emotion generation unit sets an art or craft goal when the user is feeling down. In this way, the user's motivation can be maintained by supporting long-term goal setting according to the user's emotions.

[0054] The emotion generation unit can estimate the user's emotions and support the creation of a diary or memo in accordance with the user's emotions. The emotion generation unit, for example, analyzes the user's emotions and suggests diary content in accordance with the emotions. For example, when the user experiences a fun event, the emotion generation unit suggests that the user record the event. The emotion generation unit can also support the creation of relaxing memo based on the user's emotions. For example, when the user is feeling stressed, the emotion generation unit suggests that the user make a note of relaxation methods. The emotion generation unit can also estimate the user's emotions and support the creation of creative diary or memo in accordance with the emotions. For example, when the emotion generation unit is feeling down, the emotion generation unit suggests that the user record a positive event. In this way, the user's emotions can be organized by supporting the creation of a diary or memo in accordance with the user's emotions.

[0055] The emotion generation unit can be introduced into business meetings to provide facilitation according to the emotions of the participants. The emotion generation unit can be introduced into business meetings, for example, to analyze the emotions of the participants and provide facilitation according to those emotions. For example, the emotion generation unit can provide utterances that relax a nervous participant. The emotion generation unit can also support the progress of the meeting based on the emotions of the participants. For example, the emotion generation unit can suggest a break to a participant who is feeling stressed. The emotion generation unit can also estimate the emotions of the participants and provide facilitation with humor according to those emotions. For example, the emotion generation unit can use light-hearted jokes to create a relaxed atmosphere. In this way, by introducing the emotion generation unit into business meetings, it is possible to provide facilitation according to the emotions of the participants.

[0056] The emotion generation unit can provide personal training that corresponds to the user's emotions. The emotion generation unit, for example, analyzes the user's emotions and provides personal training that corresponds to those emotions. For example, the emotion generation unit may offer words of encouragement when the user is feeling down. The emotion generation unit can also suggest a training program that will help the user relax based on the user's emotions. For example, the emotion generation unit may suggest yoga or stretching when the user is feeling stressed. The emotion generation unit can also estimate the user's emotions and provide personal training that corresponds to those emotions. For example, the emotion generation unit may offer words of motivation that will motivate the user. In this way, the user's health can be supported by providing personal training that corresponds to the user's emotions.

[0057] The emotion generation unit can provide recommendations for books and movies that correspond to the user's emotions. The emotion generation unit, for example, analyzes the user's emotions and recommends books and movies that match those emotions. For example, when the user wants to relax, the emotion generation unit suggests books and movies that are suitable for relaxation. The emotion generation unit can also recommend active books and movies based on the user's emotions. For example, when the user is feeling energetic, the emotion generation unit suggests action movies or adventure novels. The emotion generation unit can also estimate the user's emotions and recommend creative books and movies that correspond to those emotions. For example, when the user is feeling stressed, the emotion generation unit suggests comedy movies or fun novels. In this way, the user's mood can be improved by providing recommendations for books and movies that correspond to the user's emotions.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The text speech system may further include a health management unit that monitors the user's health condition. The health management unit acquires biometric data such as the user's body temperature, heart rate, and blood pressure, and evaluates the user's health condition based on this data. For example, the health management unit may detect a fever if the user's body temperature is high and generate a speech encouraging the user to rest. The health management unit may also detect stress or excessive exercise if the user's heart rate is abnormally high and generate a speech encouraging the user to relax. Furthermore, the health management unit may suggest healthy eating and exercise if the user's blood pressure is high. This allows the text speech system to provide appropriate advice according to the user's health condition.

[0060] The text-to-speech system may further include a hobby suggestion unit that provides content based on the user's hobbies and interests. The hobby suggestion unit analyzes the user's past dialogue history and behavioral data to suggest hobbies and activities that the user may be interested in. For example, if the user is a movie lover, the hobby suggestion unit may provide information on new movies and movie reviews. If the user enjoys outdoor activities, the hobby suggestion unit may also provide information on nearby hiking trails and campsites. If the user is interested in cooking, the hobby suggestion unit may also provide information on new recipes and cooking classes. In this way, the text-to-speech system can enrich the user's life by providing content that matches the user's hobbies and interests.

[0061] The text-to-speech system can further include a learning support unit that supports the user's learning. The learning support unit monitors the user's learning situation and progress and provides appropriate learning advice. For example, if the user is struggling with a particular subject, the learning support unit can suggest supplementary materials or reference books related to that subject. The learning support unit can also create a study plan tailored to the user's learning pace and periodically check the user's progress. Furthermore, if the user loses motivation to study, the learning support unit can generate speech that offers words of encouragement or conveys the fun of learning. In this way, the text-to-speech system can increase the user's motivation to study and support effective learning.

[0062] The sentence speech system may further include a sleep management unit that supports the user's sleep. The sleep management unit monitors the user's sleep patterns and quality and provides appropriate sleep advice. For example, if the user is not getting enough sleep, the sleep management unit may provide relaxing music or a meditation guide. The sleep management unit may also make suggestions to improve the user's sleep environment. For example, it may suggest ways to adjust the room temperature and lighting appropriately. Furthermore, the sleep management unit may analyze the user's sleep data and provide advice to improve sleep quality. In this way, the sentence speech system can improve the user's sleep quality and support a healthy lifestyle.

[0063] The text-to-speech system may further include a fitness support unit that supports the user's fitness activities. The fitness support unit monitors the user's exercise data and provides appropriate fitness advice. For example, if the user is not getting enough exercise, the fitness support unit may suggest simple exercises or stretches. The fitness support unit may also create a training plan tailored to the user's exercise goals and check the user's progress. Furthermore, the fitness support unit may provide encouraging words or music to help the user maintain motivation while exercising. In this way, the text-to-speech system can support the user's fitness activities and promote a healthy lifestyle.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The emotion generation unit analyzes the user's situation and emotions. For example, the emotion generation unit analyzes the user's tone of voice and choice of words to understand the user's emotions. The emotion generation unit can also analyze the user's behavioral data to estimate the user's emotions. For example, if the user speaks in a depressed voice, the emotion generation unit analyzes the tone of the voice and determines that the user is depressed. Step 2: The emotion analysis unit generates utterances with emotions based on the results of the analysis by the emotion generation unit. For example, if the user feels lonely, the emotion analysis unit utters friendly phrases such as "How was your day? Did anything fun happen?". Also, if the user says "I'm tired today," the emotion analysis unit can generate utterances such as "Thank you for your hard work! Take a good rest today." Step 3: The speech generation unit provides the utterance generated by the emotion analysis unit to the user. For example, the utterance generation unit outputs the generated utterance as audio. The utterance generation unit can also display the generated utterance as text. For example, the utterance generation unit outputs the utterance generated using speech synthesis technology as audio.

[0066] (Example 2) The sentence speech system according to the embodiment of the present invention is a system that analyzes the situation and emotions of a user and generates emotional speech, thereby reducing the user's feelings of loneliness and isolation.

[0067] A sentence speech system according to an embodiment includes an emotion generation unit, an emotion analysis unit, and an utterance generation unit. The emotion generation unit analyzes a user's situation and emotions. For example, the emotion generation unit analyzes the user's tone of voice and word choice to understand the user's emotions. The emotion generation unit can also analyze the user's behavioral data to estimate the user's emotions. For example, if a user speaks in a depressed voice, the emotion generation unit analyzes the tone of the voice and determines that the user is depressed. The emotion analysis unit generates an utterance with an emotion based on the analysis results by the emotion generation unit. For example, if a user feels lonely, the emotion analysis unit utters a friendly phrase such as "How was your day? Did anything fun happen?" If a user says "I'm tired today," the emotion analysis unit can also generate an utterance such as "Thank you for your hard work! Take a good rest today." The utterance generation unit provides the utterance generated by the emotion analysis unit to the user. For example, the utterance generation unit outputs the generated utterance as audio. The speech generation unit can also display the generated utterance as text. For example, the utterance generation unit outputs the generated utterance as a voice using speech synthesis technology. As a result, the text speech system according to the embodiment can reduce the user's sense of loneliness and isolation by providing utterances that correspond to the user's situation and emotions. For example, even if the user spends a lot of time alone, the emotion generation unit can talk to the user as if they were a friend, making the user feel less lonely. Furthermore, by generating utterances that correspond to the user's emotions, the user can feel that their feelings are understood and can gain a sense of security.

[0068] The emotion generation unit can learn the user's past interaction history and generate emotional expressions optimized for the user. For example, the emotion generation unit analyzes the user's past interaction history and learns the user's preferences and patterns. For example, the emotion generation unit generates more friendly utterances based on the user's frequently used words and phrases. The emotion generation unit can also track changes in the user's emotions based on the user's past interaction history and generate utterances accordingly. For example, the emotion generation unit can refer to past interactions when the user felt sad and generate encouraging utterances in similar situations. The emotion generation unit can also learn the user's interaction history and generate utterances related to specific events or anniversaries. For example, the emotion generation unit can remember the user's birthday or special day and utter congratulatory words on that day. In this way, by learning the user's past interaction history, it is possible to provide emotional expressions optimized for each individual user.

[0069] The emotion generation unit can provide timely emotional expressions by taking into account the user's lifestyle and daily events. The emotion generation unit, for example, analyzes the user's calendar and schedule and generates utterances that match important events and plans. For example, if the user is nervous before a meeting, the emotion generation unit generates utterances that relax the user. The emotion generation unit can also learn the user's lifestyle and generate utterances that match the time of day, such as morning or evening. For example, the emotion generation unit utters encouraging words in the morning and relaxing words in the evening. The emotion generation unit can also grasp the user's daily events and provide emotional expressions that correspond to them. For example, if the user is tired after exercising, the emotion generation unit utters encouraging words. This makes it possible to provide timely emotional expressions that match the user's lifestyle and daily events.

[0070] The emotion generation unit can estimate the user's emotion and suggest music or videos that match the user's emotion. The emotion generation unit, for example, analyzes the user's emotion and suggests music that matches that emotion. For example, if the user is feeling sad, the emotion generation unit plays uplifting music. The emotion generation unit can also suggest relaxing videos based on the user's emotion. For example, if the user is feeling stressed, the emotion generation unit plays natural scenery or relaxation videos. The emotion generation unit can also estimate the user's emotion and suggest podcasts or audiobooks that match that emotion. For example, the emotion generation unit selects and plays content that the user is likely to be interested in. In this way, the user's mood can be improved by suggesting music or videos that match the user's emotion.

[0071] The emotion generation unit can work with other smart devices in the home to achieve a unified emotional expression. The emotion generation unit, for example, works with smart lighting in the home to adjust the color and brightness of the lighting according to the user's emotion. For example, the emotion generation unit changes the lighting to a warm color when the user wants to relax. The emotion generation unit can also work with a smart refrigerator to suggest meals according to the user's emotion. For example, the emotion generation unit suggests a nutritious meal when the user is feeling down. The emotion generation unit can also work with a smart speaker to play music or audio content according to the user's emotion. For example, the emotion generation unit plays relaxation music when the user wants to relax. In this way, by working with smart devices in the home, a unified emotional expression can be provided.

[0072] The emotion generation unit can be introduced into an educational robot and provide emotional expressions to increase children's motivation to learn. For example, the emotion generation unit can be introduced into an educational robot and utter words of encouragement according to children's learning status. For example, when a child is working on a difficult problem, the emotion generation unit might say, "Do your best! You're almost there!" The emotion generation unit can also analyze children's emotions and suggest games or quizzes to increase their motivation to learn. For example, when a child is getting bored with studying, the emotion generation unit might suggest, "Let's take a break and play a game!" The emotion generation unit can also grasp children's learning progress and utter utterances to give them a sense of accomplishment. For example, when a child achieves a goal, the emotion generation unit might praise them by saying, "That's amazing! You did a great job!" In this way, when introduced into an educational robot, it can provide emotional expressions that increase children's motivation to learn.

[0073] The emotion generation unit can suggest cooking recipes that correspond to the user's emotions. The emotion generation unit, for example, analyzes the user's emotions and suggests cooking recipes that match those emotions. For example, the emotion generation unit suggests nutritious dishes when the user is feeling down. The emotion generation unit can also suggest relaxing cooking recipes based on the user's emotions. For example, the emotion generation unit suggests easy, relaxing dishes when the user is feeling stressed. The emotion generation unit can also estimate the user's emotions and suggest dessert or sweets recipes that correspond to those emotions. For example, the emotion generation unit suggests a dessert recipe when the user has a craving for something sweet. In this way, the user's mood can be improved by suggesting cooking recipes that correspond to the user's emotions.

[0074] The emotion generation unit analyzes a user's SNS posts and messages and can grasp changes in emotions in real time. The emotion generation unit, for example, analyzes a user's SNS posts and grasps changes in emotions in real time from the content of the posts. For example, the emotion generation unit determines that a user is in a positive state when there are many positive posts. The emotion generation unit can also analyze the content of the user's messaging app to detect changes in emotions. For example, the emotion generation unit estimates the user's emotions from the content of conversations with friends. The emotion generation unit can also analyze the user's SNS and message history to grasp trends in emotions. For example, the emotion generation unit learns patterns of changes in emotions from the content of past posts. This makes it possible to grasp changes in emotions in real time by analyzing a user's SNS posts and messages.

[0075] The emotion generation unit can analyze the user's biometric data and detect changes in emotions. The emotion generation unit, for example, analyzes the user's heart rate data and detects changes in emotions. For example, the emotion generation unit determines that the user is nervous when the heart rate is rising. The emotion generation unit can also grasp changes in emotions in real time based on the user's electrodermal activity data. For example, the emotion generation unit determines that the user is excited when the electrodermal activity is active. The emotion generation unit can also detect changes in emotions by comprehensively analyzing the user's biometric data. For example, the emotion generation unit infers emotions by combining heart rate and electrodermal activity data. In this way, changes in emotions can be detected by analyzing the user's biometric data.

[0076] The emotion generation unit is introduced into a medical device and can monitor the emotional state of a patient and notify medical staff. The emotion generation unit is introduced into, for example, a medical device and monitors the emotional state of a patient in real time. For example, the emotion generation unit analyzes heart rate and electrodermal activity to detect changes in emotions. The emotion generation unit can also be used to build a system that monitors the emotional state of a patient and notifies medical staff when an abnormality is detected. For example, the emotion generation unit issues an alert when stress or anxiety increases. The emotion generation unit can also analyze the emotional state of a patient and suggest appropriate ways to respond to the patient to medical staff. For example, the emotion generation unit suggests relaxing conversations or music. Thus, by introducing the emotion generation unit into a medical device, it is possible to monitor the emotional state of a patient in real time and notify medical staff.

[0077] The emotion generation unit is introduced into an in-vehicle system and can provide driving assistance according to the emotional state of the driver. The emotion generation unit is introduced into, for example, an in-vehicle system and monitors the emotional state of the driver in real time. For example, the emotion generation unit analyzes heart rate and facial expressions to detect changes in emotions. The emotion generation unit can also be used to build a system that provides driving assistance according to the emotional state of the driver. For example, the emotion generation unit plays relaxing music when stress levels rise. The emotion generation unit can also analyze the emotional state of the driver and produce utterances to encourage safe driving. For example, the emotion generation unit suggests taking a break when the driver is tired. In this way, by introducing the emotion generation unit into an in-vehicle system, driving assistance according to the emotional state of the driver can be provided.

[0078] The emotion generation unit can suggest travel plans that correspond to the user's emotions. The emotion generation unit, for example, analyzes the user's emotions and suggests travel plans that match those emotions. For example, the emotion generation unit suggests a hot spring trip when the user wants to relax. The emotion generation unit can also suggest active travel plans based on the user's emotions. For example, the emotion generation unit suggests an adventure tour when the user is feeling energetic. The emotion generation unit can also estimate the user's emotions and suggest tourist spots and activities that correspond to those emotions. For example, the emotion generation unit suggests quiet tourist spots when the user wants to relax. In this way, the user's mood can be improved by suggesting travel plans that correspond to the user's emotions.

[0079] The emotion generation unit can learn the user's preferences and hobbies and generate friendly utterances based on them. The emotion generation unit can, for example, learn the user's preferences and hobbies and generate utterances based on them. For example, the emotion generation unit can provide topics about the user's favorite movies and music. The emotion generation unit can also generate utterances that provide information related to the user's hobbies. For example, if the user likes gardening, the emotion generation unit can suggest new ways to grow plants. The emotion generation unit can also grasp the user's preferences and generate utterances that include jokes and humor based on them. For example, the emotion generation unit can quote lines from the user's favorite comedy movie. This makes it possible to provide friendly utterances based on the user's preferences and hobbies.

[0080] The emotion generation unit can generate consistent and friendly utterances by referring to the user's past dialogue history. The emotion generation unit can, for example, refer to the user's past dialogue history to generate consistent utterances. For example, the emotion generation unit revisits topics that were discussed in previous conversations. The emotion generation unit can also generate utterances that match the user's preferences and interests based on the user's dialogue history. For example, the emotion generation unit continues the discussion of a hobby that was previously discussed. The emotion generation unit can also learn the past dialogue history and predict and speak topics that the user wants to talk about. For example, the emotion generation unit naturally continues the previous conversation. In this way, consistent and friendly utterances can be provided by referring to the user's past dialogue history.

[0081] The emotion generation unit can estimate the user's emotions and generate jokes or humorous utterances in accordance with the user's emotions. The emotion generation unit, for example, analyzes the user's emotions and utters jokes in accordance with those emotions. For example, the emotion generation unit cheers up the user with a light joke when the user is feeling down. The emotion generation unit can also generate humorous utterances based on the user's emotions. For example, the emotion generation unit provides a fun topic when the user is relaxed. The emotion generation unit can also estimate the user's emotions and generate humorous utterances in accordance with those emotions. For example, the emotion generation unit tells a funny story that makes the user smile. In this way, jokes or humorous utterances in accordance with the user's emotions can be provided.

[0082] The emotion generation unit can be introduced into customer support systems to provide a friendly response that matches the customer's emotions. The emotion generation unit can be introduced into customer support systems, for example, to analyze the customer's emotions and provide a friendly response that matches those emotions. For example, the emotion generation unit provides a polite response when the customer is dissatisfied. The emotion generation unit can also generate friendly utterances based on the customer's emotions. For example, the emotion generation unit can offer words of encouragement when the customer is in trouble. The emotion generation unit can also estimate the customer's emotions and provide a humorous response that matches those emotions. For example, the emotion generation unit can use light-hearted jokes to help the customer relax. In this way, by introducing the emotion generation unit into customer support systems, a friendly response that matches the customer's emotions can be provided.

[0083] The emotion generation unit can be introduced into educational apps to provide friendly utterances that motivate children to learn. The emotion generation unit can be introduced into educational apps, for example, to provide friendly utterances that correspond to children's learning status. For example, when a child is working on a difficult problem, the emotion generation unit might say, "Good luck! You're almost there!" The emotion generation unit can also analyze children's emotions and generate utterances to motivate them to learn. For example, when a child is getting bored with studying, the emotion generation unit might suggest, "Let's take a break and play a game!" The emotion generation unit can also grasp children's learning progress and generate utterances that make them feel a sense of accomplishment. For example, when a child achieves a goal, the emotion generation unit might praise them by saying, "That's amazing! You did a great job!" In this way, when introduced into educational apps, friendly utterances that motivate children to learn can be provided.

[0084] The emotion generation unit can provide fitness coaching according to the user's emotions. The emotion generation unit, for example, analyzes the user's emotions and provides fitness coaching according to those emotions. For example, the emotion generation unit may offer words of encouragement when the user is feeling down. The emotion generation unit can also suggest a fitness program that will help the user relax based on the user's emotions. For example, the emotion generation unit may suggest yoga or stretching when the user is feeling stressed. The emotion generation unit can also estimate the user's emotions and provide fitness coaching according to those emotions. For example, the emotion generation unit may offer words of motivation to motivate the user. In this way, the user's health can be supported by providing fitness coaching according to the user's emotions.

[0085] The emotion generation unit can estimate the user's emotions and introduce online communities that correspond to the user's emotions. The emotion generation unit, for example, analyzes the user's emotions and introduces online communities that match those emotions. For example, when the user is feeling lonely, the emotion generation unit suggests communities where people with the same hobbies gather. The emotion generation unit can also introduce support groups and forums based on the user's emotions. For example, when the user is feeling stressed, the emotion generation unit suggests communities related to relaxation and mental health. The emotion generation unit can also estimate the user's emotions and introduce online events and workshops that correspond to those emotions. For example, when the user wants to relax, the emotion generation unit suggests online yoga or meditation classes. In this way, the user's sense of loneliness can be reduced by introducing online communities that correspond to the user's emotions.

[0086] The emotion generation unit can estimate the user's emotions and suggest hobbies and activities that correspond to the user's emotions. The emotion generation unit, for example, analyzes the user's emotions and suggests hobbies and activities that match those emotions. For example, the emotion generation unit suggests a relaxing hobby when the user is feeling down. The emotion generation unit can also suggest active activities based on the user's emotions. For example, the emotion generation unit suggests sports or outdoor activities when the user is feeling energetic. The emotion generation unit can also estimate the user's emotions and suggest creative activities that correspond to those emotions. For example, the emotion generation unit suggests arts and crafts activities when the user is feeling stressed. In this way, the user's sense of loneliness can be reduced by suggesting hobbies and activities that correspond to the user's emotions.

[0087] The emotion generation unit can be introduced into a care robot and provide utterances to reduce the elderly person's sense of loneliness. The emotion generation unit can be introduced into, for example, a care robot and analyze the elderly person's emotions and provide utterances in accordance with those emotions. For example, the emotion generation unit can speak encouraging words when the elderly person is feeling lonely. The emotion generation unit can also provide relaxing utterances based on the elderly person's emotions. For example, the emotion generation unit can speak relaxation words when the elderly person is feeling stressed. The emotion generation unit can also estimate the elderly person's emotions and provide humorous utterances in accordance with those emotions. For example, the emotion generation unit can tell funny stories that make the elderly person smile. In this way, by introducing the emotion generation unit into a care robot, it can provide utterances that reduce the elderly person's sense of loneliness.

[0088] The emotion generation unit can suggest volunteer activities that correspond to the user's emotions. The emotion generation unit, for example, analyzes the user's emotions and suggests volunteer activities that match those emotions. For example, the emotion generation unit suggests community activities when the user is feeling lonely. The emotion generation unit can also suggest volunteer activities that will help the user relax based on the user's emotions. For example, the emotion generation unit suggests volunteering at an animal shelter when the user is feeling stressed. The emotion generation unit can also estimate the user's emotions and suggest creative volunteer activities that correspond to those emotions. For example, the emotion generation unit suggests arts and crafts volunteer activities when the user is feeling down. In this way, the user's sense of loneliness can be alleviated by suggesting volunteer activities that correspond to the user's emotions.

[0089] The emotion generation unit can suggest art therapy that corresponds to the user's emotions. The emotion generation unit, for example, analyzes the user's emotions and suggests art therapy that matches those emotions. For example, the emotion generation unit suggests drawing when the user is feeling stressed. The emotion generation unit can also suggest relaxing art therapy based on the user's emotions. For example, the emotion generation unit suggests coloring or crafting when the user wants to relax. The emotion generation unit can also estimate the user's emotions and suggest creative art therapy that corresponds to those emotions. For example, the emotion generation unit suggests clay or sculpting activities when the user is feeling down. In this way, the user's stress can be reduced by suggesting art therapy that corresponds to the user's emotions.

[0090] The emotion generation unit can learn the user's dialogue history and generate utterances in the next dialogue that take into account the content of the previous dialogue. The emotion generation unit, for example, learns the user's dialogue history and generates utterances in the next dialogue that take into account the content of the previous dialogue. For example, the emotion generation unit revisits topics that came up in the previous conversation. The emotion generation unit can also generate utterances that match the user's preferences and interests based on the user's dialogue history. For example, the emotion generation unit continues the discussion of a hobby that was previously discussed. The emotion generation unit can also learn the past dialogue history and predict topics that the user wants to talk about and generate utterances. For example, the emotion generation unit naturally begins a continuation of the previous conversation. In this way, by learning the user's dialogue history, it is possible to provide utterances in the next dialogue that take into account the content of the previous conversation.

[0091] The emotion generation unit can estimate the user's emotions and support long-term goal setting according to the user's emotions. The emotion generation unit, for example, analyzes the user's emotions and supports long-term goal setting according to those emotions. For example, the emotion generation unit sets a small goal when the user is feeling down. The emotion generation unit can also suggest long-term goals that will help the user relax based on the user's emotions. For example, the emotion generation unit sets a goal to help the user relax when the user is feeling stressed. The emotion generation unit can also estimate the user's emotions and suggest creative long-term goals that correspond to those emotions. For example, the emotion generation unit sets an art or craft goal when the user is feeling down. In this way, the user's motivation can be maintained by supporting long-term goal setting according to the user's emotions.

[0092] The emotion generation unit can estimate the user's emotions and support the creation of a diary or memo in accordance with the user's emotions. The emotion generation unit, for example, analyzes the user's emotions and suggests diary content in accordance with the emotions. For example, when the user experiences a fun event, the emotion generation unit suggests that the user record the event. The emotion generation unit can also support the creation of relaxing memo based on the user's emotions. For example, when the user is feeling stressed, the emotion generation unit suggests that the user make a note of relaxation methods. The emotion generation unit can also estimate the user's emotions and support the creation of creative diary or memo in accordance with the emotions. For example, when the emotion generation unit is feeling down, the emotion generation unit suggests that the user record a positive event. In this way, the user's emotions can be organized by supporting the creation of a diary or memo in accordance with the user's emotions.

[0093] The emotion generation unit can be introduced into business meetings to provide facilitation according to the emotions of the participants. The emotion generation unit can be introduced into business meetings, for example, to analyze the emotions of the participants and provide facilitation according to those emotions. For example, the emotion generation unit can provide utterances that relax a nervous participant. The emotion generation unit can also support the progress of the meeting based on the emotions of the participants. For example, the emotion generation unit can suggest a break to a participant who is feeling stressed. The emotion generation unit can also estimate the emotions of the participants and provide facilitation with humor according to those emotions. For example, the emotion generation unit can use light-hearted jokes to create a relaxed atmosphere. In this way, by introducing the emotion generation unit into business meetings, it is possible to provide facilitation according to the emotions of the participants.

[0094] The emotion generation unit can provide personal training that corresponds to the user's emotions. The emotion generation unit, for example, analyzes the user's emotions and provides personal training that corresponds to those emotions. For example, the emotion generation unit may offer words of encouragement when the user is feeling down. The emotion generation unit can also suggest a training program that will help the user relax based on the user's emotions. For example, the emotion generation unit may suggest yoga or stretching when the user is feeling stressed. The emotion generation unit can also estimate the user's emotions and provide personal training that corresponds to those emotions. For example, the emotion generation unit may offer words of motivation that will motivate the user. In this way, the user's health can be supported by providing personal training that corresponds to the user's emotions.

[0095] The emotion generation unit can provide recommendations for books and movies that correspond to the user's emotions. The emotion generation unit, for example, analyzes the user's emotions and recommends books and movies that match those emotions. For example, when the user wants to relax, the emotion generation unit suggests books and movies that are suitable for relaxation. The emotion generation unit can also recommend active books and movies based on the user's emotions. For example, when the user is feeling energetic, the emotion generation unit suggests action movies or adventure novels. The emotion generation unit can also estimate the user's emotions and recommend creative books and movies that correspond to those emotions. For example, when the user is feeling stressed, the emotion generation unit suggests comedy movies or fun novels. In this way, the user's mood can be improved by providing recommendations for books and movies that correspond to the user's emotions.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The text speech system may further include a health management unit that monitors the user's health condition. The health management unit acquires biometric data such as the user's body temperature, heart rate, and blood pressure, and evaluates the user's health condition based on this data. For example, the health management unit may detect a fever if the user's body temperature is high and generate a speech encouraging the user to rest. The health management unit may also detect stress or excessive exercise if the user's heart rate is abnormally high and generate a speech encouraging the user to relax. Furthermore, the health management unit may suggest healthy eating and exercise if the user's blood pressure is high. This allows the text speech system to provide appropriate advice according to the user's health condition.

[0098] The text-to-speech system may further include a hobby suggestion unit that provides content based on the user's hobbies and interests. The hobby suggestion unit analyzes the user's past dialogue history and behavioral data to suggest hobbies and activities that the user may be interested in. For example, if the user is a movie lover, the hobby suggestion unit may provide information on new movies and movie reviews. If the user enjoys outdoor activities, the hobby suggestion unit may also provide information on nearby hiking trails and campsites. If the user is interested in cooking, the hobby suggestion unit may also provide information on new recipes and cooking classes. In this way, the text-to-speech system can enrich the user's life by providing content that matches the user's hobbies and interests.

[0099] The text-to-speech system can further include a learning support unit that supports the user's learning. The learning support unit monitors the user's learning situation and progress and provides appropriate learning advice. For example, if the user is struggling with a particular subject, the learning support unit can suggest supplementary materials or reference books related to that subject. The learning support unit can also create a study plan tailored to the user's learning pace and periodically check the user's progress. Furthermore, if the user loses motivation to study, the learning support unit can generate speech that offers words of encouragement or conveys the fun of learning. In this way, the text-to-speech system can increase the user's motivation to study and support effective learning.

[0100] The sentence speech system may further include a sleep management unit that supports the user's sleep. The sleep management unit monitors the user's sleep patterns and quality and provides appropriate sleep advice. For example, if the user is not getting enough sleep, the sleep management unit may provide relaxing music or a meditation guide. The sleep management unit may also make suggestions to improve the user's sleep environment. For example, it may suggest ways to adjust the room temperature and lighting appropriately. Furthermore, the sleep management unit may analyze the user's sleep data and provide advice to improve sleep quality. In this way, the sentence speech system can improve the user's sleep quality and support a healthy lifestyle.

[0101] The text-to-speech system may further include a fitness support unit that supports the user's fitness activities. The fitness support unit monitors the user's exercise data and provides appropriate fitness advice. For example, if the user is not getting enough exercise, the fitness support unit may suggest simple exercises or stretches. The fitness support unit may also create a training plan tailored to the user's exercise goals and check the user's progress. Furthermore, the fitness support unit may provide encouraging words or music to help the user maintain motivation while exercising. In this way, the text-to-speech system can support the user's fitness activities and promote a healthy lifestyle.

[0102] The text speech system may further include a relaxation suggestion unit that estimates the user's emotions and suggests an appropriate relaxation method based on the estimated emotions. The relaxation suggestion unit analyzes the user's emotions and suggests a relaxation method according to the emotions. For example, if the user is feeling stressed, the relaxation suggestion unit may provide guidance for deep breathing or meditation. If the user is tired, the relaxation suggestion unit may also suggest relaxing music or aromatherapy. If the user is feeling anxious, the relaxation suggestion unit may also provide relaxing images or natural sounds. In this way, the text speech system can suggest relaxation methods according to the user's emotions and reduce the user's stress.

[0103] The sentence speech system can further include a feedback unit that estimates the user's emotions and provides appropriate feedback based on the estimated emotions. The feedback unit analyzes the user's emotions and provides feedback according to the emotions. For example, if the user feels a sense of accomplishment, the feedback unit can provide words of praise or admiration. If the user feels failure or frustration, the feedback unit can also provide words of encouragement or advice for next time. Furthermore, if the user feels anxious, the feedback unit can also suggest words of reassurance or relaxation methods. In this way, the sentence speech system can provide feedback according to the user's emotions and maintain the user's motivation.

[0104] The sentence speech system can further include a learning method suggestion unit that estimates the user's emotions and suggests an appropriate learning method based on the estimated emotions. The learning method suggestion unit analyzes the user's emotions and suggests a learning method that corresponds to the emotions. For example, if the user is lacking in concentration, the learning method suggestion unit can suggest short periods of intensive learning or a learning method that includes breaks. In addition, if the user is losing motivation, the learning method suggestion unit can also suggest learning materials that allow learning in a game-like manner or an interactive learning method. Furthermore, if the user is feeling stressed, the learning method suggestion unit can suggest learning in a relaxing environment or a learning method that incorporates relaxation. In this way, the sentence speech system can suggest learning methods that correspond to the user's emotions and support effective learning.

[0105] The text-to-speech system can further include an exercise program suggestion unit that estimates the user's emotions and suggests an appropriate exercise program based on the estimated emotions. The exercise program suggestion unit analyzes the user's emotions and suggests an exercise program that corresponds to the emotions. For example, if the user is feeling stressed, the exercise program suggestion unit can suggest a relaxing yoga or stretching program. If the user is feeling low in energy, the exercise program suggestion unit can also suggest a light exercise or walking program. If the user is feeling energetic, the exercise program suggestion unit can also suggest a high-intensity interval training (HIIT) or running program. In this way, the text-to-speech system can suggest an exercise program that corresponds to the user's emotions and support the user's health.

[0106] The sentence speech system can further include a meal plan suggestion unit that estimates the user's emotions and suggests an appropriate meal plan based on the estimated emotions. The meal plan suggestion unit analyzes the user's emotions and suggests a meal plan according to the emotions. For example, if the user is feeling stressed, the meal plan suggestion unit can suggest dishes made with relaxing ingredients or herbal tea. If the user is feeling low in energy, the meal plan suggestion unit can also suggest nutritious meals or smoothies that can replenish energy. Furthermore, if the user wants to relax, the meal plan suggestion unit can suggest light meals or dishes made with ingredients that are easy to digest. In this way, the sentence speech system can suggest meal plans according to the user's emotions and support the user's health.

[0107] The processing flow of the second embodiment will be briefly explained below.

[0108] Step 1: The emotion generation unit analyzes the user's situation and emotions. For example, the emotion generation unit analyzes the user's tone of voice and choice of words to understand the user's emotions. The emotion generation unit can also analyze the user's behavioral data to estimate the user's emotions. For example, if the user speaks in a depressed voice, the emotion generation unit analyzes the tone of the voice and determines that the user is depressed. Step 2: The emotion analysis unit generates utterances with emotions based on the results of the analysis by the emotion generation unit. For example, if the user feels lonely, the emotion analysis unit utters friendly phrases such as "How was your day? Did anything fun happen?". Also, if the user says "I'm tired today," the emotion analysis unit can generate utterances such as "Thank you for your hard work! Take a good rest today." Step 3: The speech generation unit provides the utterance generated by the emotion analysis unit to the user. For example, the utterance generation unit outputs the generated utterance as audio. The utterance generation unit can also display the generated utterance as text. For example, the utterance generation unit outputs the utterance generated using speech synthesis technology as audio.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0122] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0137] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0159] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0160] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0161] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0162] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the 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.

[0163] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0164] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0165] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0166] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0167] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0168] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0169] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0170] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0171] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0172] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0173] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0174] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0175] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0176] 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 generation unit that analyzes the user's situation and emotions; an emotion analysis unit that generates an utterance having emotion based on the analysis result by the emotion generation unit; an utterance generation unit that provides the utterance generated by the emotion analysis unit to a user; A system characterized by:

2. The emotion generation unit The system learns the user's past dialogue history and generates an emotional expression optimized for the user.

2. The system of claim 1.

3. The emotion generation unit Providing timely emotional expressions taking into account the user's daily rhythm and events 2. The system of claim 1.

4. The emotion generation unit Estimating the user's emotions and suggesting music and videos according to the user's emotions 2. The system of claim 1.

5. The emotion generation unit Links with other smart devices in the home to achieve consistent emotional expression 2. The system of claim 1.

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