System

Abstract

An object of a system according to an embodiment is to realize a natural conversation with a user and realistic appearance of a character.SOLUTION: In accordance with an embodiment, a system comprises a generation AI, audio generation technology, and a character manifestation section. The generation AI analyzes the input from the user and generates an appropriate response. The voice generation technology reproduces the response generated by the generation AI as a character's voice. The character revealing section displays a character.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to realize natural conversations with users or realistic character appearances.

[0005] The system according to the embodiment aims to realize natural conversation with the user and realistic appearance of the character. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation AI, a voice generation technology, and a character manifestation unit. The generation AI analyzes input from a user and generates an appropriate response. The voice generation technology reproduces the response generated by the generation AI as the voice of the character. The character manifestation unit displays the character. [Effects of the Invention]

[0007] The system according to the embodiment can realize natural conversation with the user and realistic appearance of the character. [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 AI ​​virtual assistant system according to an embodiment of the present invention allows users to freely converse with popular characters from anime and games on their home PCs or mobile devices, providing an experience that makes it feel as if the user is actually living with the characters. This allows the user to freely converse with the characters and receive voice responses, providing a highly realistic experience.

[0029] An AI virtual assistant system according to an embodiment includes a generation AI, voice generation technology, and a character manifestation unit. The generation AI analyzes user input and generates an appropriate response. For example, when the user inputs "How was your day?", the generation AI responds with "I had a great time today! How was it for you?" The generation AI can also understand the user's input and maintain a natural conversation. The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do, and the generation AI generates a response based on the prompt. The voice generation technology reproduces the response generated by the generation AI as a character's voice. For example, when a user inputs "Good morning," the voice generation technology pronounces the characters as "Good morning" in the character's voice. The voice generation technology can also automatically convert the characters input by the user into voice and reproduce it as the character's voice. The character manifestation unit displays a character. For example, the user can converse with the character on a PC screen. The character moves and responds in voice according to the user's input, providing an experience that makes the character seem as if it were actually present. As a result, the AI ​​virtual assistant system according to the embodiment allows users to freely converse with popular characters from anime and games on their home PCs or mobile devices, providing an experience that makes it feel as if they are living with the characters.

[0030] Generative AI can learn from a user's past conversation history and generate personalized responses based on the user's preferences and interests. For example, generative AI can analyze a user's past conversation history to identify topics and interests that the user frequently discusses. For example, it can learn information about the user's hobbies and favorite movies and generate responses based on that information. Generative AI can also generate personalized questions that reflect the user's preferences and interests based on the user's past conversation history. For example, if a user is interested in travel, it can generate questions such as, "How was your recent trip?" Generative AI can also learn from a user's past conversation history and provide the latest information on topics that the user is particularly interested in. For example, if a user is interested in technology, it can generate conversations based on the latest technology news. This allows for the generation of personalized responses based on the user's preferences and interests, providing a more individualized conversation experience.

[0031] The generation AI can support conversations in different languages ​​and accommodate international users. For example, the generation AI uses multilingual natural language processing technology to understand content input by the user in different languages ​​and generate an appropriate response. For example, it supports multiple languages ​​such as English, French, and Chinese. Furthermore, if the user starts a conversation in a different language, the generation AI automatically switches to that language and generates a response. For example, if the user speaks in English, the generation AI will respond in English. Furthermore, to support conversations in different languages, the generation AI generates a response based on the language selected by the user. For example, if the user selects Japanese in the settings, the generation AI will respond in Japanese. This allows the generation AI to accommodate international users by supporting conversations in different languages.

[0032] Generative AI can support users in managing their schedules and tasks, and function as an assistant in everyday life. For example, generative AI can manage the user's schedule and send appointment reminders. For example, it can notify the user by saying, "Tomorrow's meeting starts at 10:00 AM." When the user inputs a task, generative AI manages the task and checks its progress. For example, it can check progress by asking, "Have you finished today's tasks?" Generative AI can also make suggestions for efficient time management based on the user's schedule and tasks. For example, it can make suggestions such as, "Let's take a break now to make effective use of your afternoon." In this way, it can function as an assistant in everyday life by supporting the user in managing their schedule and tasks.

[0033] Voice generation technology can enable customization of a character's voice tone and accent to suit a user's preferences. Voice generation technology, for example, provides a function for adjusting a character's voice tone and accent to suit a user's preferences. For example, a user can set a character's voice to be higher. Furthermore, a user can select a character's voice accent, and the voice generation technology generates voice based on that selection. For example, an accent such as Kansai dialect or standard Japanese can be selected. Furthermore, the voice generation technology adjusts the character's voice tone in real time according to the user's preferences. For example, if a user sets a softer voice tone, voice is generated based on that setting. This allows a more personalized voice experience to be provided by customizing a character's voice tone and accent to suit a user's preferences.

[0034] Voice generation technology can incorporate background sounds and environmental sounds to provide a more realistic conversation experience. Voice generation technology can provide a more realistic conversation experience by, for example, incorporating background sounds and playing them together with the character's voice. For example, a character speaks while the background sounds of a cafe are played. Environmental sounds can also be incorporated into voice generation technology and played together with the character's voice. For example, a character speaks with the sounds of rain or wind in the background. Voice generation technology can also automatically select background sounds according to the user's environment and play them together with the character's voice. For example, if the user is out and about, city sounds can be played in the background. In this way, by incorporating background sounds and environmental sounds, a more realistic conversation experience can be provided.

[0035] Voice generation technology can simultaneously generate voices for different characters, enabling conversations with multiple characters. Voice generation technology can, for example, simultaneously generate voices for multiple characters, allowing a user to converse with multiple characters at the same time. For example, two characters can speak alternately. Furthermore, by simultaneously generating voices for different characters, users can enjoy group conversations. For example, three characters can speak together. Furthermore, voice generation technology can generate voices for multiple characters in real time, providing a scenario in which a user converses with multiple characters at the same time. For example, characters can converse with each other. Thus, by simultaneously generating voices for different characters, conversations with multiple characters can be enabled.

[0036] Voice generation technology can mimic a user's voice and provide an experience of conversing with a character in the user's own voice. Voice generation technology, for example, analyzes the user's voice, imitates that voice, and reproduces it as a character's voice. For example, a character speaks based on the user's voice. Voice generation technology that mimics a user's voice can also be used to provide an experience of a user conversing with a character in their own voice. For example, a character based on the user's voice responds. Voice generation technology can also mimic a user's voice in real time to provide a scenario in which the user converses with a character in their own voice. For example, a character based on the user's voice speaks. In this way, by imitating the user's voice, it is possible to provide an experience of a user conversing with a character in their own voice.

[0037] The character manifestation unit can change the character's behavior and facial expression in real time according to user input, thereby realizing more natural interactions. The character manifestation unit, for example, builds a system that changes the character's behavior and facial expression in real time according to user input. For example, when a user inputs "smile," the character smiles. The character's behavior and facial expression are also adjusted in real time based on the user's input. For example, when a user inputs "wave," the character waves. By changing the character's behavior and facial expression in real time according to user input, more natural interactions are also realized. For example, when a user inputs "surprised," the character makes a surprised expression. In this way, by changing the character's behavior and facial expression in real time according to user input, more natural interactions can be realized.

[0038] The character manifestation unit can recognize the environment around the user and perform actions and responses according to the environment. In the character manifestation unit, for example, the character recognizes the environment around the user using a camera and performs actions and responses according to the environment. For example, if the user is out, the character generates a response such as "How is it outside?". The character also recognizes the environment around the user using a sensor and performs actions and responses according to the environment. For example, if the user is in a dark room, the character generates a response such as "Shall I turn on the light?". The character also recognizes the environment around the user in real time and performs actions and responses according to the environment. For example, if the user is in the kitchen, the character generates a response such as "What are you making?". In this way, the character recognizes the environment around the user and performs actions and responses according to the environment, thereby achieving more natural interactions.

[0039] The character manifestation unit allows a character to function as a user's avatar and support virtual interactions with other users. The character manifestation unit, for example, builds a system in which a character functions as a user's avatar and supports virtual interactions with other users. For example, the character participates in a virtual event on behalf of the user. Also, the character supports chats and video calls with other users as the user's avatar. For example, the character sends messages on behalf of the user. Also, the character supports virtual interactions in real time as the user's avatar. For example, the character participates in a virtual conference on behalf of the user. In this way, the character functions as the user's avatar and supports virtual interactions with other users, thereby realizing a wider range of communication.

[0040] The character manifestation unit allows a character to monitor the user's health condition and function as a health management assistant. The character manifestation unit, for example, builds a system in which a character monitors the user's health condition and functions as a health management assistant. For example, the character monitors the user's heart rate and sleep state. The character also monitors the user's health condition in real time and provides health management advice. For example, the character may make a suggestion such as "try exercising a little more today." The character also monitors the user's health condition and issues an alert if it detects an abnormality. For example, the character may issue an alert such as "your heart rate is high, so take a break." In this way, the character can monitor the user's health condition and function as a health management assistant, thereby supporting the user's health.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] Generative AI can monitor the user's health status and function as a health management assistant. For example, generative AI can monitor the user's heart rate and sleep status and provide health management advice. For example, it can make suggestions such as, "Try to exercise a little more today." Generative AI can also monitor the user's health status in real time and issue an alert if it detects an abnormality. For example, it can issue an alert such as, "Your heart rate is high, so take a break." In this way, generative AI can support the user's health by monitoring the user's health status and functioning as a health management assistant.

[0043] Generative AI can support users in managing their schedules and tasks, and function as an assistant in everyday life. For example, the generative AI can manage the user's schedule and send appointment reminders. For example, it can notify the user by saying, "Tomorrow's meeting starts at 10:00 AM." When the user inputs a task, the generative AI manages the task and checks its progress. For example, it can check progress by asking, "Have you finished today's tasks?" The generative AI can also make suggestions for efficient time management based on the user's schedule and tasks. For example, it can make suggestions such as, "Let's take a break now to make effective use of your afternoon." In this way, it can function as an assistant in everyday life by supporting the user in managing their schedule and tasks.

[0044] Generative AI can support conversations in different languages ​​and accommodate international users. For example, generative AI uses multilingual natural language processing technology to understand content input by users in different languages ​​and generate appropriate responses. For example, it supports multiple languages ​​such as English, French, and Chinese. Furthermore, if a user begins a conversation in a different language, the generative AI automatically switches to that language and generates a response. For example, if a user speaks in English, it responds in English. Furthermore, to support conversations in different languages, the generative AI generates responses based on the language selected by the user. For example, if a user selects Japanese in the settings, it responds in Japanese. This allows it to accommodate international users by supporting conversations in different languages.

[0045] Voice generation technology can enable customization of a character's voice tone and accent to suit a user's preferences. For example, the voice generation technology provides a function for adjusting a character's voice tone and accent to suit a user's preferences. For example, a user can set a character's voice to be higher. The voice generation technology can also allow a user to select a character's voice accent, and the voice generation technology generates voice based on that selection. For example, an accent such as Kansai dialect or standard Japanese can be selected. The voice generation technology also adjusts the character's voice tone in real time according to the user's preferences. For example, if a user sets a voice tone to be softer, the voice generation technology generates voice based on that setting. This allows a character's voice tone and accent to be customized to suit a user's preferences, providing a more personalized voice experience.

[0046] Voice generation technology can incorporate background sounds and environmental sounds to provide a more realistic conversation experience. For example, voice generation technology can incorporate background sounds and play them together with the character's voice to provide a more realistic conversation experience. For example, a character can speak while the background sounds of a cafe are played. Environmental sounds can also be incorporated into voice generation technology and played along with the character's voice. For example, a character can speak with the sounds of rain or wind in the background. Voice generation technology can also automatically select background sounds according to the user's environment and play them along with the character's voice. For example, if the user is out and about, city sounds can be played in the background. In this way, by incorporating background sounds and environmental sounds, a more realistic conversation experience can be provided.

[0047] The character manifestation unit allows a character to function as a user's avatar and support virtual interactions with other users. For example, the character manifestation unit builds a system in which a character functions as a user's avatar and supports virtual interactions with other users. For example, the character participates in a virtual event on behalf of the user. Also, the character supports chats and video calls with other users as the user's avatar. For example, the character sends messages on behalf of the user. Also, the character supports virtual interactions in real time as the user's avatar. For example, the character participates in a virtual conference on behalf of the user. In this way, the character functions as the user's avatar and supports virtual interactions with other users, thereby realizing a wider range of communication.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The generative AI analyzes the user's input and generates an appropriate response. For example, if the user inputs "How was your day?", the AI ​​will respond with something like "I had a great time today! How was it for you?" The generative AI can also understand the user's input and continue a natural conversation. The input to the generative AI is a prompt containing instructions on what the user wants the AI ​​to do, and the AI ​​generates a response based on that prompt. Step 2: The voice generation technology reproduces the response generated by the generation AI as a character's voice. For example, if a user types "good morning," the voice generation technology will speak the characters as "good morning" in the character's voice. The voice generation technology can also automatically convert the characters entered by the user into speech and reproduce it as a character's voice. Step 3: The character manifestation unit displays the character. For example, the user can talk to the character on the PC screen. The character moves and responds with voice according to the user's input, providing an experience as if the character were actually present.

[0050] (Example 2) The AI ​​virtual assistant system according to an embodiment of the present invention allows users to freely converse with popular characters from anime and games on their home PCs or mobile devices, providing an experience that makes it feel as if the user is actually living with the characters. This allows the user to freely converse with the characters and receive voice responses, providing a highly realistic experience.

[0051] An AI virtual assistant system according to an embodiment includes a generation AI, voice generation technology, and a character manifestation unit. The generation AI analyzes user input and generates an appropriate response. For example, when the user inputs "How was your day?", the generation AI responds with "I had a great time today! How was it for you?" The generation AI can also understand the user's input and maintain a natural conversation. The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do, and the generation AI generates a response based on the prompt. The voice generation technology reproduces the response generated by the generation AI as a character's voice. For example, when a user inputs "Good morning," the voice generation technology pronounces the characters as "Good morning" in the character's voice. The voice generation technology can also automatically convert the characters input by the user into voice and reproduce it as the character's voice. The character manifestation unit displays a character. For example, the user can converse with the character on a PC screen. The character moves and responds in voice according to the user's input, providing an experience that makes the character seem as if it were actually present. As a result, the AI ​​virtual assistant system according to the embodiment allows users to freely converse with popular characters from anime and games on their home PCs or mobile devices, providing an experience that makes it feel as if they are living with the characters.

[0052] Generative AI can learn from a user's past conversation history and generate personalized responses based on the user's preferences and interests. For example, generative AI can analyze a user's past conversation history to identify topics and interests that the user frequently discusses. For example, it can learn information about the user's hobbies and favorite movies and generate responses based on that information. Generative AI can also generate personalized questions that reflect the user's preferences and interests based on the user's past conversation history. For example, if a user is interested in travel, it can generate questions such as, "How was your recent trip?" Generative AI can also learn from a user's past conversation history and provide the latest information on topics that the user is particularly interested in. For example, if a user is interested in technology, it can generate conversations based on the latest technology news. This allows for the generation of personalized responses based on the user's preferences and interests, providing a more individualized conversation experience.

[0053] The generation AI can analyze a user's facial expression and tone of voice and generate an emotional response accordingly. For example, the generation AI can analyze a user's facial expression using a camera, and if the user is smiling, generate a positive response such as "You look like you're having fun today!" For example, if a user speaks with a smile, the generation AI can recognize that facial expression. The generation AI can also analyze the user's tone of voice using a microphone and generate a response according to the user's emotional state. For example, if the user's voice is calm, the generation AI can generate a response such as "You seem relaxed." The generation AI can also simultaneously analyze a user's facial expression and tone of voice, and if the user looks sad, it can generate an emotional response such as "Is something wrong?" For example, if a user speaks with a sad expression and tone, the generation AI can recognize that emotion. This allows for a more natural conversation experience by generating emotional responses according to the user's facial expression and tone of voice.

[0054] The generation AI can use its emotion estimation function to estimate the user's emotional state in real time and generate a response that will help them relax if they are feeling stressed. For example, the generation AI can analyze the user's emotional state in real time and generate a relaxing response such as "Shall we take a break?" if they are feeling stressed. For example, it can detect stress from the user's facial expressions and tone of voice. Furthermore, if the generation AI determines that the user is feeling stressed using the emotion estimation function, it can suggest relaxing music or meditation. For example, it can generate a response such as "I'll play some relaxing music." The generation AI can also monitor the user's emotional state in real time and suggest relaxation methods such as "Try taking a deep breath" if the user is feeling stressed. For example, it can make such suggestions if the user's emotion score is high. This allows the system to estimate the user's emotional state in real time and generate a relaxing response if the user is feeling stressed, thereby reducing the user's psychological burden.

[0055] The generation AI can support conversations in different languages ​​and accommodate international users. For example, the generation AI uses multilingual natural language processing technology to understand content input by the user in different languages ​​and generate an appropriate response. For example, it supports multiple languages ​​such as English, French, and Chinese. Furthermore, if the user starts a conversation in a different language, the generation AI automatically switches to that language and generates a response. For example, if the user speaks in English, the generation AI will respond in English. Furthermore, to support conversations in different languages, the generation AI generates a response based on the language selected by the user. For example, if the user selects Japanese in the settings, the generation AI will respond in Japanese. This allows the generation AI to accommodate international users by supporting conversations in different languages.

[0056] Generative AI can support users in managing their schedules and tasks, and function as an assistant in everyday life. For example, generative AI can manage the user's schedule and send appointment reminders. For example, it can notify the user by saying, "Tomorrow's meeting starts at 10:00 AM." When the user inputs a task, generative AI manages the task and checks its progress. For example, it can check progress by asking, "Have you finished today's tasks?" Generative AI can also make suggestions for efficient time management based on the user's schedule and tasks. For example, it can make suggestions such as, "Let's take a break now to make effective use of your afternoon." In this way, it can function as an assistant in everyday life by supporting the user in managing their schedule and tasks.

[0057] The generation AI can use the emotion estimation function to analyze the emotions a user has toward a specific character and adjust the character's responses. For example, the generation AI can use the emotion estimation function to analyze the emotions a user has toward a specific character in real time and generate responses based on those emotions. For example, if the user has positive emotions toward a character, the generation AI generates a more friendly response. The generation AI also adjusts the character's responses based on the user's emotion data. For example, if the user has negative emotions toward a character, the response is made more subdued. The generation AI also analyzes the emotions a user has toward a specific character and personalizes the character's responses. For example, if the user has a strong attachment to the character, the response is made more emotional. This makes it possible to provide a more personalized conversation experience by analyzing the emotions a user has toward a specific character and adjusting the character's responses.

[0058] Voice generation technology can enable customization of a character's voice tone and accent to suit a user's preferences. Voice generation technology, for example, provides a function for adjusting a character's voice tone and accent to suit a user's preferences. For example, a user can set a character's voice to be higher. Furthermore, a user can select a character's voice accent, and the voice generation technology generates voice based on that selection. For example, an accent such as Kansai dialect or standard Japanese can be selected. Furthermore, the voice generation technology adjusts the character's voice tone in real time according to the user's preferences. For example, if a user sets a softer voice tone, voice is generated based on that setting. This allows a more personalized voice experience to be provided by customizing a character's voice tone and accent to suit a user's preferences.

[0059] Voice generation technology can incorporate background sounds and environmental sounds to provide a more realistic conversation experience. Voice generation technology can provide a more realistic conversation experience by, for example, incorporating background sounds and playing them together with the character's voice. For example, a character speaks while the background sounds of a cafe are played. Environmental sounds can also be incorporated into voice generation technology and played together with the character's voice. For example, a character speaks with the sounds of rain or wind in the background. Voice generation technology can also automatically select background sounds according to the user's environment and play them together with the character's voice. For example, if the user is out and about, city sounds can be played in the background. In this way, by incorporating background sounds and environmental sounds, a more realistic conversation experience can be provided.

[0060] The voice generation technology can use an emotion estimation function to adjust the tone and speed of the voice according to the emotional state of the user. For example, the voice generation technology uses the emotion estimation function to adjust the tone of the voice according to the emotional state of the user. For example, if the user is relaxed, the character's voice is made calm. The voice generation technology also adjusts the speed of the character's voice based on the user's emotional state. For example, if the user is in a hurry, the speed of the character's voice is increased. The emotion estimation function also simultaneously adjusts the tone and speed of the voice according to the user's emotional state. For example, if the user is excited, the character's voice is made higher and the speed is increased. In this way, by adjusting the tone and speed of the voice according to the user's emotional state, a more natural voice experience can be provided.

[0061] Voice generation technology can simultaneously generate voices for different characters, enabling conversations with multiple characters. Voice generation technology can, for example, simultaneously generate voices for multiple characters, allowing a user to converse with multiple characters at the same time. For example, two characters can speak alternately. Furthermore, by simultaneously generating voices for different characters, users can enjoy group conversations. For example, three characters can speak together. Furthermore, voice generation technology can generate voices for multiple characters in real time, providing a scenario in which a user converses with multiple characters at the same time. For example, characters can converse with each other. Thus, by simultaneously generating voices for different characters, conversations with multiple characters can be enabled.

[0062] Voice generation technology can mimic a user's voice and provide an experience of conversing with a character in the user's own voice. Voice generation technology, for example, analyzes the user's voice, imitates that voice, and reproduces it as a character's voice. For example, a character speaks based on the user's voice. Voice generation technology that mimics a user's voice can also be used to provide an experience of a user conversing with a character in their own voice. For example, a character based on the user's voice responds. Voice generation technology can also mimic a user's voice in real time to provide a scenario in which the user converses with a character in their own voice. For example, a character based on the user's voice speaks. In this way, by imitating the user's voice, it is possible to provide an experience of a user conversing with a character in their own voice.

[0063] The voice generation technology can use an emotion estimation function to generate a voice response in which a character empathizes with a particular emotion when a user feels that emotion. The voice generation technology, for example, uses the emotion estimation function to generate a voice response in which a character empathizes with a particular emotion when a user feels that emotion. For example, if the user is sad, a response such as "Are you okay?" is generated. Also, when a user feels a particular emotion, the emotion estimation function is used to generate a voice response in which a character empathizes with the emotion. For example, if the user is happy, a response such as "That's great!" is generated. Also, when a user feels a particular emotion, the emotion estimation function is used to generate a voice response in which a character empathizes with the emotion in real time. For example, if the user is angry, a response such as "What happened?" is generated. In this way, when a user feels a particular emotion, a voice response in which a character empathizes with the emotion is generated, thereby providing a more emotional conversation experience.

[0064] The character manifestation unit can change the character's behavior and facial expression in real time according to user input, thereby realizing more natural interactions. The character manifestation unit, for example, builds a system that changes the character's behavior and facial expression in real time according to user input. For example, when a user inputs "smile," the character smiles. The character's behavior and facial expression are also adjusted in real time based on the user's input. For example, when a user inputs "wave," the character waves. By changing the character's behavior and facial expression in real time according to user input, more natural interactions are also realized. For example, when a user inputs "surprised," the character makes a surprised expression. In this way, by changing the character's behavior and facial expression in real time according to user input, more natural interactions can be realized.

[0065] The character manifestation unit can recognize the environment around the user and perform actions and responses according to the environment. In the character manifestation unit, for example, the character recognizes the environment around the user using a camera and performs actions and responses according to the environment. For example, if the user is out, the character generates a response such as "How is it outside?". The character also recognizes the environment around the user using a sensor and performs actions and responses according to the environment. For example, if the user is in a dark room, the character generates a response such as "Shall I turn on the light?". The character also recognizes the environment around the user in real time and performs actions and responses according to the environment. For example, if the user is in the kitchen, the character generates a response such as "What are you making?". In this way, the character recognizes the environment around the user and performs actions and responses according to the environment, thereby achieving more natural interactions.

[0066] The character manifestation unit can use the emotion estimation function to adjust the character's facial expression and behavior according to the user's emotional state. The character manifestation unit, for example, uses the emotion estimation function to adjust the character's facial expression in real time according to the user's emotional state. For example, if the user is sad, the character will have a worried expression. The character's behavior is also adjusted based on the user's emotional state. For example, if the user is relaxed, the character will perform calm behavior. The emotion estimation function is also used to simultaneously adjust the character's facial expression and behavior according to the user's emotional state. For example, if the user is excited, the character will perform energetic behavior. In this way, by adjusting the character's facial expression and behavior according to the user's emotional state, more natural interactions can be achieved.

[0067] The character manifestation unit allows a character to function as a user's avatar and support virtual interactions with other users. The character manifestation unit, for example, builds a system in which a character functions as a user's avatar and supports virtual interactions with other users. For example, the character participates in a virtual event on behalf of the user. Also, the character supports chats and video calls with other users as the user's avatar. For example, the character sends messages on behalf of the user. Also, the character supports virtual interactions in real time as the user's avatar. For example, the character participates in a virtual conference on behalf of the user. In this way, the character functions as the user's avatar and supports virtual interactions with other users, thereby realizing a wider range of communication.

[0068] The character manifestation unit allows a character to monitor the user's health condition and function as a health management assistant. The character manifestation unit, for example, builds a system in which a character monitors the user's health condition and functions as a health management assistant. For example, the character monitors the user's heart rate and sleep state. The character also monitors the user's health condition in real time and provides health management advice. For example, the character may make a suggestion such as "try exercising a little more today." The character also monitors the user's health condition and issues an alert if it detects an abnormality. For example, the character may issue an alert such as "your heart rate is high, so take a break." In this way, the character can monitor the user's health condition and function as a health management assistant, thereby supporting the user's health.

[0069] The character manifestation unit uses the emotion estimation function to enable a character to take an action according to a particular emotion when the user feels that emotion. The character manifestation unit, for example, uses the emotion estimation function to enable a character to take an action according to a particular emotion when the user feels that emotion. For example, if the user is angry, the character asks, "What happened?" Also, when the user feels a particular emotion, the emotion estimation function enables the character to take an appropriate action. For example, if the user is sad, the character asks, "Are you okay?" Also, when the user feels a particular emotion, the emotion estimation function enables the character to take an action according to that emotion in real time. For example, if the user is happy, the character replies, "That's great!" In this way, when the user feels a particular emotion, the character takes an action according to that emotion, thereby realizing a more emotional interaction.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] Generative AI can monitor the user's health status and function as a health management assistant. For example, generative AI can monitor the user's heart rate and sleep status and provide health management advice. For example, it can make suggestions such as, "Try to exercise a little more today." Generative AI can also monitor the user's health status in real time and issue an alert if it detects an abnormality. For example, it can issue an alert such as, "Your heart rate is high, so take a break." In this way, generative AI can support the user's health by monitoring the user's health status and functioning as a health management assistant.

[0072] Generative AI can support users in managing their schedules and tasks, and function as an assistant in everyday life. For example, the generative AI can manage the user's schedule and send appointment reminders. For example, it can notify the user by saying, "Tomorrow's meeting starts at 10:00 AM." When the user inputs a task, the generative AI manages the task and checks its progress. For example, it can check progress by asking, "Have you finished today's tasks?" The generative AI can also make suggestions for efficient time management based on the user's schedule and tasks. For example, it can make suggestions such as, "Let's take a break now to make effective use of your afternoon." In this way, it can function as an assistant in everyday life by supporting the user in managing their schedule and tasks.

[0073] The generation AI can use its emotion estimation function to estimate the user's emotional state in real time and generate a response that will help them relax if they are feeling stressed. For example, the generation AI can analyze the user's emotional state in real time and generate a relaxing response such as "Shall we take a break?" if they are feeling stressed. Furthermore, if the generation AI determines that the user is feeling stressed using the emotion estimation function, it can suggest relaxing music or meditation. For example, it can generate a response such as "I'll play some relaxing music." The generation AI can also monitor the user's emotional state in real time and suggest relaxation methods such as "Try taking a deep breath" if they are feeling stressed. In this way, the generation AI can estimate the user's emotional state in real time and generate a relaxing response if they are feeling stressed, thereby reducing the user's psychological burden.

[0074] Generative AI can support conversations in different languages ​​and accommodate international users. For example, generative AI uses multilingual natural language processing technology to understand content input by users in different languages ​​and generate appropriate responses. For example, it supports multiple languages ​​such as English, French, and Chinese. Furthermore, if a user begins a conversation in a different language, the generative AI automatically switches to that language and generates a response. For example, if a user speaks in English, it responds in English. Furthermore, to support conversations in different languages, the generative AI generates responses based on the language selected by the user. For example, if a user selects Japanese in the settings, it responds in Japanese. This allows it to accommodate international users by supporting conversations in different languages.

[0075] The generation AI can analyze the user's facial expression and tone of voice and generate an emotional response accordingly. For example, the generation AI can analyze the user's facial expression using a camera, and if the user is smiling, generate a positive response such as "You look like you're having fun today!". The generation AI can also analyze the user's tone of voice using a microphone and generate a response that corresponds to the user's emotional state. For example, if the user's voice is calm, it can generate a response such as "You seem relaxed." The generation AI can also simultaneously analyze the user's facial expression and tone of voice, and if the user looks sad, it can generate an emotional response such as "Is something wrong?". This allows for a more natural conversation experience by generating emotional responses that correspond to the user's facial expression and tone of voice.

[0076] Voice generation technology can enable customization of a character's voice tone and accent to suit a user's preferences. For example, the voice generation technology provides a function for adjusting a character's voice tone and accent to suit a user's preferences. For example, a user can set a character's voice to be higher. The voice generation technology can also allow a user to select a character's voice accent, and the voice generation technology generates voice based on that selection. For example, an accent such as Kansai dialect or standard Japanese can be selected. The voice generation technology also adjusts the character's voice tone in real time according to the user's preferences. For example, if a user sets a voice tone to be softer, the voice generation technology generates voice based on that setting. This allows a character's voice tone and accent to be customized to suit a user's preferences, providing a more personalized voice experience.

[0077] Voice generation technology can incorporate background sounds and environmental sounds to provide a more realistic conversation experience. For example, voice generation technology can incorporate background sounds and play them together with the character's voice to provide a more realistic conversation experience. For example, a character can speak while the background sounds of a cafe are played. Environmental sounds can also be incorporated into voice generation technology and played along with the character's voice. For example, a character can speak with the sounds of rain or wind in the background. Voice generation technology can also automatically select background sounds according to the user's environment and play them along with the character's voice. For example, if the user is out and about, city sounds can be played in the background. In this way, by incorporating background sounds and environmental sounds, a more realistic conversation experience can be provided.

[0078] The voice generation technology can use an emotion estimation function to adjust the tone and speed of the voice according to the emotional state of the user. For example, the voice generation technology uses the emotion estimation function to adjust the tone of the voice according to the emotional state of the user. For example, if the user is relaxed, the character's voice is made calm. The voice generation technology also adjusts the speed of the character's voice based on the user's emotional state. For example, if the user is in a hurry, the speed of the character's voice is increased. The emotion estimation function also simultaneously adjusts the tone and speed of the voice according to the user's emotional state. For example, if the user is excited, the character's voice is made higher and the speed is increased. In this way, by adjusting the tone and speed of the voice according to the user's emotional state, a more natural voice experience can be provided.

[0079] The character manifestation unit allows a character to function as a user's avatar and support virtual interactions with other users. For example, the character manifestation unit builds a system in which a character functions as a user's avatar and supports virtual interactions with other users. For example, the character participates in a virtual event on behalf of the user. Also, the character supports chats and video calls with other users as the user's avatar. For example, the character sends messages on behalf of the user. Also, the character supports virtual interactions in real time as the user's avatar. For example, the character participates in a virtual conference on behalf of the user. In this way, the character functions as the user's avatar and supports virtual interactions with other users, thereby realizing a wider range of communication.

[0080] The character manifestation unit uses the emotion estimation function to enable a character to take an action according to a particular emotion when the user feels that emotion. For example, the character manifestation unit uses the emotion estimation function to enable a character to take an action according to a particular emotion when the user feels that emotion. For example, if the user is angry, the character asks, "What happened?" Also, when the user feels a particular emotion, the emotion estimation function enables the character to take an appropriate action. For example, if the user is sad, the character asks, "Are you okay?" Also, when the user feels a particular emotion, the emotion estimation function enables the character to take an action according to that emotion in real time. For example, if the user is happy, the character replies, "That's great!" This allows the character to take an action according to the emotion when the user feels that emotion, thereby achieving more emotional interaction.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The generative AI analyzes the user's input and generates an appropriate response. For example, if the user inputs "How was your day?", the AI ​​will respond with something like "I had a great time today! How was it for you?" The generative AI can also understand the user's input and continue a natural conversation. The input to the generative AI is a prompt containing instructions on what the user wants the AI ​​to do, and the AI ​​generates a response based on that prompt. Step 2: The voice generation technology reproduces the response generated by the generation AI as a character's voice. For example, if a user types "good morning," the voice generation technology will speak the characters as "good morning" in the character's voice. The voice generation technology can also automatically convert the characters entered by the user into speech and reproduce it as a character's voice. Step 3: The character manifestation unit displays the character. For example, the user can talk to the character on the PC screen. The character moves and responds with voice according to the user's input, providing an experience as if the character were actually present.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0089] The 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.

[0090] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0091] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0092] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0093] Fig. 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.

[0094] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0096] 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.

[0097] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0098] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0100] The data processing system 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

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

[0104] The 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.

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

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

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

[0108] Fig. 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.

[0109] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0111] In the 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.

[0112] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0113] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0115] The data processing system 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The 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.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 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. Generative AI and Speech generation technology, a character manifestation unit; The generated AI is Parsing user input and generating appropriate responses, The voice generation technology includes: Reproducing the response generated by the generation AI as a character's voice; The character manifestation unit: Display the character A system characterized by:

2. The generated AI is Learns the user's past conversation history and generates personalized responses based on the user's preferences and interests 2. The system of claim 1.

3. The generated AI is Analyzing the user's facial expressions and tone of voice and generating corresponding emotional responses 2. The system of claim 1.

4. The generated AI is Estimating the user's emotional state in real time and generating a relaxing response if the user is feeling stressed 2. The system of claim 1.

5. The generated AI is Support conversations in different languages ​​and cater to international users 2. The system of claim 1.

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