system

Abstract

An object of a system according to an embodiment is to cooperate with an external service while interacting with a character selected by a user.SOLUTION: A system according to an embodiment includes a generation AI, a speech recognition unit, a character interaction unit, and an external service cooperation unit. The generative AI performs a natural language process using the generative AI. The voice recognition unit converts a user's speech into text. The character interaction unit interacts with the character selected by the user. The external service cooperation unit cooperates with an external service through the API.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 do not adequately provide systems that allow users to interact with characters of their choice while linking with external services, and there is room for improvement.

[0005] The system according to the embodiment aims to link with external services while interacting with a character selected by the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation AI, a voice recognition unit, a character dialogue unit, and an external service linkage unit. The generation AI performs natural language processing using the generation AI. The voice recognition unit converts user utterances into text. The character dialogue unit interacts with a character selected by the user. The external service linkage unit links with external services via an API. [Effects of the Invention]

[0007] The system according to the embodiment can link with external services while interacting with a character selected by the user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) A care system according to an embodiment of the present invention provides mental and physical care to people, particularly women, who are busy with childcare, work, and housework. This care system uses generative AI to perform natural language processing and speech recognition, allowing users to interact with their favorite characters, thereby reducing stress and improving motivation. It also connects with various external services via APIs to support users' daily lives. This allows the care system to provide mental and physical care to users, reducing stress and improving motivation.

[0029] The care system according to the embodiment includes a generation AI, a voice recognition unit, a character dialogue unit, and an external service linkage unit. The generation AI analyzes user input and generates an appropriate response. For example, if a user says, "I'm tired today," the generation AI responds with, "Thank you for your hard work. What happened today?" The generation AI generates a response based on the user input using a text generation AI (e.g., LLM) or a multimodal generation AI. The voice recognition unit converts the user's speech into text. For example, if a user says, "Tell me what's on the schedule for tomorrow," the voice recognition unit converts the speech into text, and the generation AI analyzes the text to generate a response. The character dialogue unit interacts with a character selected by the user. For example, if a user asks the character, "What should we cook today?" the character might suggest, "How about pasta today?" The generation AI generates a response tailored to the character's personality. The external service linkage unit links with external services such as calendar apps, reminders, and music streaming services via APIs. For example, if a user says, "Tell me what my schedule is for tomorrow," the system responds by retrieving the schedule from a calendar app. Also, if the user says, "Play some relaxing music," the system plays relaxing music from a music streaming service. This allows the care system according to the embodiment to provide care for the user's mind and body, reducing stress and improving motivation. For example, the user can reduce stress by interacting with a character, and can improve the efficiency of daily life through collaboration with external services. Furthermore, by having the generation AI grasp the user's mental and physical state and providing appropriate care, we can aim for a society in which users can live more vibrantly and actively.

[0030] Generative AI can analyze a user's past dialogue history and generate responses optimized for each individual user. For example, generative AI stores a user's past dialogue history in a database and analyzes that history. For example, it learns what the user has said in the past and frequently used phrases, and generates responses based on that. This allows for more personalized dialogue by generating responses optimized for the user.

[0031] Based on the content of the user's utterance, the generation AI can collect related information from the Internet and reflect it in the response. For example, the generation AI analyzes the content of the user's utterance and extracts related keywords. For example, if the user says, "Tell me the latest movie information," the generation AI will collect the latest movie information from the Internet and reflect it in the response. In this way, by collecting related information based on the content of the user's utterance and reflecting it in the response, more useful information can be provided.

[0032] The generation AI can suggest related events and news based on the user's hobbies and interests. For example, the generation AI analyzes the user's hobbies and interests and provides related event information based on that. For example, if the user is interested in music, the generation AI can suggest information about nearby concerts. This makes it easier to attract the user's attention by suggesting related information based on the user's hobbies and interests.

[0033] The generating AI can analyze the user's health condition and provide health advice. For example, the generating AI analyzes the user's health condition based on the content of the user's utterances and dialogue history. For example, if the user says, "I've been feeling tired a lot lately," the generating AI can provide health advice based on that information. This can support the user's health management by providing appropriate advice based on the user's health condition.

[0034] The speech recognition unit analyzes the user's speech rate and tone to more accurately understand the intent of the spoken content. The speech recognition unit, for example, analyzes the user's speech rate and tone and identifies the intent of the spoken content based on that information. For example, if the user is speaking in a hurry, the generation AI will understand the urgency and respond quickly. In this way, by analyzing the user's speech rate and tone, the intent of the spoken content can be more accurately understood.

[0035] The speech recognition unit can use speech recognition technology to translate the content of a user's speech in real time, thereby realizing multilingual support. The speech recognition unit, for example, uses speech recognition technology to build a system that translates the content of a user's speech in real time. For example, it translates what a user says in English into Japanese. This makes it possible to support multiple languages ​​by translating the content of a user's speech in real time.

[0036] The speech recognition unit can use speech recognition technology to convert the user's speech into text and save it as a diary or memo. The speech recognition unit, for example, uses speech recognition technology to build a system that converts the user's speech into text and automatically saves it as a diary. For example, when a user talks about the events of the day, it is converted into text and saved as a diary. This makes it easy to keep records by converting the user's speech into text and saving it as a diary or memo.

[0037] The speech recognition unit can use speech recognition technology to analyze the content of a user's speech and automatically generate a reminder based on the content of the speech. For example, the speech recognition unit uses speech recognition technology to build a system that analyzes the content of a user's speech and automatically generates a reminder. For example, if a user says, "Don't forget about tomorrow's meeting," a reminder is automatically generated. This allows the system to automatically generate reminders based on the content of a user's speech, helping users to remember and manage important appointments.

[0038] The character dialogue unit can customize responses to user utterances based on the character's personality. For example, the character dialogue unit stores the character's personality in a database, and the generation AI generates responses based on that personality. For example, a lively character may respond cheerfully to the user. This allows for more natural and friendly dialogue by customizing responses based on the character's personality.

[0039] The character dialogue unit allows the character to learn the user's past dialogue history, enabling more natural dialogue. For example, the character stores the user's past dialogue history in a database and learns that history. For example, the character responds naturally based on what the user has said in the past. In this way, the character learns the user's past dialogue history, enabling more natural and friendly dialogue.

[0040] The character dialogue unit allows the character to suggest related content based on the user's hobbies and interests. For example, the character analyzes the user's hobbies and interests and suggests related content based on the results. For example, if the user is interested in movies, the character provides the latest movie information. This makes it easier to attract the user's attention by allowing the character to suggest related content based on the user's hobbies and interests.

[0041] The character dialogue unit allows the character to analyze the user's health condition and provide health-related advice. For example, the character analyzes the health condition based on the user's speech content and dialogue history. For example, if the user says, "I've been feeling tired a lot lately," the character provides health-related advice based on that information. This allows the character to analyze the user's health condition and provide appropriate advice, thereby supporting the user's health management.

[0042] The external service integration unit can collect lifestyle habit data of the user through an API, and the generation AI can generate a response based on that data. The external service integration unit, for example, collects lifestyle habit data of the user through an API, and the generation AI can generate a response based on that data. For example, the generation AI can provide appropriate sleep advice based on the user's sleep data. In this way, by collecting the user's lifestyle habit data and generating a response based on that data, more personalized support can be provided.

[0043] The external service linkage unit can collect the user's health data through an API, and the generation AI can provide health advice based on that data. The external service linkage unit can, for example, collect the user's health data through an API, and the generation AI can provide health advice based on that data. For example, the generation AI can provide appropriate exercise advice based on the user's heart rate data. In this way, by collecting the user's health data and providing health advice based on that data, it is possible to support the user's health management.

[0044] The external service integration unit can suggest events and news based on the user's hobbies and interests through the API. For example, the external service integration unit provides event information based on the user's hobbies and interests through the API. For example, if the user is interested in music, the external service integration unit can obtain and suggest information about nearby concerts through the API. This makes it easier to attract the user's attention by suggesting events and news based on the user's hobbies and interests through the API.

[0045] The external service integration unit can automatically generate reminders and notifications based on the user's speech via the API. For example, the external service integration unit can analyze the user's speech via the API and build a system that automatically generates reminders. For example, if a user says, "Don't forget about tomorrow's meeting," a reminder will be automatically generated via the API. This allows users to manage important appointments without forgetting them by automatically generating reminders and notifications based on the user's speech via the API.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The care system can analyze the user's sleep patterns and provide appropriate sleep advice. For example, if a user says, "I haven't been sleeping well lately," the system can suggest ways to improve their sleep environment or relax based on the user's past sleep data. Also, if a user says, "I have to get up early tomorrow," the system can set a reminder to go to bed early. This can improve the user's sleep quality.

[0048] The care system can analyze the user's food records and suggest nutritionally balanced meal plans. For example, if a user says, "I haven't been eating many vegetables lately," the system can suggest recipes that include a lot of vegetables based on the user's diet history. Also, if a user says, "I want to start a diet," the system can provide a calorie-restricted meal plan. This can support the user's health management.

[0049] The care system can analyze the user's exercise data and suggest an appropriate exercise plan. For example, if a user says, "I haven't been exercising much lately," the system can suggest an easy-to-start exercise plan based on the user's past exercise data. Also, if a user says, "I want to try running a marathon," the system can provide a training plan. This can improve the user's exercise habits and support their health.

[0050] The care system can suggest appropriate hobbies and activities based on the user's speech. For example, if a user says, "I want to find a new hobby," the system will suggest appropriate hobbies based on the user's interests and past conversation history. Also, if a user says, "I want to do something fun on the weekend," the system can suggest nearby events and activities. This can help enrich the user's life.

[0051] The care system can suggest appropriate learning plans based on the user's speech. For example, if a user says, "I want to learn a new skill," the system will suggest an appropriate learning plan based on the user's interests and past conversation history. Also, if a user says, "I want to study a language," the system can provide language learning resources and plans. This can increase the user's motivation to learn and support skill improvement.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The generative AI analyzes the user's input and generates an appropriate response. For example, if the user says, "I'm tired today," the generative AI responds with something like, "Thank you for your hard work. What happened today?" The generative AI uses text generation AI (e.g., LLM) or multimodal generation AI to generate a response based on the user's input. Step 2: The speech recognition unit converts the user's speech into text. For example, if the user says, "Tell me what my schedule is tomorrow," the speech recognition unit converts the speech into text, and the generation AI analyzes the text and generates a response. Step 3: The character dialogue unit interacts with the character selected by the user. For example, if the user asks the character, "What should we cook today?", the character may suggest, "How about pasta today?" The generation AI generates a response that matches the character's personality. Step 4: The external service integration unit connects with external services such as calendar apps, reminders, and music streaming services via APIs. For example, if a user says, "Tell me what's on my schedule for tomorrow," the unit responds by retrieving the schedule from the calendar app. Similarly, if a user says, "Play some relaxing music," the unit plays relaxing music from a music streaming service.

[0054] (Example 2) A care system according to an embodiment of the present invention provides mental and physical care to people, particularly women, who are busy with childcare, work, and housework. This care system uses generative AI to perform natural language processing and speech recognition, allowing users to interact with their favorite characters, thereby reducing stress and improving motivation. It also connects with various external services via APIs to support users' daily lives. This allows the care system to provide mental and physical care to users, reducing stress and improving motivation.

[0055] The care system according to the embodiment includes a generation AI, a voice recognition unit, a character dialogue unit, and an external service linkage unit. The generation AI analyzes user input and generates an appropriate response. For example, if a user says, "I'm tired today," the generation AI responds with, "Thank you for your hard work. What happened today?" The generation AI generates a response based on the user input using a text generation AI (e.g., LLM) or a multimodal generation AI. The voice recognition unit converts the user's speech into text. For example, if a user says, "Tell me what's on the schedule for tomorrow," the voice recognition unit converts the speech into text, and the generation AI analyzes the text to generate a response. The character dialogue unit interacts with a character selected by the user. For example, if a user asks the character, "What should we cook today?" the character might suggest, "How about pasta today?" The generation AI generates a response tailored to the character's personality. The external service linkage unit links with external services such as calendar apps, reminders, and music streaming services via APIs. For example, if a user says, "Tell me what my schedule is for tomorrow," the system responds by retrieving the schedule from a calendar app. Also, if the user says, "Play some relaxing music," the system plays relaxing music from a music streaming service. This allows the care system according to the embodiment to provide care for the user's mind and body, reducing stress and improving motivation. For example, the user can reduce stress by interacting with a character, and can improve the efficiency of daily life through collaboration with external services. Furthermore, by having the generation AI grasp the user's mental and physical state and providing appropriate care, we can aim for a society in which users can live more vibrantly and actively.

[0056] Generative AI can analyze a user's past dialogue history and generate responses optimized for each individual user. For example, generative AI stores a user's past dialogue history in a database and analyzes that history. For example, it learns what the user has said in the past and frequently used phrases, and generates responses based on that. This allows for more personalized dialogue by generating responses optimized for the user.

[0057] Based on the content of the user's utterance, the generation AI can collect related information from the Internet and reflect it in the response. For example, the generation AI analyzes the content of the user's utterance and extracts related keywords. For example, if the user says, "Tell me the latest movie information," the generation AI will collect the latest movie information from the Internet and reflect it in the response. In this way, by collecting related information based on the content of the user's utterance and reflecting it in the response, more useful information can be provided.

[0058] The generation AI can use its emotion estimation function to analyze the user's emotional state in real time and generate a response that corresponds to that emotion. For example, the generation AI analyzes the content and tone of the user's speech and uses the emotion estimation function to identify the user's emotional state. For example, if the user speaks in a tired voice, the generation AI can detect that emotion and offer words of encouragement. This allows the system to provide more appropriate support by generating a response that corresponds to the user's emotional state.

[0059] The generation AI can suggest related events and news based on the user's hobbies and interests. For example, the generation AI analyzes the user's hobbies and interests and provides related event information based on that. For example, if the user is interested in music, the generation AI can suggest information about nearby concerts. This makes it easier to attract the user's attention by suggesting related information based on the user's hobbies and interests.

[0060] The generating AI can analyze the user's health condition and provide health advice. For example, the generating AI analyzes the user's health condition based on the content of the user's utterances and dialogue history. For example, if the user says, "I've been feeling tired a lot lately," the generating AI can provide health advice based on that information. This can support the user's health management by providing appropriate advice based on the user's health condition.

[0061] Using its emotion estimation function, the generative AI can suggest music and video content that matches the user's emotions. For example, the generative AI analyzes the user's emotional state and suggests appropriate music based on that. For example, if the user feels like relaxing, the generative AI will suggest relaxing music. This can improve the user's mood by suggesting music and video content that matches the user's emotions.

[0062] The speech recognition unit analyzes the user's speech rate and tone to more accurately understand the intent of the spoken content. The speech recognition unit, for example, analyzes the user's speech rate and tone and identifies the intent of the spoken content based on that information. For example, if the user is speaking in a hurry, the generation AI will understand the urgency and respond quickly. In this way, by analyzing the user's speech rate and tone, the intent of the spoken content can be more accurately understood.

[0063] The speech recognition unit can use speech recognition technology to translate the content of a user's speech in real time, thereby realizing multilingual support. The speech recognition unit, for example, uses speech recognition technology to build a system that translates the content of a user's speech in real time. For example, it translates what a user says in English into Japanese. This makes it possible to support multiple languages ​​by translating the content of a user's speech in real time.

[0064] The speech recognition unit can use an emotion estimation function to infer emotions from the content of a user's speech and generate a response according to the emotion. For example, the speech recognition unit uses speech recognition technology to analyze the content of a user's speech and identifies the emotion using the emotion estimation function. For example, if the user speaks in a sad voice, the generation AI can understand that emotion and offer words of comfort. This allows the system to provide more appropriate support by inferring emotions from the content of a user's speech and generating a response according to the emotion.

[0065] The speech recognition unit can use speech recognition technology to convert the user's speech into text and save it as a diary or memo. The speech recognition unit, for example, uses speech recognition technology to build a system that converts the user's speech into text and automatically saves it as a diary. For example, when a user talks about the events of the day, it is converted into text and saved as a diary. This makes it easy to keep records by converting the user's speech into text and saving it as a diary or memo.

[0066] The speech recognition unit can use speech recognition technology to analyze the content of a user's speech and automatically generate a reminder based on the content of the speech. For example, the speech recognition unit uses speech recognition technology to build a system that analyzes the content of a user's speech and automatically generates a reminder. For example, if a user says, "Don't forget about tomorrow's meeting," a reminder is automatically generated. This allows the system to automatically generate reminders based on the content of a user's speech, helping users to remember and manage important appointments.

[0067] The speech recognition unit can use an emotion estimation function to estimate emotions from the content of a user's speech and generate reminders and notifications according to the emotions. For example, the speech recognition unit uses speech recognition technology to analyze the content of a user's speech and identifies the emotion using the emotion estimation function. For example, if the user is feeling stressed, a reminder to relax is generated. This allows for more appropriate support to be provided by generating reminders and notifications according to the user's emotions.

[0068] The character dialogue unit can customize responses to user utterances based on the character's personality. For example, the character dialogue unit stores the character's personality in a database, and the generation AI generates responses based on that personality. For example, a lively character may respond cheerfully to the user. This allows for more natural and friendly dialogue by customizing responses based on the character's personality.

[0069] The character dialogue unit allows the character to learn the user's past dialogue history, enabling more natural dialogue. For example, the character stores the user's past dialogue history in a database and learns that history. For example, the character responds naturally based on what the user has said in the past. In this way, the character learns the user's past dialogue history, enabling more natural and friendly dialogue.

[0070] The character dialogue unit uses the emotion estimation function to enable the character to generate a response according to the user's emotion and provide emotional support. For example, the character dialogue unit analyzes the content and tone of the user's speech and identifies the emotion using the emotion estimation function. For example, if the user speaks in a sad voice, the character will offer words of comfort. This allows the character to generate a response according to the user's emotion and provide emotional support.

[0071] The character dialogue unit allows the character to suggest related content based on the user's hobbies and interests. For example, the character analyzes the user's hobbies and interests and suggests related content based on the results. For example, if the user is interested in movies, the character provides the latest movie information. This makes it easier to attract the user's attention by allowing the character to suggest related content based on the user's hobbies and interests.

[0072] The character dialogue unit allows the character to analyze the user's health condition and provide health-related advice. For example, the character analyzes the health condition based on the user's speech content and dialogue history. For example, if the user says, "I've been feeling tired a lot lately," the character provides health-related advice based on that information. This allows the character to analyze the user's health condition and provide appropriate advice, thereby supporting the user's health management.

[0073] The character dialogue unit uses the emotion estimation function to enable the character to suggest music or video content that matches the user's emotions. For example, the character dialogue unit analyzes the user's emotional state and suggests appropriate music based on that. For example, if the user feels like relaxing, the character will suggest relaxing music. This allows the character to suggest music or video content that matches the user's emotions, thereby improving the user's mood.

[0074] The external service integration unit can collect lifestyle habit data of the user through an API, and the generation AI can generate a response based on that data. The external service integration unit, for example, collects lifestyle habit data of the user through an API, and the generation AI can generate a response based on that data. For example, the generation AI can provide appropriate sleep advice based on the user's sleep data. In this way, by collecting the user's lifestyle habit data and generating a response based on that data, more personalized support can be provided.

[0075] The external service linkage unit can collect the user's health data through an API, and the generation AI can provide health advice based on that data. The external service linkage unit can, for example, collect the user's health data through an API, and the generation AI can provide health advice based on that data. For example, the generation AI can provide appropriate exercise advice based on the user's heart rate data. In this way, by collecting the user's health data and providing health advice based on that data, it is possible to support the user's health management.

[0076] The external service integration unit can use the emotion estimation function to analyze data collected through the API and generate a response according to the user's emotion. For example, the external service integration unit analyzes data collected through the API and identifies the user's emotion using the emotion estimation function. For example, based on the user's exercise data, the generation AI understands the user's emotional state and provides an appropriate response. This allows the system to provide more appropriate support by analyzing data collected through the API and generating a response according to the user's emotion.

[0077] The external service integration unit can suggest events and news based on the user's hobbies and interests through the API. For example, the external service integration unit provides event information based on the user's hobbies and interests through the API. For example, if the user is interested in music, the external service integration unit can obtain and suggest information about nearby concerts through the API. This makes it easier to attract the user's attention by suggesting events and news based on the user's hobbies and interests through the API.

[0078] The external service integration unit can automatically generate reminders and notifications based on the user's speech via the API. For example, the external service integration unit can analyze the user's speech via the API and build a system that automatically generates reminders. For example, if a user says, "Don't forget about tomorrow's meeting," a reminder will be automatically generated via the API. This allows users to manage important appointments without forgetting them by automatically generating reminders and notifications based on the user's speech via the API.

[0079] The external service integration unit can use the emotion estimation function to analyze data collected through the API and generate reminders and notifications according to the user's emotions. For example, the external service integration unit analyzes data collected through the API and identifies the user's emotions using the emotion estimation function. For example, based on the user's exercise data, the generation AI understands the user's emotional state and generates appropriate reminders. This allows the system to provide more appropriate support by analyzing data collected through the API and generating reminders and notifications according to the user's emotions.

[0080] To support the user's mental and physical health, the generative AI can analyze the content of the user's speech and provide appropriate care. For example, if the user says, "I'm feeling stressed today," the generative AI can suggest relaxation methods and offer words of encouragement. For example, it can teach deep breathing or meditation techniques. In this way, the generative AI can support the user's mental and physical health by analyzing the content of the user's speech and providing appropriate care.

[0081] Generative AI can understand the user's mental and physical state and provide appropriate care. For example, if the user says, "I'm tired today," the generative AI will respond by saying, "Thank you for your hard work. Please get some rest early today." This allows the AI ​​to understand the user's mental and physical state and provide appropriate care, thereby supporting the user's health.

[0082] Generative AI can analyze the content of a user's speech and provide appropriate care to support the user's mental and physical care. For example, generative AI can analyze the content of a user's speech and provide advice to support mental and physical care. For example, if a user says, "I'm tired today," generative AI can suggest ways to relax. In this way, by analyzing the content of a user's speech and providing appropriate care, it is possible to support the user's mental and physical health.

[0083] Generative AI can analyze the content of a user's speech and provide appropriate care to support the user's mental and physical care. For example, generative AI can analyze the content of a user's speech and provide advice to support mental and physical care. For example, if a user says, "I'm tired today," generative AI can suggest ways to relax. In this way, by analyzing the content of a user's speech and providing appropriate care, it is possible to support the user's mental and physical health.

[0084] Generative AI can analyze the content of a user's speech and provide appropriate care to support the user's mental and physical care. For example, generative AI can analyze the content of a user's speech and provide advice to support mental and physical care. For example, if a user says, "I'm tired today," generative AI can suggest ways to relax. In this way, by analyzing the content of a user's speech and providing appropriate care, it is possible to support the user's mental and physical health.

[0085] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0086] The care system can analyze the user's sleep patterns and provide appropriate sleep advice. For example, if a user says, "I haven't been sleeping well lately," the system can suggest ways to improve their sleep environment or relax based on the user's past sleep data. Also, if a user says, "I have to get up early tomorrow," the system can set a reminder to go to bed early. This can improve the user's sleep quality.

[0087] The care system can analyze the user's food records and suggest nutritionally balanced meal plans. For example, if a user says, "I haven't been eating many vegetables lately," the system can suggest recipes that include a lot of vegetables based on the user's diet history. Also, if a user says, "I want to start a diet," the system can provide a calorie-restricted meal plan. This can support the user's health management.

[0088] The care system can analyze the user's exercise data and suggest an appropriate exercise plan. For example, if a user says, "I haven't been exercising much lately," the system can suggest an easy-to-start exercise plan based on the user's past exercise data. Also, if a user says, "I want to try running a marathon," the system can provide a training plan. This can improve the user's exercise habits and support their health.

[0089] The care system can analyze the user's stress level and suggest relaxation methods. For example, if the user says, "I've been feeling stressed lately," the system analyzes the user's speech content and tone and uses emotion estimation to identify the stress level. Based on this, the system can suggest deep breathing or meditation techniques. Also, if the user says, "Play some relaxing music," the system can suggest relaxing music. This can help reduce the user's stress.

[0090] The care system can suggest appropriate exercises based on the user's emotional state. For example, if a user says, "I'm feeling down today," the system can identify that emotion using its emotion estimation function and suggest light exercises to improve their mood. Alternatively, if a user says, "I have plenty of energy," the system can suggest high-intensity exercises. This allows the system to support the user's mental and physical health by providing exercises tailored to their emotional state.

[0091] The care system can suggest an appropriate reading list based on the user's emotional state. For example, if a user says, "I want to relax today," the system can identify that emotion using its emotion estimation function and suggest relaxing books. Also, if a user says, "I want to read an uplifting book," the system can suggest uplifting books. This allows the system to support mental care by providing a reading list that matches the user's emotional state.

[0092] The care system can suggest appropriate movies and TV dramas based on the user's emotional state. For example, if a user says, "I want to laugh today," the system can identify that emotion using its emotion estimation function and suggest comedy movies. Also, if a user says, "I want to watch a moving movie," the system can suggest moving movies. This allows the system to support mental care by providing movies and TV dramas that match the user's emotional state.

[0093] The care system can suggest appropriate travel plans based on the user's emotional state. For example, if a user says, "I want to refresh myself," the system can identify that emotion using its emotion estimation function and suggest a refreshing travel destination. Similarly, if a user says, "I want to be adventurous," the system can suggest an adventurous travel plan. This allows the system to support mental care by providing travel plans tailored to the user's emotional state.

[0094] The care system can suggest appropriate hobbies and activities based on the user's speech. For example, if a user says, "I want to find a new hobby," the system will suggest appropriate hobbies based on the user's interests and past conversation history. Also, if a user says, "I want to do something fun on the weekend," the system can suggest nearby events and activities. This can help enrich the user's life.

[0095] The care system can suggest appropriate learning plans based on the user's speech. For example, if a user says, "I want to learn a new skill," the system will suggest an appropriate learning plan based on the user's interests and past conversation history. Also, if a user says, "I want to study a language," the system can provide language learning resources and plans. This can increase the user's motivation to learn and support skill improvement.

[0096] The processing flow of the second embodiment will be briefly explained below.

[0097] Step 1: The generative AI analyzes the user's input and generates an appropriate response. For example, if the user says, "I'm tired today," the generative AI responds with something like, "Thank you for your hard work. What happened today?" The generative AI uses text generation AI (e.g., LLM) or multimodal generation AI to generate a response based on the user's input. Step 2: The speech recognition unit converts the user's speech into text. For example, if the user says, "Tell me what my schedule is tomorrow," the speech recognition unit converts the speech into text, and the generation AI analyzes the text and generates a response. Step 3: The character dialogue unit interacts with the character selected by the user. For example, if the user asks the character, "What should we cook today?", the character may suggest, "How about pasta today?" The generation AI generates a response that matches the character's personality. Step 4: The external service integration unit connects with external services such as calendar apps, reminders, and music streaming services via APIs. For example, if a user says, "Tell me what's on my schedule for tomorrow," the unit responds by retrieving the schedule from the calendar app. Similarly, if a user says, "Play some relaxing music," the unit plays relaxing music from a music streaming service.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0102] 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.

[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 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.

[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. 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.

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

[0111] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 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.

[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 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.

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0117] 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.

[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 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.

[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 (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).

[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] 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.

[0124] 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.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0142] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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."

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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]

[0165] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A generative AI that performs natural language processing using generative AI, a speech recognition unit that converts a user's speech into text; a character dialogue unit for dialogue with the character selected by the user; An external service linking unit that links with external services through APIs. A system characterized by:

2. The generated AI is Analyzing the emotional state of the user in real time and generating a response according to the emotion.

2. The system of claim 1.

3. The generated AI is Analyzing the user's health status and providing health advice 2. The system of claim 1.

4. The voice recognition unit Analyze the user's speech rate and tone to more accurately understand the intent of what is being said 2. The system of claim 1.

5. The character dialogue unit Customize responses to the user's utterances based on the character's personality.

2. The system of claim 1.

6. The external service cooperation unit Through the API, the user's lifestyle data is collected, and the generation AI generates a response based on that data.

2. The system of claim 1.

7. The generated AI is Suggesting music and video content according to the user's emotions 2. The system of claim 1.

8. The external service cooperation unit Analyze data collected through API and generate a response according to the user's emotions 2. The system of claim 1.

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