System

Abstract

An object of a system according to an embodiment is to realize prevention of dementia of elderly people and reduction of burden on caregivers.SOLUTION: A system includes a voice generation part, a facial expression generation part, and a conversation recording part. The voice generation unit generates a voice close to a human utterance using the neural voice. The facial expression generator richly expresses facial expressions and gestures on the display. The conversation recording unit records the conversation and, after a few days, extracts the topic of the conversation from its memory.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 technology does not provide enough interactive robots that can effectively prevent dementia in the elderly or reduce the burden on caregivers, and there is room for improvement.

[0005] The system according to the embodiment aims to prevent dementia in the elderly and reduce the burden on caregivers. [Means for solving the problem]

[0006] The system according to the embodiment includes a voice generation unit, a facial expression generation unit, and a conversation recording unit. The voice generation unit generates voice that resembles human speech using neural speech. The facial expression generation unit expresses a variety of facial expressions and gestures on the display. The conversation recording unit records conversations and retrieves the topics of conversation from the memory several days later. [Effects of the Invention]

[0007] The system according to the embodiment can prevent dementia in the elderly and reduce the burden on caregivers. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The interactive robot system according to an embodiment of the present invention uses generative AI to prevent dementia in the elderly and reduce the burden on caregivers. This system uses neural speech to generate speech that sounds similar to human speech, displays a rich range of facial expressions and gestures, and can record conversations and then select new topics from the memory several days later. This allows the interactive robot system to prevent dementia in the elderly and reduce the burden on caregivers.

[0029] The interactive robot system according to the embodiment includes a voice generation unit, a facial expression generation unit, and a conversation recording unit. The voice generation unit uses neural speech to generate voices similar to human speech. For example, the generation AI generates natural-sounding voices using neural speech technologies such as WaveNet and Tacotron. The voice generation unit asks questions such as "How was your day today?" to the elderly, and the elderly responds, allowing the conversation to proceed smoothly. The facial expression generation unit displays a variety of facial expressions and gestures on the display. For example, the generation AI responds with a smile, "That's wonderful!", making the conversation with the elderly more intimate. The facial expression generation unit displays various facial expressions and hand movements on the display, making the interactive robot appear more human-like. The conversation recording unit records conversations and, several days later, suggests new topics of conversation from its memory. For example, the generation AI may suggest new topics based on past conversations, such as, "How did that story about your grandchildren go?" The conversation recording unit allows the generation AI to save the conversation content as text data, allowing it to be searched and reused as needed. As a result, the interactive robot system according to the embodiment can prevent dementia in the elderly and reduce the burden on caregivers. For example, caregivers can have more time to concentrate on other tasks, and the elderly can enjoy pleasant conversations on a daily basis. It is also expected to contribute to the early detection and prevention of dementia.

[0030] The voice generation unit can learn the tone and speed of an elderly person's voice and generate voices tailored to each individual elderly person. For example, if a particular elderly person speaks slowly, the generation AI will generate voices that match that speed. The voice generation unit can also save the characteristics of the elderly person's voice as a profile and generate voices based on that profile in future conversations. For example, the generation AI can analyze the tone and speed of an elderly person's voice and adjust the voice generation algorithm based on that data. This makes it possible to have natural conversations tailored to the elderly.

[0031] The speech generation unit supports conversations in different languages, enabling the realization of a multilingual interactive robot. For example, the generation AI in the speech generation unit supports conversations in different languages, enabling the realization of a multilingual interactive robot. For example, it can converse in multiple languages, such as Japanese, English, and French. The speech generation unit also allows the generation AI to switch languages ​​in real time. For example, if an elderly person speaks to it in English, the generation AI will automatically respond in English. The speech generation unit also allows the generation AI to learn the pronunciation and intonation of different languages ​​and generate natural speech. For example, the generation AI can learn French pronunciation and converse smoothly in French. This makes multilingual dialogue possible.

[0032] The voice generation unit can generate not only voice but also music and environmental sounds to use as background sounds for conversations, thereby providing a more relaxing atmosphere. For example, the voice generation unit uses a generation AI to generate music and environmental sounds to use as background sounds for conversations. For example, natural sounds or classical music can be played to create a relaxing atmosphere. The voice generation unit can also use the generation AI to select music that matches the preferences of the elderly person. For example, playing songs that the elderly person likes can make conversations more enjoyable. The voice generation unit can also use the generation AI to generate environmental sounds in real time to use as background sounds for conversations. For example, playing the sounds of birds chirping or waves can provide a relaxing atmosphere. This can provide a relaxing atmosphere.

[0033] The facial expression generation unit imitates the facial expressions and gestures of the elderly in real time, enabling more natural dialogue. For example, the generation AI in the facial expression generation unit imitates the facial expressions and gestures of the elderly in real time, enabling more natural dialogue. For example, when the elderly smiles, the robot smiles back. The facial expression generation unit can also imitate the hand movements of the elderly. For example, when the elderly waves their hand, the robot also waves back. The facial expression generation unit can also detect changes in the elderly's facial expression in real time and generate a corresponding facial expression. For example, when the elderly shows a surprised expression, the robot also replies with a surprised expression. This makes natural dialogue possible.

[0034] The facial expression generation unit accumulates facial expression and gesture data and can generate expressions customized for each elderly person. The facial expression generation unit accumulates, for example, facial expression and gesture data and generates expressions customized for each elderly person. For example, it learns the facial expressions and gestures that a particular elderly person frequently uses. The facial expression generation unit can also enable the generation AI to save the elderly person's facial expression data as a profile and use that profile for expressions in subsequent interactions. For example, the generation AI can learn the elderly person's smile patterns and generate facial expressions based on those patterns. The facial expression generation unit can also enable the generation AI to analyze the elderly person's gesture data and generate customized gestures based on that data. For example, it can learn the hand movements that the elderly often use and generate gestures based on those movements. This makes it possible to generate expressions tailored to each elderly person.

[0035] The facial expression generation unit can link facial expression and gesture data with other devices to produce consistent expressions across multiple devices. The facial expression generation unit, for example, links facial expression and gesture data with other devices to produce consistent expressions across multiple devices. For example, the same facial expressions and gestures are displayed on a smartphone and a tablet. The facial expression generation unit can also synchronize data between devices using the generation AI to produce consistent expressions. For example, when a robot smiles, the same smile is displayed on a smartphone and a tablet. The facial expression generation unit can also share facial expression and gesture data between devices using the generation AI to produce unified expressions. For example, when a robot waves its hand, the same movement is displayed on other devices. This enables consistent expressions across multiple devices.

[0036] The conversation recording unit can analyze the content of a conversation in detail and extract and record important keywords and phrases. For example, the generation AI can analyze the content of a conversation in detail and extract and record important keywords and phrases. For example, it can identify words and phrases that appear frequently in the conversation. The conversation recording unit can also allow the generation AI to save the content of the conversation as text data and highlight important keywords and phrases. For example, the generation AI can extract important keywords such as "grandchildren" or "travel" from the conversation and provide new topics based on them. The conversation recording unit can also allow the generation AI to analyze the content of a conversation and record specific phrases. For example, the generation AI can record phrases such as "Where do you want to go next time?" and reuse those phrases in the next conversation. This allows important keywords and phrases to be recorded.

[0037] The conversation recording unit can identify the interests and concerns of the elderly based on the conversation records and provide topics based on them. For example, the conversation recording unit allows the generation AI to identify the interests and concerns of the elderly based on the conversation records and provide topics based on them. For example, topics that frequently came up in past conversations can be provided again. The conversation recording unit can also allow the generation AI to save the interests and concerns of the elderly as a profile and provide topics based on that profile. For example, if the elderly person prefers topics related to their hobbies, the generation AI will preferentially provide those topics. The conversation recording unit can also allow the generation AI to analyze the interests of the elderly and provide new topics. For example, if the elderly person is interested in traveling, the generation AI will provide topics related to travel. This makes it possible to provide topics based on the interests and concerns of the elderly.

[0038] The conversation recording unit stores conversation records in the cloud and makes them accessible from multiple devices, enabling the conversations to be reused anywhere. The conversation recording unit, for example, stores conversation records in the cloud and makes them accessible from multiple devices. For example, conversation records can be accessed from smartphones and tablets. The conversation recording unit can also enable the generation AI to synchronize data on the cloud, enabling the conversations to be reused anywhere. For example, the generation AI can provide new topics based on conversation data stored on the cloud. The conversation recording unit can also enable the generation AI to manage conversation data on the cloud, enabling it to be searched and reused as needed. For example, the generation AI can search for a specific conversation from a database on the cloud and reuse its content. This enables the conversations to be reused anywhere.

[0039] The conversation recording unit can promote group dialogue by sharing conversation records with other elderly people and providing common topics of conversation. The conversation recording unit, for example, shares conversation records with other elderly people and provides common topics of conversation. For example, conversation records are shared between elderly people who share the same hobbies. The conversation recording unit can also enable the generation AI to share elderly people's conversation data and promote group dialogue. For example, the generation AI adjusts the conversation so that multiple elderly people enjoy conversations on common topics. The conversation recording unit can also enable the generation AI to provide new group topics of conversation based on the elderly people's conversation data. For example, the generation AI promotes conversations between elderly people who share common hobbies and interests. This can promote group dialogue.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The interactive robot system can also be equipped with a health management unit that monitors the health of the elderly. For example, the health management unit measures blood pressure and heart rate and issues an alert if an abnormality is detected. The health management unit can also use the generating AI to manage the elderly's diet and exercise records and support healthy lifestyle habits. For example, the generating AI can ask questions such as, "Did you go for a walk today?" to encourage exercise. The health management unit can also manage the elderly's medication status and prevent them from forgetting to take their medicine. For example, the generating AI can remind them, "Did you take your medicine?" This allows for more effective health management for the elderly.

[0042] The interactive robot system can further include a content providing unit that provides content based on the hobbies and interests of the elderly. For example, the content providing unit uses a generation AI to collect information about the elderly's hobbies and provide related news and articles. The content providing unit can also use the generation AI to recommend videos and music based on the elderly's interests. For example, the generation AI can ask questions such as, "Have you seen any movies recently?" and recommend movies. The content providing unit can also use the generation AI to provide event information related to the elderly's hobbies. For example, the generation AI can make suggestions such as, "Why not go to a concert nearby?" This will make the lives of the elderly more fulfilling.

[0043] The interactive robot system can further include a security monitoring unit to ensure the safety of the elderly. For example, in the security monitoring unit, the generation AI monitors the elderly's living environment and issues an alert if an abnormality is detected. The security monitoring unit can also have the generation AI suggest safe routes for the elderly when they go out. For example, the generation AI may suggest, "The weather is bad today, so let's take a different route." The security monitoring unit can also have the generation AI make emergency contact with the elderly's family or caregiver. For example, the generation AI may send an alert such as, "An elderly person has fallen." This ensures the safety of the elderly.

[0044] The interactive robot system can further include a communication support unit to promote social connections among the elderly. For example, in the communication support unit, the generation AI supports the elderly in contacting friends and family. In addition, the communication support unit can also enable the generation AI to set up video calls for the elderly. For example, the generation AI can make suggestions such as, "Let's have a video call with your grandchild." In addition, the communication support unit can also enable the generation AI to assist the elderly in writing letters and emails. For example, the generation AI can make suggestions such as, "Let's write a letter to your friend." This strengthens the social connections among the elderly.

[0045] The interactive robot system can further include an education support unit to support elderly people's learning. For example, in the education support unit, the generation AI provides learning content based on the elderly person's interests. The education support unit can also have the generation AI recommend online courses for elderly people. For example, the generation AI makes suggestions such as, "Would you like to learn a new language?" The education support unit can also have the generation AI manage the elderly person's learning progress and provide appropriate feedback. For example, the generation AI can ask questions such as, "How much did you study today?" to promote learning. This increases the elderly person's motivation to learn.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The speech generation unit uses neural speech to generate speech that sounds similar to human speech. For example, the generation AI uses neural speech technologies such as WaveNet and Tacotron to generate natural speech. The speech generation unit also allows the generation AI to ask the elderly questions such as "How was your day today?", and the elderly responds, allowing the conversation to proceed smoothly. Step 2: The facial expression generation unit expresses a variety of facial expressions and gestures on the display. For example, the generation AI can respond with a smile, "That's wonderful!", making the conversation with the elderly more friendly. The facial expression generation unit also makes the interactive robot seem almost human by displaying various facial expressions and hand movements on the display. Step 3: The conversation recording unit records the conversation and, a few days later, selects a topic from its memory. For example, the generation AI might suggest a new topic based on past conversations, such as, "How did that story about your grandchildren go in the last time?" The conversation recording unit also allows the generation AI to save the content of the conversation as text data, allowing it to be searched and reused as needed.

[0048] (Example 2) The interactive robot system according to an embodiment of the present invention uses generative AI to prevent dementia in the elderly and reduce the burden on caregivers. This system uses neural speech to generate speech that sounds similar to human speech, displays a rich range of facial expressions and gestures, and can record conversations and then select new topics from the memory several days later. This allows the interactive robot system to prevent dementia in the elderly and reduce the burden on caregivers.

[0049] The interactive robot system according to the embodiment includes a voice generation unit, a facial expression generation unit, and a conversation recording unit. The voice generation unit uses neural speech to generate voices similar to human speech. For example, the generation AI generates natural-sounding voices using neural speech technologies such as WaveNet and Tacotron. The voice generation unit asks questions such as "How was your day today?" to the elderly, and the elderly responds, allowing the conversation to proceed smoothly. The facial expression generation unit displays a variety of facial expressions and gestures on the display. For example, the generation AI responds with a smile, "That's wonderful!", making the conversation with the elderly more intimate. The facial expression generation unit displays various facial expressions and hand movements on the display, making the interactive robot appear more human-like. The conversation recording unit records conversations and, several days later, suggests new topics of conversation from its memory. For example, the generation AI may suggest new topics based on past conversations, such as, "How did that story about your grandchildren go?" The conversation recording unit allows the generation AI to save the conversation content as text data, allowing it to be searched and reused as needed. As a result, the interactive robot system according to the embodiment can prevent dementia in the elderly and reduce the burden on caregivers. For example, caregivers can have more time to concentrate on other tasks, and the elderly can enjoy pleasant conversations on a daily basis. It is also expected to contribute to the early detection and prevention of dementia.

[0050] The voice generation unit can learn the tone and speed of an elderly person's voice and generate voices tailored to each individual elderly person. For example, if a particular elderly person speaks slowly, the generation AI will generate voices that match that speed. The voice generation unit can also save the characteristics of the elderly person's voice as a profile and generate voices based on that profile in future conversations. For example, the generation AI can analyze the tone and speed of an elderly person's voice and adjust the voice generation algorithm based on that data. This makes it possible to have natural conversations tailored to the elderly.

[0051] The voice generation unit can generate voice with emotions according to the content of the conversation and express changes in emotions. For example, the generation AI analyzes the content of the conversation and generates voice with emotions. For example, to express joy, the generation AI can speak in a bright tone. The voice generation unit can also speak in a calm tone to express sadness. For example, the generation AI generates voice with emotions when using phrases such as "That's really unfortunate" in a conversation. The voice generation unit can also speak in a strong tone to express anger. For example, the generation AI expresses changes in emotions when using phrases such as "That's unforgivable." This enables natural conversation with emotions.

[0052] The voice generation unit can use the emotion estimation function to analyze the emotional state of the elderly person in real time and generate a voice tone corresponding to the state. For example, the voice generation unit can use the emotion estimation function to analyze the emotional state of the elderly person in real time and generate a voice tone corresponding to the state. For example, if the elderly person is excited, the voice generation unit can speak in a calm tone. Also, the voice generation unit can use the emotion estimation function to speak in a bright tone if the elderly person is relaxed. For example, the generation AI can analyze the elderly person's facial expressions and voice data and adjust the voice tone based on that data. Also, the voice generation unit can use the emotion estimation function to speak in a comforting tone if the elderly person is sad. For example, the generation AI can speak in a gentle tone when using phrases such as "It's okay." This makes it possible to generate a voice tone corresponding to the elderly person's emotions.

[0053] The speech generation unit supports conversations in different languages, enabling the realization of a multilingual interactive robot. For example, the generation AI in the speech generation unit supports conversations in different languages, enabling the realization of a multilingual interactive robot. For example, it can converse in multiple languages, such as Japanese, English, and French. The speech generation unit also allows the generation AI to switch languages ​​in real time. For example, if an elderly person speaks to it in English, the generation AI will automatically respond in English. The speech generation unit also allows the generation AI to learn the pronunciation and intonation of different languages ​​and generate natural speech. For example, the generation AI can learn French pronunciation and converse smoothly in French. This makes multilingual dialogue possible.

[0054] The voice generation unit can generate not only voice but also music and environmental sounds to use as background sounds for conversations, thereby providing a more relaxing atmosphere. For example, the voice generation unit uses a generation AI to generate music and environmental sounds to use as background sounds for conversations. For example, natural sounds or classical music can be played to create a relaxing atmosphere. The voice generation unit can also use the generation AI to select music that matches the preferences of the elderly person. For example, playing songs that the elderly person likes can make conversations more enjoyable. The voice generation unit can also use the generation AI to generate environmental sounds in real time to use as background sounds for conversations. For example, playing the sounds of birds chirping or waves can provide a relaxing atmosphere. This can provide a relaxing atmosphere.

[0055] The speech generation unit can use the emotion estimation function to analyze how the elderly person feels about a specific topic and provide topics based on that emotion. The speech generation unit can, for example, use the emotion estimation function to analyze how the elderly person feels about a specific topic. For example, if the elderly person has positive emotions about a specific topic, the speech generation unit can preferentially provide that topic. The speech generation unit can also use the emotion estimation function to avoid topics that the elderly person feels negative about. For example, it can avoid topics that the elderly person found stressful in past conversations. The speech generation unit can also use the emotion estimation function to provide topics that the elderly person is interested in. For example, if the elderly person likes topics related to their hobbies, the speech generation unit can provide those topics. In this way, topics based on the elderly person's emotions can be provided.

[0056] The facial expression generation unit imitates the facial expressions and gestures of the elderly in real time, enabling more natural dialogue. For example, the generation AI in the facial expression generation unit imitates the facial expressions and gestures of the elderly in real time, enabling more natural dialogue. For example, when the elderly smiles, the robot smiles back. The facial expression generation unit can also imitate the hand movements of the elderly. For example, when the elderly waves their hand, the robot also waves back. The facial expression generation unit can also detect changes in the elderly's facial expression in real time and generate a corresponding facial expression. For example, when the elderly shows a surprised expression, the robot also replies with a surprised expression. This makes natural dialogue possible.

[0057] The facial expression generation unit accumulates facial expression and gesture data and can generate expressions customized for each elderly person. The facial expression generation unit accumulates, for example, facial expression and gesture data and generates expressions customized for each elderly person. For example, it learns the facial expressions and gestures that a particular elderly person frequently uses. The facial expression generation unit can also enable the generation AI to save the elderly person's facial expression data as a profile and use that profile for expressions in subsequent interactions. For example, the generation AI can learn the elderly person's smile patterns and generate facial expressions based on those patterns. The facial expression generation unit can also enable the generation AI to analyze the elderly person's gesture data and generate customized gestures based on that data. For example, it can learn the hand movements that the elderly often use and generate gestures based on those movements. This makes it possible to generate expressions tailored to each elderly person.

[0058] The facial expression generation unit can use the emotion estimation function to generate facial expressions and gestures according to the emotional state of the elderly person. The facial expression generation unit, for example, uses the emotion estimation function to generate facial expressions and gestures according to the emotional state of the elderly person. For example, a comforting expression is shown for an elderly person who is sad. The facial expression generation unit can also use the emotion estimation function to make the elderly person smile if they are happy. For example, the generation AI analyzes the facial expression data of the elderly person and generates facial expressions based on that data. The facial expression generation unit can also use the emotion estimation function to make the elderly person show a surprised expression if they are surprised. For example, the generation AI analyzes the gesture data of the elderly person and generates gestures based on that data. In this way, facial expressions and gestures according to the emotions of the elderly person can be generated.

[0059] The facial expression generation unit can link facial expression and gesture data with other devices to produce consistent expressions across multiple devices. The facial expression generation unit, for example, links facial expression and gesture data with other devices to produce consistent expressions across multiple devices. For example, the same facial expressions and gestures are displayed on a smartphone and a tablet. The facial expression generation unit can also synchronize data between devices using the generation AI to produce consistent expressions. For example, when a robot smiles, the same smile is displayed on a smartphone and a tablet. The facial expression generation unit can also share facial expression and gesture data between devices using the generation AI to produce unified expressions. For example, when a robot waves its hand, the same movement is displayed on other devices. This enables consistent expressions across multiple devices.

[0060] The facial expression generation unit can use the emotion estimation function to analyze what emotion the elderly person has toward a specific gesture and provide a gesture based on that emotion. The facial expression generation unit can, for example, use the emotion estimation function to analyze what emotion the elderly person has toward a specific gesture. For example, if the elderly person has positive emotions toward a specific gesture, the facial expression generation unit can frequently use that gesture. The facial expression generation unit can also use the emotion estimation function to avoid gestures that the elderly person has negative emotions about. For example, the facial expression generation unit can avoid gestures that the elderly person has felt stressed about in past interactions. The facial expression generation unit can also use the emotion estimation function to provide gestures that the elderly person likes. For example, the facial expression generation unit can frequently use gestures that make the elderly happy. In this way, gestures based on the elderly person's emotions can be provided.

[0061] The conversation recording unit can analyze the content of a conversation in detail and extract and record important keywords and phrases. For example, the generation AI can analyze the content of a conversation in detail and extract and record important keywords and phrases. For example, it can identify words and phrases that appear frequently in the conversation. The conversation recording unit can also allow the generation AI to save the content of the conversation as text data and highlight important keywords and phrases. For example, the generation AI can extract important keywords such as "grandchildren" or "travel" from the conversation and provide new topics based on them. The conversation recording unit can also allow the generation AI to analyze the content of a conversation and record specific phrases. For example, the generation AI can record phrases such as "Where do you want to go next time?" and reuse those phrases in the next conversation. This allows important keywords and phrases to be recorded.

[0062] The conversation recording unit can identify the interests and concerns of the elderly based on the conversation records and provide topics based on them. For example, the conversation recording unit allows the generation AI to identify the interests and concerns of the elderly based on the conversation records and provide topics based on them. For example, topics that frequently came up in past conversations can be provided again. The conversation recording unit can also allow the generation AI to save the interests and concerns of the elderly as a profile and provide topics based on that profile. For example, if the elderly person prefers topics related to their hobbies, the generation AI will preferentially provide those topics. The conversation recording unit can also allow the generation AI to analyze the interests of the elderly and provide new topics. For example, if the elderly person is interested in traveling, the generation AI will provide topics related to travel. This makes it possible to provide topics based on the interests and concerns of the elderly.

[0063] The conversation recording unit can use the emotion estimation function to preferentially reuse topics in which the elderly showed positive emotions in past conversations. The conversation recording unit, for example, uses the emotion estimation function to preferentially reuse topics in which the elderly showed positive emotions in past conversations. For example, it may re-provide topics that elicited a lot of smiles. The conversation recording unit can also use the generation AI to analyze the elderly's emotional data and record topics that elicited positive emotions. For example, the generation AI may identify topics in past conversations that made the elderly happy and reuse those topics. The conversation recording unit can also use the emotion estimation function to preferentially provide topics that the elderly enjoyed. For example, the generation AI may re-provide topics that elicited a smile. This allows topics that elicit positive emotions to be reused.

[0064] The conversation recording unit stores conversation records in the cloud and makes them accessible from multiple devices, enabling the conversations to be reused anywhere. The conversation recording unit, for example, stores conversation records in the cloud and makes them accessible from multiple devices. For example, conversation records can be accessed from smartphones and tablets. The conversation recording unit can also enable the generation AI to synchronize data on the cloud, enabling the conversations to be reused anywhere. For example, the generation AI can provide new topics based on conversation data stored on the cloud. The conversation recording unit can also enable the generation AI to manage conversation data on the cloud, enabling it to be searched and reused as needed. For example, the generation AI can search for a specific conversation from a database on the cloud and reuse its content. This enables the conversations to be reused anywhere.

[0065] The conversation recording unit can promote group dialogue by sharing conversation records with other elderly people and providing common topics of conversation. The conversation recording unit, for example, shares conversation records with other elderly people and provides common topics of conversation. For example, conversation records are shared between elderly people who share the same hobbies. The conversation recording unit can also enable the generation AI to share elderly people's conversation data and promote group dialogue. For example, the generation AI adjusts the conversation so that multiple elderly people enjoy conversations on common topics. The conversation recording unit can also enable the generation AI to provide new group topics of conversation based on the elderly people's conversation data. For example, the generation AI promotes conversations between elderly people who share common hobbies and interests. This can promote group dialogue.

[0066] The conversation recording unit can use the emotion estimation function to avoid topics in which the elderly expressed negative emotions in past conversations. The conversation recording unit, for example, uses the emotion estimation function to avoid topics in which the elderly expressed negative emotions in past conversations. For example, sad topics and topics that cause stress are avoided. The conversation recording unit can also have the generation AI analyze the elderly's emotional data and record topics that cause negative emotions. For example, the generation AI can identify topics in past conversations that made the elderly sad and avoid those topics. The conversation recording unit can also use the emotion estimation function to avoid topics that the elderly dislike. For example, the generation AI can avoid topics that cause stress to the elderly. This makes it possible to avoid topics that elicit negative emotions.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] The interactive robot system can also be equipped with a health management unit that monitors the health of the elderly. For example, the health management unit measures blood pressure and heart rate and issues an alert if an abnormality is detected. The health management unit can also use the generating AI to manage the elderly's diet and exercise records and support healthy lifestyle habits. For example, the generating AI can ask questions such as, "Did you go for a walk today?" to encourage exercise. The health management unit can also manage the elderly's medication status and prevent them from forgetting to take their medicine. For example, the generating AI can remind them, "Did you take your medicine?" This allows for more effective health management for the elderly.

[0069] The interactive robot system can further include a content providing unit that provides content based on the hobbies and interests of the elderly. For example, the content providing unit uses a generation AI to collect information about the elderly's hobbies and provide related news and articles. The content providing unit can also use the generation AI to recommend videos and music based on the elderly's interests. For example, the generation AI can ask questions such as, "Have you seen any movies recently?" and recommend movies. The content providing unit can also use the generation AI to provide event information related to the elderly's hobbies. For example, the generation AI can make suggestions such as, "Why not go to a concert nearby?" This will make the lives of the elderly more fulfilling.

[0070] The interactive robot system can further include a security monitoring unit to ensure the safety of the elderly. For example, in the security monitoring unit, the generation AI monitors the elderly's living environment and issues an alert if an abnormality is detected. The security monitoring unit can also have the generation AI suggest safe routes for the elderly when they go out. For example, the generation AI may suggest, "The weather is bad today, so let's take a different route." The security monitoring unit can also have the generation AI make emergency contact with the elderly's family or caregiver. For example, the generation AI may send an alert such as, "An elderly person has fallen." This ensures the safety of the elderly.

[0071] The interactive robot system can further include a communication support unit to promote social connections among the elderly. For example, in the communication support unit, the generation AI supports the elderly in contacting friends and family. In addition, the communication support unit can also enable the generation AI to set up video calls for the elderly. For example, the generation AI can make suggestions such as, "Let's have a video call with your grandchild." In addition, the communication support unit can also enable the generation AI to assist the elderly in writing letters and emails. For example, the generation AI can make suggestions such as, "Let's write a letter to your friend." This strengthens the social connections among the elderly.

[0072] The interactive robot system can further include an education support unit to support elderly people's learning. For example, in the education support unit, the generation AI provides learning content based on the elderly person's interests. The education support unit can also have the generation AI recommend online courses for elderly people. For example, the generation AI makes suggestions such as, "Would you like to learn a new language?" The education support unit can also have the generation AI manage the elderly person's learning progress and provide appropriate feedback. For example, the generation AI can ask questions such as, "How much did you study today?" to promote learning. This increases the elderly person's motivation to learn.

[0073] The interactive robot system can also estimate the emotions of the elderly person and provide relaxing music or environmental sounds based on those emotions. For example, if the elderly person is feeling stressed, the generation AI will play relaxing music. Also, if the elderly person is relaxed, the generation AI can play natural sounds. For example, the generation AI can make suggestions such as, "I feel like relaxing today," and provide appropriate music. Also, if the elderly person is excited, the generation AI can play calming music. For example, the generation AI can make suggestions such as, "Let's calm down a bit," and provide appropriate music. This achieves a relaxing effect that corresponds to the elderly person's emotions.

[0074] The interactive robot system can also estimate the emotions of the elderly person and suggest appropriate exercises based on those emotions. For example, if the elderly person is feeling stressed, the generation AI can suggest relaxing yoga or stretching. If the elderly person is relaxed, the generation AI can also suggest light walking. For example, the generation AI can suggest, "Let's go for a short walk." If the elderly person is excited, the generation AI can also suggest calming exercises. For example, the generation AI can suggest, "Take some deep breaths." In this way, exercises are suggested according to the elderly person's emotions.

[0075] The interactive robot system can also estimate the emotions of the elderly person and suggest appropriate meals based on those emotions. For example, if the elderly person is feeling stressed, the generative AI can suggest a relaxing meal. Also, if the elderly person is relaxed, the generative AI can suggest a nutritionally balanced meal. For example, the generative AI can suggest, "Eat more vegetables today." Also, if the elderly person is excited, the generative AI can suggest a calming meal. For example, the generative AI can suggest, "Eat a light meal today." In this way, meals are suggested according to the elderly person's emotions.

[0076] The interactive robot system can also estimate the elderly person's emotions and suggest appropriate recreational activities based on those emotions. For example, if the elderly person is feeling stressed, the generative AI can suggest relaxing arts and crafts. If the elderly person is relaxed, the generative AI can also suggest light games. For example, the generative AI can suggest, "Let's do a puzzle today." If the elderly person is excited, the generative AI can also suggest calming recreational activities. For example, the generative AI can suggest, "Let's draw a picture today." In this way, recreational activities are suggested according to the elderly person's emotions.

[0077] The interactive robot system can also estimate the emotions of the elderly person and suggest appropriate communication methods based on those emotions. For example, if the elderly person is feeling stressed, the generation AI can suggest topics that will help them relax. Also, if the elderly person is relaxed, the generation AI can suggest fun topics. For example, the generation AI can suggest, "Let's talk about some happy memories today." Also, if the elderly person is excited, the generation AI can suggest calming topics. For example, the generation AI can suggest, "Let's talk about quiet topics today." In this way, communication methods are suggested that match the elderly person's emotions.

[0078] The processing flow of the second embodiment will be briefly explained below.

[0079] Step 1: The speech generation unit uses neural speech to generate speech that sounds similar to human speech. For example, the generation AI uses neural speech technologies such as WaveNet and Tacotron to generate natural speech. The speech generation unit also allows the generation AI to ask the elderly questions such as "How was your day today?", and the elderly responds, allowing the conversation to proceed smoothly. Step 2: The facial expression generation unit expresses a variety of facial expressions and gestures on the display. For example, the generation AI can respond with a smile, "That's wonderful!", making the conversation with the elderly more friendly. The facial expression generation unit also makes the interactive robot seem almost human by displaying various facial expressions and hand movements on the display. Step 3: The conversation recording unit records the conversation and, a few days later, selects a topic from its memory. For example, the generation AI might suggest a new topic based on past conversations, such as, "How did that story about your grandchildren go in the last time?" The conversation recording unit also allows the generation AI to save the content of the conversation as text data, allowing it to be searched and reused as needed.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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).

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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."

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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]

[0147] 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 speech generation unit that generates speech similar to human speech using neural speech; a facial expression generating unit that expresses facial expressions and gestures on the display; A conversation recording unit that records the conversation and displays the topic of the conversation from the memory after a few days. A system characterized by:

2. The voice generation unit Generates emotional voice according to the content of the conversation and expresses changes in emotion 2. The system of claim 1.

3. The voice generation unit Supporting conversations in different languages, realizing a multilingual conversational robot 2. The system of claim 1.

4. The facial expression generation unit Mimics the facial expressions and gestures of seniors in real time for more natural interactions 2. The system of claim 1.

5. The conversation recording unit Analyze the content of the conversation in detail and extract and record important keywords and phrases.

2. The system of claim 1.

6. The voice generation unit Analyzes the emotional state of the elderly in real time and generates a voice tone accordingly 2. The system of claim 1.

7. The facial expression generation unit Generating facial expressions and gestures according to the emotional states of elderly people 2. The system of claim 1.

8. The conversation recording unit Prioritize reuse of topics that the elderly person expressed positive feelings about in past conversations.

2. The system of claim 1.

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