System

Abstract

An object of a system according to an embodiment is to allow elderly people to easily perform daily communication.SOLUTION: A system according to an embodiment includes a generation AI. The generated AI provides daily communication through interaction with the senior. The senior can interact with the generated AI through a smartphone or a nursing robot.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] With conventional technology, elderly people living alone may suffer from a lack of daily communication.

[0005] The system according to the embodiment aims to make it easier for elderly people to communicate on a daily basis. [Means for solving the problem]

[0006] The system according to the embodiment includes a generative AI that provides daily communication through dialogue with elderly people. The elderly people can interact with the generative AI through their smartphones or care robots. [Effects of the Invention]

[0007] The system according to the embodiment allows elderly people to easily carry out daily communication. [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 elderly support system according to an embodiment of the present invention is a system that allows elderly people to interact with a generative AI through smartphones or care robots. This system provides daily communication through dialogue with the elderly, and can prevent feelings of isolation, depression, and the onset and progression of dementia. As a result, the elderly support system can ensure daily communication for the elderly, contributing to their mental health and dementia prevention.

[0029] An elderly support system according to an embodiment includes a generative AI, a smartphone, and a care robot. The generative AI provides daily communication through dialogue with the elderly. For example, when the elderly asks, "What's the weather like today?", the generative AI responds, "It's sunny today, and the temperature is 20 degrees. It's a good day for a walk." The generative AI receives input from the elderly, analyzes it, generates an appropriate response, and delivers it verbally. The smartphone provides an interface for interacting with the generative AI. For example, the elderly can converse with the generative AI through an app installed on the smartphone. The care robot provides an interface for interacting with the generative AI, enabling more natural dialogue. For example, the elderly can interact with the generative AI on a daily basis through the generative AI installed in the care robot. This allows the elderly to maintain daily communication and prevent feelings of isolation, depression, and the onset and progression of dementia.

[0030] Generative AI can learn from an elderly person's past conversation history and provide personalized dialogue tailored to each individual elderly person. For example, generative AI can analyze an elderly person's past conversation history and generate dialogue based on the individual elderly person's preferences and interests. For example, it can prioritize questions about topics and hobbies that a particular elderly person likes. Generative AI can also learn phrases and expressions specific to that elderly person based on the elderly person's past conversation history to provide more natural dialogue. For example, it can incorporate phrases that a particular elderly person frequently uses. Generative AI can also analyze an elderly person's past conversation history and provide dialogue tailored to that person's mood and physical condition on that day. For example, it can use conversations from previous days when the elderly person was feeling unwell to express concern for their health. This can provide more personalized dialogue for the elderly person and improve the quality of communication.

[0031] Generative AI can provide topics based on the hobbies or interests of the elderly, making the conversation more enjoyable. For example, generative AI can learn the hobbies and interests of the elderly and provide topics based on them. For example, for an elderly person who likes gardening, it can provide topics about how to grow plants. Generative AI can also provide related news and information based on the elderly person's hobbies and interests. For example, it can introduce the latest music news to an elderly person who likes music. Generative AI can also provide quizzes and games based on the elderly person's hobbies and interests, making the conversation more enjoyable. For example, it can provide a history quiz to an elderly person who likes history. This makes it possible to provide topics based on the elderly person's hobbies and interests, making the conversation more enjoyable.

[0032] The generation AI can learn the lifestyle rhythm of the elderly and begin a conversation at the appropriate time. For example, the generation AI can learn the lifestyle rhythm of the elderly and greet them with "Good morning" in time for them to wake up in the morning. The generation AI can also ask "What did you have for lunch?" in time for meals based on the elderly's lifestyle rhythm. The generation AI can also learn the lifestyle rhythm of the elderly and greet them with "Good night" before they go to bed in the evening. This allows for conversations that are tailored to the elderly's lifestyle rhythm, enabling more natural communication.

[0033] A smartphone or a care robot can monitor the elderly person's living environment and provide appropriate dialogue. For example, a smartphone or a care robot can monitor the elderly person's living environment and, if the room temperature is low, ask, "Is it warm?". Alternatively, a smartphone or a care robot can monitor the elderly person's living environment and, if the lighting is dim, suggest, "Shall we brighten the room?". Alternatively, a smartphone or a care robot can monitor the elderly person's living environment and, if music is playing, ask, "What music are you listening to?". This makes it possible to provide appropriate dialogue based on the elderly person's living environment and improve their quality of life.

[0034] A smartphone or care robot can collect health data from an elderly person and reflect it in the content of the conversation. For example, a smartphone or care robot can collect an elderly person's health data and ask, "Is your blood pressure okay?" if their blood pressure is high. Furthermore, based on the elderly person's health data, the smartphone or care robot's generative AI can ask, "Did you exercise today?" Furthermore, a smartphone or care robot can collect health data from an elderly person and ask, "How is your diet lately?" if their weight has increased. This makes it possible to adjust the content of the conversation based on the elderly person's health data and support their health management.

[0035] A smartphone or care robot can work in conjunction with an elderly person's family and convey messages from them through a generation AI. For example, a smartphone or care robot can receive a message from an elderly person's family and convey it through the generation AI, asking, "Mom, how are you?". A smartphone or care robot can also work in conjunction with an elderly person's family, with the generation AI speaking in the family's voice, asking, "Dad, what did you do today?" A smartphone or care robot can also receive a message from an elderly person's family and convey, through the generation AI, "Grandma, we'll come over to play sometime." This allows elderly people to receive messages from their families, reducing their sense of isolation and supporting their mental health.

[0036] The generative AI can learn not only the voices of family and friends, but also their speaking styles and catchphrases, allowing it to reproduce more natural conversations. For example, the generative AI can learn not only the voices of family and friends, but also their unique speaking styles and catchphrases, allowing it to reproduce more natural conversations. For example, it can reproduce the unique intonation of a family member. The generative AI can also learn the voices of elderly family members and friends, and speak in that voice, saying, "How is Mom doing?" The generative AI can also learn the speaking styles and catchphrases of family and friends, and provide conversations that incorporate their unique expressions. For example, it can reproduce phrases that family members often use. This allows it to reproduce the voices and speaking styles of family and friends, allowing it to provide more natural conversations.

[0037] The generation AI can learn the content of past conversations between family members or friends and recreate stories of memories. For example, the generation AI can learn the content of past conversations between family members and friends and recreate stories of memories by saying, "Do you remember? We talked about our trip last year." The generation AI can also say, "That birthday party was fun," based on the content of past conversations between an elderly person's family and friends. The generation AI can also learn the content of past conversations between family members and friends and recreate stories of memories by saying, "Let's talk while looking at old photos." This allows the generation AI to recreate stories with family and friends and provide conversations that evoke nostalgia.

[0038] The generation AI can have a conversation while displaying photos or videos of family or friends. For example, while displaying a photo of family or friends, the generation AI might say, "This photo is from your trip last year." In addition, to allow elderly people to enjoy conversation while watching videos of family and friends, the generation AI might say, "This video is from your grandchild's sports day." In addition, while displaying photos or videos of family and friends, the generation AI might say, "This photo is from your mother's birthday." This allows for a more friendly conversation by having a conversation while displaying photos or videos of family and friends.

[0039] The generating AI can use the voices of family members or friends to provide daily reminders or notifications. For example, the generating AI can use a family member's voice to remind them, "Mom, it's time to take your medicine." Or, an elderly person can receive a notification in a friend's voice saying, "Today you have a doctor's appointment." The generating AI can also use a family member's voice to remind them, "Dad, today is the day to go for a walk." This allows reminders and notifications to be provided in a way that is familiar to the elderly, by using the voices of family members or friends.

[0040] The generative AI can provide quizzes or puzzles that stimulate the cognitive functions of the elderly. For example, the generative AI may ask the elderly a quiz to train their memory, such as, "What did you have for dinner last night?" The generative AI may also provide puzzle games to stimulate the elderly's cognitive functions, such as a simple crossword puzzle. The generative AI may also ask the elderly calculation problems to stimulate their cognitive functions, such as, "What is 5 + 3?" This can stimulate the elderly's cognitive functions and contribute to the prevention of dementia.

[0041] The generative AI can stimulate memories by having conversations with elderly people that make them look back on past events. For example, the generative AI can ask an elderly person, "Tell me about your old travels," to stimulate their memory. The generative AI can also ask an elderly person, "Tell me about your childhood memories," to stimulate their memory. The generative AI can also ask an elderly person, "Tell me about an episode with an old friend," to stimulate their memory and help prevent dementia.

[0042] Generative AI can provide exercise guides to support the exercise habits of the elderly. For example, generative AI can guide the elderly through simple stretching exercises, for example by instructing them to "stretch your arms up." Generative AI can also provide walking guides to support the exercise habits of the elderly, for example by suggesting, "Try walking for 20 minutes today." Generative AI can also guide the elderly through exercises that can be done while sitting in a chair, for example by instructing them to "stretch your legs forward." This can support the exercise habits of the elderly and contribute to maintaining their health.

[0043] Generative AI can provide dietary or nutritional advice to the elderly. For example, generative AI can provide advice to the elderly on how to eat a balanced diet. For example, it might suggest, "Eat more vegetables." To support the elderly's nutritional status, generative AI might advise, "Drink plenty of fluids today." Generative AI can also provide healthy meal recipes to the elderly. For example, it might suggest, "Try making vegetable soup today." This can provide dietary and nutritional advice to the elderly, contributing to maintaining their health.

[0044] Generative AI can monitor the health condition of elderly people in real time and automatically notify when abnormalities are detected. For example, generative AI can monitor the heart rate and blood pressure of elderly people in real time and notify family members when abnormalities are detected. Generative AI can also monitor the health condition of elderly people and notify medical institutions when body temperature is abnormally high. Generative AI can also monitor the health condition of elderly people in real time and notify care staff when abnormalities are detected. This allows for real-time monitoring of the health condition of elderly people and allows for rapid response when abnormalities are detected.

[0045] The generating AI can learn the lifestyle patterns of the elderly and notify family members or medical institutions if there are any abnormalities. For example, the generating AI can learn the lifestyle patterns of the elderly and notify family members if they do not wake up at their usual wake-up time. Furthermore, based on the elderly's lifestyle patterns, the generating AI can notify medical institutions if they do not eat at mealtimes. The generating AI can also learn the lifestyle patterns of the elderly and notify care staff if there are any abnormalities. This allows the generating AI to learn the lifestyle patterns of the elderly and respond quickly if there are any abnormalities.

[0046] Generative AI can suggest activities based on the hobbies or interests of the elderly, improving their quality of life. For example, generative AI can suggest gardening activities based on the elderly's hobbies, suggesting, for example, "Plant some new flowers today." Generative AI can also suggest reading activities based on the elderly's interests, suggesting, for example, "Read this book today." Generative AI can also suggest cooking activities based on the elderly's hobbies and interests, suggesting, for example, "Try a new recipe today." This allows us to suggest activities based on the elderly's hobbies and interests, improving their quality of life.

[0047] Generative AI can provide online communities to support the social connections of the elderly. For example, generative AI can introduce elderly people to online communities based on their hobbies and interests. For example, it might suggest, "Why not join a community of gardening enthusiasts?" To support the social connections of elderly people, generative AI might suggest, "Why not join an online book club meeting?" Generative AI can also introduce elderly people to local online communities. For example, it might suggest, "Why not participate in a local event?" This can support the social connections of elderly people and reduce feelings of isolation.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The elderly support system can further include a health management unit. The health management unit collects health data from the elderly and supports their daily health management. For example, it periodically measures their blood pressure and heart rate and notifies them of any abnormalities. The health management unit can also manage diet and exercise records and provide advice to promote a balanced lifestyle. Furthermore, the health management unit can link with medical institutions and support regular health checks and appointment scheduling. This provides comprehensive support for the elderly's health management and improves their quality of life.

[0050] The elderly support system can further include a reminder unit. The reminder unit notifies the elderly of important plans and tasks in their daily lives. For example, it can remind them to take their medicine or make an appointment at a medical institution. The reminder unit can also notify them of meal times and exercise times to support their daily routine. Furthermore, the reminder unit has a function for linking with family members and caregivers, allowing them to share important plans. This allows the elderly to go about their daily lives smoothly and provides a sense of security.

[0051] The elderly support system can further include an entertainment unit. The entertainment unit provides content that the elderly can enjoy. For example, it provides music, movie, and reading content to provide relaxation and enjoyment. The entertainment unit can also suggest activities based on hobbies and interests. For example, it can provide gardening and cooking recipes to support time spent enjoying hobbies. Furthermore, the entertainment unit can promote participation in online events and communities and support social connections. This can provide enjoyment and a sense of fulfillment in the lives of the elderly.

[0052] The elderly support system can also be equipped with a safety management unit. The safety management unit monitors the living environment of the elderly to ensure safety. For example, it has a fall detection function that quickly notifies the user if a fall occurs. The safety management unit can also detect abnormalities such as fires and gas leaks and prompt appropriate responses. Furthermore, the safety management unit has a GPS function to support safety when going out, and can share location information with family members and caregivers. This ensures the safety of the elderly, allowing them to live with peace of mind.

[0053] The elderly support system can further include a learning support unit. The learning support unit supports elderly people in learning new knowledge and skills. For example, it can provide online courses and learning programs, and offer learning opportunities based on hobbies and interests. The learning support unit can also provide quizzes and puzzles to stimulate cognitive functions. Furthermore, the learning support unit can support the improvement of digital literacy to promote communication with family and friends. This allows elderly people to enjoy lifelong learning and receive intellectual stimulation.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The generative AI provides everyday communication through dialogue with the elderly. For example, if an elderly person asks, "What's the weather like today?", the generative AI will respond, "It's sunny today, and the temperature is 20 degrees. It's a good day to go for a walk." The generative AI receives input from the elderly person, analyzes it, and generates an appropriate response, which is then transmitted via voice. Step 2: The smartphone provides an interface for interacting with the generative AI. For example, the user can converse with the generative AI through an app installed on the smartphone. Step 3: The care robot provides an interface for interacting with the generative AI, enabling more natural interactions. For example, elderly people can interact with the generative AI on a daily basis through the generative AI installed in the care robot.

[0056] (Example 2) The elderly support system according to an embodiment of the present invention is a system that allows elderly people to interact with a generative AI through smartphones or care robots. This system provides daily communication through dialogue with the elderly, and can prevent feelings of isolation, depression, and the onset and progression of dementia. As a result, the elderly support system can ensure daily communication for the elderly, contributing to their mental health and dementia prevention.

[0057] An elderly support system according to an embodiment includes a generative AI, a smartphone, and a care robot. The generative AI provides daily communication through dialogue with the elderly. For example, when the elderly asks, "What's the weather like today?", the generative AI responds, "It's sunny today, and the temperature is 20 degrees. It's a good day for a walk." The generative AI receives input from the elderly, analyzes it, generates an appropriate response, and delivers it verbally. The smartphone provides an interface for interacting with the generative AI. For example, the elderly can converse with the generative AI through an app installed on the smartphone. The care robot provides an interface for interacting with the generative AI, enabling more natural dialogue. For example, the elderly can interact with the generative AI on a daily basis through the generative AI installed in the care robot. This allows the elderly to maintain daily communication and prevent feelings of isolation, depression, and the onset and progression of dementia.

[0058] Generative AI can learn from an elderly person's past conversation history and provide personalized dialogue tailored to each individual elderly person. For example, generative AI can analyze an elderly person's past conversation history and generate dialogue based on the individual elderly person's preferences and interests. For example, it can prioritize questions about topics and hobbies that a particular elderly person likes. Generative AI can also learn phrases and expressions specific to that elderly person based on the elderly person's past conversation history to provide more natural dialogue. For example, it can incorporate phrases that a particular elderly person frequently uses. Generative AI can also analyze an elderly person's past conversation history and provide dialogue tailored to that person's mood and physical condition on that day. For example, it can use conversations from previous days when the elderly person was feeling unwell to express concern for their health. This can provide more personalized dialogue for the elderly person and improve the quality of communication.

[0059] The generation AI can analyze the elderly person's tone of voice or speaking style, infer their emotional state, and generate an appropriate response. For example, the generation AI can analyze the elderly person's tone of voice and speaking style in real time to infer their emotional state. For example, if their voice sounds low, it can offer words of encouragement. The generation AI can also analyze the elderly person's tone of voice and speaking style to generate a response that matches their emotional state. For example, if they sound excited, it can offer words of comfort. The generation AI can also learn the elderly person's tone of voice and speaking style to infer their emotional state and generate an appropriate response. For example, if their voice sounds cheerful, it can offer words of empathy. This makes it possible to provide appropriate responses that match the elderly person's emotional state, enabling more natural dialogue.

[0060] The generation AI can generate words of encouragement or comfort according to the emotional state of the elderly. For example, using its emotion estimation function, the generation AI can generate words of comfort if the elderly is sad, such as saying, "It's okay, I'm here for you." The generation AI can also generate words of empathy if the elderly is happy, such as saying, "That's wonderful!" The generation AI can also use its emotion estimation function to generate words of reassurance if the elderly is feeling anxious, such as saying, "Don't worry, everything will be fine." This makes it possible to provide appropriate words according to the elderly's emotional state and support their mental health.

[0061] Generative AI can provide topics based on the hobbies or interests of the elderly, making the conversation more enjoyable. For example, generative AI can learn the hobbies and interests of the elderly and provide topics based on them. For example, for an elderly person who likes gardening, it can provide topics about how to grow plants. Generative AI can also provide related news and information based on the elderly person's hobbies and interests. For example, it can introduce the latest music news to an elderly person who likes music. Generative AI can also provide quizzes and games based on the elderly person's hobbies and interests, making the conversation more enjoyable. For example, it can provide a history quiz to an elderly person who likes history. This makes it possible to provide topics based on the elderly person's hobbies and interests, making the conversation more enjoyable.

[0062] The generation AI can learn the lifestyle rhythm of the elderly and begin a conversation at the appropriate time. For example, the generation AI can learn the lifestyle rhythm of the elderly and greet them with "Good morning" in time for them to wake up in the morning. The generation AI can also ask "What did you have for lunch?" in time for meals based on the elderly's lifestyle rhythm. The generation AI can also learn the lifestyle rhythm of the elderly and greet them with "Good night" before they go to bed in the evening. This allows for conversations that are tailored to the elderly's lifestyle rhythm, enabling more natural communication.

[0063] Using its emotion estimation function, the generation AI can automatically initiate a conversation when an elderly person feels lonely. For example, using its emotion estimation function, the generation AI can ask, "What's wrong?" when an elderly person feels lonely. The generation AI can also start a conversation by asking, "How are you doing lately?" when an elderly person feels lonely. The generation AI can also use its emotion estimation function to ask, "Is there anything I can help you with?" when an elderly person feels lonely. This allows the generation AI to automatically start a conversation when an elderly person feels lonely, reducing their sense of isolation.

[0064] The smartphone or care robot can analyze the elderly person's movements or facial expressions and adjust the content of the conversation. For example, the smartphone or care robot can analyze the elderly person's facial expressions and offer fun topics if they are smiling. The smartphone or care robot can also analyze the elderly person's movements and offer relaxing topics if they are tired. The smartphone or care robot can also analyze the elderly person's facial expressions and offer comforting words if they are sad. This allows the content of the conversation to be adjusted based on the elderly person's movements and facial expressions, providing more appropriate communication.

[0065] A smartphone or a care robot can monitor the elderly person's living environment and provide appropriate dialogue. For example, a smartphone or a care robot can monitor the elderly person's living environment and, if the room temperature is low, ask, "Is it warm?". Alternatively, a smartphone or a care robot can monitor the elderly person's living environment and, if the lighting is dim, suggest, "Shall we brighten the room?". Alternatively, a smartphone or a care robot can monitor the elderly person's living environment and, if music is playing, ask, "What music are you listening to?". This makes it possible to provide appropriate dialogue based on the elderly person's living environment and improve their quality of life.

[0066] A smartphone or care robot can use its emotion estimation function to provide relaxing dialogue when an elderly person feels stressed. For example, using the emotion estimation function, a smartphone or care robot can say relaxing words such as "Try taking a deep breath" when an elderly person feels stressed. In addition, when an elderly person feels stressed, the generation AI of the smartphone or care robot can suggest "Shall I play some relaxing music?". In addition, using the emotion estimation function, a smartphone or care robot can ask an elderly person "Is there something you would like to talk to me about?" This allows for relaxing dialogue when an elderly person feels stressed, supporting their mental health.

[0067] A smartphone or care robot can collect health data from an elderly person and reflect it in the content of the conversation. For example, a smartphone or care robot can collect an elderly person's health data and ask, "Is your blood pressure okay?" if their blood pressure is high. Furthermore, based on the elderly person's health data, the smartphone or care robot's generative AI can ask, "Did you exercise today?" Furthermore, a smartphone or care robot can collect health data from an elderly person and ask, "How is your diet lately?" if their weight has increased. This makes it possible to adjust the content of the conversation based on the elderly person's health data and support their health management.

[0068] A smartphone or care robot can work in conjunction with an elderly person's family and convey messages from them through a generation AI. For example, a smartphone or care robot can receive a message from an elderly person's family and convey it through the generation AI, asking, "Mom, how are you?". A smartphone or care robot can also work in conjunction with an elderly person's family, with the generation AI speaking in the family's voice, asking, "Dad, what did you do today?" A smartphone or care robot can also receive a message from an elderly person's family and convey, through the generation AI, "Grandma, we'll come over to play sometime." This allows elderly people to receive messages from their families, reducing their sense of isolation and supporting their mental health.

[0069] A smartphone or a care robot can use its emotion estimation function to notify family members when an elderly person feels anxious. For example, using the emotion estimation function, a smartphone or a care robot can notify family members by saying, "Mom is feeling anxious" when an elderly person feels anxious. Also, when an elderly person feels anxious, the smartphone or a care robot's generation AI can notify family members by saying, "Dad is feeling anxious." Also, using the emotion estimation function, a smartphone or a care robot can notify family members by saying, "Grandma is feeling anxious" when an elderly person feels anxious. This allows family members to be notified when an elderly person feels anxious, enabling a prompt response.

[0070] The generative AI can learn not only the voices of family and friends, but also their speaking styles and catchphrases, allowing it to reproduce more natural conversations. For example, the generative AI can learn not only the voices of family and friends, but also their unique speaking styles and catchphrases, allowing it to reproduce more natural conversations. For example, it can reproduce the unique intonation of a family member. The generative AI can also learn the voices of elderly family members and friends, and speak in that voice, saying, "How is Mom doing?" The generative AI can also learn the speaking styles and catchphrases of family and friends, and provide conversations that incorporate their unique expressions. For example, it can reproduce phrases that family members often use. This allows it to reproduce the voices and speaking styles of family and friends, allowing it to provide more natural conversations.

[0071] The generation AI can learn the content of past conversations between family members or friends and recreate stories of memories. For example, the generation AI can learn the content of past conversations between family members and friends and recreate stories of memories by saying, "Do you remember? We talked about our trip last year." The generation AI can also say, "That birthday party was fun," based on the content of past conversations between an elderly person's family and friends. The generation AI can also learn the content of past conversations between family members and friends and recreate stories of memories by saying, "Let's talk while looking at old photos." This allows the generation AI to recreate stories with family and friends and provide conversations that evoke nostalgia.

[0072] Using its emotion estimation function, the generative AI can provide dialogue that makes the elderly feel nostalgic. For example, the generative AI might use its emotion estimation function to provide dialogue that makes the elderly feel nostalgic, such as by saying, "Let's talk about old friends." In addition, to provide dialogue that makes the elderly feel nostalgic, the generative AI might suggest, "Let's talk while looking at old photos." In addition, the generative AI might use its emotion estimation function to provide dialogue that makes the elderly feel nostalgic, such as by saying, "Let's talk while listening to old music." In this way, the generative AI can provide dialogue that makes the elderly feel nostalgic, supporting their mental health.

[0073] The generation AI can have a conversation while displaying photos or videos of family or friends. For example, while displaying a photo of family or friends, the generation AI might say, "This photo is from your trip last year." In addition, to allow elderly people to enjoy conversation while watching videos of family and friends, the generation AI might say, "This video is from your grandchild's sports day." In addition, while displaying photos or videos of family and friends, the generation AI might say, "This photo is from your mother's birthday." This allows for a more friendly conversation by having a conversation while displaying photos or videos of family and friends.

[0074] The generating AI can use the voices of family members or friends to provide daily reminders or notifications. For example, the generating AI can use a family member's voice to remind them, "Mom, it's time to take your medicine." Or, an elderly person can receive a notification in a friend's voice saying, "Today you have a doctor's appointment." The generating AI can also use a family member's voice to remind them, "Dad, today is the day to go for a walk." This allows reminders and notifications to be provided in a way that is familiar to the elderly, by using the voices of family members or friends.

[0075] The generation AI can use its emotion estimation function to start a conversation with the voice of a family member or friend when an elderly person feels lonely. For example, using the emotion estimation function, the generation AI can speak to the elderly person in the voice of a family member, asking, "Mom, how are you doing?" When an elderly person feels lonely, the generation AI can also speak to the elderly person in the voice of a friend, asking, "How are you doing lately?" When an elderly person feels lonely, the generation AI can also use the emotion estimation function to speak to the elderly person in the voice of a family member, asking, "Dad, is there anything you'd like to talk to me about?" In this way, when an elderly person feels lonely, they can start a conversation with the voice of a family member or friend, reducing their sense of isolation.

[0076] The generative AI can provide quizzes or puzzles that stimulate the cognitive functions of the elderly. For example, the generative AI may ask the elderly a quiz to train their memory, such as, "What did you have for dinner last night?" The generative AI may also provide puzzle games to stimulate the elderly's cognitive functions, such as a simple crossword puzzle. The generative AI may also ask the elderly calculation problems to stimulate their cognitive functions, such as, "What is 5 + 3?" This can stimulate the elderly's cognitive functions and contribute to the prevention of dementia.

[0077] The generative AI can stimulate memories by having conversations with elderly people that make them look back on past events. For example, the generative AI can ask an elderly person, "Tell me about your old travels," to stimulate their memory. The generative AI can also ask an elderly person, "Tell me about your childhood memories," to stimulate their memory. The generative AI can also ask an elderly person, "Tell me about an episode with an old friend," to stimulate their memory and help prevent dementia.

[0078] The generation AI can use its emotion estimation function to provide relaxation music or meditation guides that match the mood of the elderly. For example, using the emotion estimation function, the generation AI can provide relaxation music if the elderly is feeling stressed, for example by playing classical music. The generation AI can also provide meditation guides that match the elderly's mood, for example by guiding them to "take a deep breath and relax." The generation AI can also use its emotion estimation function to provide relaxation music if the elderly is feeling anxious, for example by playing the sounds of nature. This allows the generation AI to provide relaxation music or meditation guides that match the elderly's mood, supporting their mental health.

[0079] Generative AI can provide exercise guides to support the exercise habits of the elderly. For example, generative AI can guide the elderly through simple stretching exercises, for example by instructing them to "stretch your arms up." Generative AI can also provide walking guides to support the exercise habits of the elderly, for example by suggesting, "Try walking for 20 minutes today." Generative AI can also guide the elderly through exercises that can be done while sitting in a chair, for example by instructing them to "stretch your legs forward." This can support the exercise habits of the elderly and contribute to maintaining their health.

[0080] Generative AI can provide dietary or nutritional advice to the elderly. For example, generative AI can provide advice to the elderly on how to eat a balanced diet. For example, it might suggest, "Eat more vegetables." To support the elderly's nutritional status, generative AI might advise, "Drink plenty of fluids today." Generative AI can also provide healthy meal recipes to the elderly. For example, it might suggest, "Try making vegetable soup today." This can provide dietary and nutritional advice to the elderly, contributing to maintaining their health.

[0081] The generative AI can use its emotion estimation function to suggest relaxation exercises when the elderly feel stressed. For example, using its emotion estimation function, the generative AI can suggest relaxation exercises such as "Take a deep breath and relax" when the elderly feel stressed. The generative AI can also suggest "Try a yoga pose" when the elderly feel stressed. The generative AI can also use its emotion estimation function to suggest relaxation exercises such as "Meditate to calm your mind" when the elderly feel stressed. This makes it possible to suggest relaxation exercises when the elderly feel stressed and support their mental health.

[0082] Generative AI can monitor the health condition of elderly people in real time and automatically notify when abnormalities are detected. For example, generative AI can monitor the heart rate and blood pressure of elderly people in real time and notify family members when abnormalities are detected. Generative AI can also monitor the health condition of elderly people and notify medical institutions when body temperature is abnormally high. Generative AI can also monitor the health condition of elderly people in real time and notify care staff when abnormalities are detected. This allows for real-time monitoring of the health condition of elderly people and allows for rapid response when abnormalities are detected.

[0083] The generating AI can learn the lifestyle patterns of the elderly and notify family members or medical institutions if there are any abnormalities. For example, the generating AI can learn the lifestyle patterns of the elderly and notify family members if they do not wake up at their usual wake-up time. Furthermore, based on the elderly's lifestyle patterns, the generating AI can notify medical institutions if they do not eat at mealtimes. The generating AI can also learn the lifestyle patterns of the elderly and notify care staff if there are any abnormalities. This allows the generating AI to learn the lifestyle patterns of the elderly and respond quickly if there are any abnormalities.

[0084] The generation AI can use its emotion estimation function to monitor the emotional state of the elderly and notify family members if there is an abnormality. For example, the generation AI can use its emotion estimation function to notify family members if the elderly person has been feeling sad for a long period of time. The generation AI can also monitor the emotional state of the elderly and notify medical institutions if there is an abnormality. The generation AI can also use its emotion estimation function to notify care staff if the elderly person has been feeling anxious for a long period of time. This allows the emotional state of the elderly to be monitored and a prompt response to be made if there is an abnormality.

[0085] Generative AI can suggest activities based on the hobbies or interests of the elderly, improving their quality of life. For example, generative AI can suggest gardening activities based on the elderly's hobbies, suggesting, for example, "Plant some new flowers today." Generative AI can also suggest reading activities based on the elderly's interests, suggesting, for example, "Read this book today." Generative AI can also suggest cooking activities based on the elderly's hobbies and interests, suggesting, for example, "Try a new recipe today." This allows us to suggest activities based on the elderly's hobbies and interests, improving their quality of life.

[0086] Generative AI can provide online communities to support the social connections of the elderly. For example, generative AI can introduce elderly people to online communities based on their hobbies and interests. For example, it might suggest, "Why not join a community of gardening enthusiasts?" To support the social connections of elderly people, generative AI might suggest, "Why not join an online book club meeting?" Generative AI can also introduce elderly people to local online communities. For example, it might suggest, "Why not participate in a local event?" This can support the social connections of elderly people and reduce feelings of isolation.

[0087] The generative AI can use its emotion estimation function to suggest that elderly people join online communities when they feel lonely. For example, using its emotion estimation function, the generative AI can suggest, "Why not join an online book club?" when an elderly person feels lonely. Also, using its emotion estimation function, the generative AI can suggest, "Why not participate in a local online event?" when an elderly person feels lonely. Also, using its emotion estimation function, the generative AI can suggest, "Why not join an online hobby community?" when an elderly person feels lonely. In this way, the generative AI can suggest that elderly people join online communities when they feel lonely, thereby reducing their sense of isolation.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The elderly support system can further include a health management unit. The health management unit collects health data from the elderly and supports their daily health management. For example, it periodically measures their blood pressure and heart rate and notifies them of any abnormalities. The health management unit can also manage diet and exercise records and provide advice to promote a balanced lifestyle. Furthermore, the health management unit can link with medical institutions and support regular health checks and appointment scheduling. This provides comprehensive support for the elderly's health management and improves their quality of life.

[0090] The elderly support system can further include a reminder unit. The reminder unit notifies the elderly of important plans and tasks in their daily lives. For example, it can remind them to take their medicine or make an appointment at a medical institution. The reminder unit can also notify them of meal times and exercise times to support their daily routine. Furthermore, the reminder unit has a function for linking with family members and caregivers, allowing them to share important plans. This allows the elderly to go about their daily lives smoothly and provides a sense of security.

[0091] The elderly support system can further include an entertainment unit. The entertainment unit provides content that the elderly can enjoy. For example, it provides music, movie, and reading content to provide relaxation and enjoyment. The entertainment unit can also suggest activities based on hobbies and interests. For example, it can provide gardening and cooking recipes to support time spent enjoying hobbies. Furthermore, the entertainment unit can promote participation in online events and communities and support social connections. This can provide enjoyment and a sense of fulfillment in the lives of the elderly.

[0092] The elderly support system can also be equipped with a safety management unit. The safety management unit monitors the living environment of the elderly to ensure safety. For example, it has a fall detection function that quickly notifies the user if a fall occurs. The safety management unit can also detect abnormalities such as fires and gas leaks and prompt appropriate responses. Furthermore, the safety management unit has a GPS function to support safety when going out, and can share location information with family members and caregivers. This ensures the safety of the elderly, allowing them to live with peace of mind.

[0093] The elderly support system can further include a learning support unit. The learning support unit supports elderly people in learning new knowledge and skills. For example, it can provide online courses and learning programs, and offer learning opportunities based on hobbies and interests. The learning support unit can also provide quizzes and puzzles to stimulate cognitive functions. Furthermore, the learning support unit can support the improvement of digital literacy to promote communication with family and friends. This allows elderly people to enjoy lifelong learning and receive intellectual stimulation.

[0094] The elderly support system can also use its emotion estimation function to provide music and videos that correspond to the elderly person's emotional state. For example, if an elderly person is feeling stressed, it can provide relaxation music to encourage relaxation. If an elderly person is happy, it can provide fun videos and music to further improve their mood. Furthermore, if an elderly person is sad, it can provide comforting videos and music to help stabilize their mind. In this way, it is possible to provide appropriate content according to the elderly person's emotional state and support their mental health.

[0095] The elderly support system also uses emotion estimation functions to automatically contact family and friends when an elderly person feels lonely. For example, if an elderly person feels lonely, it can notify family members, saying, "Your mother is lonely," encouraging them to contact them. It can also send a message to friends, asking, "How are you doing lately?" Furthermore, when an elderly person feels lonely, it can suggest that they join an online community and support social connections. This allows for a quick response when an elderly person feels lonely and reduces their sense of isolation.

[0096] The elderly support system can also use its emotion estimation function to provide guidance to help the elderly relax when they feel anxious. For example, if an elderly person feels anxious, the system can guide them by saying, "Take a deep breath and relax." It can also suggest meditation or yoga poses to promote mental stability. Furthermore, when an elderly person feels anxious, it can play relaxation music to encourage relaxation. This allows the system to provide appropriate guidance when the elderly feel anxious and support their mental health.

[0097] The elderly support system can also use its emotion estimation function to suggest relaxation exercises when the elderly feel stressed. For example, if an elderly person feels stressed, it can suggest relaxation exercises such as "take a deep breath and relax." It can also suggest yoga poses or meditation to promote mental stability. Furthermore, when the elderly feel stressed, it can provide relaxation music to encourage relaxation. This makes it possible to suggest appropriate exercises when the elderly feel stressed and support their mental health.

[0098] The elderly support system also uses emotion estimation functionality to initiate a conversation with the voice of a family member or friend when the elderly person feels lonely. For example, if an elderly person feels lonely, it can speak to them in the voice of a family member, asking, "Mom, how are you?" It can also speak to them in the voice of a friend, asking, "How are you doing lately?" Furthermore, when an elderly person feels lonely, it can speak to them in the voice of a family member or friend, asking, "Is there anything you'd like to talk to me about?" This allows elderly people to start a conversation with the voice of a family member or friend when they feel lonely, helping to reduce their sense of isolation.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The generative AI provides everyday communication through dialogue with the elderly. For example, if an elderly person asks, "What's the weather like today?", the generative AI will respond, "It's sunny today, and the temperature is 20 degrees. It's a good day to go for a walk." The generative AI receives input from the elderly person, analyzes it, and generates an appropriate response, which is then transmitted via voice. Step 2: The smartphone provides an interface for interacting with the generative AI. For example, the user can converse with the generative AI through an app installed on the smartphone. Step 3: The care robot provides an interface for interacting with the generative AI, enabling more natural interactions. For example, elderly people can interact with the generative AI on a daily basis through the generative AI installed in the care robot.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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]

[0168] 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. Equipped with generative AI, The generative AI provides daily communication through dialogue with the elderly, The user can interact with the AI ​​through a smartphone or a care robot. A system characterized by:

2. The generated AI is Analyzing the tone of voice or manner of speaking of the elderly person to infer their emotional state and generate an appropriate response.

2. The system of claim 1.

3. The generated AI is Providing topics based on the elderly person's hobbies or interests to make conversation more enjoyable 2. The system of claim 1.

4. The smartphone or the care robot is Analyzing the movements or facial expressions of the elderly person and adjusting the content of the dialogue 2. The system of claim 1.

5. The generated AI is It not only learns the voices of family and friends, but also their speaking styles and catchphrases to reproduce more natural conversations.

2. The system of claim 1.

6. The generated AI is Providing a quiz or puzzle that stimulates the cognitive function of the elderly person 2. The system of claim 1.

7. The generated AI is By monitoring the health status of the elderly in real time and automatically notifying them when an abnormality is detected, 2. The system of claim 1.

8. The generated AI is By suggesting that the elderly join online communities when they feel lonely, 2. The system of claim 1.

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