System

Abstract

An object of a system according to an embodiment is to reduce the risk of dementia or lonely death of an elderly person living alone.SOLUTION: A system according to an embodiment includes a dialogue unit, a health monitoring unit, and a cognitive training unit. The interaction unit interacts with the elderly person on a daily basis. The health monitoring unit monitors the health condition of the elderly person on the basis of the information obtained by the interaction unit. The cognitive training unit maintains and improves the cognitive function of the elderly person based on the information obtained by the health monitoring unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not provide sufficient means to effectively reduce the risk of dementia and lonely death among elderly people living alone, and there is room for improvement.

[0005] The system according to the embodiment aims to reduce the risk of dementia and lonely death among elderly people living alone. [Means for solving the problem]

[0006] The system according to the embodiment includes a dialogue unit, a health monitoring unit, and a cognitive training unit. The dialogue unit dialogues with the elderly on a daily basis. The health monitoring unit monitors the health status of the elderly based on information obtained by the dialogue unit. The cognitive training unit maintains and improves the elderly's cognitive function based on the information obtained by the health monitoring unit. [Effects of the Invention]

[0007] The system according to the embodiment can reduce the risk of dementia and lonely death among elderly people living alone. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The AI ​​assistant system according to the embodiment of the present invention is a system that interacts with elderly people on a daily basis to maintain their cognitive function and reduce their sense of loneliness, thereby improving their quality of life.

[0029] The AI ​​assistant system according to the embodiment includes a dialogue unit, a health monitoring unit, and a cognitive training unit. The dialogue unit engages in daily dialogue with the elderly. For example, the dialogue unit stimulates the elderly's cognitive function through morning greetings, talking about the weather, sharing news, and so on. The dialogue unit also uses a generation AI (e.g., a text generation AI or a multimodal generation AI) to generate appropriate responses to input from the elderly (e.g., "What's the weather like today?") and responds by voice. The health monitoring unit monitors the elderly's health status based on information obtained by the dialogue unit. For example, the health monitoring unit checks the elderly's daily physical condition, dietary habits, medication status, etc., and notifies family members or medical institutions if any abnormalities are detected. The health monitoring unit also analyzes input from the elderly (e.g., "I have a headache today") and suggests necessary measures. The cognitive training unit maintains and improves the elderly's cognitive function based on the information obtained by the health monitoring unit. For example, the cognitive training unit provides quizzes, puzzles, memory-training games, and other activities to stimulate the elderly's brain. The cognitive training unit also uses a generation AI to provide appropriate content according to the user's cognitive function. As a result, the AI ​​assistant system according to the embodiment can maintain the cognitive function of the elderly, reduce loneliness, and monitor their health.

[0030] The dialogue unit can learn the elderly person's past dialogue history and provide personalized conversations tailored to each individual elderly person. For example, the dialogue unit, an AI assistant, analyzes the elderly person's past dialogue history and generates conversations based on the preferences and interests of each individual elderly person. For example, it may revisit hobbies or favorite foods that have been discussed in the past. The dialogue unit also provides topics appropriate for specific times of the day based on the elderly person's dialogue history. For example, it may select health-related topics in the morning and relaxing topics in the evening. The dialogue unit also learns the dialogue history and behaves as if it remembers what the elderly person has previously discussed. For example, it may revisit a travel topic from the previous conversation and continue the conversation. This improves the quality of dialogue by providing personalized conversations tailored to the elderly person.

[0031] The dialogue unit can suggest music and video content based on the hobbies and interests of the elderly person and share it during the dialogue. For example, the dialogue unit suggests music and video content based on the hobbies and interests of the elderly person and shares it during the dialogue. For example, the dialogue unit may introduce a new song by a favorite artist. The dialogue unit may also suggest movies and documentaries in genres that the elderly person is interested in and watch them together. For example, if the elderly person is interested in history, the dialogue unit may suggest historical documentaries. The dialogue unit may also suggest content related to the elderly person's hobby and discuss the content during the dialogue. For example, if gardening is a hobby, the dialogue unit may suggest gardening videos. In this way, by suggesting content based on the elderly person's hobbies and interests and sharing it during the dialogue, the quality of the dialogue can be improved.

[0032] The dialogue unit can hold a dialogue that elicits reminiscences based on places the elderly have visited in the past and events they have experienced. The dialogue unit holds a dialogue that elicits reminiscences based on places the elderly have visited in the past and events they have experienced, for example, by talking about travel destinations or childhood memories. The dialogue unit also elicits reminiscences by showing photos or videos related to past events. For example, the dialogue unit shows family photos and talks about family memories. The dialogue unit also talks about special events or happenings that the elderly experienced in the past, and shares emotions and memories from those times. For example, the dialogue unit talks about weddings or graduation ceremonies. In this way, the quality of the dialogue can be improved by eliciting reminiscences based on the elderly's past experiences.

[0033] The health monitoring unit can record the elderly person's dietary content using photographs, analyze nutritional balance, and provide advice. For example, the health monitoring unit allows the elderly person to take photos of their meals, and the AI ​​assistant analyzes the photos to evaluate nutritional balance. For example, if the elderly person is not consuming enough vegetables, the health monitoring unit can provide advice to increase their vegetable intake. The health monitoring unit also records meal content using photographs, and the AI ​​assistant analyzes nutritional balance based on past dietary data. For example, it can analyze the dietary content of the past week and suggest areas for improving nutritional balance. The health monitoring unit can also analyze meal photos and suggest meal plans based on nutritional balance. For example, if a specific nutrient is lacking, it can suggest ingredients that contain that nutrient. This allows the elderly person's dietary content to be recorded, nutritional balance analyzed, and advice provided, thereby supporting health management.

[0034] The health monitoring unit can monitor the sleep patterns of elderly people and suggest appropriate measures if any abnormalities are found. For example, the health monitoring unit monitors the sleep patterns of elderly people and suggests appropriate measures if any abnormalities are detected. For example, if the sleep time is short, it suggests relaxation methods. The health monitoring unit also analyzes sleep data and evaluates sleep quality. For example, if there is little deep sleep, it suggests improving the sleep environment. The health monitoring unit also monitors the sleep patterns of elderly people over a long period of time and suggests consulting a medical institution if any abnormalities persist. For example, if signs of a sleep disorder are found, it recommends consulting a specialist. In this way, health management can be supported by monitoring the sleep patterns of elderly people and suggesting appropriate measures if any abnormalities are found.

[0035] The health monitoring unit can record the elderly person's exercise status and suggest an appropriate exercise plan. For example, the health monitoring unit records the elderly person's exercise status, and the AI ​​assistant suggests an appropriate exercise plan. For example, the amount of exercise is evaluated based on the number of steps and exercise time, and an exercise plan is provided. The health monitoring unit also analyzes exercise data and suggests an exercise plan based on the elderly person's physical strength and health condition. For example, it suggests light stretching or walking. The health monitoring unit also monitors the elderly person's exercise status over a long period of time and evaluates the effectiveness of the exercise plan. For example, it suggests a new exercise plan if the amount of exercise increases. In this way, health management can be supported by recording the elderly person's exercise status and suggesting an appropriate exercise plan.

[0036] The health monitoring unit can provide a reminder function that checks the elderly person's medication status via voice and prevents them from forgetting to take their medication. For example, the health monitoring unit can check the elderly person's medication status via voice and provide a reminder function that prevents them from forgetting to take their medication. For example, it can set a voice reminder at the time to take medication every day. The health monitoring unit also records the medication status and provides a reminder for the AI ​​assistant to prevent them from forgetting to take their medication. For example, it can sound an alarm when it's time to take the medication. The health monitoring unit also monitors the elderly person's medication status and notifies family members or medical institutions if they continue to forget to take their medication. For example, it can notify them if they have forgotten to take their medication for three days or more. This makes it possible to support health management by checking the elderly person's medication status and providing a reminder function that prevents them from forgetting to take their medication.

[0037] The cognitive training unit can periodically evaluate changes in the cognitive function of the elderly person and provide an individually optimized training program. The cognitive training unit, for example, periodically evaluates the cognitive function of the elderly person and provides an individually optimized training program. For example, it suggests games to train memory and attention. The cognitive training unit also creates an individually optimized training program based on cognitive function evaluation data. For example, if a decline in cognitive function is observed, it strengthens specific training. The cognitive training unit also monitors the cognitive function of the elderly person over a long period of time and evaluates the effectiveness of the training program. For example, it adjusts the program according to the progress of the training. In this way, by periodically evaluating changes in the cognitive function of the elderly person and providing an individually optimized training program, it is possible to maintain and improve cognitive function.

[0038] The cognitive training unit can analyze the content of conversations between elderly people and issue an early warning if a decline in cognitive function is detected. The cognitive training unit, for example, analyzes the content of conversations between elderly people and builds a system that issues an early warning if a decline in cognitive function is detected. For example, it issues a warning if abnormalities are detected in word choice or speech flow. The cognitive training unit also analyzes the content of conversations and notifies family members or medical institutions if a decline in cognitive function is suspected. For example, it issues a notification if a decline in memory or confusion is detected. The cognitive training unit also monitors the content of conversations between elderly people over a long period of time and tracks changes in cognitive function. For example, it detects a decline in cognitive function based on changes in the frequency and content of conversations. This makes it possible to analyze the content of conversations between elderly people and issue an early warning if a decline in cognitive function is detected, enabling early response.

[0039] The cognitive training unit can share the progress of an elderly person's cognitive function training with family members and encourage support. For example, the cognitive training unit has an AI assistant record the progress of an elderly person's cognitive function training and share it with family members. For example, the cognitive training unit periodically reports the results and progress of the training. The cognitive training unit also builds a system that shares cognitive function training progress data with family members and encourages support. For example, family members can check the training progress and send encouraging messages. The cognitive training unit also shares the progress of an elderly person's cognitive function training with family members and evaluates the effectiveness of the training. For example, family members can monitor the training progress and provide support as needed. In this way, the effectiveness of the training can be improved by sharing the progress of an elderly person's cognitive function training with family members and encouraging support.

[0040] The cognitive training unit can promote social interaction by providing elderly people with a function that allows them to enjoy online conversations and games with other users. For example, the cognitive training unit provides elderly people with a function that allows them to enjoy online conversations and games with other users, thereby promoting social interaction. For example, the cognitive training unit allows elderly people to interact with other users online, thereby promoting social interaction. For example, the cognitive training unit provides multiplayer quiz and puzzle games. The cognitive training unit also builds a system that allows elderly people to enjoy online conversations and games with other users, thereby preventing social isolation. For example, the cognitive training unit holds regular online events. This allows elderly people to enjoy online conversations and games with other users, thereby promoting social interaction and reducing feelings of loneliness.

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

[0042] The AI ​​assistant system can also suggest music and video content based on the elderly person's hobbies and interests, and share it during the conversation. For example, it can introduce a new song by a favorite artist. The dialogue unit can also suggest movies and documentaries in genres that interest the elderly person and watch them together. For example, if the elderly person is interested in history, it can suggest historical documentaries. The dialogue unit can also suggest content related to hobbies and discuss the content during the conversation. For example, if gardening is a hobby, it can suggest gardening videos. In this way, the quality of the conversation can be improved by suggesting content based on the elderly person's hobbies and interests and sharing it during the conversation.

[0043] The AI ​​assistant system can also engage in dialogue that elicits reminiscences based on places the elderly have visited or events they have experienced in the past. For example, the system can talk about travel destinations or childhood memories. The dialogue unit can also elicit reminiscences by showing photos and videos related to past events. For example, the system can show family photos and talk about family memories. The dialogue unit can also talk about special events or moments that the elderly experienced in the past, sharing the emotions and memories they had at the time. For example, the system can talk about weddings or graduation ceremonies. This can improve the quality of the dialogue by eliciting reminiscences based on the elderly's past experiences.

[0044] The health monitoring unit can record the elderly person's dietary content using photographs, analyze nutritional balance, and provide advice. For example, the elderly person takes a photo of their meal, and the AI ​​assistant analyzes the photo to evaluate nutritional balance. For example, if the elderly person is not consuming enough vegetables, the AI ​​assistant can provide advice to increase their vegetable intake. The health monitoring unit also records meal content using photographs, and the AI ​​assistant analyzes nutritional balance based on past dietary data. For example, it can analyze the dietary content of the past week and suggest areas for improving nutritional balance. The health monitoring unit also analyzes meal photos and proposes a meal plan based on nutritional balance. For example, if a specific nutrient is lacking, it can suggest ingredients that contain that nutrient. This makes it possible to support health management by recording the elderly person's dietary content, analyzing nutritional balance, and providing advice.

[0045] The health monitoring unit can monitor the sleep patterns of elderly people and suggest appropriate measures if any abnormalities are found. For example, it can monitor the sleep patterns of elderly people and suggest appropriate measures if any abnormalities are detected. For example, if the sleep time is short, it can suggest relaxation methods. The health monitoring unit also analyzes sleep data and evaluates sleep quality. For example, if there is little deep sleep, it can suggest improvements to the sleep environment. The health monitoring unit can also monitor the sleep patterns of elderly people over a long period of time and suggest consulting a medical institution if any abnormalities persist. For example, if signs of a sleep disorder are found, it can recommend consulting a specialist. In this way, health management can be supported by monitoring the sleep patterns of elderly people and suggesting appropriate measures if any abnormalities are found.

[0046] The cognitive training unit can periodically evaluate changes in the cognitive function of the elderly person and provide an individually optimized training program. For example, the cognitive training unit can periodically evaluate the cognitive function of the elderly person and provide an individually optimized training program. For example, it can suggest games to train memory and attention. The cognitive training unit also creates an individually optimized training program based on the cognitive function evaluation data. For example, it can strengthen specific training if a decline in cognitive function is observed. The cognitive training unit also monitors the cognitive function of the elderly person over a long period of time and evaluates the effectiveness of the training program. For example, it can adjust the program according to the progress of the training. In this way, by periodically evaluating changes in the cognitive function of the elderly person and providing an individually optimized training program, it is possible to maintain and improve cognitive function.

[0047] The cognitive training unit can promote social interaction by providing a function for elderly people to enjoy online conversations and games with other users. For example, elderly people can interact with other users online through online chat or video calls. The cognitive training unit can also promote social interaction by providing a function for playing games with other users online. For example, multiplayer quiz or puzzle games can be provided. The cognitive training unit can also build a system that prevents social isolation by allowing elderly people to interact with other users online and enjoy games. For example, regular online events can be held. This allows elderly people to interact with other users online and enjoy games, promoting social interaction and reducing feelings of loneliness.

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

[0049] Step 1: The dialogue unit engages in daily conversations with the elderly. For example, the dialogue unit stimulates the elderly's cognitive functions through morning greetings, talking about the weather, sharing the news, etc. The dialogue unit also uses generative AI (e.g., text generation AI or multimodal generation AI) to generate appropriate responses to input from the elderly (e.g., "What's the weather like today?") and responds in voice. Step 2: The health monitoring unit monitors the elderly person's health condition based on the information obtained by the dialogue unit. For example, the health monitoring unit checks the elderly person's daily physical condition, dietary habits, medication status, etc., and notifies family members or medical institutions if any abnormalities are detected. The health monitoring unit also analyzes input from the elderly person (for example, "I have a headache today") and suggests necessary measures. Step 3: The cognitive training unit maintains and improves the elderly's cognitive function based on the information obtained by the health monitoring unit. For example, the cognitive training unit provides quizzes, puzzles, and memory training games to activate the elderly's brains. The cognitive training unit also uses generative AI to provide appropriate content according to the user's cognitive function.

[0050] (Example 2) The AI ​​assistant system according to the embodiment of the present invention is a system that interacts with elderly people on a daily basis to maintain their cognitive function and reduce their sense of loneliness, thereby improving their quality of life.

[0051] The AI ​​assistant system according to the embodiment includes a dialogue unit, a health monitoring unit, and a cognitive training unit. The dialogue unit engages in daily dialogue with the elderly. For example, the dialogue unit stimulates the elderly's cognitive function through morning greetings, talking about the weather, sharing news, and so on. The dialogue unit also uses a generation AI (e.g., a text generation AI or a multimodal generation AI) to generate appropriate responses to input from the elderly (e.g., "What's the weather like today?") and responds by voice. The health monitoring unit monitors the elderly's health status based on information obtained by the dialogue unit. For example, the health monitoring unit checks the elderly's daily physical condition, dietary habits, medication status, etc., and notifies family members or medical institutions if any abnormalities are detected. The health monitoring unit also analyzes input from the elderly (e.g., "I have a headache today") and suggests necessary measures. The cognitive training unit maintains and improves the elderly's cognitive function based on the information obtained by the health monitoring unit. For example, the cognitive training unit provides quizzes, puzzles, memory-training games, and other activities to stimulate the elderly's brain. The cognitive training unit also uses a generation AI to provide appropriate content according to the user's cognitive function. As a result, the AI ​​assistant system according to the embodiment can maintain the cognitive function of the elderly, reduce loneliness, and monitor their health.

[0052] The dialogue unit can learn the elderly person's past dialogue history and provide personalized conversations tailored to each individual elderly person. For example, the dialogue unit, an AI assistant, analyzes the elderly person's past dialogue history and generates conversations based on the preferences and interests of each individual elderly person. For example, it may revisit hobbies or favorite foods that have been discussed in the past. The dialogue unit also provides topics appropriate for specific times of the day based on the elderly person's dialogue history. For example, it may select health-related topics in the morning and relaxing topics in the evening. The dialogue unit also learns the dialogue history and behaves as if it remembers what the elderly person has previously discussed. For example, it may revisit a travel topic from the previous conversation and continue the conversation. This improves the quality of dialogue by providing personalized conversations tailored to the elderly person.

[0053] The dialogue unit analyzes changes in the elderly person's tone of voice and speaking style, detects changes in their emotions, and can engage in appropriate dialogue. For example, the dialogue unit analyzes changes in the elderly person's tone of voice and speaking style in real time to detect changes in their emotions. For example, if their voice sounds low, it will offer words of encouragement. The dialogue unit also analyzes changes in speaking speed and rhythm to detect signs of stress or fatigue. For example, if their speech becomes slower, it will suggest topics that will help them relax. The dialogue unit also estimates changes in their emotions based on changes in their tone of voice and engages in appropriate dialogue. For example, if their voice becomes brighter, it will continue with positive topics. In this way, it is possible to detect changes in the elderly person's emotions and engage in appropriate dialogue, thereby stabilizing their emotions.

[0054] The dialogue unit uses the emotion estimation function to generate dialogue content that corresponds to the emotional state of the elderly person and can elicit positive emotions. The dialogue unit, for example, uses the emotion estimation function to analyze the emotional state of the elderly person in real time and generate dialogue content that elicits positive emotions. For example, if the elderly person is feeling down, the dialogue unit provides a cheerful topic. The dialogue unit also adjusts the tone and content of the dialogue according to the elderly person's emotional state. For example, if the elderly person is feeling excited, the dialogue unit selects a topic that shares the elderly person's excitement. The dialogue unit also uses the emotion estimation function to select a topic that will relax the elderly person and engage in dialogue. For example, if the elderly person is feeling unstable, the dialogue unit provides a topic that will give them a sense of security. In this way, dialogue content that corresponds to the elderly person's emotional state can be generated to elicit positive emotions, thereby maintaining mental health.

[0055] The dialogue unit can suggest music and video content based on the hobbies and interests of the elderly person and share it during the dialogue. For example, the dialogue unit suggests music and video content based on the hobbies and interests of the elderly person and shares it during the dialogue. For example, the dialogue unit may introduce a new song by a favorite artist. The dialogue unit may also suggest movies and documentaries in genres that the elderly person is interested in and watch them together. For example, if the elderly person is interested in history, the dialogue unit may suggest historical documentaries. The dialogue unit may also suggest content related to the elderly person's hobby and discuss the content during the dialogue. For example, if gardening is a hobby, the dialogue unit may suggest gardening videos. In this way, by suggesting content based on the elderly person's hobbies and interests and sharing it during the dialogue, the quality of the dialogue can be improved.

[0056] The dialogue unit can hold a dialogue that elicits reminiscences based on places the elderly have visited in the past and events they have experienced. The dialogue unit holds a dialogue that elicits reminiscences based on places the elderly have visited in the past and events they have experienced, for example, by talking about travel destinations or childhood memories. The dialogue unit also elicits reminiscences by showing photos or videos related to past events. For example, the dialogue unit shows family photos and talks about family memories. The dialogue unit also talks about special events or happenings that the elderly experienced in the past, and shares emotions and memories from those times. For example, the dialogue unit talks about weddings or graduation ceremonies. In this way, the quality of the dialogue can be improved by eliciting reminiscences based on the elderly's past experiences.

[0057] The dialogue unit can use the emotion estimation function to select a topic that will most relax the elderly person and engage in a dialogue. The dialogue unit, for example, uses the emotion estimation function to select a topic that will most relax the elderly person and engage in a dialogue. For example, when the elderly person's emotions are stable, a topic that will make them feel relaxed is provided. The dialogue unit also analyzes the elderly person's emotional state and selects a topic that will make them feel relaxed. For example, when the elderly person's emotions are unstable, a topic that will give them a sense of security is provided. The dialogue unit also uses the emotion estimation function to select a topic that will create an environment where the elderly person can relax. For example, a topic about nature or a topic related to hobbies is provided. In this way, by selecting a topic that will relax the elderly person and engaging in a dialogue, it is possible to maintain their mental health.

[0058] The health monitoring unit can record the elderly person's dietary content using photographs, analyze nutritional balance, and provide advice. For example, the health monitoring unit allows the elderly person to take photos of their meals, and the AI ​​assistant analyzes the photos to evaluate nutritional balance. For example, if the elderly person is not consuming enough vegetables, the health monitoring unit can provide advice to increase their vegetable intake. The health monitoring unit also records meal content using photographs, and the AI ​​assistant analyzes nutritional balance based on past dietary data. For example, it can analyze the dietary content of the past week and suggest areas for improving nutritional balance. The health monitoring unit can also analyze meal photos and suggest meal plans based on nutritional balance. For example, if a specific nutrient is lacking, it can suggest ingredients that contain that nutrient. This allows the elderly person's dietary content to be recorded, nutritional balance analyzed, and advice provided, thereby supporting health management.

[0059] The health monitoring unit can monitor the sleep patterns of elderly people and suggest appropriate measures if any abnormalities are found. For example, the health monitoring unit monitors the sleep patterns of elderly people and suggests appropriate measures if any abnormalities are detected. For example, if the sleep time is short, it suggests relaxation methods. The health monitoring unit also analyzes sleep data and evaluates sleep quality. For example, if there is little deep sleep, it suggests improving the sleep environment. The health monitoring unit also monitors the sleep patterns of elderly people over a long period of time and suggests consulting a medical institution if any abnormalities persist. For example, if signs of a sleep disorder are found, it recommends consulting a specialist. In this way, health management can be supported by monitoring the sleep patterns of elderly people and suggesting appropriate measures if any abnormalities are found.

[0060] The health monitoring unit can use the emotion estimation function to analyze the stress level of the elderly person and suggest relaxation methods. For example, the health monitoring unit uses the emotion estimation function to analyze the stress level of the elderly person in real time and suggest relaxation methods. For example, if stress is high, it suggests deep breathing or meditation. The health monitoring unit also analyzes the emotional state of the elderly person and suggests relaxing activities if the stress level is high. For example, it suggests taking walks or spending more time on hobbies. The health monitoring unit also uses the emotion estimation function to monitor the stress level of the elderly person over a long period of time and suggests consulting a professional if stress continues. For example, it recommends receiving counseling. In this way, by analyzing the stress level of the elderly person and suggesting relaxation methods, mental health can be maintained.

[0061] The health monitoring unit can record the elderly person's exercise status and suggest an appropriate exercise plan. For example, the health monitoring unit records the elderly person's exercise status, and the AI ​​assistant suggests an appropriate exercise plan. For example, the amount of exercise is evaluated based on the number of steps and exercise time, and an exercise plan is provided. The health monitoring unit also analyzes exercise data and suggests an exercise plan based on the elderly person's physical strength and health condition. For example, it suggests light stretching or walking. The health monitoring unit also monitors the elderly person's exercise status over a long period of time and evaluates the effectiveness of the exercise plan. For example, it suggests a new exercise plan if the amount of exercise increases. In this way, health management can be supported by recording the elderly person's exercise status and suggesting an appropriate exercise plan.

[0062] The health monitoring unit can provide a reminder function that checks the elderly person's medication status via voice and prevents them from forgetting to take their medication. For example, the health monitoring unit can check the elderly person's medication status via voice and provide a reminder function that prevents them from forgetting to take their medication. For example, it can set a voice reminder at the time to take medication every day. The health monitoring unit also records the medication status and provides a reminder for the AI ​​assistant to prevent them from forgetting to take their medication. For example, it can sound an alarm when it's time to take the medication. The health monitoring unit also monitors the elderly person's medication status and notifies family members or medical institutions if they continue to forget to take their medication. For example, it can notify them if they have forgotten to take their medication for three days or more. This makes it possible to support health management by checking the elderly person's medication status and providing a reminder function that prevents them from forgetting to take their medication.

[0063] The health monitoring unit can use the emotion estimation function to provide health advice according to the mood of the elderly person. The health monitoring unit, for example, uses the emotion estimation function to provide health advice according to the mood of the elderly person. For example, if the elderly person is feeling depressed, it may suggest relaxation methods. The health monitoring unit also analyzes the emotional state of the elderly person and provides health advice according to the mood. For example, if the elderly person is feeling excited, it may suggest light exercise. The health monitoring unit also uses the emotion estimation function to provide dietary and exercise advice according to the elderly person's mood. For example, if the elderly person is feeling unstable, it may suggest a balanced diet. In this way, health management can be supported by providing health advice according to the elderly person's mood.

[0064] The cognitive training unit can periodically evaluate changes in the cognitive function of the elderly person and provide an individually optimized training program. The cognitive training unit, for example, periodically evaluates the cognitive function of the elderly person and provides an individually optimized training program. For example, it suggests games to train memory and attention. The cognitive training unit also creates an individually optimized training program based on cognitive function evaluation data. For example, if a decline in cognitive function is observed, it strengthens specific training. The cognitive training unit also monitors the cognitive function of the elderly person over a long period of time and evaluates the effectiveness of the training program. For example, it adjusts the program according to the progress of the training. In this way, by periodically evaluating changes in the cognitive function of the elderly person and providing an individually optimized training program, it is possible to maintain and improve cognitive function.

[0065] The cognitive training unit can analyze the content of conversations between elderly people and issue an early warning if a decline in cognitive function is detected. The cognitive training unit, for example, analyzes the content of conversations between elderly people and builds a system that issues an early warning if a decline in cognitive function is detected. For example, it issues a warning if abnormalities are detected in word choice or speech flow. The cognitive training unit also analyzes the content of conversations and notifies family members or medical institutions if a decline in cognitive function is suspected. For example, it issues a notification if a decline in memory or confusion is detected. The cognitive training unit also monitors the content of conversations between elderly people over a long period of time and tracks changes in cognitive function. For example, it detects a decline in cognitive function based on changes in the frequency and content of conversations. This makes it possible to analyze the content of conversations between elderly people and issue an early warning if a decline in cognitive function is detected, enabling early response.

[0066] The cognitive training unit can use the emotion estimation function to provide games that allow elderly people to train their cognitive functions while having fun. The cognitive training unit, for example, uses the emotion estimation function to provide games that allow elderly people to train their cognitive functions while having fun. For example, when emotions are high, it suggests a more difficult game. The cognitive training unit also analyzes the emotional state of the elderly and selects games that allow elderly people to train their cognitive functions while having fun. For example, when emotions are low, it suggests a game that allows them to relax. The cognitive training unit also uses the emotion estimation function to provide games that elderly people can enjoy and train their cognitive functions. For example, when emotions are stable, it suggests a game that trains concentration. In this way, by providing games that allow elderly people to train their cognitive functions while having fun, it is possible to maintain and improve cognitive functions.

[0067] The cognitive training unit can share the progress of an elderly person's cognitive function training with family members and encourage support. For example, the cognitive training unit has an AI assistant record the progress of an elderly person's cognitive function training and share it with family members. For example, the cognitive training unit periodically reports the results and progress of the training. The cognitive training unit also builds a system that shares cognitive function training progress data with family members and encourages support. For example, family members can check the training progress and send encouraging messages. The cognitive training unit also shares the progress of an elderly person's cognitive function training with family members and evaluates the effectiveness of the training. For example, family members can monitor the training progress and provide support as needed. In this way, the effectiveness of the training can be improved by sharing the progress of an elderly person's cognitive function training with family members and encouraging support.

[0068] The cognitive training unit can promote social interaction by providing elderly people with a function that allows them to enjoy online conversations and games with other users. For example, the cognitive training unit provides elderly people with a function that allows them to enjoy online conversations and games with other users, thereby promoting social interaction. For example, the cognitive training unit allows elderly people to interact with other users online, thereby promoting social interaction. For example, the cognitive training unit provides multiplayer quiz and puzzle games. The cognitive training unit also builds a system that allows elderly people to enjoy online conversations and games with other users, thereby preventing social isolation. For example, the cognitive training unit holds regular online events. This allows elderly people to enjoy online conversations and games with other users, thereby promoting social interaction and reducing feelings of loneliness.

[0069] The cognitive training unit can use the emotion estimation function to provide cognitive training based on topics that interest the elderly most. For example, the cognitive training unit uses the emotion estimation function to provide cognitive training based on topics that interest the elderly most. For example, it provides quizzes related to emotionally uplifting topics. The cognitive training unit also analyzes the emotional state of the elderly and selects cognitive training based on topics of interest. For example, it provides training to improve concentration when the emotions are stable. The cognitive training unit also uses the emotion estimation function to provide cognitive training based on topics that interest the elderly, allowing them to train their cognitive function while having fun. For example, it selects topics related to hobbies. In this way, by providing cognitive training based on topics that interest the elderly most, it is possible to maintain and improve cognitive function.

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

[0071] The AI ​​assistant system can also suggest music and video content based on the elderly person's hobbies and interests, and share it during the conversation. For example, it can introduce a new song by a favorite artist. The dialogue unit can also suggest movies and documentaries in genres that interest the elderly person and watch them together. For example, if the elderly person is interested in history, it can suggest historical documentaries. The dialogue unit can also suggest content related to hobbies and discuss the content during the conversation. For example, if gardening is a hobby, it can suggest gardening videos. In this way, the quality of the conversation can be improved by suggesting content based on the elderly person's hobbies and interests and sharing it during the conversation.

[0072] The AI ​​assistant system can also engage in dialogue that elicits reminiscences based on places the elderly have visited or events they have experienced in the past. For example, the system can talk about travel destinations or childhood memories. The dialogue unit can also elicit reminiscences by showing photos and videos related to past events. For example, the system can show family photos and talk about family memories. The dialogue unit can also talk about special events or moments that the elderly experienced in the past, sharing the emotions and memories they had at the time. For example, the system can talk about weddings or graduation ceremonies. This can improve the quality of the dialogue by eliciting reminiscences based on the elderly's past experiences.

[0073] The dialogue unit analyzes changes in the elderly person's tone of voice and speaking style, detects changes in their emotions, and can engage in appropriate dialogue. For example, it can analyze changes in the elderly person's tone of voice and speaking style in real time to detect changes in their emotions. For example, if their voice sounds low, it can offer words of encouragement. The dialogue unit can also analyze changes in speaking speed and rhythm to detect signs of stress or fatigue. For example, if their speech becomes slower, it can suggest topics that will help them relax. The dialogue unit can also estimate changes in emotions based on changes in voice tone and engage in appropriate dialogue. For example, if their voice becomes brighter, it can continue with positive topics. In this way, it is possible to detect changes in the elderly person's emotions and engage in appropriate dialogue, thereby stabilizing their emotions.

[0074] The dialogue unit uses the emotion estimation function to generate dialogue content that corresponds to the emotional state of the elderly person and can elicit positive emotions. For example, the emotion estimation function is used to analyze the emotional state of the elderly person in real time and generate dialogue content that elicits positive emotions. For example, if the elderly person is feeling down, cheerful topics are provided. The dialogue unit also adjusts the tone and content of the dialogue according to the elderly person's emotional state. For example, if the elderly person is feeling excited, a topic that shares the elderly person's excitement is selected. The dialogue unit also uses the emotion estimation function to select a topic that will help the elderly person relax and engage in dialogue. For example, if the elderly person is feeling unstable, a topic that gives a sense of security is provided. In this way, dialogue content that corresponds to the elderly person's emotional state can be generated to elicit positive emotions, thereby maintaining mental health.

[0075] The health monitoring unit can record the elderly person's dietary content using photographs, analyze nutritional balance, and provide advice. For example, the elderly person takes a photo of their meal, and the AI ​​assistant analyzes the photo to evaluate nutritional balance. For example, if the elderly person is not consuming enough vegetables, the AI ​​assistant can provide advice to increase their vegetable intake. The health monitoring unit also records meal content using photographs, and the AI ​​assistant analyzes nutritional balance based on past dietary data. For example, it can analyze the dietary content of the past week and suggest areas for improving nutritional balance. The health monitoring unit also analyzes meal photos and proposes a meal plan based on nutritional balance. For example, if a specific nutrient is lacking, it can suggest ingredients that contain that nutrient. This makes it possible to support health management by recording the elderly person's dietary content, analyzing nutritional balance, and providing advice.

[0076] The health monitoring unit can monitor the sleep patterns of elderly people and suggest appropriate measures if any abnormalities are found. For example, it can monitor the sleep patterns of elderly people and suggest appropriate measures if any abnormalities are detected. For example, if the sleep time is short, it can suggest relaxation methods. The health monitoring unit also analyzes sleep data and evaluates sleep quality. For example, if there is little deep sleep, it can suggest improvements to the sleep environment. The health monitoring unit can also monitor the sleep patterns of elderly people over a long period of time and suggest consulting a medical institution if any abnormalities persist. For example, if signs of a sleep disorder are found, it can recommend consulting a specialist. In this way, health management can be supported by monitoring the sleep patterns of elderly people and suggesting appropriate measures if any abnormalities are found.

[0077] The health monitoring unit can use the emotion estimation function to analyze the stress level of the elderly person and suggest relaxation methods. For example, the emotion estimation function can be used to analyze the stress level of the elderly person in real time and suggest relaxation methods. For example, if stress is high, deep breathing or meditation can be suggested. The health monitoring unit can also analyze the emotional state of the elderly person and, if the stress level is high, suggest relaxing activities. For example, it can suggest taking more walks or spending more time on hobbies. The health monitoring unit can also use the emotion estimation function to monitor the stress level of the elderly person over a long period of time and, if stress continues, suggest consulting a specialist. For example, it can recommend receiving counseling. In this way, by analyzing the stress level of the elderly person and suggesting relaxation methods, mental health can be maintained.

[0078] The cognitive training unit can periodically evaluate changes in the cognitive function of the elderly person and provide an individually optimized training program. For example, the cognitive training unit can periodically evaluate the cognitive function of the elderly person and provide an individually optimized training program. For example, it can suggest games to train memory and attention. The cognitive training unit also creates an individually optimized training program based on the cognitive function evaluation data. For example, it can strengthen specific training if a decline in cognitive function is observed. The cognitive training unit also monitors the cognitive function of the elderly person over a long period of time and evaluates the effectiveness of the training program. For example, it can adjust the program according to the progress of the training. In this way, by periodically evaluating changes in the cognitive function of the elderly person and providing an individually optimized training program, it is possible to maintain and improve cognitive function.

[0079] The cognitive training unit can use the emotion estimation function to provide games that allow elderly people to train their cognitive functions while having fun. For example, the emotion estimation function is used to provide games that allow elderly people to train their cognitive functions while having fun. For example, when emotions are high, a more difficult game is suggested. The cognitive training unit also analyzes the emotional state of the elderly and selects games that allow elderly people to train their cognitive functions while having fun. For example, when emotions are low, a relaxing game is suggested. The cognitive training unit also uses the emotion estimation function to provide games that elderly people can enjoy and train their cognitive functions. For example, when emotions are stable, a game that trains concentration is suggested. In this way, by providing games that allow elderly people to train their cognitive functions while having fun, it is possible to maintain and improve cognitive functions.

[0080] The cognitive training unit can promote social interaction by providing a function for elderly people to enjoy online conversations and games with other users. For example, elderly people can interact with other users online through online chat or video calls. The cognitive training unit can also promote social interaction by providing a function for playing games with other users online. For example, multiplayer quiz or puzzle games can be provided. The cognitive training unit can also build a system that prevents social isolation by allowing elderly people to interact with other users online and enjoy games. For example, regular online events can be held. This allows elderly people to interact with other users online and enjoy games, promoting social interaction and reducing feelings of loneliness.

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

[0082] Step 1: The dialogue unit engages in daily conversations with the elderly. For example, the dialogue unit stimulates the elderly's cognitive functions through morning greetings, talking about the weather, sharing the news, etc. The dialogue unit also uses generative AI (e.g., text generation AI or multimodal generation AI) to generate appropriate responses to input from the elderly (e.g., "What's the weather like today?") and responds in voice. Step 2: The health monitoring unit monitors the elderly person's health condition based on the information obtained by the dialogue unit. For example, the health monitoring unit checks the elderly person's daily physical condition, dietary habits, medication status, etc., and notifies family members or medical institutions if any abnormalities are detected. The health monitoring unit also analyzes input from the elderly person (for example, "I have a headache today") and suggests necessary measures. Step 3: The cognitive training unit maintains and improves the elderly's cognitive function based on the information obtained by the health monitoring unit. For example, the cognitive training unit provides quizzes, puzzles, and memory training games to activate the elderly's brains. The cognitive training unit also uses generative AI to provide appropriate content according to the user's cognitive function.

[0083] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0087] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0089] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

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

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

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

[0093] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0100] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

[0104] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

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

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

[0108] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0111] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0113] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0115] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0117] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0119] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

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

[0123] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0124] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0133] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0134] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0135] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0136] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0137] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0138] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0139] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0140] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0141] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0142] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0143] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0144] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0145] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0146] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0147] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0148] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0149] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0150] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. The dialogue department engages in daily dialogue with the elderly, a health monitoring unit that monitors the health condition of the elderly person based on the information obtained by the dialogue unit; and a cognitive training unit that maintains and improves the cognitive function of the elderly based on the information obtained by the health monitoring unit. A system characterized by:

2. The dialogue unit The system learns the elderly person's past conversation history and provides personalized conversations tailored to each individual elderly person.

2. The system of claim 1.

3. The dialogue unit Analyze changes in the elderly person's tone of voice and speaking style, detect changes in their emotions, and engage in appropriate dialogue.

2. The system of claim 1.

4. The dialogue unit Generate dialogue content that corresponds to the emotional state of the elderly person, and elicit positive emotions.

2. The system of claim 1.

5. The dialogue unit Propose music and video content based on the elderly person's hobbies and interests and share it through conversations 2. The system of claim 1.

6. The dialogue unit Conduct a conversation to elicit memories based on the places the elderly person has visited and events they have experienced in the past.

2. The system of claim 1.

7. The dialogue unit Select a topic that the elderly person finds most relaxing and engage in conversation.

2. The system of claim 1.

8. The health monitoring unit: The elderly person's diet is recorded with photographs, and nutritional balance is analyzed and advice is provided.

2. The system of claim 1.

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