system

Abstract

The system according to this embodiment aims to detect the degree of mental illness in elderly people living alone in real time and prevent the worsening of symptoms through appropriate dialogue. [Solution] The system according to the embodiment comprises a conversation unit, an analysis unit, a detection unit, and a dialogue unit. The conversation unit engages in voice conversation with the elderly person. The analysis unit analyzes the content of the conversation conducted by the conversation unit in real time. The detection unit detects the degree of mental illness based on the content analyzed by the analysis unit. The dialogue unit engages in dialogue to prevent the worsening of symptoms based on the degree of mental illness detected by the detection unit.

Description

Technical Field

[0004] ,

[0006] , , , ,

[0005] , , , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that the degree of mental illness of elderly people living alone has not been sufficiently detected in real time, and the deterioration of symptoms has not been prevented through appropriate conversations.

[0005] The system according to the embodiment aims to detect in real time the degree of mental illness of elderly people living alone and prevent the deterioration of symptoms through appropriate conversations.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a conversation unit, an analysis unit, a detection unit, and a dialogue unit. The conversation unit engages in voice conversation with the elderly person. The analysis unit analyzes the content of the conversation conducted by the conversation unit in real time. The detection unit detects the degree of mental illness based on the content analyzed by the analysis unit. The dialogue unit engages in dialogue to prevent the worsening of symptoms based on the degree of mental illness detected by the detection unit. [Effects of the Invention]

[0007] The system according to this embodiment can detect the severity of mental illness in elderly people living alone in real time and prevent the worsening of symptoms through appropriate dialogue. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The system according to an embodiment of the present invention is a system in which an AI agent converses with elderly people living alone via voice, instead of caregivers or care managers, analyzes their mental and emotional state, and detects the degree of mental illness such as dementia. This system uses the AI ​​agent to engage in dialogue in a way that prevents the worsening of the person's symptoms or improves them, thereby contributing to the prevention and improvement of illness. For example, the AI ​​agent converses with an elderly person living alone via voice. In this case, the AI ​​agent uses natural language processing technology to understand what the elderly person says and makes an appropriate response. For example, if the elderly person says, "The weather is nice today," the AI ​​agent might respond, "Yes, it might be nice to go for a walk today." Next, the AI ​​agent analyzes the content of the conversation in real time to grasp the mental and emotional state of the elderly person. For example, if the elderly person says, "I've been forgetting things a lot lately, and it's bothering me," the AI ​​agent records the statement and determines whether there are signs of dementia. Furthermore, the AI ​​agent detects the degree of the elderly person's mental illness and engages in dialogue in a way that prevents the symptoms from worsening or improves them. For example, if an elderly person says, "I've been feeling down lately," the AI ​​agent might suggest, "Why don't you try doing something fun to cheer yourself up?" This system allows elderly people to live healthy and independent lives, both physically and mentally, without becoming isolated. It also reduces the burden on family members and caregivers and promotes efficiency in care and medical settings. Ultimately, the goal is to create a sustainable society where technology and humans work in harmony, and everyone can live with peace of mind. This allows the system to analyze the mental and emotional state of elderly people, detect the degree of mental illnesses such as dementia, and engage in dialogue to prevent the worsening of symptoms.

[0029] The system according to this embodiment comprises a conversation unit, an analysis unit, a detection unit, and a dialogue unit. The conversation unit engages in voice conversation with the elderly person. The conversation unit can, for example, record what the elderly person says. For example, if the elderly person says, "The weather is nice today," the conversation unit might respond, "Yes, it might be nice to go for a walk today." For example, if the elderly person says, "I'm having trouble because I'm forgetting things a lot lately," the conversation unit can record that statement. For example, if the elderly person says, "I've been feeling down lately," the conversation unit might suggest, "Why don't you try doing something fun to cheer yourself up?" The analysis unit analyzes the content of the conversation conducted by the conversation unit in real time. For example, the analysis unit can analyze what the elderly person says and understand their mental and emotional state. For example, if the elderly person says, "I'm having trouble because I'm forgetting things a lot lately," the analysis unit analyzes that statement and determines whether there are signs of dementia. The analysis unit can analyze a statement, for example, if an elderly person says, "I've been feeling down lately," and understand their mental state. The detection unit detects the degree of mental illness based on the analysis by the analysis unit. The detection unit can determine, for example, whether there are signs of dementia. The detection unit can understand the mental state and detect the degree of mental illness. The detection unit can evaluate the degree of mental illness and engage in dialogue to prevent the symptoms from worsening. The dialogue unit engages in dialogue to prevent the symptoms from worsening based on the degree of mental illness detected by the detection unit. For example, if an elderly person says, "I've been feeling down lately," the dialogue unit might suggest, "Why don't you try doing something fun to cheer yourself up?" For example, if an elderly person says, "I've been forgetting things a lot lately," the dialogue unit might record the statement and determine whether there are signs of dementia. For example, if an elderly person says, "The weather is nice today," the dialogue unit might respond, "Yes, it might be nice to go for a walk today." This allows the system to analyze the mental and emotional state of elderly individuals, detect the severity of mental illnesses such as dementia, and engage in dialogue to prevent the worsening of symptoms.

[0030] The conversation unit engages in voice conversations with elderly individuals. For example, the conversation unit can record what the elderly person says. Specifically, it uses a high-sensitivity microphone and speech recognition technology to accurately capture the elderly person's statements and record them as text data. The conversation unit utilizes natural language processing technology to understand what the elderly person says and generate appropriate responses. For example, if an elderly person says, "The weather is nice today," the conversation unit analyzes the statement and not only provides information about the weather but also suggests activities such as taking a walk, allowing the conversation to continue naturally. Also, if an elderly person says, "I've been forgetting things a lot lately, and it's bothering me," the conversation unit records this statement and uses it for analysis in the subsequent analysis unit. The conversation unit can use emotion analysis technology to detect changes in the elderly person's emotions from their statements and provide appropriate responses. For example, if an elderly person says, "I've been feeling down lately," the conversation unit emotionally understands the statement and suggests, "Why don't you try doing something fun to cheer yourself up?" In this way, the conversation unit can achieve natural communication with the elderly and provide emotional support. Furthermore, the conversational unit can provide more consistent responses by saving the conversation history and referring to past statements. For example, it can improve the quality of the conversation by remembering what the elderly person said previously and providing related topics. This allows the conversational unit to build trust with the elderly person and provide a sense of security.

[0031] The analysis department analyzes the content of conversations conducted by the conversation department in real time. For example, the analysis department can analyze the statements of elderly people to understand their mental and emotional state. Specifically, the analysis department uses natural language processing technology to analyze the content of elderly people's statements in detail and understand the emotions and intentions behind them. For example, if an elderly person says, "I'm having trouble because I've been forgetting things a lot lately," the analysis department will analyze the statement and determine whether it is a sign of dementia. The analysis department analyzes not only the content of statements but also the frequency and patterns of statements to understand changes in mental state. For example, if an elderly person says, "I've been feeling down lately," the analysis department can analyze the statement and understand their mental state. The analysis department can refer to past conversation history and analyze changes and trends in the content of statements to understand long-term changes in mental state. Furthermore, the analysis department uses AI to classify the content of statements and assess the risk of mental illness. For example, it can detect specific keywords and phrases and evaluate the mental state based on them. This allows the analysis department to understand the mental state of elderly people in real time and provide information for appropriate responses. Furthermore, the analysis department can share the results of its analysis of speech content with other departments, strengthening the overall system coordination. This allows the analysis department to comprehensively understand the mental state of elderly individuals and provide a foundation for appropriate responses.

[0032] The detection unit detects the degree of mental illness based on the analysis performed by the analysis unit. Specifically, the detection unit assesses the risk of mental illness based on the data provided by the analysis unit. For example, to determine whether there are signs of dementia, it analyzes specific speech patterns and frequencies and calculates a risk score. The detection unit uses AI to analyze speech content and assess the risk of mental illness. For example, if an elderly person says, "I've been forgetting things a lot lately, and it's bothering me," the detection unit analyzes the statement and assesses the risk of dementia. The detection unit can grasp the mental state and detect the degree of mental illness. For example, if an elderly person says, "I've been feeling down lately," the detection unit analyzes the statement and assesses the risk of depression. The detection unit can assess the degree of mental illness and engage in dialogue to prevent the worsening of symptoms. For example, if a certain risk score is exceeded, it notifies the dialogue unit and instructs it to take appropriate action. This allows the detection unit to accurately grasp the mental state of elderly people and take appropriate action early. Furthermore, the detection unit can refer to past data and monitor changes in mental state over the long term. This allows the detection unit to continuously monitor the mental state of elderly individuals and detect abnormalities early.

[0033] The dialogue unit engages in conversations to prevent the worsening of symptoms based on the degree of mental illness detected by the detection unit. Specifically, the dialogue unit provides emotional support by engaging in appropriate conversations according to the elderly person's mental state. For example, if an elderly person says, "I've been feeling down lately," it might suggest, "Why don't you try doing something fun to cheer yourself up?" The dialogue unit uses AI to analyze what the elderly person says and generates appropriate responses. For example, if an elderly person says, "I've been forgetting things a lot lately, and it's bothering me," it records the statement and determines whether there are signs of dementia. Based on what the elderly person says, the dialogue unit can provide appropriate advice and suggestions. For example, if an elderly person says, "The weather is nice today," it might respond, "Yes, it might be nice to go for a walk today." This allows the dialogue unit to achieve natural communication with the elderly person and provide emotional support. Furthermore, the dialogue unit can refer to the conversation history and provide consistent responses based on past statements. This allows the dialogue unit to build trust with the elderly person and provide a sense of security. The dialogue unit can take appropriate action upon receiving notifications from the detection unit. For example, if a certain risk score is exceeded, the dialogue unit will alert the elderly person and provide the necessary support. This allows the dialogue unit to continuously support the elderly person's mental state and prevent the worsening of symptoms.

[0034] The conversation unit includes a recording unit that records the elderly person's statements. The conversation unit can, for example, record the elderly person's statements in audio format. The conversation unit can, for example, record the elderly person's statements in text format. The conversation unit can, for example, record the elderly person's statements in video format. This allows for later analysis and reference of the elderly person's statements. Some or all of the above-described processing in the recording unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recording unit can input the elderly person's statements into a generative AI and have the generative AI perform the conversion from audio data to text data.

[0035] The analysis unit includes a comprehension unit for understanding the mental state of elderly individuals. The analysis unit can, for example, analyze the statements of elderly individuals to understand their mental state. The analysis unit can also, for example, analyze the statements of elderly individuals using sentiment analysis technology to understand their mental state. The analysis unit can also, for example, observe the behavior of elderly individuals to understand their mental state. By understanding the mental state of elderly individuals, the degree of mental illness can be detected more accurately. Some or all of the above-described processes in the comprehension unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the comprehension unit can input the statements of elderly individuals into a generative AI and have the generative AI perform sentiment analysis.

[0036] The dialogue unit includes a suggestion unit that proposes improvements to symptoms. The dialogue unit can, for example, analyze the statements of elderly people and propose improvements to their symptoms. The dialogue unit can also, for example, suggest exercise recommendations based on the statements of elderly people. The dialogue unit can also, for example, suggest dietary improvements based on the statements of elderly people. In this way, by proposing improvements to symptoms, it is possible to support the improvement of mental illness in elderly people. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the statements of elderly people into a generative AI and have the generative AI execute suggestions for improving symptoms.

[0037] The conversation unit analyzes the elderly person's past conversation history and selects the optimal way to conduct the conversation. For example, the conversation unit can select topics of interest based on what the elderly person has talked about in the past. For example, the conversation unit can reduce stress by avoiding topics that the elderly person has avoided in the past. For example, the conversation unit can conduct the conversation by referring to the tone and pace of conversation that the elderly person has preferred in the past. In this way, the optimal way to conduct the conversation can be selected by analyzing the past conversation history. Some or all of the above processing in the conversation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the conversation unit can input the elderly person's past conversation history into a generative AI and have the generative AI select the optimal way to conduct the conversation.

[0038] The conversation unit automatically selects appropriate topics according to the content of the conversation, thereby facilitating the smooth progress of the conversation. For example, if an elderly person talks about the weather, the conversation unit can continue with seasonal topics. For example, if an elderly person talks about health, the conversation unit can provide health advice. For example, if an elderly person talks about hobbies, the conversation unit can provide topics related to hobbies. In this way, by selecting appropriate topics according to the content of the conversation, the conversation can proceed smoothly. Some or all of the above processing in the conversation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the conversation unit can input the elderly person's statements into a generative AI and have the generative AI select appropriate topics.

[0039] The conversation unit provides relevant topics during conversations based on the elderly person's living environment and daily activities. For example, if the elderly person is gardening, the conversation unit can provide topics related to gardening. If the elderly person is cooking, the conversation unit can discuss recipes and cooking tips. If the elderly person is taking a walk, the conversation unit can provide topics related to walking routes and nature. In this way, conversations can be stimulated by providing relevant topics based on the elderly person's living environment and daily activities. Some or all of the above processing in the conversation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the conversation unit can input information about the elderly person's living environment and daily activities into a generative AI and have the generative AI provide relevant topics.

[0040] The conversation unit selects topics based on the elderly person's hobbies and interests during a conversation, thereby stimulating the conversation. For example, if the elderly person likes music, the conversation unit can offer topics related to music. For example, if the elderly person likes reading, the conversation unit can offer topics related to books they have recently read. For example, if the elderly person likes sports, the conversation unit can bring up recent sports news. In this way, by selecting topics based on the elderly person's hobbies and interests, the conversation can be stimulated. Some or all of the above processing in the conversation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the conversation unit can input information about the elderly person's hobbies and interests into a generative AI and have the generative AI perform the topic selection.

[0041] The analysis unit analyzes the content of conversations along a timeline and tracks changes in mental state. For example, the analysis unit can analyze the content of elderly people's conversations on a daily basis and track changes in their mental state. For example, the analysis unit can analyze the content of elderly people's conversations on a weekly basis and track changes in their mental state. For example, the analysis unit can analyze the content of elderly people's conversations on a monthly basis and track changes in their mental state. In this way, changes in mental state can be tracked by analyzing the content of conversations along a timeline. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the content of elderly people's conversations into a generative AI and have the generative AI perform an analysis along a timeline.

[0042] The analysis unit extracts keywords from the conversation content and performs a detailed analysis of the mental state. For example, the analysis unit can extract keywords such as "sad" and "lonely" from the conversation of an elderly person and analyze their mental state. For example, the analysis unit can extract keywords such as "happy" and "joyful" from the conversation of an elderly person and analyze their mental state. For example, the analysis unit can extract keywords such as "tired" and "exhausted" from the conversation of an elderly person and analyze their mental state. This allows for a detailed analysis of the mental state by extracting keywords from the conversation content. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the conversation content of an elderly person into a generative AI and have the generative AI perform keyword extraction.

[0043] The analysis unit integrates the content of conversations with other data (e.g., health data, lifestyle data) and analyzes it. For example, the analysis unit can integrate the content of conversations with the health data of elderly people and analyze their mental state. For example, the analysis unit can integrate the content of conversations with the lifestyle data of elderly people and analyze their mental state. For example, the analysis unit can integrate the content of conversations with the exercise data of elderly people and analyze their mental state. By integrating the content of conversations with other data and analyzing it, the accuracy of the analysis of mental state can be improved. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the content of elderly people's conversations and other data into a generative AI and have the generative AI perform the integrated analysis.

[0044] The analysis unit analyzes the content of conversations based on regional and cultural backgrounds to deepen the understanding of mental states. For example, the analysis unit can analyze conversations considering the cultural background of the elderly person's region. For example, the analysis unit can analyze conversations considering the customs and traditions of the elderly person's region. For example, the analysis unit can analyze conversations considering the language and dialect of the elderly person's region. In this way, by analyzing the content of conversations based on regional and cultural backgrounds, a deeper understanding of mental states can be achieved. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the elderly person's conversation content into a generative AI and have the generative AI perform an analysis based on regional and cultural backgrounds.

[0045] The detection unit detects early signs of mental illness based on the content of conversations. For example, the detection unit can detect early signs such as "frequent forgetfulness" from the conversations of elderly people. For example, the detection unit can detect early signs such as "feeling depressed" from the conversations of elderly people. For example, the detection unit can detect early signs such as "difficulty sleeping" from the conversations of elderly people. This enables early intervention by detecting early signs of mental illness based on the content of conversations. Some or all of the above processing in the detection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the detection unit can input the content of an elderly person's conversation into a generative AI and have the generative AI perform the detection of early signs.

[0046] The detection unit monitors changes in the frequency and content of conversations to detect the progression of mental illness. For example, the detection unit can detect the progression of mental illness if the frequency of conversations of elderly people decreases. For example, the detection unit can detect the progression of mental illness if the content of conversations of elderly people changes. For example, the detection unit can detect the progression of mental illness if the tone of conversations of elderly people changes. In this way, the progression of mental illness can be detected by monitoring changes in the frequency and content of conversations. Some or all of the above processing in the detection unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the detection unit can input the content of conversations of elderly people into a generating AI and have the generating AI perform monitoring of changes in frequency and content.

[0047] The detection unit combines the content of conversations with other health data to comprehensively assess the risk of mental illness. For example, the detection unit can integrate the health data of elderly individuals with the content of conversations to assess the risk of mental illness. For example, the detection unit can integrate the lifestyle data of elderly individuals with the content of conversations to assess the risk of mental illness. For example, the detection unit can integrate the exercise data of elderly individuals with the content of conversations to assess the risk of mental illness. This allows for a more accurate risk assessment by comprehensively evaluating the risk of mental illness by combining the content of conversations with other health data. Some or all of the above processing in the detection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the detection unit can input the content of elderly individuals' conversations and other health data into a generative AI and have the generative AI perform the risk assessment.

[0048] The detection unit analyzes the content of conversations and classifies the type and severity of mental illnesses in detail. For example, the detection unit can classify the type and severity of dementia from the content of conversations of elderly people. For example, the detection unit can classify the type and severity of depression from the content of conversations of elderly people. For example, the detection unit can classify the type and severity of anxiety disorders from the content of conversations of elderly people. In this way, by analyzing the content of conversations, the type and severity of mental illnesses can be classified in detail. Some or all of the above processing in the detection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the detection unit can input the content of conversations of elderly people into a generative AI and have the generative AI perform the classification of the type and severity of mental illnesses.

[0049] The dialogue unit compares the content of the dialogue with past dialogue history and selects the optimal dialogue method. For example, the dialogue unit can select topics of interest based on what the elderly person has talked about in the past. For example, the dialogue unit can reduce stress by avoiding topics that the elderly person has avoided in the past. For example, the dialogue unit can guide the conversation by referring to the tone and pace of conversation that the elderly person has preferred in the past. By doing so, the optimal dialogue method can be selected by comparing it with past dialogue history. Some or all of the above processing in the dialogue unit may be performed using, for example, a generative AI, or without a generative AI. For example, the dialogue unit can input the elderly person's past dialogue history into a generative AI and have the generative AI select the optimal dialogue method.

[0050] The dialogue unit proposes and supports the implementation of an appropriate action plan based on the content of the dialogue. For example, if an elderly person says, "I've been forgetting things a lot lately," the dialogue unit can suggest taking notes as a habit. For example, if an elderly person says, "I've been feeling down lately," the dialogue unit can suggest spending time enjoying a hobby. For example, if an elderly person says, "I haven't been able to sleep lately," the dialogue unit can suggest ways to relax. In this way, by proposing an appropriate action plan based on the content of the dialogue, the dialogue unit can support the lives of elderly people. Some or all of the above processing in the dialogue unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the dialogue unit can input the elderly person's statements into a generative AI and have the generative AI execute the action plan proposal.

[0051] The dialogue unit provides relevant advice during the conversation based on the elderly person's living environment and daily activities. For example, if the elderly person is gardening, the dialogue unit can provide gardening advice. For example, if the elderly person is cooking, the dialogue unit can provide advice on recipes and cooking tips. For example, if the elderly person is taking a walk, the dialogue unit can provide advice on walking routes and nature. This allows for more engaging conversations by providing relevant advice based on the elderly person's living environment and daily activities. Some or all of the above processing in the dialogue unit may be performed using, for example, a generative AI, or without a generative AI. For example, the dialogue unit can input information about the elderly person's living environment and daily activities into a generative AI and have the generative AI provide relevant advice.

[0052] The dialogue unit, during a conversation, selects topics based on the elderly person's hobbies and interests to stimulate the conversation. For example, if the elderly person likes music, the dialogue unit can offer topics related to music. For example, if the elderly person likes reading, the dialogue unit can offer topics related to books they have recently read. For example, if the elderly person likes sports, the dialogue unit can bring up recent sports news. In this way, the conversation can be stimulated by selecting topics based on the elderly person's hobbies and interests. Some or all of the above processing in the dialogue unit may be performed using, for example, a generative AI, or without a generative AI. For example, the dialogue unit can input information about the elderly person's hobbies and interests into a generative AI and have the generative AI select the topics of conversation.

[0053] The recording unit records the content of conversations in detail so that they can be analyzed and referenced later. For example, the recording unit can record the content of conversations of elderly people in detail in text format. For example, the recording unit can record the content of conversations of elderly people in detail in audio format. For example, the recording unit can record the content of conversations of elderly people in detail in video format. This allows for detailed recording of conversation content, which can then be analyzed and referenced later. Some or all of the above-described processes in the recording unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recording unit can input the content of conversations of elderly people into a generative AI and have the generative AI perform detailed recording.

[0054] The recording unit integrates and stores the recorded data with other data (e.g., health data, lifestyle data). For example, the recording unit can integrate and store the health data and conversation content of an elderly person. For example, the recording unit can integrate and store the lifestyle data and conversation content of an elderly person. For example, the recording unit can integrate and store the exercise data and conversation content of an elderly person. This enables centralized data management by integrating and storing the recorded data with other data. Some or all of the above processing in the recording unit may be performed using, for example, a generation AI, or without a generation AI. For example, the recording unit can input the conversation content of an elderly person and other data into a generation AI and have the generation AI perform the integrated storage.

[0055] The comprehension unit comprehends the content of conversations along a timeline and tracks changes in mental state. For example, the comprehension unit can comprehend the content of an elderly person's conversations on a daily basis and track changes in their mental state. For example, the comprehension unit can comprehend the content of an elderly person's conversations on a weekly basis and track changes in their mental state. For example, the comprehension unit can comprehend the content of an elderly person's conversations on a monthly basis and track changes in their mental state. In this way, changes in mental state can be tracked by comprehending the content of conversations along a timeline. Some or all of the above processing in the comprehension unit may be performed using, for example, a generative AI, or without a generative AI. For example, the comprehension unit can input the content of an elderly person's conversations into a generative AI and have the generative AI perform comprehension along a timeline.

[0056] The comprehension unit understands the content of conversations by integrating it with other data (e.g., health data, lifestyle data). For example, the comprehension unit can integrate the content of conversations with the health data of elderly people. For example, the comprehension unit can integrate the content of conversations with the lifestyle data of elderly people. For example, the comprehension unit can integrate the content of conversations with the exercise data of elderly people. By integrating the content of conversations with other data, the accuracy of understanding the mental state can be improved. Some or all of the above processing in the comprehension unit may be performed using, for example, a generative AI, or without a generative AI. For example, the comprehension unit can input the content of elderly people's conversations and other data into a generative AI and have the generative AI perform the integrated comprehension.

[0057] The proposal unit compares the content of the proposal with past proposal history and selects the optimal proposal method. For example, the proposal unit can make the optimal proposal based on proposals that the elderly person has accepted in the past. For example, the proposal unit can avoid proposals that the elderly person has rejected in the past, thereby reducing stress. For example, the proposal unit can make proposals by referring to the content and methods of proposals that the elderly person has preferred in the past. This allows the optimal proposal method to be selected by comparing it with past proposal history. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input the elderly person's past proposal history into a generative AI and have the generative AI select the optimal proposal method.

[0058] The suggestion unit provides relevant advice based on the elderly person's living environment and daily activities when making a suggestion. For example, if the elderly person is gardening, the suggestion unit can provide gardening advice. For example, if the elderly person is cooking, the suggestion unit can provide advice on recipes and cooking tips. For example, if the elderly person is taking a walk, the suggestion unit can provide advice on walking routes and nature. In this way, suggestions can be made more effective by providing relevant advice based on the elderly person's living environment and daily activities. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input information about the elderly person's living environment and daily activities into a generative AI and have the generative AI provide relevant advice.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The conversational unit can estimate the hobbies and interests of elderly individuals based on the content of the conversation and adjust the conversation accordingly. For example, if an elderly person shows interest in music, the conversational unit can offer topics related to music. Similarly, if an elderly person shows interest in gardening, the conversational unit can offer topics related to gardening. Furthermore, if an elderly person shows interest in travel, the conversational unit can offer topics related to travel. By adjusting the conversation content based on the hobbies and interests of elderly individuals, more engaging conversations become possible.

[0061] The analysis department can identify stressors in the daily lives of elderly individuals based on their statements and provide advice to reduce stress. For example, if an elderly person says, "Housework has been difficult lately," the analysis department can analyze the statement and identify housework as a stressor. Similarly, if an elderly person says, "The noise from the neighbors bothers me," the analysis department can analyze the statement and identify the noise as a stressor. Furthermore, if an elderly person says, "I feel lonely when I'm alone," the analysis department can analyze the statement and identify loneliness as a stressor. By identifying stressors in daily life and providing advice to reduce stress, the department can support the mental health of elderly individuals.

[0062] The detection unit can assess the risk of social isolation based on what the elderly person says and notify caregivers or family members as needed. For example, if an elderly person says, "I haven't talked to anyone lately," the detection unit can analyze the statement and assess the risk of social isolation as high. Similarly, if an elderly person says, "I'm lonely because I don't have any friends," the detection unit can analyze the statement and assess the risk of social isolation as high. Furthermore, if an elderly person says, "It's a hassle to go out," the detection unit can analyze the statement and assess the risk of social isolation as high. In this way, by assessing the risk of social isolation and notifying caregivers or family members as needed, it is possible to maintain the social connections of the elderly.

[0063] The dialogue function can provide advice on health management in daily life based on what the elderly person says. For example, if an elderly person says, "I haven't been getting enough exercise lately," the dialogue function can analyze the statement, explain the importance of exercise, and suggest simple exercises. If an elderly person says, "My diet is unbalanced," the dialogue function can analyze the statement, explain the importance of a balanced diet, and suggest specific meals. Furthermore, if an elderly person says, "I'm not getting enough sleep," the dialogue function can analyze the statement and provide advice on how to get quality sleep. In this way, the system can support the health of the elderly by providing advice on health management in daily life.

[0064] The recording unit can record the content of conversations in detail, allowing for later analysis and reference. For example, it can record the content of conversations with elderly people in detail in text format. It can also record the content of conversations with elderly people in detail in audio format. Furthermore, it can record the content of conversations with elderly people in detail in video format. This allows for detailed recording of conversation content, which can then be analyzed and referenced later.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The conversation unit engages in voice conversations with the elderly person. For example, if the elderly person says, "The weather is nice today," the unit responds, "Yes, it might be nice to go for a walk today." Also, if the elderly person says, "I'm having trouble with my memory lately," the unit records that statement, and if they say, "I've been feeling down lately," the unit can suggest, "Why don't you try doing something fun to cheer yourself up?" Step 2: The analysis unit analyzes the content of conversations conducted by the conversation unit in real time. For example, it can analyze the statements of elderly people to understand their mental and emotional state. If someone says, "I've been forgetting things a lot lately, and it's bothering me," the analysis unit will determine whether there are signs of dementia. Similarly, if someone says, "I've been feeling down lately," the analysis unit will understand their mental state. Step 3: The detection unit detects the degree of mental illness based on the analysis performed by the analysis unit. For example, it can determine whether there are signs of dementia, assess the mental state, and detect the degree of mental illness. Step 4: The dialogue unit engages in conversation to prevent the worsening of symptoms based on the degree of mental illness detected by the detection unit. For example, if an elderly person says, "I've been feeling down lately," it suggests, "Why don't you try doing something fun to cheer yourself up?" Also, if an elderly person says, "I've been forgetting things a lot lately, and it's bothering me," it records the statement and determines whether it is a sign of dementia. Furthermore, if an elderly person says, "The weather is nice today," it responds, "Yes, it might be nice to go for a walk today."

[0067] (Example of form 2) The system according to an embodiment of the present invention is a system in which an AI agent converses with elderly people living alone via voice, instead of caregivers or care managers, analyzes their mental and emotional state, and detects the degree of mental illness such as dementia. This system uses the AI ​​agent to engage in dialogue in a way that prevents the worsening of the person's symptoms or improves them, thereby contributing to the prevention and improvement of illness. For example, the AI ​​agent converses with an elderly person living alone via voice. In this case, the AI ​​agent uses natural language processing technology to understand what the elderly person says and makes an appropriate response. For example, if the elderly person says, "The weather is nice today," the AI ​​agent might respond, "Yes, it might be nice to go for a walk today." Next, the AI ​​agent analyzes the content of the conversation in real time to grasp the mental and emotional state of the elderly person. For example, if the elderly person says, "I've been forgetting things a lot lately, and it's bothering me," the AI ​​agent records the statement and determines whether there are signs of dementia. Furthermore, the AI ​​agent detects the degree of the elderly person's mental illness and engages in dialogue in a way that prevents the symptoms from worsening or improves them. For example, if an elderly person says, "I've been feeling down lately," the AI ​​agent might suggest, "Why don't you try doing something fun to cheer yourself up?" This system allows elderly people to live healthy and independent lives, both physically and mentally, without becoming isolated. It also reduces the burden on family members and caregivers and promotes efficiency in care and medical settings. Ultimately, the goal is to create a sustainable society where technology and humans work in harmony, and everyone can live with peace of mind. This allows the system to analyze the mental and emotional state of elderly people, detect the degree of mental illnesses such as dementia, and engage in dialogue to prevent the worsening of symptoms.

[0068] The system according to this embodiment comprises a conversation unit, an analysis unit, a detection unit, and a dialogue unit. The conversation unit engages in voice conversation with the elderly person. The conversation unit can, for example, record what the elderly person says. For example, if the elderly person says, "The weather is nice today," the conversation unit might respond, "Yes, it might be nice to go for a walk today." For example, if the elderly person says, "I'm having trouble because I'm forgetting things a lot lately," the conversation unit can record that statement. For example, if the elderly person says, "I've been feeling down lately," the conversation unit might suggest, "Why don't you try doing something fun to cheer yourself up?" The analysis unit analyzes the content of the conversation conducted by the conversation unit in real time. For example, the analysis unit can analyze what the elderly person says and understand their mental and emotional state. For example, if the elderly person says, "I'm having trouble because I'm forgetting things a lot lately," the analysis unit analyzes that statement and determines whether there are signs of dementia. The analysis unit can analyze a statement, for example, if an elderly person says, "I've been feeling down lately," and understand their mental state. The detection unit detects the degree of mental illness based on the analysis by the analysis unit. The detection unit can determine, for example, whether there are signs of dementia. The detection unit can understand the mental state and detect the degree of mental illness. The detection unit can evaluate the degree of mental illness and engage in dialogue to prevent the symptoms from worsening. The dialogue unit engages in dialogue to prevent the symptoms from worsening based on the degree of mental illness detected by the detection unit. For example, if an elderly person says, "I've been feeling down lately," the dialogue unit might suggest, "Why don't you try doing something fun to cheer yourself up?" For example, if an elderly person says, "I've been forgetting things a lot lately," the dialogue unit might record the statement and determine whether there are signs of dementia. For example, if an elderly person says, "The weather is nice today," the dialogue unit might respond, "Yes, it might be nice to go for a walk today." This allows the system to analyze the mental and emotional state of elderly individuals, detect the severity of mental illnesses such as dementia, and engage in dialogue to prevent the worsening of symptoms.

[0069] The conversation unit engages in voice conversations with elderly individuals. For example, the conversation unit can record what the elderly person says. Specifically, it uses a high-sensitivity microphone and speech recognition technology to accurately capture the elderly person's statements and record them as text data. The conversation unit utilizes natural language processing technology to understand what the elderly person says and generate appropriate responses. For example, if an elderly person says, "The weather is nice today," the conversation unit analyzes the statement and not only provides information about the weather but also suggests activities such as taking a walk, allowing the conversation to continue naturally. Also, if an elderly person says, "I've been forgetting things a lot lately, and it's bothering me," the conversation unit records this statement and uses it for analysis in the subsequent analysis unit. The conversation unit can use emotion analysis technology to detect changes in the elderly person's emotions from their statements and provide appropriate responses. For example, if an elderly person says, "I've been feeling down lately," the conversation unit emotionally understands the statement and suggests, "Why don't you try doing something fun to cheer yourself up?" In this way, the conversation unit can achieve natural communication with the elderly and provide emotional support. Furthermore, the conversational unit can provide more consistent responses by saving the conversation history and referring to past statements. For example, it can improve the quality of the conversation by remembering what the elderly person said previously and providing related topics. This allows the conversational unit to build trust with the elderly person and provide a sense of security.

[0070] The analysis department analyzes the content of conversations conducted by the conversation department in real time. For example, the analysis department can analyze the statements of elderly people to understand their mental and emotional state. Specifically, the analysis department uses natural language processing technology to analyze the content of elderly people's statements in detail and understand the emotions and intentions behind them. For example, if an elderly person says, "I'm having trouble because I've been forgetting things a lot lately," the analysis department will analyze the statement and determine whether it is a sign of dementia. The analysis department analyzes not only the content of statements but also the frequency and patterns of statements to understand changes in mental state. For example, if an elderly person says, "I've been feeling down lately," the analysis department can analyze the statement and understand their mental state. The analysis department can refer to past conversation history and analyze changes and trends in the content of statements to understand long-term changes in mental state. Furthermore, the analysis department uses AI to classify the content of statements and assess the risk of mental illness. For example, it can detect specific keywords and phrases and evaluate the mental state based on them. This allows the analysis department to understand the mental state of elderly people in real time and provide information for appropriate responses. Furthermore, the analysis department can share the results of its analysis of speech content with other departments, strengthening the overall system coordination. This allows the analysis department to comprehensively understand the mental state of elderly individuals and provide a foundation for appropriate responses.

[0071] The detection unit detects the degree of mental illness based on the analysis performed by the analysis unit. Specifically, the detection unit assesses the risk of mental illness based on the data provided by the analysis unit. For example, to determine whether there are signs of dementia, it analyzes specific speech patterns and frequencies and calculates a risk score. The detection unit uses AI to analyze speech content and assess the risk of mental illness. For example, if an elderly person says, "I've been forgetting things a lot lately, and it's bothering me," the detection unit analyzes the statement and assesses the risk of dementia. The detection unit can grasp the mental state and detect the degree of mental illness. For example, if an elderly person says, "I've been feeling down lately," the detection unit analyzes the statement and assesses the risk of depression. The detection unit can assess the degree of mental illness and engage in dialogue to prevent the worsening of symptoms. For example, if a certain risk score is exceeded, it notifies the dialogue unit and instructs it to take appropriate action. This allows the detection unit to accurately grasp the mental state of elderly people and take appropriate action early. Furthermore, the detection unit can refer to past data and monitor changes in mental state over the long term. This allows the detection unit to continuously monitor the mental state of elderly individuals and detect abnormalities early.

[0072] The dialogue unit engages in conversations to prevent the worsening of symptoms based on the degree of mental illness detected by the detection unit. Specifically, the dialogue unit provides emotional support by engaging in appropriate conversations according to the elderly person's mental state. For example, if an elderly person says, "I've been feeling down lately," it might suggest, "Why don't you try doing something fun to cheer yourself up?" The dialogue unit uses AI to analyze what the elderly person says and generates appropriate responses. For example, if an elderly person says, "I've been forgetting things a lot lately, and it's bothering me," it records the statement and determines whether there are signs of dementia. Based on what the elderly person says, the dialogue unit can provide appropriate advice and suggestions. For example, if an elderly person says, "The weather is nice today," it might respond, "Yes, it might be nice to go for a walk today." This allows the dialogue unit to achieve natural communication with the elderly person and provide emotional support. Furthermore, the dialogue unit can refer to the conversation history and provide consistent responses based on past statements. This allows the dialogue unit to build trust with the elderly person and provide a sense of security. The dialogue unit can take appropriate action upon receiving notifications from the detection unit. For example, if a certain risk score is exceeded, the dialogue unit will alert the elderly person and provide the necessary support. This allows the dialogue unit to continuously support the elderly person's mental state and prevent the worsening of symptoms.

[0073] The conversation unit includes a recording unit that records the elderly person's statements. The conversation unit can, for example, record the elderly person's statements in audio format. The conversation unit can, for example, record the elderly person's statements in text format. The conversation unit can, for example, record the elderly person's statements in video format. This allows for later analysis and reference of the elderly person's statements. Some or all of the above-described processing in the recording unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recording unit can input the elderly person's statements into a generative AI and have the generative AI perform the conversion from audio data to text data.

[0074] The analysis unit includes a comprehension unit for understanding the mental state of elderly individuals. The analysis unit can, for example, analyze the statements of elderly individuals to understand their mental state. The analysis unit can also, for example, analyze the statements of elderly individuals using sentiment analysis technology to understand their mental state. The analysis unit can also, for example, observe the behavior of elderly individuals to understand their mental state. By understanding the mental state of elderly individuals, the degree of mental illness can be detected more accurately. Some or all of the above-described processes in the comprehension unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the comprehension unit can input the statements of elderly individuals into a generative AI and have the generative AI perform sentiment analysis.

[0075] The dialogue unit includes a suggestion unit that proposes improvements to symptoms. The dialogue unit can, for example, analyze the statements of elderly people and propose improvements to their symptoms. The dialogue unit can also, for example, suggest exercise recommendations based on the statements of elderly people. The dialogue unit can also, for example, suggest dietary improvements based on the statements of elderly people. In this way, by proposing improvements to symptoms, it is possible to support the improvement of mental illness in elderly people. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the statements of elderly people into a generative AI and have the generative AI execute suggestions for improving symptoms.

[0076] The conversation unit estimates the emotions of the elderly person and adjusts the tone and content of the conversation based on the estimated emotions. For example, if the elderly person is sad, the conversation unit can offer words of encouragement in a gentle tone. For example, if the elderly person is agitated, the conversation unit can speak in a calm tone to help them calm down. For example, if the elderly person is tired, the conversation unit can engage in short, concise conversation to reduce their burden. By adjusting the tone and content of the conversation according to the elderly person's emotions, more appropriate dialogue becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the conversation unit may be performed using a generative AI, or not using a generative AI. For example, the conversation unit can input the elderly person's statements into a generative AI and have the generative AI perform emotion estimation.

[0077] The conversation unit analyzes the elderly person's past conversation history and selects the optimal way to conduct the conversation. For example, the conversation unit can select topics of interest based on what the elderly person has talked about in the past. For example, the conversation unit can reduce stress by avoiding topics that the elderly person has avoided in the past. For example, the conversation unit can conduct the conversation by referring to the tone and pace of conversation that the elderly person has preferred in the past. In this way, the optimal way to conduct the conversation can be selected by analyzing the past conversation history. Some or all of the above processing in the conversation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the conversation unit can input the elderly person's past conversation history into a generative AI and have the generative AI select the optimal way to conduct the conversation.

[0078] The conversation unit automatically selects appropriate topics according to the content of the conversation, thereby facilitating the smooth progress of the conversation. For example, if an elderly person talks about the weather, the conversation unit can continue with seasonal topics. For example, if an elderly person talks about health, the conversation unit can provide health advice. For example, if an elderly person talks about hobbies, the conversation unit can provide topics related to hobbies. In this way, by selecting appropriate topics according to the content of the conversation, the conversation can proceed smoothly. Some or all of the above processing in the conversation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the conversation unit can input the elderly person's statements into a generative AI and have the generative AI select appropriate topics.

[0079] The conversation unit estimates the emotions of the elderly person and adjusts the frequency of conversation based on the estimated emotions. For example, if the elderly person is feeling lonely, the conversation unit can talk to them more frequently. For example, if the elderly person is feeling stressed, the conversation unit can reduce the frequency of conversation to help them relax. For example, if the elderly person is feeling energetic, the conversation unit can talk at an appropriate frequency. By adjusting the frequency of conversation according to the elderly person's emotions, more appropriate dialogue becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the conversation unit may be performed using a generative AI, or not using a generative AI. For example, the conversation unit can input the elderly person's statements into a generative AI and have the generative AI perform emotion estimation.

[0080] The conversation unit provides relevant topics during conversations based on the elderly person's living environment and daily activities. For example, if the elderly person is gardening, the conversation unit can provide topics related to gardening. If the elderly person is cooking, the conversation unit can discuss recipes and cooking tips. If the elderly person is taking a walk, the conversation unit can provide topics related to walking routes and nature. In this way, conversations can be stimulated by providing relevant topics based on the elderly person's living environment and daily activities. Some or all of the above processing in the conversation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the conversation unit can input information about the elderly person's living environment and daily activities into a generative AI and have the generative AI provide relevant topics.

[0081] The conversation unit selects topics based on the elderly person's hobbies and interests during a conversation, thereby stimulating the conversation. For example, if the elderly person likes music, the conversation unit can offer topics related to music. For example, if the elderly person likes reading, the conversation unit can offer topics related to books they have recently read. For example, if the elderly person likes sports, the conversation unit can bring up recent sports news. In this way, by selecting topics based on the elderly person's hobbies and interests, the conversation can be stimulated. Some or all of the above processing in the conversation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the conversation unit can input information about the elderly person's hobbies and interests into a generative AI and have the generative AI perform the topic selection.

[0082] The analysis unit estimates the emotions of elderly individuals and improves the accuracy of the analysis of their mental state based on the estimated emotions. For example, if an elderly person is sad, the analysis unit can take that emotion into account and analyze their mental state in detail. For example, if an elderly person is excited, the analysis unit can take that emotion into account and analyze their mental state in detail. For example, if an elderly person is tired, the analysis unit can take that emotion into account and analyze their mental state in detail. This improves the accuracy of the analysis by analyzing the mental state based on the emotions of elderly individuals. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, for example, or without a generative AI. For example, the analysis unit can input the elderly person's statements into a generative AI and have the generative AI perform emotion estimation.

[0083] The analysis unit analyzes the content of conversations along a timeline and tracks changes in mental state. For example, the analysis unit can analyze the content of elderly people's conversations on a daily basis and track changes in their mental state. For example, the analysis unit can analyze the content of elderly people's conversations on a weekly basis and track changes in their mental state. For example, the analysis unit can analyze the content of elderly people's conversations on a monthly basis and track changes in their mental state. In this way, changes in mental state can be tracked by analyzing the content of conversations along a timeline. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the content of elderly people's conversations into a generative AI and have the generative AI perform an analysis along a timeline.

[0084] The analysis unit extracts keywords from the conversation content and performs a detailed analysis of the mental state. For example, the analysis unit can extract keywords such as "sad" and "lonely" from the conversation of an elderly person and analyze their mental state. For example, the analysis unit can extract keywords such as "happy" and "joyful" from the conversation of an elderly person and analyze their mental state. For example, the analysis unit can extract keywords such as "tired" and "exhausted" from the conversation of an elderly person and analyze their mental state. This allows for a detailed analysis of the mental state by extracting keywords from the conversation content. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the conversation content of an elderly person into a generative AI and have the generative AI perform keyword extraction.

[0085] The analysis unit estimates the emotions of elderly individuals and adjusts the display method of the analysis results based on the estimated emotions. For example, if an elderly individual is tense, the analysis unit can provide a simple and highly visible display method. For example, if an elderly individual is relaxed, the analysis unit can provide a display method that includes detailed information. For example, if an elderly individual is in a hurry, the analysis unit can provide a display method that gets straight to the point. By adjusting the display method of the analysis results based on the emotions of elderly individuals, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, for example, or without a generative AI. For example, the analysis unit can input the elderly individual's statements into a generative AI and have the generative AI perform emotion estimation.

[0086] The analysis unit integrates the content of conversations with other data (e.g., health data, lifestyle data) and analyzes it. For example, the analysis unit can integrate the content of conversations with the health data of elderly people and analyze their mental state. For example, the analysis unit can integrate the content of conversations with the lifestyle data of elderly people and analyze their mental state. For example, the analysis unit can integrate the content of conversations with the exercise data of elderly people and analyze their mental state. By integrating the content of conversations with other data and analyzing it, the accuracy of the analysis of mental state can be improved. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the content of elderly people's conversations and other data into a generative AI and have the generative AI perform the integrated analysis.

[0087] The analysis unit analyzes the content of conversations based on regional and cultural backgrounds to deepen the understanding of mental states. For example, the analysis unit can analyze conversations considering the cultural background of the elderly person's region. For example, the analysis unit can analyze conversations considering the customs and traditions of the elderly person's region. For example, the analysis unit can analyze conversations considering the language and dialect of the elderly person's region. In this way, by analyzing the content of conversations based on regional and cultural backgrounds, a deeper understanding of mental states can be achieved. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the elderly person's conversation content into a generative AI and have the generative AI perform an analysis based on regional and cultural backgrounds.

[0088] The detection unit estimates the emotions of elderly individuals and improves the accuracy of detecting mental illness based on the estimated emotions. For example, if an elderly individual is sad, the detection unit can improve the accuracy of detecting mental illness by considering that emotion. For example, if an elderly individual is excited, the detection unit can improve the accuracy of detecting mental illness by considering that emotion. For example, if an elderly individual is tired, the detection unit can improve the accuracy of detecting mental illness by considering that emotion. In this way, detection accuracy can be improved by detecting mental illness based on the emotions of elderly individuals. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the detection unit may be performed using a generative AI, for example, or without a generative AI. For example, the detection unit can input the elderly individual's statements into a generative AI and have the generative AI perform emotion estimation.

[0089] The detection unit detects early signs of mental illness based on the content of conversations. For example, the detection unit can detect early signs such as "frequent forgetfulness" from the conversations of elderly people. For example, the detection unit can detect early signs such as "feeling depressed" from the conversations of elderly people. For example, the detection unit can detect early signs such as "difficulty sleeping" from the conversations of elderly people. This enables early intervention by detecting early signs of mental illness based on the content of conversations. Some or all of the above processing in the detection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the detection unit can input the content of an elderly person's conversation into a generative AI and have the generative AI perform the detection of early signs.

[0090] The detection unit monitors changes in the frequency and content of conversations to detect the progression of mental illness. For example, the detection unit can detect the progression of mental illness if the frequency of conversations of elderly people decreases. For example, the detection unit can detect the progression of mental illness if the content of conversations of elderly people changes. For example, the detection unit can detect the progression of mental illness if the tone of conversations of elderly people changes. In this way, the progression of mental illness can be detected by monitoring changes in the frequency and content of conversations. Some or all of the above processing in the detection unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the detection unit can input the content of conversations of elderly people into a generating AI and have the generating AI perform monitoring of changes in frequency and content.

[0091] The detection unit estimates the emotions of the elderly person and adjusts the notification method of the detection result based on the estimated emotions. For example, if the elderly person is tense, the detection unit can provide a simple and highly visible notification method. For example, if the elderly person is relaxed, the detection unit can provide a notification method that includes detailed information. For example, if the elderly person is in a hurry, the detection unit can provide a notification method that gets straight to the point. By adjusting the notification method of the detection result based on the emotions of the elderly person, more appropriate notifications become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the detection unit may be performed using a generative AI, for example, or without a generative AI. For example, the detection unit can input the elderly person's statements into a generative AI and have the generative AI perform emotion estimation.

[0092] The detection unit combines the content of conversations with other health data to comprehensively assess the risk of mental illness. For example, the detection unit can integrate the health data of elderly individuals with the content of conversations to assess the risk of mental illness. For example, the detection unit can integrate the lifestyle data of elderly individuals with the content of conversations to assess the risk of mental illness. For example, the detection unit can integrate the exercise data of elderly individuals with the content of conversations to assess the risk of mental illness. This allows for a more accurate risk assessment by comprehensively evaluating the risk of mental illness by combining the content of conversations with other health data. Some or all of the above processing in the detection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the detection unit can input the content of elderly individuals' conversations and other health data into a generative AI and have the generative AI perform the risk assessment.

[0093] The detection unit analyzes the content of conversations and classifies the type and severity of mental illnesses in detail. For example, the detection unit can classify the type and severity of dementia from the content of conversations of elderly people. For example, the detection unit can classify the type and severity of depression from the content of conversations of elderly people. For example, the detection unit can classify the type and severity of anxiety disorders from the content of conversations of elderly people. In this way, by analyzing the content of conversations, the type and severity of mental illnesses can be classified in detail. Some or all of the above processing in the detection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the detection unit can input the content of conversations of elderly people into a generative AI and have the generative AI perform the classification of the type and severity of mental illnesses.

[0094] The dialogue unit estimates the emotions of the elderly person and adjusts the content and method of the dialogue based on the estimated emotions. For example, if the elderly person is sad, the dialogue unit can offer words of encouragement in a gentle tone. For example, if the elderly person is agitated, the dialogue unit can speak in a calm tone to help them calm down. For example, if the elderly person is tired, the dialogue unit can engage in short, concise conversation to reduce their burden. This allows for more appropriate dialogue by adjusting the content and method of the dialogue according to the elderly person's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the dialogue unit may be performed using a generative AI, or not using a generative AI. For example, the dialogue unit can input the elderly person's statements into a generative AI and have the generative AI perform emotion estimation.

[0095] The dialogue unit compares the content of the dialogue with past dialogue history and selects the optimal dialogue method. For example, the dialogue unit can select topics of interest based on what the elderly person has talked about in the past. For example, the dialogue unit can reduce stress by avoiding topics that the elderly person has avoided in the past. For example, the dialogue unit can guide the conversation by referring to the tone and pace of conversation that the elderly person has preferred in the past. By doing so, the optimal dialogue method can be selected by comparing it with past dialogue history. Some or all of the above processing in the dialogue unit may be performed using, for example, a generative AI, or without a generative AI. For example, the dialogue unit can input the elderly person's past dialogue history into a generative AI and have the generative AI select the optimal dialogue method.

[0096] The dialogue unit proposes and supports the implementation of an appropriate action plan based on the content of the dialogue. For example, if an elderly person says, "I've been forgetting things a lot lately," the dialogue unit can suggest taking notes as a habit. For example, if an elderly person says, "I've been feeling down lately," the dialogue unit can suggest spending time enjoying a hobby. For example, if an elderly person says, "I haven't been able to sleep lately," the dialogue unit can suggest ways to relax. In this way, by proposing an appropriate action plan based on the content of the dialogue, the dialogue unit can support the lives of elderly people. Some or all of the above processing in the dialogue unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the dialogue unit can input the elderly person's statements into a generative AI and have the generative AI execute the action plan proposal.

[0097] The dialogue unit estimates the emotions of the elderly person and adjusts the frequency of dialogue based on the estimated emotions. For example, if the elderly person is feeling lonely, the dialogue unit can speak to them more frequently. For example, if the elderly person is feeling stressed, the dialogue unit can reduce the frequency of dialogue to help them relax. For example, if the elderly person is feeling energetic, the dialogue unit can engage in dialogue at an appropriate frequency. By adjusting the frequency of dialogue according to the elderly person's emotions, more appropriate dialogue becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the dialogue unit may be performed using a generative AI, or not using a generative AI. For example, the dialogue unit can input the elderly person's statements into a generative AI and have the generative AI perform emotion estimation.

[0098] The dialogue unit provides relevant advice during the conversation based on the elderly person's living environment and daily activities. For example, if the elderly person is gardening, the dialogue unit can provide gardening advice. For example, if the elderly person is cooking, the dialogue unit can provide advice on recipes and cooking tips. For example, if the elderly person is taking a walk, the dialogue unit can provide advice on walking routes and nature. This allows for more engaging conversations by providing relevant advice based on the elderly person's living environment and daily activities. Some or all of the above processing in the dialogue unit may be performed using, for example, a generative AI, or without a generative AI. For example, the dialogue unit can input information about the elderly person's living environment and daily activities into a generative AI and have the generative AI provide relevant advice.

[0099] The dialogue unit, during a conversation, selects topics based on the elderly person's hobbies and interests to stimulate the conversation. For example, if the elderly person likes music, the dialogue unit can offer topics related to music. For example, if the elderly person likes reading, the dialogue unit can offer topics related to books they have recently read. For example, if the elderly person likes sports, the dialogue unit can bring up recent sports news. In this way, the conversation can be stimulated by selecting topics based on the elderly person's hobbies and interests. Some or all of the above processing in the dialogue unit may be performed using, for example, a generative AI, or without a generative AI. For example, the dialogue unit can input information about the elderly person's hobbies and interests into a generative AI and have the generative AI select the topics of conversation.

[0100] The recording unit estimates the emotions of elderly individuals and adjusts the recording method and content based on the estimated emotions. For example, if an elderly person is sad, the recording unit can record changes in emotion in detail. For example, if an elderly person is excited, the recording unit can record changes in emotion in detail. For example, if an elderly person is tired, the recording unit can record changes in emotion in detail. This allows for more appropriate recording by adjusting the recording method and content based on the elderly person's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the recording unit may be performed using a generative AI, or not using a generative AI. For example, the recording unit can input the elderly person's statements into a generative AI and have the generative AI perform emotion estimation.

[0101] The recording unit records the content of conversations in detail so that they can be analyzed and referenced later. For example, the recording unit can record the content of conversations of elderly people in detail in text format. For example, the recording unit can record the content of conversations of elderly people in detail in audio format. For example, the recording unit can record the content of conversations of elderly people in detail in video format. This allows for detailed recording of conversation content, which can then be analyzed and referenced later. Some or all of the above-described processes in the recording unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recording unit can input the content of conversations of elderly people into a generative AI and have the generative AI perform detailed recording.

[0102] The recording unit estimates the emotions of the elderly person and adjusts the recording frequency based on the estimated emotions. For example, if the elderly person is feeling lonely, the recording unit can record more frequently. For example, if the elderly person is feeling stressed, the recording unit can reduce the recording frequency. For example, if the elderly person is feeling well, the recording unit can record at an appropriate frequency. By adjusting the recording frequency based on the elderly person's emotions, more appropriate recording becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the recording unit may be performed using a generative AI, or not using a generative AI. For example, the recording unit can input the elderly person's statements into a generative AI and have the generative AI perform emotion estimation.

[0103] The recording unit integrates and stores the recorded data with other data (e.g., health data, lifestyle data). For example, the recording unit can integrate and store the health data and conversation content of an elderly person. For example, the recording unit can integrate and store the lifestyle data and conversation content of an elderly person. For example, the recording unit can integrate and store the exercise data and conversation content of an elderly person. This enables centralized data management by integrating and storing the recorded data with other data. Some or all of the above processing in the recording unit may be performed using, for example, a generation AI, or without a generation AI. For example, the recording unit can input the conversation content of an elderly person and other data into a generation AI and have the generation AI perform the integrated storage.

[0104] The understanding unit estimates the emotions of elderly people and improves the accuracy of understanding their mental state based on the estimated emotions. For example, if an elderly person is sad, the understanding unit can take that emotion into account and understand their mental state in detail. For example, if an elderly person is excited, the understanding unit can take that emotion into account and understand their mental state in detail. For example, if an elderly person is tired, the understanding unit can take that emotion into account and understand their mental state in detail. In this way, the accuracy of understanding can be improved by understanding the mental state based on the emotions of elderly people. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the understanding unit may be performed using a generative AI, for example, or without a generative AI. For example, the understanding unit can input the elderly person's statements into a generative AI and have the generative AI perform emotion estimation.

[0105] The comprehension unit comprehends the content of conversations along a timeline and tracks changes in mental state. For example, the comprehension unit can comprehend the content of an elderly person's conversations on a daily basis and track changes in their mental state. For example, the comprehension unit can comprehend the content of an elderly person's conversations on a weekly basis and track changes in their mental state. For example, the comprehension unit can comprehend the content of an elderly person's conversations on a monthly basis and track changes in their mental state. In this way, changes in mental state can be tracked by comprehending the content of conversations along a timeline. Some or all of the above processing in the comprehension unit may be performed using, for example, a generative AI, or without a generative AI. For example, the comprehension unit can input the content of an elderly person's conversations into a generative AI and have the generative AI perform comprehension along a timeline.

[0106] The understanding unit estimates the emotions of elderly individuals and adjusts the display method of the understanding results based on the estimated emotions. For example, if an elderly person is tense, the understanding unit can provide a simple and highly visible display method. For example, if an elderly person is relaxed, the understanding unit can provide a display method that includes detailed information. For example, if an elderly person is in a hurry, the understanding unit can provide a display method that gets straight to the point. By adjusting the display method of the understanding results based on the emotions of elderly individuals, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the understanding unit may be performed using a generative AI, for example, or without a generative AI. For example, the understanding unit can input the elderly person's statements into a generative AI and have the generative AI perform emotion estimation.

[0107] The comprehension unit understands the content of conversations by integrating it with other data (e.g., health data, lifestyle data). For example, the comprehension unit can integrate the content of conversations with the health data of elderly people. For example, the comprehension unit can integrate the content of conversations with the lifestyle data of elderly people. For example, the comprehension unit can integrate the content of conversations with the exercise data of elderly people. By integrating the content of conversations with other data, the accuracy of understanding the mental state can be improved. Some or all of the above processing in the comprehension unit may be performed using, for example, a generative AI, or without a generative AI. For example, the comprehension unit can input the content of elderly people's conversations and other data into a generative AI and have the generative AI perform the integrated comprehension.

[0108] The suggestion unit estimates the emotions of the elderly person and adjusts the content and method of the suggestions based on the estimated emotions. For example, if the elderly person is sad, the suggestion unit can make suggestions for a change of pace. For example, if the elderly person is excited, the suggestion unit can make suggestions for relaxation. For example, if the elderly person is tired, the suggestion unit can make suggestions for rest. By adjusting the content and method of suggestions based on the elderly person's emotions, more appropriate suggestions become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the processing described above in the suggestion unit may be performed using a generative AI, or not using a generative AI. For example, the suggestion unit can input the elderly person's statements into a generative AI and have the generative AI perform emotion estimation.

[0109] The proposal unit compares the content of the proposal with past proposal history and selects the optimal proposal method. For example, the proposal unit can make the optimal proposal based on proposals that the elderly person has accepted in the past. For example, the proposal unit can avoid proposals that the elderly person has rejected in the past, thereby reducing stress. For example, the proposal unit can make proposals by referring to the content and methods of proposals that the elderly person has preferred in the past. This allows the optimal proposal method to be selected by comparing it with past proposal history. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input the elderly person's past proposal history into a generative AI and have the generative AI select the optimal proposal method.

[0110] The suggestion unit estimates the emotions of elderly individuals and adjusts the frequency of suggestions based on the estimated emotions. For example, if an elderly person is feeling lonely, the suggestion unit can make suggestions more frequently. For example, if an elderly person is feeling stressed, the suggestion unit can reduce the frequency of suggestions. For example, if an elderly person is feeling energetic, the suggestion unit can make suggestions at an appropriate frequency. By adjusting the frequency of suggestions based on the emotions of elderly individuals, more appropriate suggestions become possible. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using a generative AI, or not using a generative AI. For example, the suggestion unit can input the elderly person's statements into a generative AI and have the generative AI perform emotion estimation.

[0111] The suggestion unit provides relevant advice based on the elderly person's living environment and daily activities when making a suggestion. For example, if the elderly person is gardening, the suggestion unit can provide gardening advice. For example, if the elderly person is cooking, the suggestion unit can provide advice on recipes and cooking tips. For example, if the elderly person is taking a walk, the suggestion unit can provide advice on walking routes and nature. In this way, suggestions can be made more effective by providing relevant advice based on the elderly person's living environment and daily activities. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input information about the elderly person's living environment and daily activities into a generative AI and have the generative AI provide relevant advice.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The conversational unit can estimate the hobbies and interests of elderly individuals based on the content of the conversation and adjust the conversation accordingly. For example, if an elderly person shows interest in music, the conversational unit can offer topics related to music. Similarly, if an elderly person shows interest in gardening, the conversational unit can offer topics related to gardening. Furthermore, if an elderly person shows interest in travel, the conversational unit can offer topics related to travel. By adjusting the conversation content based on the hobbies and interests of elderly individuals, more engaging conversations become possible.

[0114] The analysis department can identify stressors in the daily lives of elderly individuals based on their statements and provide advice to reduce stress. For example, if an elderly person says, "Housework has been difficult lately," the analysis department can analyze the statement and identify housework as a stressor. Similarly, if an elderly person says, "The noise from the neighbors bothers me," the analysis department can analyze the statement and identify the noise as a stressor. Furthermore, if an elderly person says, "I feel lonely when I'm alone," the analysis department can analyze the statement and identify loneliness as a stressor. By identifying stressors in daily life and providing advice to reduce stress, the department can support the mental health of elderly individuals.

[0115] The detection unit can assess the risk of social isolation based on what the elderly person says and notify caregivers or family members as needed. For example, if an elderly person says, "I haven't talked to anyone lately," the detection unit can analyze the statement and assess the risk of social isolation as high. Similarly, if an elderly person says, "I'm lonely because I don't have any friends," the detection unit can analyze the statement and assess the risk of social isolation as high. Furthermore, if an elderly person says, "It's a hassle to go out," the detection unit can analyze the statement and assess the risk of social isolation as high. In this way, by assessing the risk of social isolation and notifying caregivers or family members as needed, it is possible to maintain the social connections of the elderly.

[0116] The dialogue function can provide advice on health management in daily life based on what the elderly person says. For example, if an elderly person says, "I haven't been getting enough exercise lately," the dialogue function can analyze the statement, explain the importance of exercise, and suggest simple exercises. If an elderly person says, "My diet is unbalanced," the dialogue function can analyze the statement, explain the importance of a balanced diet, and suggest specific meals. Furthermore, if an elderly person says, "I'm not getting enough sleep," the dialogue function can analyze the statement and provide advice on how to get quality sleep. In this way, the system can support the health of the elderly by providing advice on health management in daily life.

[0117] The conversational unit can estimate the emotions of elderly people and adjust the content of the conversation based on those estimated emotions. For example, if an elderly person is sad, the conversational unit can offer words of comfort. If an elderly person is happy, the conversational unit can share in their joy. Furthermore, if an elderly person is angry, the conversational unit can calmly listen and offer words to alleviate their anger. In this way, by adjusting the content of the conversation according to the emotions of the elderly person, more appropriate dialogue becomes possible.

[0118] The analysis unit can estimate the emotions of elderly individuals and improve the accuracy of the analysis of their mental state based on these estimated emotions. For example, if an elderly person is sad, their mental state can be analyzed in detail, taking that emotion into consideration. Similarly, if an elderly person is agitated, their mental state can be analyzed in detail, taking that emotion into consideration. Furthermore, if an elderly person is tired, their mental state can be analyzed in detail, taking that emotion into consideration. In this way, the accuracy of the analysis can be improved by analyzing the mental state based on the emotions of elderly individuals.

[0119] The detection unit can estimate the emotions of elderly individuals and improve the accuracy of detecting mental illness based on these estimated emotions. For example, if an elderly person is sad, this emotion can be taken into account to improve the accuracy of detecting mental illness. Similarly, if an elderly person is agitated, this emotion can be taken into account to improve the accuracy of detecting mental illness. Furthermore, if an elderly person is tired, this emotion can be taken into account to improve the accuracy of detecting mental illness. In this way, detection accuracy can be improved by detecting mental illness based on the emotions of elderly individuals.

[0120] The dialogue unit can estimate the emotions of elderly individuals and adjust the content and method of the dialogue based on those estimates. For example, if an elderly person is sad, it can offer words of encouragement in a gentle tone. If an elderly person is agitated, it can speak in a calm tone to help them relax. Furthermore, if an elderly person is tired, it can engage in short, concise conversations to reduce their burden. In this way, by adjusting the content and method of the dialogue according to the emotions of the elderly person, more appropriate dialogue becomes possible.

[0121] The recording unit can estimate the emotions of elderly individuals and adjust the recording method and content based on the estimated emotions. For example, if an elderly person is sad, the system can record their emotional changes in detail. Similarly, if an elderly person is agitated, the system can record their emotional changes in detail. Furthermore, if an elderly person is tired, the system can record their emotional changes in detail. This allows for more appropriate recording by adjusting the recording method and content based on the elderly person's emotions.

[0122] The recording unit can record the content of conversations in detail, allowing for later analysis and reference. For example, it can record the content of conversations with elderly people in detail in text format. It can also record the content of conversations with elderly people in detail in audio format. Furthermore, it can record the content of conversations with elderly people in detail in video format. This allows for detailed recording of conversation content, which can then be analyzed and referenced later.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The conversation unit engages in voice conversations with the elderly person. For example, if the elderly person says, "The weather is nice today," the unit responds, "Yes, it might be nice to go for a walk today." Also, if the elderly person says, "I'm having trouble with my memory lately," the unit records that statement, and if they say, "I've been feeling down lately," the unit can suggest, "Why don't you try doing something fun to cheer yourself up?" Step 2: The analysis unit analyzes the content of conversations conducted by the conversation unit in real time. For example, it can analyze the statements of elderly people to understand their mental and emotional state. If someone says, "I've been forgetting things a lot lately, and it's bothering me," the analysis unit will determine whether there are signs of dementia. Similarly, if someone says, "I've been feeling down lately," the analysis unit will understand their mental state. Step 3: The detection unit detects the degree of mental illness based on the analysis performed by the analysis unit. For example, it can determine whether there are signs of dementia, assess the mental state, and detect the degree of mental illness. Step 4: The dialogue unit engages in conversation to prevent the worsening of symptoms based on the degree of mental illness detected by the detection unit. For example, if an elderly person says, "I've been feeling down lately," it suggests, "Why don't you try doing something fun to cheer yourself up?" Also, if an elderly person says, "I've been forgetting things a lot lately, and it's bothering me," it records the statement and determines whether it is a sign of dementia. Furthermore, if an elderly person says, "The weather is nice today," it responds, "Yes, it might be nice to go for a walk today."

[0125] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0126] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0127] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0128] Each of the multiple elements described above, including the conversation unit, analysis unit, detection unit, and dialogue unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the conversation unit is implemented by the control unit 46A of the smart device 14 and can record the elderly person's statements in voice format. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and can analyze the elderly person's statements and understand their mental state. The detection unit is implemented by the identification processing unit 290 of the data processing unit 12 and can detect the degree of mental illness. The dialogue unit is implemented by the control unit 46A of the smart device 14 and can engage in dialogue to prevent the worsening of symptoms. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the conversation unit, analysis unit, detection unit, and dialogue unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the conversation unit is implemented by the control unit 46A of the smart glasses 214 and can record the elderly person's statements in voice format. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and can analyze the elderly person's statements and understand their mental state. The detection unit is implemented by the identification processing unit 290 of the data processing unit 12 and can detect the degree of mental illness. The dialogue unit is implemented by the control unit 46A of the smart glasses 214 and can engage in dialogue to prevent the worsening of symptoms. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0153] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0154] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0155] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0156] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0157] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0158] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0159] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0160] Each of the multiple elements described above, including the conversation unit, analysis unit, detection unit, and dialogue unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the conversation unit is implemented by the control unit 46A of the headset terminal 314 and can record the elderly person's statements in voice format. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and can analyze the elderly person's statements and understand their mental state. The detection unit is implemented by the identification processing unit 290 of the data processing unit 12 and can detect the degree of mental illness. The dialogue unit is implemented by the control unit 46A of the headset terminal 314 and can engage in dialogue to prevent the worsening of symptoms. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] As shown in Figure 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.

[0163] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0164] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0165] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0166] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0167] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0168] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0169] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0170] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0171] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0172] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0173] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0174] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0175] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0176] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0177] Each of the multiple elements described above, including the conversation unit, analysis unit, detection unit, and dialogue unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the conversation unit is implemented by the control unit 46A of the robot 414 and can record the elderly person's statements in voice format. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and can analyze the elderly person's statements and understand their mental state. The detection unit is implemented by the specific processing unit 290 of the data processing unit 12 and can detect the degree of mental illness. The dialogue unit is implemented by the control unit 46A of the robot 414 and can engage in dialogue to prevent the worsening of symptoms. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0178] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0179] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0180] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0181] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0182] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0183] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0184] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0185] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0186] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0187] 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.

[0188] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0189] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0190] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0191] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0192] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0193] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0194] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0195] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0196] (Note 1) A conversation unit that engages in voice conversations with the elderly, An analysis unit analyzes the content of conversations conducted by the aforementioned conversation unit in real time, A detection unit that detects the degree of mental illness based on the content analyzed by the aforementioned analysis unit, A dialogue unit that engages in dialogue to prevent the worsening of symptoms based on the degree of mental illness detected by the aforementioned detection unit, Equipped with A system characterized by the following features. (Note 2) The aforementioned conversation section is, It is equipped with a recording unit for recording the statements of elderly people. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is It is equipped with a unit for assessing the mental state of elderly people. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned dialogue unit, It is equipped with a proposal department that suggests ways to improve symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned conversation section is, It estimates the emotions of elderly people and adjusts the tone and content of conversations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned conversation section is, Analyze the past conversation history of elderly individuals to select the most appropriate way to conduct a conversation. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned conversation section is, It automatically selects appropriate topics based on the content of the conversation, facilitating a smooth exchange. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned conversation section is, The system estimates the emotions of elderly individuals and adjusts the frequency of conversations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned conversation section is, During conversations, provide topics relevant to the elderly person's living environment and daily activities. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned conversation section is, During conversations, select topics based on the hobbies and interests of elderly people to make the conversation more engaging. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is This system estimates the emotions of elderly individuals and improves the accuracy of mental state analysis based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is Analyze the content of conversations along a timeline to track changes in mental state. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is Extract keywords from the conversation and conduct a detailed analysis of the mental state. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is The system estimates the emotions of elderly individuals and adjusts the display method of the analysis results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is Analyze the content of the conversation by integrating it with other data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is Analyzing conversation content based on regional and cultural backgrounds deepens our understanding of mental states. The system described in Appendix 1, characterized by the features described herein. (Note 17) The detection unit, This project aims to estimate the emotions of elderly individuals and improve the accuracy of detecting mental illnesses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The detection unit, Based on the content of conversations, early signs of mental illness can be detected. The system described in Appendix 1, characterized by the features described herein. (Note 19) The detection unit, By monitoring changes in the frequency and content of conversations, the progression of mental illness can be detected. The system described in Appendix 1, characterized by the features described herein. (Note 20) The detection unit, The system estimates the emotions of elderly individuals and adjusts the notification method of detection results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The detection unit, The content of conversations is combined with other health data to comprehensively assess the risk of mental illness. The system described in Appendix 1, characterized by the features described herein. (Note 22) The detection unit, Analyze the content of the conversation and classify the type and severity of the mental illness in detail. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned dialogue unit, The system estimates the emotions of elderly individuals and adjusts the content and method of dialogue based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned dialogue unit, The content of the conversation is compared with past conversation history to select the optimal conversation method. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned dialogue unit, Based on the content of the dialogue, we propose an appropriate action plan and support its implementation. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned dialogue unit, The system estimates the emotions of elderly individuals and adjusts the frequency of conversations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned dialogue unit, During the conversation, provide relevant advice based on the elderly person's living environment and daily activities. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned dialogue unit, During conversations, select topics based on the hobbies and interests of elderly individuals to revitalize the dialogue. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned recording unit is We estimate the emotions of elderly people and adjust the recording methods and content based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned recording unit is Record the conversation in detail so that it can be analyzed and referenced later. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned recording unit is The system estimates the emotions of older adults and adjusts the frequency of recordings based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned recording unit is The recorded data is integrated with other data and stored. The system described in Appendix 2, characterized by the features described herein. (Note 33) The gripping part is, This system estimates the emotions of elderly individuals and improves the accuracy of understanding their mental state based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 34) The gripping part is, The content of the conversation is understood chronologically, and changes in mental state are tracked. The system described in Appendix 3, characterized by the features described herein. (Note 35) The gripping part is, The system estimates the emotions of elderly individuals and adjusts the display method of the assessment results based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The gripping part is, Integrate the content of the conversation with other data to understand it. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned proposal section is, We estimate the emotions of elderly people and adjust the content and methods of our proposals based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 38) The aforementioned proposal section is, Compare the proposed content with past proposal history and select the most suitable proposal method. The system described in Appendix 4, characterized by the features described herein. (Note 39) The aforementioned proposal section is, The system estimates the emotions of older adults and adjusts the frequency of suggestions based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 40) The aforementioned proposal section is, When making a proposal, provide relevant advice based on the elderly person's living environment and daily activities. The system described in Appendix 4, characterized by the features described herein. [Explanation of symbols]

[0197] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A conversation unit that engages in voice conversations with the elderly, An analysis unit analyzes the content of conversations conducted by the aforementioned conversation unit in real time, A detection unit that detects the degree of mental illness based on the content analyzed by the aforementioned analysis unit, A dialogue unit that engages in dialogue to prevent the worsening of symptoms based on the degree of mental illness detected by the aforementioned detection unit, Equipped with A system characterized by the following features.

2. The aforementioned conversation section is, It is equipped with a recording unit for recording the statements of elderly people. The system according to feature 1.

3. The aforementioned analysis unit is It is equipped with a unit for assessing the mental state of elderly people. The system according to feature 1.

4. The aforementioned dialogue unit, It is equipped with a proposal department that suggests ways to improve symptoms. The system according to feature 1.

5. The aforementioned conversation section is, It estimates the emotions of elderly people and adjusts the tone and content of conversations based on those estimated emotions. The system according to feature 1.

6. The aforementioned conversation section is, Analyze the past conversation history of elderly individuals to select the most appropriate way to conduct a conversation. The system according to feature 1.

7. The aforementioned conversation section is, It automatically selects appropriate topics based on the content of the conversation, facilitating a smooth exchange. The system according to feature 1.

8. The aforementioned conversation section is, The system estimates the emotions of elderly individuals and adjusts the frequency of conversations based on those estimated emotions. The system according to feature 1.

9. The aforementioned conversation section is, During conversations, provide topics relevant to the elderly person's living environment and daily activities. The system according to feature 1.

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