System

Abstract

An object of a system according to an embodiment is to find signs of dementia at an early stage through daily conversation with an elderly person.SOLUTION: A system includes a conversation execution part, an analysis part, and a notification part. The conversation performing unit performs conversation with the elderly person on a daily basis. The analysis unit analyzes the content of the conversation with the elderly person performed by the conversation execution unit. The notifier performs notification when the analyzer detects the sign of dementia.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to detect early signs of dementia in elderly people.

[0005] The system according to the embodiment aims to detect early signs of dementia through everyday conversations with elderly people. [Means for solving the problem]

[0006] The system according to the embodiment includes a conversation unit, an analysis unit, and a notification unit. The conversation unit has daily conversations with elderly people. The analysis unit analyzes the content of conversations with elderly people conducted by the conversation unit. The notification unit notifies when the analysis unit detects signs of dementia. [Effects of the Invention]

[0007] The system according to the embodiment can detect early signs of dementia through everyday conversations with elderly people. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The automated voice conversation system according to an embodiment of the present invention utilizes a generative AI-based automated voice conversation system for healthy or frail elderly people living alone, contributing to the early detection of dementia. This system detects signs of dementia early by having the generative AI have daily conversations with the elderly and analyzing the content of those conversations. As a result, the automated voice conversation system can detect signs of dementia early through daily conversations with the elderly and encourage appropriate responses.

[0029] An automated voice conversation system according to an embodiment includes a conversation implementation unit, an analysis unit, and a notification unit. The conversation implementation unit engages in daily conversations with elderly people. For example, the generation AI asks questions such as, "What did you do today?" and "How are you feeling lately?" The generation AI uses speech recognition technology to understand the elderly person's answers and generate appropriate responses. The generation AI receives inputs such as prompts including the topic of the conversation and the question, and the generation AI advances the conversation based on the prompts. The analysis unit analyzes the content of the conversation with the elderly conducted by the conversation implementation unit. For example, the generation AI converts the content of the conversation into text and analyzes the text data. The generation AI evaluates the word choice, flow of the conversation, and memory consistency in the conversation, and uses an algorithm to detect signs of dementia. The generation AI receives inputs such as text data of the conversation, and analyzes the data to detect signs of dementia. The notification unit issues a notification when signs of dementia are detected by the analysis unit. For example, the notification may be sent to a family member or a medical institution, such as, "Your recent conversations suggest signs of dementia. We recommend further examination." In addition, the generation AI periodically conducts follow-up conversations and continuously monitors changes in the situation. As a result, the automated voice conversation system according to the embodiment can detect early signs of dementia through everyday conversations with elderly people and encourage appropriate responses.

[0030] The conversation implementation unit can generate individually customized questions based on the elderly person's past conversation history, thereby promoting deeper dialogue. The conversation implementation unit, for example, analyzes the elderly person's past conversation history and generates individually customized questions. For example, the conversation can deepen the conversation by asking again about hobbies that were previously discussed. The conversation implementation unit also identifies the elderly person's interests from the past conversation history and asks questions based on that. For example, an elderly person who likes to travel can be asked about recent trips they have taken. Furthermore, the conversation implementation unit identifies the elderly person's health condition based on the past conversation history and asks questions based on that. For example, the conversation implementation unit can check the progress of a health problem that was previously discussed. In this way, more personalized dialogue is possible by utilizing the elderly person's past conversation history.

[0031] The analysis unit can detect signs of dementia by analyzing the frequency of specific keywords and phrases in the conversation content. The analysis unit, for example, analyzes the frequency of specific keywords and phrases in the conversation content to detect signs of dementia. For example, it measures how often the same question is repeated. The analysis unit also analyzes the appearance pattern of specific keywords and phrases in the conversation content to detect signs of dementia. For example, it measures how often the conversation loses coherence. Furthermore, the analysis unit analyzes fluctuations in the frequency of use of specific keywords and phrases in the conversation content to detect signs of dementia. For example, it detects cases where the frequency of use of specific words increases suddenly. In this way, by analyzing the frequency of specific keywords and phrases, signs of dementia can be detected early.

[0032] The analysis unit can evaluate the consistency and logic of the elderly person's statements and issue a warning if an abnormality is found. For example, the analysis unit evaluates the consistency and logic of the elderly person's statements and issues a warning if an abnormality is found. For example, a warning is issued if the conversation does not make sense. The analysis unit also evaluates the consistency and logic of the elderly person's statements and notifies family members or medical institutions if an abnormality is found. For example, a notification is issued if the content of the conversation is contradictory. Furthermore, the analysis unit evaluates the consistency and logic of the elderly person's statements and conducts a follow-up conversation if an abnormality is found. For example, the analysis unit checks for details if the content of the conversation is unclear. In this way, by evaluating the consistency and logic of speech, signs of dementia can be detected early.

[0033] The analysis unit can integrate the content of the elderly person's conversation with other health data to evaluate their overall health condition. For example, the analysis unit can integrate the content of the elderly person's conversation with their sleep patterns to evaluate their overall health condition. For example, it can analyze whether lack of sleep is affecting the content of the conversation. The analysis unit can also integrate the content of the elderly person's conversation with their food records to evaluate their overall health condition. For example, it can analyze whether lack of nutrition is affecting the content of the conversation. Furthermore, the analysis unit can integrate the content of the elderly person's conversation with their exercise data to evaluate their overall health condition. For example, it can analyze whether lack of exercise is affecting the content of the conversation. In this way, the overall health condition can be evaluated by integrating the content of the conversation with other health data.

[0034] The analysis unit translates the content of the elderly's conversation into different languages ​​and can evaluate signs of dementia from an international perspective. For example, the analysis unit translates the content of the elderly's conversation into different languages ​​and evaluates signs of dementia from an international perspective. For example, it translates into English and French and analyzes the content. The analysis unit also translates the content of the elderly's conversation into different languages ​​and evaluates signs of dementia taking into account different cultural backgrounds. For example, it analyzes changes in language usage due to cultural differences. Furthermore, the analysis unit translates the content of the elderly's conversation into different languages ​​and evaluates signs of dementia by comparing it with international research data. For example, it makes a comparison with elderly people in other countries. In this way, by translating into different languages, signs of dementia can be evaluated from an international perspective.

[0035] The notification unit can send a notification of abnormality detection not only to family members and medical institutions but also to a local support network. For example, the notification unit sends a notification of abnormality detection not only to family members and medical institutions but also to a local support network. For example, it notifies a local nursing care service or volunteer group. The notification unit also sends a notification of abnormality detection to a local welfare service. For example, the local welfare service understands the elderly person's situation and provides appropriate support. Furthermore, the notification unit also sends a notification of abnormality detection to a local community center. For example, the community center understands the elderly person's situation and coordinates local support. In this way, by notifying the local support network, more extensive support can be provided.

[0036] The notification unit can provide specific advice and support in a follow-up conversation after an abnormality is detected. The notification unit provides specific advice and support in a follow-up conversation after an abnormality is detected, for example. For example, it may provide advice recommending that the person visit a medical institution. The notification unit also provides specific advice on lifestyle improvements in a follow-up conversation after an abnormality is detected. For example, it may provide advice regarding diet and exercise. Furthermore, the notification unit provides specific psychological support in a follow-up conversation after an abnormality is detected. For example, it may provide advice on stress management and relaxation techniques. In this way, by providing specific advice and support in a follow-up conversation after an abnormality is detected, an appropriate response can be made.

[0037] The notification unit can send the notification of abnormality detection not only by voice but also by text message or video message. For example, the notification unit sends the notification of abnormality detection not only by voice but also by text message. For example, the notification is sent to family members or medical institutions by email or SMS. The notification unit also sends the notification of abnormality detection by video message. For example, the notification is sent to family members or medical institutions by video call or recorded message. Furthermore, the notification unit sends the notification of abnormality detection in multiple formats. For example, the notification unit selects and sends one of voice, text message, and video message. In this way, by sending the notification of abnormality detection in multiple formats, the recipient can respond appropriately.

[0038] The notification unit can provide content incorporating expert advice in a follow-up conversation after an abnormality is detected. For example, the notification unit provides content incorporating expert advice in a follow-up conversation after an abnormality is detected. For example, health management advice based on a doctor's opinion. The notification unit also provides content incorporating advice from a psychological counselor in a follow-up conversation after an abnormality is detected. For example, advice on stress management and relaxation techniques is provided. Furthermore, the notification unit also provides content incorporating advice from a nutritionist in a follow-up conversation after an abnormality is detected. For example, advice on improving diet is provided. This makes it possible to incorporate expert advice and provide more appropriate follow-up.

[0039] The conversation implementing unit can periodically evaluate the psychological state of the elderly person and suggest professional counseling as necessary. The conversation implementing unit, for example, periodically evaluates the psychological state of the elderly person and suggests professional counseling as necessary. For example, it may conduct regular psychological tests and recommend counseling based on the results. The conversation implementing unit also monitors the psychological state of the elderly person and suggests professional counseling as necessary. For example, it may recommend counseling if the stress level is high. Furthermore, the conversation implementing unit continuously evaluates the psychological state of the elderly person and suggests professional counseling as necessary. For example, it may recommend counseling if the elderly person's mood swings drastically. This makes it possible to provide psychological support by periodically evaluating the psychological state of the elderly person and suggesting professional counseling as necessary.

[0040] The conversation implementation unit can provide topics based on the hobbies and interests of the elderly person, thereby increasing psychological satisfaction. The conversation implementation unit can, for example, provide topics based on the hobbies and interests of the elderly person, thereby increasing psychological satisfaction. For example, for an elderly person whose hobby is gardening, the conversation implementation unit can talk about how to grow plants. The conversation implementation unit can also provide topics based on the hobbies and interests of the elderly person, thereby increasing psychological satisfaction. For example, for an elderly person who likes music, the conversation implementation unit can talk about their favorite artists. The conversation implementation unit can also provide topics based on the hobbies and interests of the elderly person, thereby increasing psychological satisfaction. For example, for an elderly person who likes traveling, the conversation unit can talk about places they would like to go. In this way, by providing topics based on the hobbies and interests of the elderly person, psychological satisfaction can be increased.

[0041] The conversation implementing unit can introduce online communities in which the elderly can participate and promote social connections. The conversation implementing unit, for example, introduces online communities in which the elderly can participate and promote social connections. For example, it introduces online forums based on hobbies or interests. The conversation implementing unit can also introduce online communities in which the elderly can participate and promote social connections. For example, it can introduce local online events. The conversation implementing unit can also introduce online communities in which the elderly can participate and promote social connections. For example, it can promote online interactions with people who share the same hobbies. In this way, social connections can be promoted by introducing online communities in which the elderly can participate.

[0042] The conversation implementing unit can share the elderly person's psychological state with family members, allowing the family members to provide appropriate support. The conversation implementing unit, for example, shares the elderly person's psychological state with family members, allowing the family members to provide appropriate support. For example, by sending regular reports to the family members. The conversation implementing unit also shares the elderly person's psychological state with family members, allowing the family members to provide appropriate support. For example, by sharing information through an online platform. The conversation implementing unit also shares the elderly person's psychological state with family members, allowing the family members to provide appropriate support. For example, by promoting regular communication with family members. This allows the elderly person's psychological state to be shared with family members, allowing the family members to provide appropriate support.

[0043] The analysis unit can anonymize the conversation data of the elderly and share it with research institutions to help with dementia research. For example, the analysis unit anonymizes the conversation data of the elderly and shares it with research institutions to help with dementia research. For example, the analysis unit anonymizes the data and provides it to a university research project. The analysis unit also anonymizes the conversation data of the elderly and shares it with medical institutions to help with dementia research. For example, the analysis unit anonymizes the data and provides it to a hospital research team. The analysis unit also anonymizes the conversation data of the elderly and shares it with international research institutions to help with dementia research. For example, the analysis unit anonymizes the data and provides it to an international research project. In this way, anonymizing the conversation data and sharing it with research institutions can help with dementia research.

[0044] The analysis unit can periodically generate individual health reports based on the conversation data and provide them to the elderly and their families. The analysis unit, for example, periodically generates individual health reports based on the conversation data and provides them to the elderly and their families. For example, it reports changes in health status as a monthly report. The analysis unit also generates individual health reports based on the conversation data and provides them to the elderly and their families. For example, it provides the reports through an online platform. Furthermore, the analysis unit continuously generates individual health reports based on the conversation data and provides them to the elderly and their families. For example, it provides a report in real time when a change in health status is observed. This makes it easier to understand the health status by generating individual health reports based on the conversation data and providing them to the elderly and their families.

[0045] The analysis unit can integrate the elderly person's conversation data with other health data to generate a comprehensive health report. For example, the analysis unit can integrate the elderly person's conversation data with exercise data to generate a comprehensive health report. For example, it can analyze whether lack of exercise is affecting the content of the conversation. The analysis unit can also integrate the elderly person's conversation data with dietary data to generate a comprehensive health report. For example, it can analyze whether lack of nutrition is affecting the content of the conversation. The analysis unit can also integrate the elderly person's conversation data with sleep data to generate a comprehensive health report. For example, it can analyze whether lack of sleep is affecting the content of the conversation. In this way, by integrating the conversation data with other health data, a comprehensive health report can be generated.

[0046] The analysis unit can develop an application that provides personalized health advice based on the conversation data. The analysis unit, for example, develops an application that provides personalized health advice based on the conversation data. For example, advice on diet and exercise is provided. The analysis unit also develops an application that provides personalized health advice based on the conversation data. For example, advice on stress management and relaxation techniques is provided. The analysis unit also develops an application that provides personalized health advice based on the conversation data. For example, advice is provided according to changes in health status. In this way, by developing an application that provides personalized health advice based on the conversation data, more personalized health management is possible.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The conversation implementation unit can generate individually customized questions based on the elderly person's past conversation history to promote deeper dialogue. For example, it analyzes the elderly person's past conversation history to generate individually customized questions. For example, it may ask again about hobbies that were previously discussed to deepen the conversation. The conversation implementation unit also identifies the elderly person's interests from the past conversation history and asks questions based on that. For example, an elderly person who likes to travel may be asked about recent trips they have taken. Furthermore, the conversation implementation unit identifies the elderly person's health condition based on the past conversation history and asks questions based on that. For example, it may check the progress of a health problem that was previously discussed. In this way, more personalized dialogue is possible by utilizing the elderly person's past conversation history.

[0049] The analysis unit can detect signs of dementia by analyzing the frequency of specific keywords and phrases in the conversation. For example, it can analyze the frequency of specific keywords and phrases in the conversation to detect signs of dementia. For example, it can measure how often the same questions are repeated. The analysis unit can also analyze the appearance patterns of specific keywords and phrases in the conversation to detect signs of dementia. For example, it can measure how often the conversation loses coherence. Furthermore, the analysis unit can analyze fluctuations in the frequency of use of specific keywords and phrases in the conversation to detect signs of dementia. For example, it can detect cases where the frequency of use of specific words increases suddenly. In this way, by analyzing the frequency of specific keywords and phrases, it is possible to detect signs of dementia at an early stage.

[0050] The analysis unit can evaluate the consistency and logic of the elderly person's statements and issue a warning if an abnormality is detected. For example, it evaluates the consistency and logic of the elderly person's statements and issues a warning if an abnormality is detected. For example, it issues a warning if the conversation does not make sense. The analysis unit also evaluates the consistency and logic of the elderly person's statements and notifies family members or medical institutions if an abnormality is detected. For example, it notifies if the content of the conversation is contradictory. Furthermore, the analysis unit evaluates the consistency and logic of the elderly person's statements and conducts a follow-up conversation if an abnormality is detected. For example, it checks for details if the content of the conversation is unclear. In this way, by evaluating the consistency and logic of speech, signs of dementia can be detected early.

[0051] The analysis unit can integrate the elderly person's conversation content with other health data to evaluate their overall health condition. For example, the analysis unit can integrate the elderly person's conversation content with their sleep patterns to evaluate their overall health condition. For example, it can analyze whether lack of sleep is affecting the conversation content. The analysis unit can also integrate the elderly person's conversation content with their food records to evaluate their overall health condition. For example, it can analyze whether lack of nutrition is affecting the conversation content. The analysis unit can also integrate the elderly person's conversation content with exercise data to evaluate their overall health condition. For example, it can analyze whether lack of exercise is affecting the conversation content. In this way, the analysis unit can evaluate their overall health condition by integrating the conversation content with other health data.

[0052] The analysis unit translates the content of the elderly's conversation into different languages ​​and can evaluate signs of dementia from an international perspective. For example, the content of the elderly's conversation can be translated into different languages ​​and evaluated for signs of dementia from an international perspective. For example, the content can be translated into English or French and analyzed. The analysis unit can also translate the content of the elderly's conversation into different languages ​​and evaluate signs of dementia taking into account different cultural backgrounds. For example, it can analyze changes in language usage due to cultural differences. Furthermore, the analysis unit can translate the content of the elderly's conversation into different languages ​​and evaluate signs of dementia by comparing it with international research data. For example, it can compare it with elderly people in other countries. In this way, by translating it into different languages, signs of dementia can be evaluated from an international perspective.

[0053] The notification unit can send a notification of an abnormality detection not only to family members and medical institutions but also to the local support network. For example, the notification of an abnormality detection is sent not only to family members and medical institutions but also to the local support network. For example, local nursing care services and volunteer groups are notified. The notification unit also sends a notification of an abnormality detection to local welfare services. For example, the local welfare services understand the situation of the elderly person and provide appropriate support. Furthermore, the notification unit also sends a notification of an abnormality detection to the local community center. For example, the community center understands the situation of the elderly person and coordinates local support. In this way, by notifying the local support network, more widespread support can be provided.

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

[0055] Step 1: The conversation execution unit conducts daily conversations with the elderly. For example, the generation AI asks questions such as "What did you do today?" and "How are you feeling lately?" The generation AI uses voice recognition technology to understand the elderly's answers and generate appropriate responses. The input to the generation AI is prompts that include the topic of the conversation and the content of the questions, and the generation AI proceeds with the conversation based on those prompts. Step 2: The analysis unit analyzes the content of the conversation with the elderly person conducted by the conversation implementation unit. For example, the generation AI converts the content of the conversation into text and analyzes that text data. The generation AI uses an algorithm to evaluate the choice of words in the conversation, the flow of the story, and the consistency of memory, and detect signs of dementia. The input to the generation AI is the text data of the conversation, and the generation AI analyzes that data to detect signs of dementia. Step 3: The notification unit notifies the user if the analysis unit detects signs of dementia. For example, it may notify family members or medical institutions by saying, "Your recent conversations suggest signs of dementia. We recommend further testing." The generation AI also regularly conducts follow-up conversations and continuously monitors changes in the user's condition.

[0056] (Example 2) The automated voice conversation system according to an embodiment of the present invention utilizes a generative AI-based automated voice conversation system for healthy or frail elderly people living alone, contributing to the early detection of dementia. This system detects signs of dementia early by having the generative AI have daily conversations with the elderly and analyzing the content of those conversations. As a result, the automated voice conversation system can detect signs of dementia early through daily conversations with the elderly and encourage appropriate responses.

[0057] An automated voice conversation system according to an embodiment includes a conversation implementation unit, an analysis unit, and a notification unit. The conversation implementation unit engages in daily conversations with elderly people. For example, the generation AI asks questions such as, "What did you do today?" and "How are you feeling lately?" The generation AI uses speech recognition technology to understand the elderly person's answers and generate appropriate responses. The generation AI receives inputs such as prompts including the topic of the conversation and the question, and the generation AI advances the conversation based on the prompts. The analysis unit analyzes the content of the conversation with the elderly conducted by the conversation implementation unit. For example, the generation AI converts the content of the conversation into text and analyzes the text data. The generation AI evaluates the word choice, flow of the conversation, and memory consistency in the conversation, and uses an algorithm to detect signs of dementia. The generation AI receives inputs such as text data of the conversation, and analyzes the data to detect signs of dementia. The notification unit issues a notification when signs of dementia are detected by the analysis unit. For example, the notification may be sent to a family member or a medical institution, such as, "Your recent conversations suggest signs of dementia. We recommend further examination." In addition, the generation AI periodically conducts follow-up conversations and continuously monitors changes in the situation. As a result, the automated voice conversation system according to the embodiment can detect early signs of dementia through everyday conversations with elderly people and encourage appropriate responses.

[0058] The conversation implementation unit can analyze the elderly person's tone of voice and speaking speed, estimate their emotional state, and adjust the content of the conversation. The conversation implementation unit, for example, analyzes the elderly person's tone of voice and speaking speed in real time to estimate their emotional state. For example, if the elderly person speaks in a low, slow voice, they may be tired, so the conversation implementation unit can provide relaxing topics. On the other hand, if the elderly person speaks in a high, fast voice, they may be excited, so the conversation implementation unit can provide calming topics. Furthermore, the conversation implementation unit continuously monitors changes in tone of voice and speaking speed to understand fluctuations in their emotional state. This allows for more appropriate communication by providing conversation content that matches the elderly person's emotional state.

[0059] The conversation implementation unit can generate individually customized questions based on the elderly person's past conversation history, thereby promoting deeper dialogue. The conversation implementation unit, for example, analyzes the elderly person's past conversation history and generates individually customized questions. For example, the conversation can deepen the conversation by asking again about hobbies that were previously discussed. The conversation implementation unit also identifies the elderly person's interests from the past conversation history and asks questions based on that. For example, an elderly person who likes to travel can be asked about recent trips they have taken. Furthermore, the conversation implementation unit identifies the elderly person's health condition based on the past conversation history and asks questions based on that. For example, the conversation implementation unit can check the progress of a health problem that was previously discussed. In this way, more personalized dialogue is possible by utilizing the elderly person's past conversation history.

[0060] The conversation implementation unit uses the emotion estimation function to grasp the emotional state of the elderly person in real time and can hold a conversation that elicits positive emotions. The conversation implementation unit, for example, uses the emotion estimation function to grasp the emotional state of the elderly person in real time and hold a conversation that elicits positive emotions. For example, if the elderly person smiles a lot, the conversation implementation unit continues with pleasant topics. The conversation implementation unit also uses the emotion estimation function to monitor the elderly person's emotional state and, if negative emotions are observed, offers words of encouragement or comfort. Furthermore, the conversation implementation unit uses the emotion estimation function to grasp fluctuations in the elderly person's emotional state and provide positive topics at appropriate times. In this way, positive emotions can be elicited by holding a conversation that is appropriate for the elderly person's emotional state.

[0061] The analysis unit can detect signs of dementia by analyzing the frequency of specific keywords and phrases in the conversation content. The analysis unit, for example, analyzes the frequency of specific keywords and phrases in the conversation content to detect signs of dementia. For example, it measures how often the same question is repeated. The analysis unit also analyzes the appearance pattern of specific keywords and phrases in the conversation content to detect signs of dementia. For example, it measures how often the conversation loses coherence. Furthermore, the analysis unit analyzes fluctuations in the frequency of use of specific keywords and phrases in the conversation content to detect signs of dementia. For example, it detects cases where the frequency of use of specific words increases suddenly. In this way, by analyzing the frequency of specific keywords and phrases, signs of dementia can be detected early.

[0062] The analysis unit can evaluate the consistency and logic of the elderly person's statements and issue a warning if an abnormality is found. For example, the analysis unit evaluates the consistency and logic of the elderly person's statements and issues a warning if an abnormality is found. For example, a warning is issued if the conversation does not make sense. The analysis unit also evaluates the consistency and logic of the elderly person's statements and notifies family members or medical institutions if an abnormality is found. For example, a notification is issued if the content of the conversation is contradictory. Furthermore, the analysis unit evaluates the consistency and logic of the elderly person's statements and conducts a follow-up conversation if an abnormality is found. For example, the analysis unit checks for details if the content of the conversation is unclear. In this way, by evaluating the consistency and logic of speech, signs of dementia can be detected early.

[0063] The analysis unit can use the emotion estimation function to analyze the emotional fluctuations of the elderly person and evaluate whether the emotional instability is a sign of dementia. For example, the analysis unit uses the emotion estimation function to analyze the emotional fluctuations of the elderly person and evaluate whether the emotional instability is a sign of dementia. For example, it detects sudden changes in emotions. The analysis unit also uses the emotion estimation function to analyze the pattern of the elderly person's emotional fluctuations and evaluate whether the emotional instability is a sign of dementia. For example, it detects cases where emotional fluctuations are frequently observed. Furthermore, the analysis unit uses the emotion estimation function to analyze the duration of the elderly person's emotional fluctuations and evaluate whether the emotional instability is a sign of dementia. For example, it detects cases where emotional fluctuations continue for a long period of time. In this way, by analyzing emotional fluctuations, signs of dementia can be detected early.

[0064] The analysis unit can integrate the content of the elderly person's conversation with other health data to evaluate their overall health condition. For example, the analysis unit can integrate the content of the elderly person's conversation with their sleep patterns to evaluate their overall health condition. For example, it can analyze whether lack of sleep is affecting the content of the conversation. The analysis unit can also integrate the content of the elderly person's conversation with their food records to evaluate their overall health condition. For example, it can analyze whether lack of nutrition is affecting the content of the conversation. Furthermore, the analysis unit can integrate the content of the elderly person's conversation with their exercise data to evaluate their overall health condition. For example, it can analyze whether lack of exercise is affecting the content of the conversation. In this way, the overall health condition can be evaluated by integrating the content of the conversation with other health data.

[0065] The analysis unit translates the content of the elderly's conversation into different languages ​​and can evaluate signs of dementia from an international perspective. For example, the analysis unit translates the content of the elderly's conversation into different languages ​​and evaluates signs of dementia from an international perspective. For example, it translates into English and French and analyzes the content. The analysis unit also translates the content of the elderly's conversation into different languages ​​and evaluates signs of dementia taking into account different cultural backgrounds. For example, it analyzes changes in language usage due to cultural differences. Furthermore, the analysis unit translates the content of the elderly's conversation into different languages ​​and evaluates signs of dementia by comparing it with international research data. For example, it makes a comparison with elderly people in other countries. In this way, by translating into different languages, signs of dementia can be evaluated from an international perspective.

[0066] The analysis unit uses the emotion estimation function to collect the emotional reactions of the family to the content of the elderly person's conversation, and can evaluate signs of dementia from the family's perspective as well. The analysis unit, for example, uses the emotion estimation function to collect the emotional reactions of the family to the content of the elderly person's conversation, and evaluate signs of dementia from the family's perspective as well. For example, it issues a warning if the family is worried. The analysis unit also uses the emotion estimation function to analyze the emotional reactions of the family to the content of the elderly person's conversation, and evaluate signs of dementia from the family's perspective as well. For example, it holds a follow-up conversation if the family is feeling anxious. The analysis unit also uses the emotion estimation function to monitor the emotional reactions of the family to the content of the elderly person's conversation, and evaluate signs of dementia from the family's perspective as well. For example, it determines that there is no particular problem if the family feels relieved. In this way, by collecting the emotional reactions of the family, it is possible to evaluate signs of dementia from the family's perspective as well.

[0067] The notification unit can send a notification of abnormality detection not only to family members and medical institutions but also to a local support network. For example, the notification unit sends a notification of abnormality detection not only to family members and medical institutions but also to a local support network. For example, it notifies a local nursing care service or volunteer group. The notification unit also sends a notification of abnormality detection to a local welfare service. For example, the local welfare service understands the elderly person's situation and provides appropriate support. Furthermore, the notification unit also sends a notification of abnormality detection to a local community center. For example, the community center understands the elderly person's situation and coordinates local support. In this way, by notifying the local support network, more extensive support can be provided.

[0068] The notification unit can provide specific advice and support in a follow-up conversation after an abnormality is detected. The notification unit provides specific advice and support in a follow-up conversation after an abnormality is detected, for example. For example, it may provide advice recommending that the person visit a medical institution. The notification unit also provides specific advice on lifestyle improvements in a follow-up conversation after an abnormality is detected. For example, it may provide advice regarding diet and exercise. Furthermore, the notification unit provides specific psychological support in a follow-up conversation after an abnormality is detected. For example, it may provide advice on stress management and relaxation techniques. In this way, by providing specific advice and support in a follow-up conversation after an abnormality is detected, an appropriate response can be made.

[0069] The notification unit can use the emotion estimation function to analyze the emotional reactions of family members or medical professionals who receive the notification and provide appropriate follow-up. The notification unit, for example, uses the emotion estimation function to analyze the emotional reactions of family members or medical professionals who receive the notification and provide appropriate follow-up. For example, if a family member is feeling anxious, the notification unit provides information to reassure the family member. The notification unit also uses the emotion estimation function to monitor the emotional reactions of family members or medical professionals who receive the notification and provide appropriate follow-up. For example, if a medical professional senses an emergency, the notification unit urges the medical professional to take prompt action. Furthermore, the notification unit uses the emotion estimation function to continuously analyze the emotional reactions of family members or medical professionals who receive the notification and provide appropriate follow-up. For example, the notification unit continues to support the family member until their emotional state improves. This makes it possible to provide appropriate follow-up by analyzing the emotional reactions of family members or medical professionals who receive the notification.

[0070] The notification unit can send the notification of abnormality detection not only by voice but also by text message or video message. For example, the notification unit sends the notification of abnormality detection not only by voice but also by text message. For example, the notification is sent to family members or medical institutions by email or SMS. The notification unit also sends the notification of abnormality detection by video message. For example, the notification is sent to family members or medical institutions by video call or recorded message. Furthermore, the notification unit sends the notification of abnormality detection in multiple formats. For example, the notification unit selects and sends one of voice, text message, and video message. In this way, by sending the notification of abnormality detection in multiple formats, the recipient can respond appropriately.

[0071] The notification unit can provide content incorporating expert advice in a follow-up conversation after an abnormality is detected. For example, the notification unit provides content incorporating expert advice in a follow-up conversation after an abnormality is detected. For example, health management advice based on a doctor's opinion. The notification unit also provides content incorporating advice from a psychological counselor in a follow-up conversation after an abnormality is detected. For example, advice on stress management and relaxation techniques is provided. Furthermore, the notification unit also provides content incorporating advice from a nutritionist in a follow-up conversation after an abnormality is detected. For example, advice on improving diet is provided. This makes it possible to incorporate expert advice and provide more appropriate follow-up.

[0072] The notification unit can use the emotion estimation function to evaluate the effectiveness of the follow-up conversation and adjust the content of the conversation as necessary. The notification unit, for example, uses the emotion estimation function to evaluate the effectiveness of the follow-up conversation and adjust the content of the conversation as necessary. For example, it checks whether the elderly person's emotions have improved after the follow-up. The notification unit also uses the emotion estimation function to monitor the effectiveness of the follow-up conversation and adjust the content of the conversation as necessary. For example, if the elderly person's emotions are unstable after the follow-up, it provides additional support. Furthermore, the notification unit also uses the emotion estimation function to continuously evaluate the effectiveness of the follow-up conversation and adjust the content of the conversation as necessary. For example, it continues support until the elderly person's emotions stabilize after the follow-up. This enables more effective support by evaluating the effectiveness of the follow-up conversation and adjusting the content of the conversation as necessary.

[0073] The conversation implementing unit can periodically evaluate the psychological state of the elderly person and suggest professional counseling as necessary. The conversation implementing unit, for example, periodically evaluates the psychological state of the elderly person and suggests professional counseling as necessary. For example, it may conduct regular psychological tests and recommend counseling based on the results. The conversation implementing unit also monitors the psychological state of the elderly person and suggests professional counseling as necessary. For example, it may recommend counseling if the stress level is high. Furthermore, the conversation implementing unit continuously evaluates the psychological state of the elderly person and suggests professional counseling as necessary. For example, it may recommend counseling if the elderly person's mood swings drastically. This makes it possible to provide psychological support by periodically evaluating the psychological state of the elderly person and suggesting professional counseling as necessary.

[0074] The conversation implementation unit can provide topics based on the hobbies and interests of the elderly person, thereby increasing psychological satisfaction. The conversation implementation unit can, for example, provide topics based on the hobbies and interests of the elderly person, thereby increasing psychological satisfaction. For example, for an elderly person whose hobby is gardening, the conversation implementation unit can talk about how to grow plants. The conversation implementation unit can also provide topics based on the hobbies and interests of the elderly person, thereby increasing psychological satisfaction. For example, for an elderly person who likes music, the conversation implementation unit can talk about their favorite artists. The conversation implementation unit can also provide topics based on the hobbies and interests of the elderly person, thereby increasing psychological satisfaction. For example, for an elderly person who likes traveling, the conversation unit can talk about places they would like to go. In this way, by providing topics based on the hobbies and interests of the elderly person, psychological satisfaction can be increased.

[0075] The conversation implementation unit can use the emotion estimation function to grasp the emotional state of the elderly person in real time and provide appropriate psychological support. The conversation implementation unit, for example, uses the emotion estimation function to grasp the emotional state of the elderly person in real time and provide appropriate psychological support. For example, if the elderly person is emotionally unstable, it provides relaxing topics. The conversation implementation unit also uses the emotion estimation function to monitor the elderly person's emotional state and provide appropriate psychological support. For example, if the elderly person is feeling depressed, it offers words of encouragement. Furthermore, the conversation implementation unit uses the emotion estimation function to continuously grasp the elderly person's emotional state and provide appropriate psychological support. For example, it continues providing support until the elderly person's emotions stabilize. In this way, by grasping the elderly person's emotional state in real time and providing appropriate psychological support, it is possible to achieve psychological stability.

[0076] The conversation implementing unit can introduce online communities in which the elderly can participate and promote social connections. The conversation implementing unit, for example, introduces online communities in which the elderly can participate and promote social connections. For example, it introduces online forums based on hobbies or interests. The conversation implementing unit can also introduce online communities in which the elderly can participate and promote social connections. For example, it can introduce local online events. The conversation implementing unit can also introduce online communities in which the elderly can participate and promote social connections. For example, it can promote online interactions with people who share the same hobbies. In this way, social connections can be promoted by introducing online communities in which the elderly can participate.

[0077] The conversation implementing unit can share the elderly person's psychological state with family members, allowing the family members to provide appropriate support. The conversation implementing unit, for example, shares the elderly person's psychological state with family members, allowing the family members to provide appropriate support. For example, by sending regular reports to the family members. The conversation implementing unit also shares the elderly person's psychological state with family members, allowing the family members to provide appropriate support. For example, by sharing information through an online platform. The conversation implementing unit also shares the elderly person's psychological state with family members, allowing the family members to provide appropriate support. For example, by promoting regular communication with family members. This allows the elderly person's psychological state to be shared with family members, allowing the family members to provide appropriate support.

[0078] The conversation execution unit can use the emotion estimation function to provide relaxation music or meditation guidance in accordance with the elderly person's psychological state. The conversation execution unit, for example, uses the emotion estimation function to provide relaxation music in accordance with the elderly person's psychological state. For example, if the elderly person is emotionally unstable, relaxing music is played. The conversation execution unit also uses the emotion estimation function to provide meditation guidance in accordance with the elderly person's psychological state. For example, if the elderly person is emotionally unstable, an audio guide for meditation is provided. The conversation execution unit also uses the emotion estimation function to continuously provide relaxation music or meditation guidance in accordance with the elderly person's psychological state. For example, relaxation music or meditation guidance is provided until the elderly person's emotions stabilize. In this way, psychological stability can be achieved by providing relaxation music or meditation guidance in accordance with the elderly person's psychological state.

[0079] The analysis unit can anonymize the conversation data of the elderly and share it with research institutions to help with dementia research. For example, the analysis unit anonymizes the conversation data of the elderly and shares it with research institutions to help with dementia research. For example, the analysis unit anonymizes the data and provides it to a university research project. The analysis unit also anonymizes the conversation data of the elderly and shares it with medical institutions to help with dementia research. For example, the analysis unit anonymizes the data and provides it to a hospital research team. The analysis unit also anonymizes the conversation data of the elderly and shares it with international research institutions to help with dementia research. For example, the analysis unit anonymizes the data and provides it to an international research project. In this way, anonymizing the conversation data and sharing it with research institutions can help with dementia research.

[0080] The analysis unit can periodically generate individual health reports based on the conversation data and provide them to the elderly and their families. The analysis unit, for example, periodically generates individual health reports based on the conversation data and provides them to the elderly and their families. For example, it reports changes in health status as a monthly report. The analysis unit also generates individual health reports based on the conversation data and provides them to the elderly and their families. For example, it provides the reports through an online platform. Furthermore, the analysis unit continuously generates individual health reports based on the conversation data and provides them to the elderly and their families. For example, it provides a report in real time when a change in health status is observed. This makes it easier to understand the health status by generating individual health reports based on the conversation data and providing them to the elderly and their families.

[0081] The analysis unit can use the emotion estimation function to analyze long-term emotional fluctuations and evaluate the psychological health state. The analysis unit, for example, uses the emotion estimation function to analyze long-term emotional fluctuations and evaluate the psychological health state. For example, the analysis unit analyzes emotional fluctuation patterns to evaluate the psychological state. The analysis unit also uses the emotion estimation function to monitor long-term emotional fluctuations and evaluate the psychological health state. For example, it issues a warning if emotional fluctuations are observed frequently. Furthermore, the analysis unit uses the emotion estimation function to continuously analyze long-term emotional fluctuations and evaluate the psychological health state. For example, it conducts a follow-up conversation if emotional fluctuations continue for a long period of time. In this way, the psychological health state can be evaluated by analyzing long-term emotional fluctuations.

[0082] The analysis unit can integrate the elderly person's conversation data with other health data to generate a comprehensive health report. For example, the analysis unit can integrate the elderly person's conversation data with exercise data to generate a comprehensive health report. For example, it can analyze whether lack of exercise is affecting the content of the conversation. The analysis unit can also integrate the elderly person's conversation data with dietary data to generate a comprehensive health report. For example, it can analyze whether lack of nutrition is affecting the content of the conversation. The analysis unit can also integrate the elderly person's conversation data with sleep data to generate a comprehensive health report. For example, it can analyze whether lack of sleep is affecting the content of the conversation. In this way, by integrating the conversation data with other health data, a comprehensive health report can be generated.

[0083] The analysis unit can develop an application that provides personalized health advice based on the conversation data. The analysis unit, for example, develops an application that provides personalized health advice based on the conversation data. For example, advice on diet and exercise is provided. The analysis unit also develops an application that provides personalized health advice based on the conversation data. For example, advice on stress management and relaxation techniques is provided. The analysis unit also develops an application that provides personalized health advice based on the conversation data. For example, advice is provided according to changes in health status. In this way, by developing an application that provides personalized health advice based on the conversation data, more personalized health management is possible.

[0084] The analysis unit can use the emotion estimation function to share emotional fluctuations based on the conversation data with family members, allowing the family members to provide appropriate support. For example, the analysis unit uses the emotion estimation function to share emotional fluctuations based on the conversation data with family members, allowing the family members to provide appropriate support. For example, if emotional fluctuations are large, the analysis unit notifies the family members. The analysis unit also uses the emotion estimation function to share emotional fluctuations based on the conversation data with family members, allowing the family members to provide appropriate support. For example, if emotional fluctuations are observed frequently, the analysis unit also uses the emotion estimation function to share emotional fluctuations based on the conversation data with family members, allowing the family members to provide appropriate support. For example, if emotional fluctuations continue for a long period of time, the analysis unit provides a detailed report to the family members. In this way, by sharing emotional fluctuations based on the conversation data with family members, the family members can provide appropriate support.

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

[0086] The conversation implementation unit can analyze the elderly person's tone of voice and speaking speed, estimate their emotional state, and adjust the content of the conversation. For example, the conversation implementation unit can analyze the elderly person's tone of voice and speaking speed in real time to estimate their emotional state. For example, if the elderly person's voice is low and slow, they may be tired, so the conversation implementation unit can provide relaxing topics. If the elderly person's voice is high and fast, they may be excited, so the conversation implementation unit can provide calming topics. Furthermore, the conversation implementation unit can continuously monitor changes in tone of voice and speaking speed to understand fluctuations in their emotional state. This allows for more appropriate communication by providing conversation content that suits the elderly person's emotional state.

[0087] The conversation implementation unit can generate individually customized questions based on the elderly person's past conversation history to promote deeper dialogue. For example, it analyzes the elderly person's past conversation history to generate individually customized questions. For example, it may ask again about hobbies that were previously discussed to deepen the conversation. The conversation implementation unit also identifies the elderly person's interests from the past conversation history and asks questions based on that. For example, an elderly person who likes to travel may be asked about recent trips they have taken. Furthermore, the conversation implementation unit identifies the elderly person's health condition based on the past conversation history and asks questions based on that. For example, it may check the progress of a health problem that was previously discussed. In this way, more personalized dialogue is possible by utilizing the elderly person's past conversation history.

[0088] The analysis unit can detect signs of dementia by analyzing the frequency of specific keywords and phrases in the conversation. For example, it can analyze the frequency of specific keywords and phrases in the conversation to detect signs of dementia. For example, it can measure how often the same questions are repeated. The analysis unit can also analyze the appearance patterns of specific keywords and phrases in the conversation to detect signs of dementia. For example, it can measure how often the conversation loses coherence. Furthermore, the analysis unit can analyze fluctuations in the frequency of use of specific keywords and phrases in the conversation to detect signs of dementia. For example, it can detect cases where the frequency of use of specific words increases suddenly. In this way, by analyzing the frequency of specific keywords and phrases, it is possible to detect signs of dementia at an early stage.

[0089] The analysis unit can evaluate the consistency and logic of the elderly person's statements and issue a warning if an abnormality is detected. For example, it evaluates the consistency and logic of the elderly person's statements and issues a warning if an abnormality is detected. For example, it issues a warning if the conversation does not make sense. The analysis unit also evaluates the consistency and logic of the elderly person's statements and notifies family members or medical institutions if an abnormality is detected. For example, it notifies if the content of the conversation is contradictory. Furthermore, the analysis unit evaluates the consistency and logic of the elderly person's statements and conducts a follow-up conversation if an abnormality is detected. For example, it checks for details if the content of the conversation is unclear. In this way, by evaluating the consistency and logic of speech, signs of dementia can be detected early.

[0090] The analysis unit can use the emotion estimation function to analyze the emotional fluctuations of the elderly person and evaluate whether the emotional instability is a sign of dementia. For example, the emotion estimation function can be used to analyze the emotional fluctuations of the elderly person and evaluate whether the emotional instability is a sign of dementia. For example, sudden changes in emotion can be detected. The analysis unit can also use the emotion estimation function to analyze the pattern of the elderly person's emotional fluctuations and evaluate whether the emotional instability is a sign of dementia. For example, it can detect cases where emotional fluctuations are frequent. The analysis unit can also use the emotion estimation function to analyze the duration of the elderly person's emotional fluctuations and evaluate whether the emotional instability is a sign of dementia. For example, it can detect cases where emotional fluctuations continue for a long period of time. In this way, by analyzing emotional fluctuations, signs of dementia can be detected early.

[0091] The analysis unit can integrate the elderly person's conversation content with other health data to evaluate their overall health condition. For example, the analysis unit can integrate the elderly person's conversation content with their sleep patterns to evaluate their overall health condition. For example, it can analyze whether lack of sleep is affecting the conversation content. The analysis unit can also integrate the elderly person's conversation content with their food records to evaluate their overall health condition. For example, it can analyze whether lack of nutrition is affecting the conversation content. The analysis unit can also integrate the elderly person's conversation content with exercise data to evaluate their overall health condition. For example, it can analyze whether lack of exercise is affecting the conversation content. In this way, the analysis unit can evaluate their overall health condition by integrating the conversation content with other health data.

[0092] The analysis unit translates the content of the elderly's conversation into different languages ​​and can evaluate signs of dementia from an international perspective. For example, the content of the elderly's conversation can be translated into different languages ​​and evaluated for signs of dementia from an international perspective. For example, the content can be translated into English or French and analyzed. The analysis unit can also translate the content of the elderly's conversation into different languages ​​and evaluate signs of dementia taking into account different cultural backgrounds. For example, it can analyze changes in language usage due to cultural differences. Furthermore, the analysis unit can translate the content of the elderly's conversation into different languages ​​and evaluate signs of dementia by comparing it with international research data. For example, it can compare it with elderly people in other countries. In this way, by translating it into different languages, signs of dementia can be evaluated from an international perspective.

[0093] The analysis unit uses the emotion estimation function to collect the family's emotional reactions to the elderly person's conversation content, and can evaluate signs of dementia from the family's perspective as well. For example, the emotion estimation function is used to collect the family's emotional reactions to the elderly person's conversation content, and evaluate signs of dementia from the family's perspective as well. For example, a warning is issued if the family is worried. The analysis unit also uses the emotion estimation function to analyze the family's emotional reactions to the elderly person's conversation content, and evaluate signs of dementia from the family's perspective as well. For example, a follow-up conversation is held if the family is feeling anxious. The analysis unit also uses the emotion estimation function to monitor the family's emotional reactions to the elderly person's conversation content, and evaluate signs of dementia from the family's perspective as well. For example, if the family feels relieved, it determines that there is no particular problem. In this way, by collecting the family's emotional reactions, signs of dementia can be evaluated from the family's perspective as well.

[0094] The notification unit can send a notification of an abnormality detection not only to family members and medical institutions but also to the local support network. For example, the notification of an abnormality detection is sent not only to family members and medical institutions but also to the local support network. For example, local nursing care services and volunteer groups are notified. The notification unit also sends a notification of an abnormality detection to local welfare services. For example, the local welfare services understand the situation of the elderly person and provide appropriate support. Furthermore, the notification unit also sends a notification of an abnormality detection to the local community center. For example, the community center understands the situation of the elderly person and coordinates local support. In this way, by notifying the local support network, more widespread support can be provided.

[0095] The notification unit can use the emotion estimation function to analyze the emotional reactions of family members or medical professionals who receive the notification and provide appropriate follow-up. For example, the emotion estimation function can be used to analyze the emotional reactions of family members or medical professionals who receive the notification and provide appropriate follow-up. For example, if a family member is feeling anxious, the notification unit can provide information to reassure the family member. The notification unit also uses the emotion estimation function to monitor the emotional reactions of family members or medical professionals who receive the notification and provide appropriate follow-up. For example, if a medical professional feels an emergency, the notification unit can urge them to take prompt action. Furthermore, the notification unit uses the emotion estimation function to continuously analyze the emotional reactions of family members or medical professionals who receive the notification and provide appropriate follow-up. For example, the notification unit can continue to support the family member until their emotional state improves. This makes it possible to provide appropriate follow-up by analyzing the emotional reactions of family members or medical professionals who receive the notification.

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

[0097] Step 1: The conversation execution unit conducts daily conversations with the elderly. For example, the generation AI asks questions such as "What did you do today?" and "How are you feeling lately?" The generation AI uses voice recognition technology to understand the elderly's answers and generate appropriate responses. The input to the generation AI is prompts that include the topic of the conversation and the content of the questions, and the generation AI proceeds with the conversation based on those prompts. Step 2: The analysis unit analyzes the content of the conversation with the elderly person conducted by the conversation implementation unit. For example, the generation AI converts the content of the conversation into text and analyzes that text data. The generation AI uses an algorithm to evaluate the choice of words in the conversation, the flow of the story, and the consistency of memory, and detect signs of dementia. The input to the generation AI is the text data of the conversation, and the generation AI analyzes that data to detect signs of dementia. Step 3: The notification unit notifies the user if the analysis unit detects signs of dementia. For example, it may notify family members or medical institutions by saying, "Your recent conversations suggest signs of dementia. We recommend further testing." The generation AI also regularly conducts follow-up conversations and continuously monitors changes in the user's condition.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0100] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0102] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0104] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

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

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

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

[0108] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0115] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0117] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

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

[0119] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

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

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

[0123] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0132] 7, 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.

[0133] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0138] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0139] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0140] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0143] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0144] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0146] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0147] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0148] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0149] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0150] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0151] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0152] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0153] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0154] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0155] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0156] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0157] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0158] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0159] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0160] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0161] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0162] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0163] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0164] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

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

Claims

1. A conversation implementation unit that conducts daily conversations with elderly people; an analysis unit that analyzes the content of the conversation with the elderly person that has been conducted by the conversation conducting unit; a notification unit that notifies when a symptom of dementia is detected by the analysis unit. A system characterized by:

2. The conversation implementation unit: The tone of voice and speaking rate of the elderly person are analyzed, and the emotional state of the elderly person is estimated and the content of the conversation is adjusted accordingly.

2. The system of claim 1.

3. The analysis unit Analyzing the frequency of specific keywords and phrases in the conversation content to detect signs of dementia 2. The system of claim 1.

4. The notification unit Send notifications of detected anomalies to family members, medical institutions, and even local support networks 2. The system of claim 1.

5. The conversation implementation unit: Regularly assess the psychological status of the elderly person and offer professional counseling as needed.

2. The system of claim 1.

6. The analysis unit Analyze long-term emotional fluctuations and assess psychological well-being 2. The system of claim 1.

7. The conversation implementation unit: Grasp the emotional state of the elderly person in real time and engage in conversations that elicit positive emotions 2. The system of claim 1.

8. The analysis unit Analyzing the emotional fluctuations of the elderly person and evaluating whether the emotional instability is a sign of dementia.

2. The system of claim 1.

Images