System

Abstract

To grasp a health state and a mental state of an elderly person through daily conversation and quickly cope with a case where a problem occurs.SOLUTION: A system includes a collection unit, an analysis unit, and a cooperation unit. The collection unit collects information by having daily conversation with an elderly person of a specific age group. The analysis unit analyzes the information collected by the collection unit. The cooperation unit cooperates when a specific problem occurs based on the information analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult to understand the health condition, mental state, cognitive function, and living situation of elderly people through everyday conversation, making it difficult to respond quickly when problems arose.

[0005] The system according to the embodiment aims to grasp the health and mental state of elderly people through their daily conversations and to respond quickly if any problems arise. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a collaboration unit. The collection unit collects information by having daily conversations with elderly people of a specific age group. The analysis unit analyzes the information collected by the collection unit. The collaboration unit collaborates when a specific problem occurs based on the information analyzed by the analysis unit. Effect of the Invention

[0007] The system according to the embodiment is able to grasp the health and mental state of elderly people through their everyday conversations and can quickly respond if a problem arises. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the 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 specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) The system according to the embodiment of the present invention is a system that listens to the health, mental state, cognitive function, and living situation of the elderly through a voice assistant in a script similar to daily conversation. This system first has a daily conversation with the elderly and collects information. For example, the voice assistant asks questions such as "What kind of day was it today?" and "How are you feeling lately?" At this time, the voice assistant records the elderly's answers and collects information. Next, the collected information is analyzed. AI is used for the analysis to evaluate the elderly's health, mental state, cognitive function, living situation, and the like. For example, signs of stress or anxiety are detected from the elderly's answers, and signs indicating a decline in cognitive function are found. If a problem occurs as a result of the analysis, follow-up is performed in cooperation with a family doctor or a public institution. For example, if signs of stress or anxiety are detected, the family doctor is notified and an appropriate response is requested. In addition, if a decline in cognitive function is observed, a public institution is contacted and the necessary support is provided. As a result, the health, mental state, cognitive function, and living situation of the elderly can be monitored on a daily basis, and if a problem occurs, it can be quickly responded to. This is expected to improve the quality of life of the elderly. This will enable the system to monitor the health, mental state, cognitive function, and living conditions of elderly people on a daily basis, and respond quickly if any problems arise.

[0029] The system according to the embodiment includes a collection unit, an analysis unit, and a linking unit. The collection unit collects information by having daily conversations with the elderly. For example, the collection unit collects information by asking questions such as "What kind of day was it today?" and "How are you feeling lately?". The collection unit can, for example, use a voice assistant to record the elderly's answers and collect information. The analysis unit analyzes the information collected by the collection unit. For example, natural language processing technology is used for the analysis. For example, the analysis unit evaluates the elderly's health condition, mental state, cognitive function, and living situation based on the collected information. For example, the analysis unit detects signs of stress or anxiety from the elderly's answers, or finds signs indicating a decline in cognitive function. The linking unit links when a problem occurs based on the information analyzed by the analysis unit. For example, when a problem is detected, the linking unit automatically sends a notification to a family doctor or a public institution and requests an appropriate response. For example, when a sign of stress or anxiety is detected, the linking unit notifies the family doctor and requests an appropriate response. In addition, if the coordination unit detects a decline in cognitive function, it will contact a public institution and provide necessary support. In this way, the system according to the embodiment can monitor the health, mental state, cognitive function, and living situation of the elderly on a daily basis, and can quickly respond when a problem occurs.

[0030] The collection unit collects information by having daily conversations with the elderly. For example, the collection unit collects information by asking questions such as "What kind of day was it today?" and "How are you feeling lately?". The collection unit can, for example, use a voice assistant to record the elderly's answers and collect information. Specifically, the voice assistant recognizes the elderly's voice with high accuracy and draws out information through natural conversation. The voice assistant uses voice recognition technology to convert the elderly's remarks into text data and stores this in a database. Furthermore, the collection unit collects not only the content of the conversation, but also paralinguistic information such as tone of voice, speaking speed, and pauses. This allows the elderly's emotions and mental state to be understood in more detail. The collection unit regularly talks with the elderly and continuously monitors daily changes. For example, the voice assistant talks to the elderly at a fixed time every day and asks about daily events and physical condition. This allows the collection unit to grasp the changes in the elderly's life rhythm and health condition in real time. The collection unit also transmits the collected information to a cloud server so that the analysis unit and the linking unit can access it. This enables the collection unit to efficiently collect information on elderly people and play an important role in supporting the functionality of the entire system.

[0031] The analysis unit analyzes the information collected by the collection unit. For example, natural language processing technology is used for the analysis. Specifically, the collected text data is analyzed to evaluate the health condition, mental state, cognitive function, and living situation of the elderly. Natural language processing technology extracts keywords and phrases from the text data and evaluates the condition of the elderly based on these. For example, if keywords such as "tired," "can't sleep," and "forgetful" appear frequently in the elderly's answers, the analysis unit detects this as a sign of stress, anxiety, and declining cognitive function. The analysis unit also analyzes the collected paralinguistic information and evaluates emotional fluctuations from changes in tone of voice and speaking speed. Furthermore, the analysis unit can detect abnormal patterns by comparing with past data. For example, if negative keywords have increased in recent conversations compared to past conversation data, the analysis unit detects this as an abnormality and notifies the collaboration unit. The analysis unit uses AI to automate these analyses and can quickly and accurately evaluate the condition of the elderly. As a result, the analysis unit can comprehensively evaluate the health condition, mental state, cognitive function, and living situation of the elderly, and provide information to quickly respond when a problem occurs.

[0032] The linking unit links when a problem occurs based on the information analyzed by the analysis unit. Specifically, when a problem is detected, it automatically sends a notification to the family doctor or public institutions and requests appropriate action. For example, if the analysis unit detects signs of stress or anxiety from the elderly person's answers, the linking unit sends a notification to the family doctor and requests appropriate action. The notification is sent via email, SMS, or a dedicated medical linking system. In addition, if the linking unit detects a decline in cognitive function, it contacts public institutions and provides necessary support. For example, it can contact local welfare services and nursing care services to request home care and life support for the elderly person. The linking unit can flexibly set the content and destination of the notification, realizing the optimal response according to the elderly person's condition and situation. In addition, the linking unit manages the notification sending history and response status, and follows up as necessary. For example, it collects feedback from medical institutions and public institutions that have received the notification, and checks the progress of the response. As a result, the linking unit can support quick and appropriate responses to the health condition and living situation of the elderly person, improving the reliability and effectiveness of the entire system.

[0033] The collection unit can collect information by asking questions such as "What kind of day was it today?" or "How are you feeling recently?". The collection unit can collect information by asking questions such as "What kind of day was it today?" or "How are you feeling recently?" to the elderly person. The collection unit can record the elderly person's answers using a voice assistant, for example, and collect information. This makes it possible to collect information in a natural way through daily conversations with the elderly person. A part or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can record the elderly person's answers using a voice assistant and collect information.

[0034] The analysis unit can analyze the collected information using natural language processing and evaluate the health condition, mental state, cognitive function, and living situation of the elderly person. The analysis unit analyzes the collected information using, for example, natural language processing technology. The analysis unit evaluates the health condition, mental state, cognitive function, and living situation of the elderly person based on the collected information. For example, the analysis unit detects signs of stress or anxiety from the elderly person's answers, or finds signs indicating a decline in cognitive function. In this way, by using natural language processing, the collected information can be accurately analyzed and the condition of the elderly person can be evaluated. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit inputs the collected information into a generation AI, which can analyze the information using natural language processing technology and evaluate the condition of the elderly person.

[0035] The linking unit can automatically send a notification to a family doctor or a public institution when a problem is detected and request a specific response. For example, when a problem is detected, the linking unit automatically sends a notification to a family doctor or a public institution and requests an appropriate response. For example, when a sign of stress or anxiety is detected, the linking unit notifies the family doctor and requests an appropriate response. In addition, when a decline in cognitive function is observed, the linking unit contacts a public institution and provides necessary support. This makes it possible to respond quickly when a problem is detected. Some or all of the above-mentioned processing in the linking unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, when a problem is detected, the linking unit can send a notification to the generation AI, and the generation AI can request an appropriate response.

[0036] The collection unit can analyze the elderly person's past conversation history and select the optimal question. The collection unit, for example, analyzes the elderly person's past conversation history and selects the optimal question. For example, the collection unit asks a question based on a topic in which the elderly person has shown interest in the past. The collection unit can also ask a question while avoiding a topic that the elderly person has avoided in the past. The collection unit can also analyze the elderly person's past answer pattern and determine an appropriate order of questions. As a result, more effective questions can be asked by analyzing the past conversation history. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the elderly person's past conversation history into AI, and the AI ​​can select the optimal question.

[0037] The collection unit can filter based on the elderly person's current living situation and areas of interest when asking a question. For example, the collection unit filters based on the elderly person's current living situation and areas of interest when asking a question. For example, the collection unit asks a question based on an event that the elderly person recently participated in. The collection unit can also ask a question related to a hobby or activity that the elderly person is currently engaged in. The collection unit can also adjust the question based on the elderly person's living environment (for example, season or weather). This makes it possible to collect more relevant information by asking a question based on the elderly person's living situation and areas of interest. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the elderly person's current living situation and areas of interest into AI, and the AI ​​can adjust the content and timing of the question.

[0038] The collection unit can prioritize collecting highly relevant information in consideration of the geographical location information of the elderly person when asking a question. For example, the collection unit prioritizes collecting highly relevant information in consideration of the geographical location information of the elderly person when asking a question. For example, the collection unit collects information about medical facilities in the area where the elderly person lives. The collection unit can also ask questions related to places that the elderly person often visits. The collection unit can also collect appropriate life support information based on the living environment of the elderly person. In this way, more relevant information can be collected by considering the geographical location information of the elderly person. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the geographical location information of the elderly person to AI, and the AI ​​can collect highly relevant information preferentially.

[0039] The collection unit can analyze the social media activity of the elderly person when asking a question and collect related information. For example, the collection unit can analyze the social media activity of the elderly person when asking a question and collect related information. For example, the collection unit can ask a question based on health information shared by the elderly person on social media. The collection unit can also ask a question based on topics in which the elderly person has shown interest on social media. The collection unit can also analyze the activity pattern of the elderly person on social media and select an appropriate question. In this way, more relevant information can be collected by analyzing the social media activity of the elderly person. A part or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or can be performed without using AI. For example, the collection unit can input the social media activity of the elderly person to AI, and the AI ​​can collect related information.

[0040] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information during the analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the collected information during the analysis. For example, the analysis unit performs a detailed analysis when information on the health condition is important. The analysis unit can also perform a detailed analysis when information on the living situation is important. The analysis unit can also perform a detailed analysis when information on the mental state is important. This allows for more effective analysis by adjusting the level of detail of the analysis based on the importance of the information. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the importance of the collected information to an AI, which can adjust the level of detail of the analysis.

[0041] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. The analysis unit can apply different analysis algorithms depending on the category of information during analysis, for example. For example, the analysis unit applies a medical data analysis algorithm to information on health status. The analysis unit can also apply a psychological analysis algorithm to information on mental status. The analysis unit can also apply a sociological analysis algorithm to information on living conditions. This allows for more accurate analysis by applying different analysis algorithms depending on the category of information. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the category of information to AI, and the AI ​​can apply different analysis algorithms.

[0042] The analysis unit can determine the priority of the analysis based on the time when the information was collected during the analysis. The analysis unit, for example, determines the priority of the analysis based on the time when the information was collected during the analysis. For example, the analysis unit prioritizes the analysis of recently collected information. The analysis unit can also analyze current information while referring to past information. The analysis unit can also perform long-term analysis based on information collected periodically. This allows more effective analysis to be performed by determining the priority of the analysis based on the time when the information was collected. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the information was collected into AI, and the AI ​​can determine the priority of the analysis.

[0043] The analysis unit can adjust the order of analysis based on the relevance of information during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of information during analysis. For example, the analysis unit preferentially analyzes information related to health conditions. The analysis unit can also preferentially analyze information related to mental conditions. The analysis unit can also preferentially analyze information related to living conditions. This allows for more effective analysis by adjusting the order of analysis based on the relevance of information. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of information to AI, which can adjust the order of analysis.

[0044] The coordination unit can select an optimal coordination method by analyzing past coordination history at the time of coordination. For example, the coordination unit selects an optimal coordination method by analyzing past coordination history at the time of coordination. For example, the coordination unit selects an optimal coordination method based on a coordination method that was successful in the past. The coordination unit can also select a new coordination method by avoiding a coordination method that has had problems in the past. The coordination unit can also analyze past coordination history and determine an optimal coordination timing. As a result, a more effective coordination method can be selected by analyzing past coordination history. A part or all of the above-mentioned processing in the coordination unit may be performed, for example, using AI, or may be performed without using AI. For example, the coordination unit can input past coordination history into AI, which can select an optimal coordination method.

[0045] The linking unit can customize the means of linking based on the elderly person's current living situation at the time of linking. The linking unit, for example, customizes the means of linking based on the elderly person's current living situation at the time of linking. For example, when the elderly person is at home, the linking unit prioritizes phone calls and visits. In addition, when the elderly person is out, the linking unit can also prioritize emails and messages. In addition, the linking unit can select the optimal means of linking in accordance with the elderly person's life rhythm. As a result, by customizing the means of linking based on the elderly person's living situation, more appropriate responses can be made. Part or all of the above-mentioned processing in the linking unit may be performed, for example, using AI, or may be performed without using AI. For example, the linking unit can input the elderly person's current living situation into AI, and the AI ​​can customize the means of linking.

[0046] The linking unit can select the optimal linking method in consideration of the geographical location information of the elderly person at the time of linking. For example, the linking unit selects the optimal linking method in consideration of the geographical location information of the elderly person at the time of linking. For example, when the elderly person is at home, the linking unit prioritizes visits and phone calls. In addition, the linking unit can also prioritize emails and messages when the elderly person is out. In addition, the linking unit can select the optimal linking means based on the geographical location of the elderly person. In this way, a more appropriate linking method can be selected by considering the geographical location information of the elderly person. A part or all of the above-mentioned processing in the linking unit may be performed, for example, using AI, or may be performed without using AI. For example, the linking unit can input the geographical location information of the elderly person to AI, which can select the optimal linking method.

[0047] The linking unit can analyze the social media activity of the elderly person at the time of linking and propose a means of linking. For example, the linking unit can analyze the social media activity of the elderly person at the time of linking and propose a means of linking. For example, the linking unit prioritizes messages when the elderly person frequently uses social media. The linking unit can also propose an appropriate means of linking based on information shared by the elderly person on social media. The linking unit can also analyze the social media activity pattern of the elderly person and select the optimal means of linking. In this way, a more appropriate means of linking can be proposed by analyzing the social media activity of the elderly person. A part or all of the above-mentioned processing in the linking unit may be performed, for example, using AI or may be performed without using AI. For example, the linking unit can input the social media activity of the elderly person to AI, which can then propose a means of linking.

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

[0049] When asking questions, the collection unit can customize the questions taking into consideration the hobbies and interests of the elderly person. For example, if the elderly person is interested in gardening, the collection unit can ask questions about how to grow plants and the current state of the garden. If the elderly person is interested in music, the collection unit can also ask questions about music they have listened to recently and their favorite artists. Furthermore, if the elderly person is interested in traveling, the collection unit can also ask questions about recent travel destinations and places they would like to go. In this way, by asking questions based on the hobbies and interests of the elderly person, information can be collected through more natural conversation.

[0050] The analysis unit can analyze the elderly person's life rhythm based on the collected information and suggest appropriate lifestyle habits. For example, the analysis unit can analyze the elderly person's sleep pattern and suggest appropriate sleep time and relaxation methods. The analysis unit can also analyze the elderly person's eating pattern and suggest a balanced meal menu. Furthermore, the analysis unit can analyze the elderly person's exercise habits and suggest an appropriate exercise plan. In this way, it is possible to support health maintenance by suggesting appropriate lifestyle habits based on the elderly person's life rhythm.

[0051] The analysis unit can analyze the social connections of the elderly based on the collected information and make suggestions to prevent isolation. For example, the analysis unit can analyze the frequency of contact with friends and family of the elderly and make suggestions to encourage regular contact. The analysis unit can also analyze community activities in which the elderly participate and suggest participation in new activities. Furthermore, the analysis unit can suggest interactions with people who have the same hobbies based on the hobbies and interests of the elderly. This can strengthen the social connections of the elderly and prevent isolation.

[0052] The collection unit can analyze the elderly person's past conversation history and select optimal questions. For example, the collection unit can ask questions based on topics that the elderly person has shown interest in in the past. The collection unit can also ask questions that avoid topics that the elderly person has avoided in the past. Furthermore, the collection unit can analyze the elderly person's past answer patterns and determine an appropriate order of questions. In this way, more effective questions can be asked by analyzing the elderly person's past conversation history.

[0053] The analysis unit can analyze the elderly person's living environment based on the collected information and make appropriate suggestions for improving the environment. For example, the analysis unit can analyze the elderly person's living environment and suggest appropriate furniture arrangements and safety measures. The analysis unit can also analyze the elderly person's daily rhythm and suggest appropriate lighting and temperature adjustments. Furthermore, the analysis unit can analyze the elderly person's lifestyle habits and suggest appropriate exercise and relaxation methods. In this way, the quality of life can be improved by making appropriate suggestions for improving the environment based on the elderly person's living environment.

[0054] The collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of the elderly person when asking a question. For example, the collection unit collects information about medical facilities in the area where the elderly person lives. The collection unit can also ask questions related to places frequently visited by the elderly person. Furthermore, the collection unit can also collect appropriate life support information based on the living environment of the elderly person. In this way, more relevant information can be collected by taking into account the geographical location information of the elderly person.

[0055] The process flow of the first embodiment will be briefly described below.

[0056] Step 1: The collection unit collects information by having daily conversations with the elderly. For example, the collection unit collects information by asking questions such as "How was your day today?" and "How are you feeling recently?" The collection unit can use a voice assistant to record the elderly's answers and collect information. Step 2: The analysis unit analyzes the information collected by the collection unit. Natural language processing technology is used for the analysis, and the analysis unit evaluates the elderly person's health condition, mental state, cognitive function, and living situation based on the collected information. For example, the analysis unit detects signs of stress or anxiety from the elderly person's responses, and finds signs that indicate a decline in cognitive function. Step 3: The collaboration unit collaborates when a problem occurs based on the information analyzed by the analysis unit. For example, when a problem is detected, it automatically sends a notification to the patient's family doctor or public institutions to request appropriate action. If signs of stress or anxiety are detected, the collaboration unit notifies the patient's family doctor, and if a decline in cognitive function is observed, it contacts a public institution and provides the necessary support.

[0057] (Example 2) The system according to the embodiment of the present invention is a system that listens to the health, mental state, cognitive function, and living situation of the elderly through a voice assistant in a script similar to daily conversation. This system first has a daily conversation with the elderly and collects information. For example, the voice assistant asks questions such as "What kind of day was it today?" and "How are you feeling lately?" At this time, the voice assistant records the elderly's answers and collects information. Next, the collected information is analyzed. AI is used for the analysis to evaluate the elderly's health, mental state, cognitive function, living situation, and the like. For example, signs of stress or anxiety are detected from the elderly's answers, and signs indicating a decline in cognitive function are found. If a problem occurs as a result of the analysis, follow-up is performed in cooperation with a family doctor or a public institution. For example, if signs of stress or anxiety are detected, the family doctor is notified and an appropriate response is requested. In addition, if a decline in cognitive function is observed, a public institution is contacted and the necessary support is provided. As a result, the health, mental state, cognitive function, and living situation of the elderly can be monitored on a daily basis, and if a problem occurs, it can be quickly responded to. This is expected to improve the quality of life of the elderly. This will enable the system to monitor the health, mental state, cognitive function, and living conditions of elderly people on a daily basis, and respond quickly if any problems arise.

[0058] The system according to the embodiment includes a collection unit, an analysis unit, and a linking unit. The collection unit collects information by having daily conversations with the elderly. For example, the collection unit collects information by asking questions such as "What kind of day was it today?" and "How are you feeling lately?". The collection unit can, for example, use a voice assistant to record the elderly's answers and collect information. The analysis unit analyzes the information collected by the collection unit. For example, natural language processing technology is used for the analysis. For example, the analysis unit evaluates the elderly's health condition, mental state, cognitive function, and living situation based on the collected information. For example, the analysis unit detects signs of stress or anxiety from the elderly's answers, or finds signs indicating a decline in cognitive function. The linking unit links when a problem occurs based on the information analyzed by the analysis unit. For example, when a problem is detected, the linking unit automatically sends a notification to a family doctor or a public institution and requests an appropriate response. For example, when a sign of stress or anxiety is detected, the linking unit notifies the family doctor and requests an appropriate response. In addition, if the coordination unit detects a decline in cognitive function, it will contact a public institution and provide necessary support. In this way, the system according to the embodiment can monitor the health, mental state, cognitive function, and living situation of the elderly on a daily basis, and can quickly respond when a problem occurs.

[0059] The collection unit collects information by having daily conversations with the elderly. For example, the collection unit collects information by asking questions such as "What kind of day was it today?" and "How are you feeling lately?". The collection unit can, for example, use a voice assistant to record the elderly's answers and collect information. Specifically, the voice assistant recognizes the elderly's voice with high accuracy and draws out information through natural conversation. The voice assistant uses voice recognition technology to convert the elderly's remarks into text data and stores this in a database. Furthermore, the collection unit collects not only the content of the conversation, but also paralinguistic information such as tone of voice, speaking speed, and pauses. This allows the elderly's emotions and mental state to be understood in more detail. The collection unit regularly talks with the elderly and continuously monitors daily changes. For example, the voice assistant talks to the elderly at a fixed time every day and asks about daily events and physical condition. This allows the collection unit to grasp the changes in the elderly's life rhythm and health condition in real time. The collection unit also transmits the collected information to a cloud server so that the analysis unit and the linking unit can access it. This enables the collection unit to efficiently collect information on elderly people and play an important role in supporting the functioning of the entire system.

[0060] The analysis unit analyzes the information collected by the collection unit. For example, natural language processing technology is used for the analysis. Specifically, the collected text data is analyzed to evaluate the health condition, mental state, cognitive function, and living situation of the elderly. Natural language processing technology extracts keywords and phrases from the text data and evaluates the condition of the elderly based on these. For example, if keywords such as "tired," "can't sleep," and "forgetful" appear frequently in the elderly's answers, the analysis unit detects this as a sign of stress, anxiety, and declining cognitive function. The analysis unit also analyzes the collected paralinguistic information and evaluates emotional fluctuations from changes in tone of voice and speaking speed. Furthermore, the analysis unit can detect abnormal patterns by comparing with past data. For example, if negative keywords have increased in recent conversations compared to past conversation data, the analysis unit detects this as an abnormality and notifies the collaboration unit. The analysis unit uses AI to automate these analyses and can quickly and accurately evaluate the condition of the elderly. As a result, the analysis unit can comprehensively evaluate the health condition, mental state, cognitive function, and living situation of the elderly, and provide information to quickly respond when a problem occurs.

[0061] The linking unit links when a problem occurs based on the information analyzed by the analysis unit. Specifically, when a problem is detected, it automatically sends a notification to the family doctor or public institutions and requests appropriate action. For example, if the analysis unit detects signs of stress or anxiety from the elderly person's answers, the linking unit sends a notification to the family doctor and requests appropriate action. The notification is sent via email, SMS, or a dedicated medical linking system. In addition, if the linking unit detects a decline in cognitive function, it contacts public institutions and provides necessary support. For example, it can contact local welfare services and nursing care services to request home care and life support for the elderly person. The linking unit can flexibly set the content and destination of the notification, realizing the optimal response according to the elderly person's condition and situation. In addition, the linking unit manages the notification sending history and response status, and follows up as necessary. For example, it collects feedback from medical institutions and public institutions that have received the notification, and checks the progress of the response. As a result, the linking unit can support quick and appropriate responses to the health condition and living situation of the elderly person, improving the reliability and effectiveness of the entire system.

[0062] The collection unit can collect information by asking questions such as "What kind of day was it today?" or "How are you feeling recently?". The collection unit can collect information by asking questions such as "What kind of day was it today?" or "How are you feeling recently?" to the elderly person. The collection unit can record the elderly person's answers using a voice assistant, for example, and collect information. This makes it possible to collect information in a natural way through daily conversations with the elderly person. A part or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can record the elderly person's answers using a voice assistant and collect information.

[0063] The analysis unit can analyze the collected information using natural language processing and evaluate the health condition, mental state, cognitive function, and living situation of the elderly person. The analysis unit analyzes the collected information using, for example, natural language processing technology. The analysis unit evaluates the health condition, mental state, cognitive function, and living situation of the elderly person based on the collected information. For example, the analysis unit detects signs of stress or anxiety from the elderly person's answers, or finds signs indicating a decline in cognitive function. In this way, by using natural language processing, the collected information can be accurately analyzed and the condition of the elderly person can be evaluated. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit inputs the collected information into a generation AI, which can analyze the information using natural language processing technology and evaluate the condition of the elderly person.

[0064] The linking unit can automatically send a notification to a family doctor or a public institution when a problem is detected and request a specific response. For example, when a problem is detected, the linking unit automatically sends a notification to a family doctor or a public institution and requests an appropriate response. For example, when a sign of stress or anxiety is detected, the linking unit notifies the family doctor and requests an appropriate response. In addition, when a decline in cognitive function is observed, the linking unit contacts a public institution and provides necessary support. This makes it possible to respond quickly when a problem is detected. Some or all of the above-mentioned processing in the linking unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, when a problem is detected, the linking unit can send a notification to the generation AI, and the generation AI can request an appropriate response.

[0065] The collection unit can estimate the elderly person's emotions and adjust the content and timing of questions based on the estimated emotions. The collection unit, for example, estimates the elderly person's emotions and adjusts the content and timing of questions based on the estimated emotions. For example, when the elderly person is relaxed, the collection unit asks detailed questions to collect more information. Also, when the elderly person is stressed, the collection unit can ask simple and short questions to reduce the burden. Also, when the elderly person is excited, the collection unit can ask questions in a calm tone to elicit a calm answer. In this way, more appropriate information can be collected by adjusting the content and timing of questions according to the elderly person's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the elderly person's emotions to the generation AI, which can estimate the emotions and adjust the content and timing of questions.

[0066] The collection unit can analyze the elderly person's past conversation history and select the optimal question. The collection unit, for example, analyzes the elderly person's past conversation history and selects the optimal question. For example, the collection unit asks a question based on a topic in which the elderly person has shown interest in the past. The collection unit can also ask a question while avoiding a topic that the elderly person has avoided in the past. The collection unit can also analyze the elderly person's past answer pattern and determine an appropriate order of questions. As a result, more effective questions can be asked by analyzing the past conversation history. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the elderly person's past conversation history into AI, and the AI ​​can select the optimal question.

[0067] The collection unit can filter based on the elderly person's current living situation and areas of interest when asking a question. For example, the collection unit filters based on the elderly person's current living situation and areas of interest when asking a question. For example, the collection unit asks a question based on an event that the elderly person recently participated in. The collection unit can also ask a question related to a hobby or activity that the elderly person is currently engaged in. The collection unit can also adjust the question based on the elderly person's living environment (for example, season or weather). This makes it possible to collect more relevant information by asking a question based on the elderly person's living situation and areas of interest. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the elderly person's current living situation and areas of interest into AI, and the AI ​​can adjust the content and timing of the question.

[0068] The collection unit can estimate the emotion of the elderly person and determine the priority of the information to be collected based on the estimated emotion. The collection unit, for example, estimates the emotion of the elderly person and determines the priority of the information to be collected based on the estimated emotion. For example, when the elderly person feels anxious, the collection unit preferentially collects information on the health condition. In addition, when the elderly person is relaxed, the collection unit can also preferentially collect information on the living situation. In addition, when the elderly person is excited, the collection unit can also preferentially collect information on the mental state. In this way, important information can be preferentially collected by determining the priority of information according to the emotion of the elderly person. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the emotion of the elderly person to the generation AI, which can estimate the emotion and determine the priority of the information to be collected.

[0069] The collection unit can prioritize collecting highly relevant information in consideration of the geographical location information of the elderly person when asking a question. For example, the collection unit prioritizes collecting highly relevant information in consideration of the geographical location information of the elderly person when asking a question. For example, the collection unit collects information about medical facilities in the area where the elderly person lives. The collection unit can also ask questions related to places that the elderly person often visits. The collection unit can also collect appropriate life support information based on the living environment of the elderly person. In this way, more relevant information can be collected by considering the geographical location information of the elderly person. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the geographical location information of the elderly person to AI, and the AI ​​can collect highly relevant information preferentially.

[0070] The collection unit can analyze the social media activity of the elderly person when asking a question and collect related information. For example, the collection unit can analyze the social media activity of the elderly person when asking a question and collect related information. For example, the collection unit can ask a question based on health information shared by the elderly person on social media. The collection unit can also ask a question based on topics in which the elderly person has shown interest on social media. The collection unit can also analyze the activity pattern of the elderly person on social media and select an appropriate question. In this way, more relevant information can be collected by analyzing the social media activity of the elderly person. A part or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or can be performed without using AI. For example, the collection unit can input the social media activity of the elderly person to AI, and the AI ​​can collect related information.

[0071] The analysis unit can estimate the emotion of the elderly person and adjust the expression method of the analysis based on the estimated emotion. The analysis unit, for example, estimates the emotion of the elderly person and adjusts the expression method of the analysis based on the estimated emotion. For example, the analysis unit provides a detailed analysis result when the elderly person is relaxed. In addition, the analysis unit can provide a concise and to the point analysis result when the elderly person is stressed. In addition, the analysis unit can provide a visually easy-to-understand analysis result when the elderly person is excited. In this way, by adjusting the expression method of the analysis according to the emotion of the elderly person, a more appropriate analysis result can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the emotion of the elderly person to the generation AI, which can estimate the emotion and adjust the expression method of the analysis.

[0072] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information during the analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the collected information during the analysis. For example, the analysis unit performs a detailed analysis when information on the health condition is important. The analysis unit can also perform a detailed analysis when information on the living situation is important. The analysis unit can also perform a detailed analysis when information on the mental state is important. This allows for more effective analysis by adjusting the level of detail of the analysis based on the importance of the information. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the importance of the collected information to an AI, which can adjust the level of detail of the analysis.

[0073] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. The analysis unit can apply different analysis algorithms depending on the category of information during analysis, for example. For example, the analysis unit applies a medical data analysis algorithm to information on health status. The analysis unit can also apply a psychological analysis algorithm to information on mental status. The analysis unit can also apply a sociological analysis algorithm to information on living conditions. This allows for more accurate analysis by applying different analysis algorithms depending on the category of information. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the category of information to AI, and the AI ​​can apply different analysis algorithms.

[0074] The analysis unit can estimate the emotion of the elderly person and adjust the length of the analysis based on the estimated emotion. The analysis unit, for example, estimates the emotion of the elderly person and adjusts the length of the analysis based on the estimated emotion. For example, the analysis unit performs a detailed analysis when the elderly person is relaxed. In addition, the analysis unit can also perform a brief analysis when the elderly person is stressed. In addition, the analysis unit can also perform a visually easy-to-understand analysis when the elderly person is excited. In this way, by adjusting the length of the analysis according to the emotion of the elderly person, a more appropriate analysis result can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the emotion of the elderly person to the generation AI, which can estimate the emotion and adjust the length of the analysis.

[0075] The analysis unit can determine the priority of the analysis based on the time when the information was collected during the analysis. The analysis unit, for example, determines the priority of the analysis based on the time when the information was collected during the analysis. For example, the analysis unit prioritizes the analysis of recently collected information. The analysis unit can also analyze current information while referring to past information. The analysis unit can also perform long-term analysis based on information collected periodically. This allows more effective analysis to be performed by determining the priority of the analysis based on the time when the information was collected. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the information was collected into AI, and the AI ​​can determine the priority of the analysis.

[0076] The analysis unit can adjust the order of analysis based on the relevance of information during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of information during analysis. For example, the analysis unit preferentially analyzes information related to health conditions. The analysis unit can also preferentially analyze information related to mental conditions. The analysis unit can also preferentially analyze information related to living conditions. This allows for more effective analysis by adjusting the order of analysis based on the relevance of information. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of information to AI, which can adjust the order of analysis.

[0077] The linking unit can estimate the emotion of the elderly person and adjust the linking method based on the estimated emotion. The linking unit, for example, estimates the emotion of the elderly person and adjusts the linking method based on the estimated emotion. For example, the linking unit provides a detailed linking method when the elderly person is relaxed. In addition, the linking unit can also provide a simple and quick linking method when the elderly person is stressed. In addition, the linking unit can also provide a linking method that encourages a calm response when the elderly person is excited. In this way, by adjusting the linking method according to the emotion of the elderly person, a more appropriate response can be made. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the linking unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the linking unit can input the emotion of the elderly person to the generation AI, which can estimate the emotion and adjust the linking method.

[0078] The coordination unit can select an optimal coordination method by analyzing past coordination history at the time of coordination. For example, the coordination unit selects an optimal coordination method by analyzing past coordination history at the time of coordination. For example, the coordination unit selects an optimal coordination method based on a coordination method that was successful in the past. The coordination unit can also select a new coordination method by avoiding a coordination method that has had problems in the past. The coordination unit can also analyze past coordination history and determine an optimal coordination timing. As a result, a more effective coordination method can be selected by analyzing past coordination history. A part or all of the above-mentioned processing in the coordination unit may be performed, for example, using AI, or may be performed without using AI. For example, the coordination unit can input past coordination history into AI, which can select an optimal coordination method.

[0079] The linking unit can customize the means of linking based on the elderly person's current living situation at the time of linking. The linking unit, for example, customizes the means of linking based on the elderly person's current living situation at the time of linking. For example, when the elderly person is at home, the linking unit prioritizes phone calls and visits. In addition, when the elderly person is out, the linking unit can also prioritize emails and messages. In addition, the linking unit can select the optimal means of linking in accordance with the elderly person's life rhythm. As a result, by customizing the means of linking based on the elderly person's living situation, more appropriate responses can be made. Part or all of the above-mentioned processing in the linking unit may be performed, for example, using AI, or may be performed without using AI. For example, the linking unit can input the elderly person's current living situation into AI, and the AI ​​can customize the means of linking.

[0080] The linking unit can estimate the emotion of the elderly person and determine the priority of linking based on the estimated emotion. The linking unit, for example, estimates the emotion of the elderly person and determines the priority of linking based on the estimated emotion. For example, when the elderly person feels anxious, the linking unit prioritizes linking with a high degree of urgency. In addition, when the elderly person is relaxed, the linking unit can also perform normal linking. In addition, when the elderly person is excited, the linking unit can also prioritize linking that encourages a calm response. In this way, by determining the priority of linking according to the emotion of the elderly person, a more appropriate response can be performed. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the linking unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the linking unit can input the emotion of the elderly person to the generation AI, which can estimate the emotion and determine the priority of linking.

[0081] The linking unit can select the optimal linking method in consideration of the geographical location information of the elderly person at the time of linking. For example, the linking unit selects the optimal linking method in consideration of the geographical location information of the elderly person at the time of linking. For example, when the elderly person is at home, the linking unit prioritizes visits and phone calls. In addition, the linking unit can also prioritize emails and messages when the elderly person is out. In addition, the linking unit can select the optimal linking means based on the geographical location of the elderly person. In this way, a more appropriate linking method can be selected by considering the geographical location information of the elderly person. A part or all of the above-mentioned processing in the linking unit may be performed, for example, using AI, or may be performed without using AI. For example, the linking unit can input the geographical location information of the elderly person to AI, which can select the optimal linking method.

[0082] The linking unit can analyze the social media activity of the elderly person at the time of linking and propose a means of linking. For example, the linking unit can analyze the social media activity of the elderly person at the time of linking and propose a means of linking. For example, the linking unit prioritizes messages when the elderly person frequently uses social media. The linking unit can also propose an appropriate means of linking based on information shared by the elderly person on social media. The linking unit can also analyze the social media activity pattern of the elderly person and select the optimal means of linking. In this way, a more appropriate means of linking can be proposed by analyzing the social media activity of the elderly person. A part or all of the above-mentioned processing in the linking unit may be performed, for example, using AI or may be performed without using AI. For example, the linking unit can input the social media activity of the elderly person to AI, which can then propose a means of linking.

[0083] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0084] When asking questions, the collection unit can customize the questions taking into consideration the hobbies and interests of the elderly person. For example, if the elderly person is interested in gardening, the collection unit can ask questions about how to grow plants and the current state of the garden. If the elderly person is interested in music, the collection unit can also ask questions about music they have listened to recently and their favorite artists. Furthermore, if the elderly person is interested in traveling, the collection unit can also ask questions about recent travel destinations and places they would like to go. In this way, by asking questions based on the hobbies and interests of the elderly person, information can be collected through more natural conversation.

[0085] The analysis unit can analyze the elderly person's life rhythm based on the collected information and suggest appropriate lifestyle habits. For example, the analysis unit can analyze the elderly person's sleep pattern and suggest appropriate sleep time and relaxation methods. The analysis unit can also analyze the elderly person's eating pattern and suggest a balanced meal menu. Furthermore, the analysis unit can analyze the elderly person's exercise habits and suggest an appropriate exercise plan. In this way, it is possible to support health maintenance by suggesting appropriate lifestyle habits based on the elderly person's life rhythm.

[0086] The coordination unit can estimate the emotion of the elderly person and adjust the timing of coordination based on the estimated emotion. For example, when the elderly person is relaxed, the coordination unit performs detailed coordination. In addition, when the elderly person is feeling stressed, the coordination unit can also perform concise and quick coordination. Furthermore, when the elderly person is excited, the coordination unit can also perform coordination that encourages a calm response. In this way, by adjusting the timing of coordination according to the emotion of the elderly person, a more appropriate response can be made.

[0087] The collection unit can estimate the emotion of the elderly person and determine the priority of information to be collected based on the estimated emotion. For example, when the elderly person feels anxious, the collection unit can preferentially collect information about the health condition. Also, when the elderly person feels relaxed, the collection unit can also preferentially collect information about the living situation. Furthermore, when the elderly person is excited, the collection unit can also preferentially collect information about the mental state. In this way, by determining the priority of information according to the emotion of the elderly person, important information can be preferentially collected.

[0088] The analysis unit can analyze the social connections of the elderly based on the collected information and make suggestions to prevent isolation. For example, the analysis unit can analyze the frequency of contact with friends and family of the elderly and make suggestions to encourage regular contact. The analysis unit can also analyze community activities in which the elderly participate and suggest participation in new activities. Furthermore, the analysis unit can suggest interactions with people who have the same hobbies based on the hobbies and interests of the elderly. This can strengthen the social connections of the elderly and prevent isolation.

[0089] The coordination unit can estimate the emotion of the elderly person and adjust the coordination method based on the estimated emotion. For example, the coordination unit can provide a detailed coordination method when the elderly person is relaxed. The coordination unit can also provide a simple and quick coordination method when the elderly person is stressed. Furthermore, the coordination unit can provide a coordination method that encourages the elderly person to respond calmly when the elderly person is excited. In this way, a more appropriate response can be made by adjusting the coordination method according to the emotion of the elderly person.

[0090] The collection unit can analyze the elderly person's past conversation history and select optimal questions. For example, the collection unit can ask questions based on topics that the elderly person has shown interest in in the past. The collection unit can also ask questions that avoid topics that the elderly person has avoided in the past. Furthermore, the collection unit can analyze the elderly person's past answer patterns and determine an appropriate order of questions. In this way, more effective questions can be asked by analyzing the elderly person's past conversation history.

[0091] The analysis unit can analyze the elderly person's living environment based on the collected information and make appropriate suggestions for improving the environment. For example, the analysis unit can analyze the elderly person's living environment and suggest appropriate furniture arrangements and safety measures. The analysis unit can also analyze the elderly person's daily rhythm and suggest appropriate lighting and temperature adjustments. Furthermore, the analysis unit can analyze the elderly person's lifestyle habits and suggest appropriate exercise and relaxation methods. In this way, the quality of life can be improved by making appropriate suggestions for improving the environment based on the elderly person's living environment.

[0092] The coordination unit can estimate the emotions of the elderly person and determine the priority order of coordination based on the estimated emotions. For example, when the elderly person feels anxious, the coordination unit prioritizes coordination with a high degree of urgency. In addition, when the elderly person feels relaxed, the coordination unit can also perform normal coordination. Furthermore, when the elderly person is excited, the coordination unit can also prioritize coordination that encourages a calm response. In this way, by determining the priority order of coordination according to the emotions of the elderly person, a more appropriate response can be made.

[0093] The collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of the elderly person when asking a question. For example, the collection unit collects information about medical facilities in the area where the elderly person lives. The collection unit can also ask questions related to places frequently visited by the elderly person. Furthermore, the collection unit can also collect appropriate life support information based on the living environment of the elderly person. In this way, more relevant information can be collected by taking into account the geographical location information of the elderly person.

[0094] The process flow of the second embodiment will be briefly described below.

[0095] Step 1: The collection unit collects information by having daily conversations with the elderly. For example, the collection unit collects information by asking questions such as "How was your day today?" and "How are you feeling recently?" The collection unit can use a voice assistant to record the elderly's answers and collect information. Step 2: The analysis unit analyzes the information collected by the collection unit. Natural language processing technology is used for the analysis, and the analysis unit evaluates the elderly person's health condition, mental state, cognitive function, and living situation based on the collected information. For example, the analysis unit detects signs of stress or anxiety from the elderly person's responses, and finds signs that indicate a decline in cognitive function. Step 3: The collaboration unit collaborates when a problem occurs based on the information analyzed by the analysis unit. For example, when a problem is detected, it automatically sends a notification to the patient's family doctor or public institutions to request appropriate action. If signs of stress or anxiety are detected, the collaboration unit notifies the patient's family doctor, and if a decline in cognitive function is observed, it contacts a public institution and provides the necessary support.

[0096] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0097] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0098] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0099] For example, the collection unit is realized by the microphone 38B and the control unit 46A of the smart device 14, and records the voice of the elderly person and collects information. The analysis unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and analyzes the collected information to evaluate the elderly person's health condition, mental state, cognitive function, and living situation. The linking unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and when a problem is detected, sends a notification to the family doctor or a public institution and requests appropriate action. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0100] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0101] 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.

[0102] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0103] 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.

[0104] 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 the voice according to instructions from the processor 46.

[0105] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0106] 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 performed in a secure state.

[0107] Fig. 4 shows an example of 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.

[0108] 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.

[0109] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0110] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 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 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0111] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0112] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0113] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0114] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0115] For example, the collection unit is realized by the microphone 238 of the smart glasses 214 and the control unit 46A, and records the voice of the elderly person and collects information. The analysis unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and analyzes the collected information to evaluate the elderly person's health condition, mental state, cognitive function, and living situation. The linking unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and when a problem is detected, sends a notification to the family doctor or a public institution and requests appropriate action. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[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, the RAM 30, and the 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 the voice according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average 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 performed 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0126] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) 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 voice indicating a user input for 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.

[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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0131] For example, the collection unit is realized by the microphone 238 of the headset terminal 314 and the control unit 46A, and records the voice of the elderly person and collects information. The analysis unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and analyzes the collected information to evaluate the elderly person's health condition, mental state, cognitive function, and living situation. The linking unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and when a problem is detected, sends a notification to the family doctor or a public institution and requests an appropriate response. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0134] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0135] 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. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0136] 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 the voice according to instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0138] 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 performed in a secure state.

[0139] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0140] 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.

[0141] 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.

[0142] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0143] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0144] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0145] 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 a voice indicating a user input for 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.

[0146] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0147] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0148] For example, the collection unit is realized by the microphone 238 and the control unit 46A of the robot 414, and records the voice of the elderly person and collects information. The analysis unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and analyzes the collected information to evaluate the elderly person's health condition, mental state, cognitive function, and living situation. The linking unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and when a problem is detected, sends a notification to the elderly person's family doctor or a public institution and requests them to take appropriate action. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0149] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0150] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0151] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0152] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0153] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0154] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0155] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0156] 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.

[0157] 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 Universal Serial Bus (USB) 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.

[0158] In addition, 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 upon request from the data processing device 12.

[0159] 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.

[0160] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0161] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0162] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0163] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0164] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0165] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0166] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0167] (Appendix 1) The collection department conducts daily conversations with seniors of a specific age group to collect information; an analysis unit that analyzes the information collected by the collection unit; A linking unit that links when a specific problem occurs based on the information analyzed by the analysis unit. A system characterized by: (Appendix 2) The collecting unit includes: Gather information by asking questions like, "How was your day?" or "How are you feeling lately?" 2. The system of claim 1, (Appendix 3) The analysis unit is Analyze collected information using natural language processing to assess the health, mental state, cognitive function, and living conditions of elderly people 2. The system of claim 1, (Appendix 4) The linking portion is When a problem is detected, notifications are automatically sent to your doctor or public authorities to request specific action. 2. The system of claim 1, (Appendix 5) The collecting unit includes: Estimate the emotions of the elderly and adjust the content and timing of questions based on the estimated emotions 2. The system of claim 1, (Appendix 6) The collecting unit includes: Analyzing the elderly person's past conversation history and selecting the most appropriate questions 2. The system of claim 1, (Appendix 7) The collecting unit includes: Filter questions based on seniors' current living situations and areas of interest 2. The system of claim 1, (Appendix 8) The collecting unit includes: Estimate the emotions of the elderly and prioritize the information to be collected based on the estimated emotions. 2. The system of claim 1, (Appendix 9) The collecting unit includes: When asking questions, the system takes into account the geographic location of the senior and prioritizes collecting the most relevant information. 2. The system of claim 1, (Appendix 10) The collecting unit includes: Analyze seniors' social media activity and collect relevant information when making inquiries 2. The system of claim 1, (Appendix 11) The analysis unit is Estimate the emotions of the elderly and adjust the way the analysis is presented based on the estimated emotions. 2. The system of claim 1, (Appendix 12) The analysis unit is During analysis, adjust the level of detail of the analysis based on the importance of the information collected 2. The system of claim 1, (Appendix 13) The analysis unit is During analysis, different analysis algorithms are applied depending on the category of information 2. The system of claim 1, (Appendix 14) The analysis unit is Estimate the emotions of the elderly and adjust the length of the analysis based on the estimated emotions. 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, prioritize analysis based on when the information was collected 2. The system of claim 1, (Appendix 16) The analysis unit is During analysis, adjust the order of analysis based on the relevance of information 2. The system of claim 1, (Appendix 17) The linking portion is Estimate the emotions of the elderly and adjust the way of interacting based on the estimated emotions 2. The system of claim 1, (Appendix 18) The linking portion is When linking, analyze past link history to select the optimal linking method 2. The system of claim 1, (Appendix 19) The linking portion is When engaging, customize the means of engagement based on the senior's current living situation 2. The system of claim 1, (Appendix 20) The linking portion is Estimating the emotions of elderly people and determining collaboration priorities based on the estimated emotions 2. The system of claim 1, (Appendix 21) The linking portion is When linking, the optimal linking method is selected taking into account the geographic location information of the elderly person. 2. The system of claim 1, (Appendix 22) The linking portion is When connecting, analyze the social media activity of seniors and suggest ways to connect. 2. The system of claim 1, [Explanation of symbols]

[0168] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. The collection department conducts daily conversations with seniors of a specific age group to collect information; an analysis unit that analyzes the information collected by the collection unit; A linking unit that links when a specific problem occurs based on the information analyzed by the analysis unit. A system characterized by:

2. The analysis unit is Analyze collected information using natural language processing to assess the health, mental state, cognitive function, and living conditions of elderly people 2. The system of claim 1 .

3. The linking portion is When a problem is detected, notifications are automatically sent to your doctor or public authorities to request specific action.

2. The system of claim 1 .

4. The collecting unit includes: Estimate the emotions of the elderly and adjust the content and timing of questions based on the estimated emotions 2. The system of claim 1 .

5. The collecting unit includes: Analyzing the elderly person's past conversation history and selecting the most appropriate questions 2. The system of claim 1 .

6. The collecting unit includes: Filter questions based on seniors' current living situations and areas of interest 2. The system of claim 1 .

7. The collecting unit includes: Estimate the emotions of the elderly and prioritize the information to be collected based on the estimated emotions.

2. The system of claim 1 .

8. The collecting unit includes: When asking questions, the system takes into account the geographic location of the senior and prioritizes collecting the most relevant information.

2. The system of claim 1 .

9. The collecting unit includes: Analyze seniors' social media activity and collect relevant information when making inquiries 2. The system of claim 1 .

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