system

Abstract

The system according to the embodiment aims to grasp changes in the physical condition and mood of elderly people and take appropriate measures. [Solution] The system according to the embodiment comprises an analysis unit, a transmission unit, a linking unit, and a support unit. The analysis unit analyzes the content of the conversation and detects changes in physical condition and mood. The transmission unit summarizes the information detected by the analysis unit and transmits it to the child. The linking unit contacts welfare services and hospitals based on the information from the analysis unit. The support unit provides specific information and advice regarding health and lifestyle.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to grasp changes in the physical condition and mood of elderly people on a daily basis and take appropriate measures.

[0005] The system according to the embodiment aims to grasp changes in the physical condition and mood of elderly people and take appropriate measures. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a communication unit, a linking unit, and a support unit. The analysis unit analyzes the content of the conversation and detects changes in physical condition and mood. The communication unit summarizes the information detected by the analysis unit and communicates it to the child. The linking unit contacts welfare services and hospitals based on the information from the analysis unit. The support unit provides specific information and advice regarding health and lifestyle. [Effects of the Invention]

[0007] The system according to the embodiment can grasp changes in the physical condition and mood of elderly people and take appropriate measures. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A voice-interactive robot system according to an embodiment of the present invention is a system that checks the health and status of elderly people, assesses their condition, and automatically responds appropriately. This robot system utilizes a generation AI to have everyday conversations with the elderly and analyzes the conversation to detect changes in their health and mood. The robot system then summarizes the parents' condition and transmits the summary to the purchaser (child). Furthermore, the robot system automatically connects with welfare services, hospitals, etc. based on the user's condition. The robot system also provides information, advice, and support on health and lifestyles to help the parents live comfortably. For example, the robot system may have everyday conversations with elderly parents. For example, the robot system understands the parents' condition through everyday conversations, such as morning greetings, meal plans, and health checks. During this time, the generation AI analyzes the conversation to detect changes in their health and mood. The robot system then summarizes the parents' condition and transmits the summary to the purchaser (child). For example, the robot system may report that the parents are in good health and that there are no particular concerns. This allows the child to understand the parents' condition and feel reassured. Furthermore, the robot system automatically connects with welfare services, hospitals, etc. based on the user's condition. For example, if a parent's health deteriorates, a prompt response is possible by contacting the appropriate welfare service or hospital. The robot system can also provide information, advice, and support on health and lifestyle. For example, it can provide advice on proper diet and exercise, supporting parents in living a healthy lifestyle. This can reduce the parents' sense of loneliness and alleviate the children's anxiety. The robot system can have daily conversations and understand changes in their health and mood, allowing the parents to live with peace of mind. Children can also understand their parents' situation and take the necessary measures quickly, allowing them to live with peace of mind. This allows a voice-enabled robot system to check the health of elderly people, understand their situation, and automatically take appropriate measures.

[0029] A robot system capable of voice interaction according to an embodiment includes an analysis unit, a communication unit, a linking unit, and a support unit. The analysis unit analyzes the content of a conversation and detects changes in physical condition or mood. The analysis unit analyzes the content of a conversation using, for example, a generation AI and detects changes in physical condition or mood. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, and can analyze the content of a conversation in detail. For example, the analysis unit can detect changes in body temperature, heart rate, emotional changes, etc. from the content of a conversation. The analysis unit can also estimate changes in physical condition or mood based on the content of a conversation. For example, the analysis unit analyzes changes in tone, speed, and content of a conversation to estimate changes in physical condition or mood. The communication unit summarizes the information detected by the analysis unit and communicates it to the child. For example, the communication unit summarizes the information using, for example, a generation AI and communicates it to the child. The generation AI can summarize the information based on a specific method or criterion for summarization. For example, the communication unit reports that the parents are in good health and that there are no particular concerns. The communication unit can also adjust the level of detail of the summary based on the importance and type of information. For example, important information is summarized in detail, and general information is summarized briefly. The coordination unit contacts welfare services or hospitals based on the information from the analysis unit. For example, the coordination unit uses a generation AI to contact the appropriate welfare services or hospitals. The generation AI can select contact persons and implement the contact based on specific contact methods and criteria. For example, the coordination unit contacts welfare services or hospitals if the patient's health deteriorates. The coordination unit can also determine the priority of contact based on the urgency and importance of the contact. For example, if the urgency is high, contact is made promptly, and if the importance is low, contact is made periodically. The support unit provides specific information and advice regarding health and lifestyle. For example, the support unit uses a generation AI to provide information and advice regarding health and lifestyle. The generation AI can provide information and advice based on specific content and criteria for the information and advice. For example, the support unit provides advice on appropriate diet and exercise. The support unit can also support parents in living a healthy lifestyle. For example, the support unit provides health information and supports improving lifestyle habits.As a result, the robot system capable of voice interaction according to the embodiment can check the physical condition of elderly people, grasp their situation, and automatically take appropriate measures.

[0030] The analysis unit can analyze everyday conversations to detect changes in physical condition or mood. The analysis unit, for example, analyzes everyday conversations to detect changes in physical condition or mood. For example, the analysis unit can detect changes in body temperature, heart rate, and emotional changes from the content of the conversation. The analysis unit can also estimate changes in physical condition or mood based on the content of the conversation. For example, the analysis unit analyzes changes in the tone, speed, and content of the conversation to estimate changes in physical condition or mood. This makes it possible to detect changes in physical condition or mood through everyday conversations. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input audio data of everyday conversations into a generation AI, which can analyze the audio data to detect changes in physical condition or mood.

[0031] The transmission unit can summarize the parents' situations and transmit them to the child. For example, the transmission unit summarizes the parents' situations and transmits them to the child. For example, the transmission unit may report that the parents are in good health and that there are no particular concerns. The transmission unit can also adjust the level of detail of the summary depending on the importance and type of information. For example, important information may be summarized in detail, and general information may be summarized briefly. In this way, by summarizing the parents' situations and transmitting them to the child, the child can understand their parents' situations. Some or all of the above-mentioned processing in the transmission unit may be performed using, or without, a generation AI. For example, the transmission unit may input data regarding the parents' situations into the generation AI, which may summarize the data and transmit it to the child.

[0032] The coordination unit can contact welfare services or a hospital if the person's health condition worsens (for example, if the body temperature rises above 38 degrees). The coordination unit contacts welfare services or a hospital if the person's health condition worsens. For example, the coordination unit contacts welfare services or a hospital if the body temperature rises above 38 degrees or if blood pressure suddenly rises. The coordination unit can also determine the priority of contact depending on the urgency and importance of the contact. For example, if the urgency is high, contact is made promptly, and if the importance is low, contact is made periodically. This enables a prompt and appropriate response if the person's health condition worsens. Some or all of the above-mentioned processing in the coordination unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the coordination unit can input data regarding changes in health condition into the generation AI, which can analyze the data and contact the appropriate welfare services or hospital.

[0033] The support unit can provide advice on diet and exercise. The support unit provides, for example, diet and exercise advice. For example, the support unit provides advice on a nutritionally balanced diet and an appropriate amount of exercise. The support unit can also support parents in living a healthy lifestyle. For example, the support unit provides health information and supports improving lifestyle habits. This makes it possible to support a healthy lifestyle by providing advice on appropriate diet and exercise. Some or all of the above-mentioned processing in the support unit may be performed using, or without, a generation AI. For example, the support unit can input data on diet and exercise into the generation AI, which then analyzes the data and provides appropriate advice.

[0034] The analysis unit can detect specific keywords (e.g., "painful" or "tired") in everyday conversations and determine changes in physical condition based on their frequency and context. The analysis unit can detect specific keywords in everyday conversations and determine changes in physical condition based on their frequency and context. For example, if the analysis unit detects that parents frequently use keywords such as "tired" or "painful," the generation AI can detect a deterioration in physical condition. The analysis unit can also detect changes in physical condition if keywords such as "hospital" or "medicine" increase in the parents' conversations. Furthermore, the analysis unit can detect abnormalities in physical condition if the parents frequently use keywords such as "can't sleep" or "no appetite." This allows for early detection of abnormalities in physical condition by determining changes in physical condition based on the frequency and context of specific keywords. Some or all of the above-described processing in the analysis unit can be performed using, or without, the generation AI. For example, the analysis unit can input audio data of everyday conversations into the generation AI, which can analyze the audio data to detect specific keywords and determine changes in physical condition based on their frequency and context.

[0035] When analyzing the content of the conversation, the analysis unit can detect changes in the parents' physical condition or mood by analyzing their tone of voice and speaking rate. For example, when analyzing the content of the conversation, the analysis unit can detect changes in the parents' physical condition or mood by analyzing their tone of voice and speaking rate. For example, if the analysis unit detects a decrease in the tone of the parents' voices, the generation AI can detect depression. Furthermore, if the parents' speaking rate decreases, the analysis unit can detect fatigue or poor physical condition. Furthermore, if the parents' voice rate increases, the analysis unit can detect excitement or stress. This allows for more accurate detection of changes in physical condition or mood by analyzing the tone of voice and speaking rate. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input audio data of the conversation into the generation AI, which can then analyze the audio data to analyze the tone of voice and speaking rate and detect changes in physical condition or mood.

[0036] When analyzing the conversation content, the analysis unit can compare it with the parents' past health data to detect changes in their physical condition. For example, when analyzing the conversation content, the analysis unit can compare it with the parents' past health data to detect changes in their physical condition. For example, the analysis unit can have the generation AI compare the parents' past health data with the current conversation content to detect changes in their physical condition. The analysis unit can also have the generation AI compare the parents' past medical history with the current conversation content to detect abnormalities in their physical condition. Furthermore, the analysis unit can have the generation AI compare the parents' past health checkup results with the current conversation content to detect changes in their physical condition. This allows for more accurate detection of changes in physical condition by comparing it with past health data. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, the generation AI. For example, the analysis unit can input the parents' past health data into the generation AI, which can then analyze the data to detect changes in their physical condition.

[0037] When analyzing the content of the conversation, the analysis unit can detect changes in the parents' physical condition or mood by taking into account their living environment (e.g., the temperature and humidity of the home) and daily activity patterns. When analyzing the content of the conversation, the analysis unit can detect changes in the parents' physical condition or mood by taking into account their living environment and daily activity patterns. For example, the analysis unit detects changes in the parents' physical condition by having the generation AI take into account the parents' living environment (e.g., the temperature and humidity of the home). The analysis unit can also detect changes in the parents' physical condition by having the generation AI take into account their daily activity patterns (e.g., the amount of exercise and dietary content). Furthermore, the analysis unit can detect changes in the parents' physical condition by having the generation AI take into account their daily rhythms (e.g., sleep time and wake-up time). In this way, by taking into account the living environment and daily activity patterns, changes in the physical condition and mood can be detected more accurately. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input data regarding the parents' living environment and daily activity patterns into the generation AI, and the generation AI can analyze the data to detect changes in the parents' physical condition and mood.

[0038] The communication unit can adjust the level of detail of the communication content based on the importance of changes in the parents' physical condition or mood at the time of communication. For example, the communication unit can adjust the level of detail of the communication content based on the importance of changes in the parents' physical condition or mood at the time of communication. For example, if the parents' physical condition is deteriorating, the communication unit can cause the generation AI to provide communication content including detailed information. Furthermore, if the parents' mood is good, the communication unit can also cause the generation AI to provide concise communication content. Furthermore, if the parents' physical condition is stable, the communication unit can also provide communication content including the minimum necessary information. In this way, by adjusting the level of detail of the communication content based on the importance of changes in physical condition or mood, necessary information can be appropriately communicated. Some or all of the above-mentioned processing in the communication unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the communication unit can input data regarding changes in the parents' physical condition or mood into the generation AI, and the generation AI can analyze the data and adjust the level of detail of the communication content.

[0039] The communication unit can select a communication means according to the parents' situation when transmitting information. For example, the communication unit can select different communication means (e.g., text, audio, video) according to the parents' situation when transmitting information. For example, if the parents prefer visual information, the communication unit can have the generation AI provide the communication content in video format. If the parents prefer auditory information, the communication unit can also have the generation AI provide the communication content in audio format. Furthermore, if the parents prefer text information, the communication unit can have the generation AI provide the communication content in text format. In this way, by selecting the optimal communication means according to the parents' situation, information can be transmitted effectively. Some or all of the above-mentioned processing in the communication unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the communication unit can input data regarding the parents' situation into the generation AI, which can analyze the data and select the optimal communication means.

[0040] The transmission unit can select the timing of transmission taking into account the parents' lifestyle rhythms and daily schedules when transmitting information. For example, the transmission unit selects the optimal timing of transmission taking into account the parents' lifestyle rhythms and daily schedules when transmitting information. For example, the transmission unit causes the generation AI to transmit the content of transmission in accordance with the parents' wake-up time. The transmission unit can also cause the generation AI to transmit the content of transmission so as to avoid the parents' mealtimes. Furthermore, the transmission unit can cause the generation AI to transmit the content of transmission so as to avoid the parents' bedtimes. In this way, information can be transmitted at the optimal timing by taking into account the lifestyle rhythms and daily schedules. Some or all of the above-described processing in the transmission unit may be performed using, or without, the generation AI. For example, the transmission unit can input data regarding the parents' lifestyle rhythms and daily schedules into the generation AI, and the generation AI can analyze the data and select the optimal timing of transmission.

[0041] The transmission unit can customize the transmission content by referring to the parents' past health data and lifestyle history when transmitting the information. The transmission unit, for example, customizes the transmission content by referring to the parents' past health data and lifestyle history when transmitting the information. For example, the transmission unit provides transmission content customized by the generation AI based on the parents' past health data. The transmission unit can also provide transmission content customized by the generation AI based on the parents' lifestyle history. Furthermore, the transmission unit can also provide transmission content customized by the generation AI based on the parents' past medical history. In this way, more appropriate transmission content can be provided by referring to the past health data and lifestyle history. Some or all of the above-mentioned processing in the transmission unit may be performed using, or without, the generation AI. For example, the transmission unit can input data regarding the parents' past health data and lifestyle history into the generation AI, which can analyze the data and customize the transmission content.

[0042] The linking unit can select contacts based on the urgency of changes in the parents' physical condition or mood during linking. For example, the linking unit selects contacts based on the urgency of changes in the parents' physical condition or mood during linking. For example, if the parents' physical condition suddenly worsens, the linking unit causes the generation AI to contact an emergency contact. In addition, if the parents' mood suddenly drops, the linking unit can cause the generation AI to contact appropriate welfare services. Furthermore, if the parents' physical condition is stable, the linking unit can cause the generation AI to contact regular contacts. This enables a prompt response by selecting appropriate contacts based on the urgency of changes in physical condition or mood. Some or all of the above-mentioned processing in the linking unit may be performed using, or without, the generation AI. For example, the linking unit can input data regarding changes in the parents' physical condition or mood into the generation AI, which can analyze the data and select appropriate contacts.

[0043] The collaboration unit can select a collaboration method by referring to the parents' past medical histories and welfare service usage histories at the time of collaboration. For example, the collaboration unit selects a collaboration method by referring to the parents' past medical histories and welfare service usage histories at the time of collaboration. For example, the collaboration unit causes the generation AI to contact the most appropriate medical institution based on the parents' past medical histories. The collaboration unit can also cause the generation AI to contact the most appropriate welfare service based on the parents' welfare service usage histories. Furthermore, the collaboration unit can cause the generation AI to select the most appropriate collaboration method based on the parents' past medical histories and welfare service usage histories. In this way, the most appropriate collaboration method can be selected by referring to the past medical histories and welfare service usage histories. Some or all of the above-mentioned processing in the collaboration unit may be performed using, or without, the generation AI. For example, the collaboration unit can input data regarding the parents' past medical histories and welfare service usage histories into the generation AI, and the generation AI can analyze the data and select the most appropriate collaboration method.

[0044] The collaboration unit can select welfare services and hospitals taking into account the geographical location information of the parents (e.g., residential address) during collaboration. The collaboration unit, for example, selects the optimal welfare services and hospitals taking into account the geographical location information of the parents during collaboration. For example, the collaboration unit causes the generation AI to select the nearest welfare service based on the current locations of the parents. The collaboration unit can also cause the generation AI to select the nearest hospital based on the current locations of the parents. Furthermore, the collaboration unit can cause the generation AI to select the optimal welfare service and hospital based on the geographical location information of the parents. In this way, the optimal welfare service and hospital can be selected by taking the geographical location information into consideration. Some or all of the above-mentioned processing in the collaboration unit may be performed using, or without, the generation AI. For example, the collaboration unit can input data regarding the geographical location information of the parents into the generation AI, and the generation AI can analyze the data and select the optimal welfare service and hospital.

[0045] The coordination unit can customize the coordination method by taking into account the living environment and daily activity patterns of the parents when linking. For example, the coordination unit customizes the coordination method by taking into account the living environment and daily activity patterns of the parents when linking. For example, the coordination unit allows the generation AI to select the optimal coordination method based on the living environment of the parents (e.g., temperature and humidity of the residence). The coordination unit can also allow the generation AI to select the optimal coordination method based on the parents' daily activity patterns (e.g., amount of exercise and dietary content). Furthermore, the coordination unit can also allow the generation AI to select the optimal coordination method based on the parents' daily rhythms (e.g., sleep time and wake-up time). In this way, the optimal coordination method can be selected by taking into account the living environment and daily activity patterns. Some or all of the above-mentioned processing in the coordination unit may be performed using, or without, the generation AI. For example, the coordination unit can input data regarding the living environment and daily activity patterns of the parents into the generation AI, and the generation AI can analyze the data and select the optimal coordination method.

[0046] The support unit can adjust the level of detail of the support content based on the importance of changes in the parents' physical condition or mood during support. For example, the support unit adjusts the level of detail of the support content based on the importance of changes in the parents' physical condition or mood during support. For example, if the parents' physical condition is deteriorating, the support unit causes the generation AI to provide support content including detailed information. Furthermore, if the parents' mood is good, the support unit can also cause the generation AI to provide concise support content. Furthermore, if the parents' physical condition is stable, the support unit can also provide support content including the minimum necessary information. In this way, by adjusting the level of detail of the support content based on the importance of changes in physical condition or mood, necessary information can be appropriately provided. Some or all of the above-mentioned processing in the support unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the support unit can input data regarding changes in the parents' physical condition or mood into the generation AI, and the generation AI can analyze the data and adjust the level of detail of the support content.

[0047] The support unit can select a support method taking into account the parents' lifestyle rhythms and daily schedules when providing support. For example, the support unit selects the optimal support method taking into account the parents' lifestyle rhythms and daily schedules when providing support. For example, the support unit has the generation AI provide support content based on the parents' wake-up time. The support unit can also have the generation AI provide support content that avoids the parents' mealtimes. Furthermore, the support unit can have the generation AI provide support content that avoids the parents' bedtimes. This allows support to be provided at the optimal time by taking into account the parents' lifestyle rhythms and daily schedules. Some or all of the above-described processing in the support unit may be performed using, or without, the generation AI. For example, the support unit can input data regarding the parents' lifestyle rhythms and daily schedules into the generation AI, and the generation AI can analyze the data and select the optimal support method.

[0048] The support unit can customize the support content by referring to the parents' past health data and lifestyle history when providing support. For example, the support unit customizes the support content by referring to the parents' past health data and lifestyle history when providing support. For example, the support unit provides support content customized by the generation AI based on the parents' past health data. The support unit can also provide support content customized by the generation AI based on the parents' lifestyle history. Furthermore, the support unit can also provide support content customized by the generation AI based on the parents' past medical history. This makes it possible to provide more appropriate support content by referring to the past health data and lifestyle history. Some or all of the above-described processing in the support unit may be performed using, or without, the generation AI. For example, the support unit can input data regarding the parents' past health data and lifestyle history into the generation AI, which can then analyze the data and customize the support content.

[0049] The support unit can select a support method taking into account the geographical location information of the parents when providing support. For example, the support unit selects the optimal support method taking into account the geographical location information of the parents when providing support. For example, the support unit allows the generation AI to suggest the nearest welfare service based on the current locations of the parents. The support unit can also allow the generation AI to suggest the nearest hospital based on the current locations of the parents. Furthermore, the support unit can allow the generation AI to select the optimal support method based on the geographical location information of the parents. In this way, the optimal support method can be selected by taking geographical location information into account. Some or all of the above-mentioned processing in the support unit may be performed using, or without, the generation AI. For example, the support unit can input data regarding the geographical location information of the parents into the generation AI, and the generation AI can analyze the data and select the optimal support method.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] When analyzing the content of the parents' conversation, the analysis unit can also detect changes in their physical condition or mood by taking into account background noise and environmental sounds. For example, the analysis unit can analyze the sound of the television or external noise heard during the conversation to determine whether these sounds are affecting the parents' stress or fatigue. The analysis unit can also analyze the type and volume of music heard during the conversation to estimate how this is affecting the parents' mood. Furthermore, the analysis unit can analyze natural sounds heard during the conversation (e.g., birds chirping or the sound of the wind) to determine how this is affecting the parents' level of relaxation. In this way, by taking into account background noise and environmental sounds, changes in their physical condition or mood can be detected more accurately.

[0052] When analyzing the content of parents' conversations, the analysis unit can also detect changes in physical condition or mood by taking into account the context of the conversation and changes in topic. For example, the analysis unit can analyze topics that frequently come up in the conversation (e.g., health, family, hobbies) and determine how these topics affect the parents' mood. The analysis unit can also estimate whether a sudden change in topic in the conversation is a sign of stress or anxiety in the parents. Furthermore, the analysis unit can determine whether a positive topic (e.g., a fun event, a successful experience) that comes up in the conversation improves the parents' mood. This allows for more accurate detection of changes in physical condition or mood by taking into account the context of the conversation and changes in topic.

[0053] The communication unit may also have a function to visually display the summary when summarizing the parents' status. For example, the communication unit may display changes in the parents' physical condition and mood in graphs or charts, allowing the child to understand the situation at a glance. The communication unit may also use icons or colors to indicate the parents' health status for a visually easy-to-understand display. Furthermore, the communication unit may display the parents' daily rhythms and activity patterns in a calendar format, allowing the child to check daily changes. This allows the use of visual displays to more effectively communicate the parents' status.

[0054] If a parent's health deteriorates, the liaison department can contact not only emergency contacts but also local support networks (e.g., friends in the neighborhood and local volunteers). For example, if a parent's health suddenly deteriorates, the liaison department can contact friends in the neighborhood to request emergency support. The liaison department can also contact local volunteers to ask them to watch over the parents when they go out alone. Furthermore, the liaison department can collect information about local events and activities that the parents regularly participate in and contact them as needed. This makes it possible to respond quickly and effectively by utilizing the local support network.

[0055] The support unit can customize dietary and exercise advice by taking into account parents' preferences and allergy information. For example, the support unit can suggest nutritionally balanced recipes based on parents' favorite ingredients and dishes. The support unit can also provide allergen-free meal plans by taking into account parents' allergy information. Furthermore, the support unit can suggest appropriate exercise programs by taking into account parents' exercise preferences and physical fitness levels. This allows for more effective support by providing dietary and exercise advice tailored to individual needs.

[0056] When analyzing the content of a conversation, the analysis unit can detect changes in physical condition or mood by analyzing not only the tone and speaking speed of the parents' voices, but also the strength and rhythm of their voices. For example, the analysis unit may determine that a sudden change in the volume or volume of the parents' voices indicates a change in emotion. The analysis unit may also determine that an irregular speech rhythm of the parents indicates stress or anxiety. Furthermore, the analysis unit may determine that an inconsistent strength or volume of the parents' voices indicates a change in physical condition. Thus, by analyzing the strength and rhythm of the voices, changes in physical condition or mood can be detected more accurately.

[0057] When analyzing the content of a conversation, the analysis unit can compare it not only with the parents' past health data but also with the health data of other family members to detect changes in physical condition. For example, the analysis unit can compare the health data of the parents with the health data of the child to determine whether genetic factors are affecting the physical condition. The analysis unit can also compare the health data of the parents with the health data of siblings to determine whether common lifestyle habits are affecting the physical condition. Furthermore, the analysis unit can compare the health data of the parents with the health data of the spouse to determine whether common environmental factors are affecting the physical condition. This allows for more accurate detection of changes in physical condition by comparing it with the health data of other family members.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The analysis unit analyzes the conversation content and detects changes in physical condition or mood. The analysis unit uses generative AI to analyze the conversation content and detect changes in physical condition or mood. For example, it can detect changes in body temperature, heart rate, and emotions from the conversation content. It can also analyze changes in the tone, speed, and content of the conversation to estimate changes in physical condition or mood. Step 2: The communication unit summarizes the information detected by the analysis unit and transmits it to the child. The communication unit uses generative AI to summarize the information and transmit it to the child. For example, it may report that both parents are in good health and that there are no particular concerns. It can also adjust the level of detail in the summary depending on the importance and type of information. Step 3: The coordination unit contacts welfare services or hospitals based on the information from the analysis unit. The coordination unit uses generative AI to contact the appropriate welfare services or hospitals. For example, if the person's health deteriorates, the coordination unit may contact welfare services or hospitals. It can also determine the priority of contacts based on the urgency and importance of the contact. Step 4: The support department provides specific information and advice on health and lifestyle. The support department uses generative AI to provide information and advice on health and lifestyle. For example, it can provide advice on proper diet and exercise. It can also support parents in living a healthy lifestyle.

[0060] (Example 2) A voice-interactive robot system according to an embodiment of the present invention is a system that checks the health and status of elderly people, assesses their condition, and automatically responds appropriately. This robot system utilizes a generation AI to have everyday conversations with the elderly and analyzes the conversation to detect changes in their health and mood. The robot system then summarizes the parents' condition and transmits the summary to the purchaser (child). Furthermore, the robot system automatically connects with welfare services, hospitals, etc. based on the user's condition. The robot system also provides information, advice, and support on health and lifestyles to help the parents live comfortably. For example, the robot system may have everyday conversations with elderly parents. For example, the robot system understands the parents' condition through everyday conversations, such as morning greetings, meal plans, and health checks. During this time, the generation AI analyzes the conversation to detect changes in their health and mood. The robot system then summarizes the parents' condition and transmits the summary to the purchaser (child). For example, the robot system may report that the parents are in good health and that there are no particular concerns. This allows the child to understand the parents' condition and feel reassured. Furthermore, the robot system automatically connects with welfare services, hospitals, etc. based on the user's condition. For example, if a parent's health deteriorates, a prompt response is possible by contacting the appropriate welfare service or hospital. The robot system can also provide information, advice, and support on health and lifestyle. For example, it can provide advice on proper diet and exercise, supporting parents in living a healthy lifestyle. This can reduce the parents' sense of loneliness and alleviate the children's anxiety. The robot system can have daily conversations and understand changes in their health and mood, allowing the parents to live with peace of mind. Children can also understand their parents' situation and take the necessary measures quickly, allowing them to live with peace of mind. This allows a voice-enabled robot system to check the health of elderly people, understand their situation, and automatically take appropriate measures.

[0061] A robot system capable of voice interaction according to an embodiment includes an analysis unit, a communication unit, a linking unit, and a support unit. The analysis unit analyzes the content of a conversation and detects changes in physical condition or mood. The analysis unit analyzes the content of a conversation using, for example, a generation AI and detects changes in physical condition or mood. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, and can analyze the content of a conversation in detail. For example, the analysis unit can detect changes in body temperature, heart rate, emotional changes, etc. from the content of a conversation. The analysis unit can also estimate changes in physical condition or mood based on the content of a conversation. For example, the analysis unit analyzes changes in tone, speed, and content of a conversation to estimate changes in physical condition or mood. The communication unit summarizes the information detected by the analysis unit and communicates it to the child. For example, the communication unit summarizes the information using, for example, a generation AI and communicates it to the child. The generation AI can summarize the information based on a specific method or criterion for summarization. For example, the communication unit reports that the parents are in good health and that there are no particular concerns. The communication unit can also adjust the level of detail of the summary based on the importance and type of information. For example, important information is summarized in detail, and general information is summarized briefly. The coordination unit contacts welfare services or hospitals based on the information from the analysis unit. For example, the coordination unit uses a generation AI to contact the appropriate welfare services or hospitals. The generation AI can select contact persons and implement the contact based on specific contact methods and criteria. For example, the coordination unit contacts welfare services or hospitals if the patient's health deteriorates. The coordination unit can also determine the priority of contact based on the urgency and importance of the contact. For example, if the urgency is high, contact is made promptly, and if the importance is low, contact is made periodically. The support unit provides specific information and advice regarding health and lifestyle. For example, the support unit uses a generation AI to provide information and advice regarding health and lifestyle. The generation AI can provide information and advice based on specific content and criteria for the information and advice. For example, the support unit provides advice on appropriate diet and exercise. The support unit can also support parents in living a healthy lifestyle. For example, the support unit provides health information and supports improving lifestyle habits.As a result, the robot system capable of voice interaction according to the embodiment can check the physical condition of elderly people, grasp their situation, and automatically take appropriate measures.

[0062] The analysis unit can analyze everyday conversations to detect changes in physical condition or mood. The analysis unit, for example, analyzes everyday conversations to detect changes in physical condition or mood. For example, the analysis unit can detect changes in body temperature, heart rate, and emotional changes from the content of the conversation. The analysis unit can also estimate changes in physical condition or mood based on the content of the conversation. For example, the analysis unit analyzes changes in the tone, speed, and content of the conversation to estimate changes in physical condition or mood. This makes it possible to detect changes in physical condition or mood through everyday conversations. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input audio data of everyday conversations into a generation AI, which can analyze the audio data to detect changes in physical condition or mood.

[0063] The transmission unit can summarize the parents' situations and transmit them to the child. For example, the transmission unit summarizes the parents' situations and transmits them to the child. For example, the transmission unit may report that the parents are in good health and that there are no particular concerns. The transmission unit can also adjust the level of detail of the summary depending on the importance and type of information. For example, important information may be summarized in detail, and general information may be summarized briefly. In this way, by summarizing the parents' situations and transmitting them to the child, the child can understand their parents' situations. Some or all of the above-mentioned processing in the transmission unit may be performed using, or without, a generation AI. For example, the transmission unit may input data regarding the parents' situations into the generation AI, which may summarize the data and transmit it to the child.

[0064] The coordination unit can contact welfare services or a hospital if the person's health condition worsens (for example, if the body temperature rises above 38 degrees). The coordination unit contacts welfare services or a hospital if the person's health condition worsens. For example, the coordination unit contacts welfare services or a hospital if the body temperature rises above 38 degrees or if blood pressure suddenly rises. The coordination unit can also determine the priority of contact depending on the urgency and importance of the contact. For example, if the urgency is high, contact is made promptly, and if the importance is low, contact is made periodically. This enables a prompt and appropriate response if the person's health condition worsens. Some or all of the above-mentioned processing in the coordination unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the coordination unit can input data regarding changes in health condition into the generation AI, which can analyze the data and contact the appropriate welfare services or hospital.

[0065] The support unit can provide advice on diet and exercise. The support unit provides, for example, diet and exercise advice. For example, the support unit provides advice on a nutritionally balanced diet and an appropriate amount of exercise. The support unit can also support parents in living a healthy lifestyle. For example, the support unit provides health information and supports improving lifestyle habits. This makes it possible to support a healthy lifestyle by providing advice on appropriate diet and exercise. Some or all of the above-mentioned processing in the support unit may be performed using, or without, a generation AI. For example, the support unit can input data on diet and exercise into the generation AI, which then analyzes the data and provides appropriate advice.

[0066] The analysis unit can estimate the emotions of the parents and detect changes in their physical condition or mood based on the estimated emotions. The analysis unit, for example, estimates the emotions of the parents and detects changes in their physical condition or mood based on the estimated emotions of the parents. For example, the analysis unit uses a generation AI to estimate emotions from the content of a conversation and detect abnormalities in their physical condition based on changes in emotions. The analysis unit can also analyze the tone of voice and speaking rate of the parents and detect changes in their physical condition based on changes in emotions. Furthermore, the analysis unit can analyze the facial expressions and gestures of the parents and detect abnormalities in their physical condition based on changes in emotions. This enables more accurate situation understanding by detecting changes in their physical condition or mood based on the emotions of the parents. The emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or without the generation AI. For example, the analysis unit can input data about the parents' emotions into the generation AI, which can then analyze the data to detect changes in their physical condition or mood.

[0067] The analysis unit can detect specific keywords (e.g., "painful" or "tired") in everyday conversations and determine changes in physical condition based on their frequency and context. The analysis unit can detect specific keywords in everyday conversations and determine changes in physical condition based on their frequency and context. For example, if the analysis unit detects that parents frequently use keywords such as "tired" or "painful," the generation AI can detect a deterioration in physical condition. The analysis unit can also detect changes in physical condition if keywords such as "hospital" or "medicine" increase in the parents' conversations. Furthermore, the analysis unit can detect abnormalities in physical condition if the parents frequently use keywords such as "can't sleep" or "no appetite." This allows for early detection of abnormalities in physical condition by determining changes in physical condition based on the frequency and context of specific keywords. Some or all of the above-described processing in the analysis unit can be performed using, or without, the generation AI. For example, the analysis unit can input audio data of everyday conversations into the generation AI, which can analyze the audio data to detect specific keywords and determine changes in physical condition based on their frequency and context.

[0068] When analyzing the content of the conversation, the analysis unit can detect changes in the parents' physical condition or mood by analyzing their tone of voice and speaking rate. For example, when analyzing the content of the conversation, the analysis unit can detect changes in the parents' physical condition or mood by analyzing their tone of voice and speaking rate. For example, if the analysis unit detects a decrease in the tone of the parents' voices, the generation AI can detect depression. Furthermore, if the parents' speaking rate decreases, the analysis unit can detect fatigue or poor physical condition. Furthermore, if the parents' voice rate increases, the analysis unit can detect excitement or stress. This allows for more accurate detection of changes in physical condition or mood by analyzing the tone of voice and speaking rate. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input audio data of the conversation into the generation AI, which can then analyze the audio data to analyze the tone of voice and speaking rate and detect changes in physical condition or mood.

[0069] The analysis unit can estimate the emotions of the parents and prioritize the analysis results based on the estimated emotions of the parents. For example, the analysis unit can estimate the emotions of the parents and prioritize the analysis results based on the estimated emotions of the parents. For example, if the parents are feeling stressed, the analysis unit can cause the generation AI to prioritize reporting stress-related analysis results. Furthermore, if the parents are feeling relaxed, the analysis unit can cause the generation AI to prioritize reporting relaxation-related analysis results. Furthermore, if the parents are feeling anxious, the analysis unit can cause the generation AI to prioritize reporting anxiety-related analysis results. In this way, by prioritizing the analysis results based on the emotions of the parents, important information can be reported preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using the generation AI, or without the generation AI. For example, the analysis unit can input data about the parents' emotions into the generation AI, which can then analyze the data and determine the priorities of the analysis results.

[0070] When analyzing the conversation content, the analysis unit can compare it with the parents' past health data to detect changes in their physical condition. For example, when analyzing the conversation content, the analysis unit can compare it with the parents' past health data to detect changes in their physical condition. For example, the analysis unit can have the generation AI compare the parents' past health data with the current conversation content to detect changes in their physical condition. The analysis unit can also have the generation AI compare the parents' past medical history with the current conversation content to detect abnormalities in their physical condition. Furthermore, the analysis unit can have the generation AI compare the parents' past health checkup results with the current conversation content to detect changes in their physical condition. This allows for more accurate detection of changes in physical condition by comparing it with past health data. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, the generation AI. For example, the analysis unit can input the parents' past health data into the generation AI, which can then analyze the data to detect changes in their physical condition.

[0071] When analyzing the content of the conversation, the analysis unit can detect changes in the parents' physical condition or mood by taking into account their living environment (e.g., the temperature and humidity of the home) and daily activity patterns. When analyzing the content of the conversation, the analysis unit can detect changes in the parents' physical condition or mood by taking into account their living environment and daily activity patterns. For example, the analysis unit detects changes in the parents' physical condition by having the generation AI take into account the parents' living environment (e.g., the temperature and humidity of the home). The analysis unit can also detect changes in the parents' physical condition by having the generation AI take into account their daily activity patterns (e.g., the amount of exercise and dietary content). Furthermore, the analysis unit can detect changes in the parents' physical condition by having the generation AI take into account their daily rhythms (e.g., sleep time and wake-up time). In this way, by taking into account the living environment and daily activity patterns, changes in the physical condition and mood can be detected more accurately. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input data regarding the parents' living environment and daily activity patterns into the generation AI, and the generation AI can analyze the data to detect changes in the parents' physical condition and mood.

[0072] The communication unit can estimate the emotions of the parents and adjust the way in which the communication content is expressed based on the estimated emotions of the parents. For example, the communication unit can estimate the emotions of the parents and adjust the way in which the communication content is expressed based on the estimated emotions of the parents. For example, if the parents are feeling stressed, the communication unit can have the generation AI express the communication content in gentle words. Also, if the parents are relaxed, the communication unit can have the generation AI express the communication content including detailed information. Furthermore, if the parents are feeling anxious, the communication unit can have the generation AI express the communication content in words that give a sense of security. This enables more appropriate communication by adjusting the way in which the communication content is expressed based on the emotions of the parents. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the communication unit can be performed, for example, using the generation AI, or without using the generation AI. For example, the communication unit can input data about the parents' emotions into the generation AI, which can then analyze the data and adjust the way the communication is expressed.

[0073] The communication unit can adjust the level of detail of the communication content based on the importance of changes in the parents' physical condition or mood at the time of communication. For example, the communication unit can adjust the level of detail of the communication content based on the importance of changes in the parents' physical condition or mood at the time of communication. For example, if the parents' physical condition is deteriorating, the communication unit can cause the generation AI to provide communication content including detailed information. Furthermore, if the parents' mood is good, the communication unit can also cause the generation AI to provide concise communication content. Furthermore, if the parents' physical condition is stable, the communication unit can also provide communication content including the minimum necessary information. In this way, by adjusting the level of detail of the communication content based on the importance of changes in physical condition or mood, necessary information can be appropriately communicated. Some or all of the above-mentioned processing in the communication unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the communication unit can input data regarding changes in the parents' physical condition or mood into the generation AI, and the generation AI can analyze the data and adjust the level of detail of the communication content.

[0074] The communication unit can select a communication means according to the parents' situation when transmitting information. For example, the communication unit can select different communication means (e.g., text, audio, video) according to the parents' situation when transmitting information. For example, if the parents prefer visual information, the communication unit can have the generation AI provide the communication content in video format. If the parents prefer auditory information, the communication unit can also have the generation AI provide the communication content in audio format. Furthermore, if the parents prefer text information, the communication unit can have the generation AI provide the communication content in text format. In this way, by selecting the optimal communication means according to the parents' situation, information can be transmitted effectively. Some or all of the above-mentioned processing in the communication unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the communication unit can input data regarding the parents' situation into the generation AI, which can analyze the data and select the optimal communication means.

[0075] The transmission unit can estimate the emotions of the parents and adjust the timing of the transmission based on the estimated emotions of the parents. For example, the transmission unit estimates the emotions of the parents and adjusts the timing of the transmission based on the estimated emotions of the parents. For example, the transmission unit causes the generation AI to send the message content during a time when the parents are relaxed. The transmission unit can also cause the generation AI to send the message content to avoid times when the parents are busy. Furthermore, the transmission unit can cause the generation AI to send the message content to avoid times when the parents are stressed. In this way, by adjusting the timing of the transmission based on the emotions of the parents, information can be transmitted at a more appropriate time. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the transmission unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the transmission unit can input data regarding the emotions of the parents into the generation AI, and the generation AI can analyze the data and adjust the timing of the transmission.

[0076] The transmission unit can select the timing of transmission taking into account the parents' lifestyle rhythms and daily schedules when transmitting information. For example, the transmission unit selects the optimal timing of transmission taking into account the parents' lifestyle rhythms and daily schedules when transmitting information. For example, the transmission unit causes the generation AI to transmit the content of transmission in accordance with the parents' wake-up time. The transmission unit can also cause the generation AI to transmit the content of transmission so as to avoid the parents' mealtimes. Furthermore, the transmission unit can cause the generation AI to transmit the content of transmission so as to avoid the parents' bedtimes. In this way, information can be transmitted at the optimal timing by taking into account the lifestyle rhythms and daily schedules. Some or all of the above-described processing in the transmission unit may be performed using, or without, the generation AI. For example, the transmission unit can input data regarding the parents' lifestyle rhythms and daily schedules into the generation AI, and the generation AI can analyze the data and select the optimal timing of transmission.

[0077] The transmission unit can customize the transmission content by referring to the parents' past health data and lifestyle history when transmitting the information. The transmission unit, for example, customizes the transmission content by referring to the parents' past health data and lifestyle history when transmitting the information. For example, the transmission unit provides transmission content customized by the generation AI based on the parents' past health data. The transmission unit can also provide transmission content customized by the generation AI based on the parents' lifestyle history. Furthermore, the transmission unit can also provide transmission content customized by the generation AI based on the parents' past medical history. In this way, more appropriate transmission content can be provided by referring to the past health data and lifestyle history. Some or all of the above-mentioned processing in the transmission unit may be performed using, or without, the generation AI. For example, the transmission unit can input data regarding the parents' past health data and lifestyle history into the generation AI, which can analyze the data and customize the transmission content.

[0078] The collaboration unit can estimate the emotions of the parents and determine collaboration priorities based on the estimated emotions of the parents. For example, the collaboration unit can estimate the emotions of the parents and determine collaboration priorities based on the estimated emotions of the parents. For example, if the parents are feeling stressed, the collaboration unit can cause the generation AI to prioritize stress-related collaboration. Also, if the parents are feeling relaxed, the collaboration unit can cause the generation AI to prioritize relaxation-related collaboration. Furthermore, if the parents are feeling anxious, the collaboration unit can cause the generation AI to prioritize anxiety-related collaboration. In this way, by determining collaboration priorities based on the emotions of the parents, important collaborations can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collaboration unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the collaboration unit can input data regarding the emotions of the parents into the generation AI, which can analyze the data and determine collaboration priorities.

[0079] The linking unit can select contacts based on the urgency of changes in the parents' physical condition or mood during linking. For example, the linking unit selects contacts based on the urgency of changes in the parents' physical condition or mood during linking. For example, if the parents' physical condition suddenly worsens, the linking unit causes the generation AI to contact an emergency contact. In addition, if the parents' mood suddenly drops, the linking unit can cause the generation AI to contact appropriate welfare services. Furthermore, if the parents' physical condition is stable, the linking unit can cause the generation AI to contact regular contacts. This enables a prompt response by selecting appropriate contacts based on the urgency of changes in physical condition or mood. Some or all of the above-mentioned processing in the linking unit may be performed using, or without, the generation AI. For example, the linking unit can input data regarding changes in the parents' physical condition or mood into the generation AI, which can analyze the data and select appropriate contacts.

[0080] The collaboration unit can select a collaboration method by referring to the parents' past medical histories and welfare service usage histories at the time of collaboration. For example, the collaboration unit selects a collaboration method by referring to the parents' past medical histories and welfare service usage histories at the time of collaboration. For example, the collaboration unit causes the generation AI to contact the most appropriate medical institution based on the parents' past medical histories. The collaboration unit can also cause the generation AI to contact the most appropriate welfare service based on the parents' welfare service usage histories. Furthermore, the collaboration unit can cause the generation AI to select the most appropriate collaboration method based on the parents' past medical histories and welfare service usage histories. In this way, the most appropriate collaboration method can be selected by referring to the past medical histories and welfare service usage histories. Some or all of the above-mentioned processing in the collaboration unit may be performed using, or without, the generation AI. For example, the collaboration unit can input data regarding the parents' past medical histories and welfare service usage histories into the generation AI, and the generation AI can analyze the data and select the most appropriate collaboration method.

[0081] The collaboration unit can estimate the emotions of the parents and adjust the timing of collaboration based on the estimated emotions of the parents. For example, the collaboration unit estimates the emotions of the parents and adjusts the timing of collaboration based on the estimated emotions of the parents. For example, the collaboration unit causes the generation AI to collaborate during a time when the parents are relaxed. The collaboration unit can also cause the generation AI to collaborate by avoiding a time when the parents are busy. Furthermore, the collaboration unit can cause the generation AI to collaborate by avoiding a time when the parents are stressed. In this way, by adjusting the timing of collaboration based on the emotions of the parents, collaboration can be performed at a more appropriate time. Emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collaboration unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collaboration unit can input data regarding the emotions of the parents into the generation AI, and the generation AI can analyze the data and adjust the timing of collaboration.

[0082] The collaboration unit can select welfare services and hospitals taking into account the geographical location information of the parents (e.g., residential address) during collaboration. The collaboration unit, for example, selects the optimal welfare services and hospitals taking into account the geographical location information of the parents during collaboration. For example, the collaboration unit causes the generation AI to select the nearest welfare service based on the current locations of the parents. The collaboration unit can also cause the generation AI to select the nearest hospital based on the current locations of the parents. Furthermore, the collaboration unit can cause the generation AI to select the optimal welfare service and hospital based on the geographical location information of the parents. In this way, the optimal welfare service and hospital can be selected by taking the geographical location information into consideration. Some or all of the above-mentioned processing in the collaboration unit may be performed using, or without, the generation AI. For example, the collaboration unit can input data regarding the geographical location information of the parents into the generation AI, and the generation AI can analyze the data and select the optimal welfare service and hospital.

[0083] The coordination unit can customize the coordination method by taking into account the living environment and daily activity patterns of the parents when linking. For example, the coordination unit customizes the coordination method by taking into account the living environment and daily activity patterns of the parents when linking. For example, the coordination unit allows the generation AI to select the optimal coordination method based on the living environment of the parents (e.g., temperature and humidity of the residence). The coordination unit can also allow the generation AI to select the optimal coordination method based on the parents' daily activity patterns (e.g., amount of exercise and dietary content). Furthermore, the coordination unit can also allow the generation AI to select the optimal coordination method based on the parents' daily rhythms (e.g., sleep time and wake-up time). In this way, the optimal coordination method can be selected by taking into account the living environment and daily activity patterns. Some or all of the above-mentioned processing in the coordination unit may be performed using, or without, the generation AI. For example, the coordination unit can input data regarding the living environment and daily activity patterns of the parents into the generation AI, and the generation AI can analyze the data and select the optimal coordination method.

[0084] The support unit can estimate the emotions of the parents and adjust the way in which the support content is presented based on the estimated emotions of the parents. For example, the support unit estimates the emotions of the parents and adjusts the way in which the support content is presented based on the estimated emotions of the parents. For example, if the parents are feeling stressed, the support unit generates support content in gentle language. If the parents are relaxed, the support unit generates support content that includes detailed information. Furthermore, if the parents are feeling anxious, the support unit generates support content in words that give a sense of security. This allows for more appropriate support by adjusting the way in which the support content is presented based on the parents' emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the support unit may be performed using, for example, the generation AI, or without the generation AI. For example, the support department can input data about the parents' emotions into the generation AI, which can then analyze the data and adjust how the support is presented.

[0085] The support unit can adjust the level of detail of the support content based on the importance of changes in the parents' physical condition or mood during support. For example, the support unit adjusts the level of detail of the support content based on the importance of changes in the parents' physical condition or mood during support. For example, if the parents' physical condition is deteriorating, the support unit causes the generation AI to provide support content including detailed information. Furthermore, if the parents' mood is good, the support unit can also cause the generation AI to provide concise support content. Furthermore, if the parents' physical condition is stable, the support unit can also provide support content including the minimum necessary information. In this way, by adjusting the level of detail of the support content based on the importance of changes in physical condition or mood, necessary information can be appropriately provided. Some or all of the above-mentioned processing in the support unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the support unit can input data regarding changes in the parents' physical condition or mood into the generation AI, and the generation AI can analyze the data and adjust the level of detail of the support content.

[0086] The support unit can select a support method taking into account the parents' lifestyle rhythms and daily schedules when providing support. For example, the support unit selects the optimal support method taking into account the parents' lifestyle rhythms and daily schedules when providing support. For example, the support unit has the generation AI provide support content based on the parents' wake-up time. The support unit can also have the generation AI provide support content that avoids the parents' mealtimes. Furthermore, the support unit can have the generation AI provide support content that avoids the parents' bedtimes. This allows support to be provided at the optimal time by taking into account the parents' lifestyle rhythms and daily schedules. Some or all of the above-described processing in the support unit may be performed using, or without, the generation AI. For example, the support unit can input data regarding the parents' lifestyle rhythms and daily schedules into the generation AI, and the generation AI can analyze the data and select the optimal support method.

[0087] The support unit can estimate the emotions of the parents and adjust the timing of support based on the estimated emotions of the parents. For example, the support unit estimates the emotions of the parents and adjusts the timing of support based on the estimated emotions of the parents. For example, the support unit has the generation AI provide support content during times when the parents are relaxed. The support unit can also have the generation AI provide support content to avoid times when the parents are busy. Furthermore, the support unit can have the generation AI provide support content to avoid times when the parents are stressed. In this way, by adjusting the timing of support based on the emotions of the parents, support can be provided at a more appropriate time. Emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the support unit may be performed using, for example, the generation AI. For example, the support unit can input data regarding the emotions of the parents into the generation AI, and the generation AI can analyze the data and adjust the timing of support.

[0088] The support unit can customize the support content by referring to the parents' past health data and lifestyle history when providing support. For example, the support unit customizes the support content by referring to the parents' past health data and lifestyle history when providing support. For example, the support unit provides support content customized by the generation AI based on the parents' past health data. The support unit can also provide support content customized by the generation AI based on the parents' lifestyle history. Furthermore, the support unit can also provide support content customized by the generation AI based on the parents' past medical history. This makes it possible to provide more appropriate support content by referring to the past health data and lifestyle history. Some or all of the above-described processing in the support unit may be performed using, or without, the generation AI. For example, the support unit can input data regarding the parents' past health data and lifestyle history into the generation AI, which can then analyze the data and customize the support content.

[0089] The support unit can select a support method taking into account the geographical location information of the parents when providing support. For example, the support unit selects the optimal support method taking into account the geographical location information of the parents when providing support. For example, the support unit allows the generation AI to suggest the nearest welfare service based on the current locations of the parents. The support unit can also allow the generation AI to suggest the nearest hospital based on the current locations of the parents. Furthermore, the support unit can allow the generation AI to select the optimal support method based on the geographical location information of the parents. In this way, the optimal support method can be selected by taking geographical location information into account. Some or all of the above-mentioned processing in the support unit may be performed using, or without, the generation AI. For example, the support unit can input data regarding the geographical location information of the parents into the generation AI, and the generation AI can analyze the data and select the optimal support method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned analysis unit, transmission unit, linking unit, and support unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart device 14 and analyzes the content of the conversation to detect changes in physical condition and mood. The transmission unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and summarizes the detected information and transmits it to the child. The linking unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and contacts welfare services or hospitals. The support unit is realized, for example, by the control unit 46A of the smart device 14 and provides information and advice on health and lifestyle. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned analysis unit, transmission unit, linking unit, and support unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart glasses 214 and analyzes the content of the conversation to detect changes in physical condition or mood. The transmission unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and summarizes the detected information and transmits it to the child. The linking unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and contacts welfare services or hospitals. The support unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides information and advice regarding health and lifestyle. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned analysis unit, transmission unit, linking unit, and support unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the headset-type terminal 314 and analyzes the content of the conversation to detect changes in physical condition or mood. The transmission unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and summarizes the detected information and transmits it to the child. The linking unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and contacts welfare services or hospitals. The support unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and provides information and advice regarding health and lifestyle. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned analysis unit, transmission unit, collaboration unit, and support unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the robot 414 and analyzes the content of the conversation to detect changes in physical condition and mood. The transmission unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and summarizes the detected information and communicates it to the child. The collaboration unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and contacts welfare services or hospitals. The support unit is realized, for example, by the control unit 46A of the robot 414 and provides information and advice regarding health and lifestyle.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] When analyzing the content of the parents' conversation, the analysis unit can also detect changes in their physical condition or mood by taking into account background noise and environmental sounds. For example, the analysis unit can analyze the sound of the television or external noise heard during the conversation to determine whether these sounds are affecting the parents' stress or fatigue. The analysis unit can also analyze the type and volume of music heard during the conversation to estimate how this is affecting the parents' mood. Furthermore, the analysis unit can analyze natural sounds heard during the conversation (e.g., birds chirping or the sound of the wind) to determine how this is affecting the parents' level of relaxation. In this way, by taking into account background noise and environmental sounds, changes in their physical condition or mood can be detected more accurately.

[0092] When analyzing the content of parents' conversations, the analysis unit can also detect changes in physical condition or mood by taking into account the context of the conversation and changes in topic. For example, the analysis unit can analyze topics that frequently come up in the conversation (e.g., health, family, hobbies) and determine how these topics affect the parents' mood. The analysis unit can also estimate whether a sudden change in topic in the conversation is a sign of stress or anxiety in the parents. Furthermore, the analysis unit can determine whether a positive topic (e.g., a fun event, a successful experience) that comes up in the conversation improves the parents' mood. This allows for more accurate detection of changes in physical condition or mood by taking into account the context of the conversation and changes in topic.

[0093] The communication unit may also have a function to visually display the summary when summarizing the parents' status. For example, the communication unit may display changes in the parents' physical condition and mood in graphs or charts, allowing the child to understand the situation at a glance. The communication unit may also use icons or colors to indicate the parents' health status for a visually easy-to-understand display. Furthermore, the communication unit may display the parents' daily rhythms and activity patterns in a calendar format, allowing the child to check daily changes. This allows the use of visual displays to more effectively communicate the parents' status.

[0094] If a parent's health deteriorates, the liaison department can contact not only emergency contacts but also local support networks (e.g., friends in the neighborhood and local volunteers). For example, if a parent's health suddenly deteriorates, the liaison department can contact friends in the neighborhood to request emergency support. The liaison department can also contact local volunteers to ask them to watch over the parents when they go out alone. Furthermore, the liaison department can collect information about local events and activities that the parents regularly participate in and contact them as needed. This makes it possible to respond quickly and effectively by utilizing the local support network.

[0095] The support unit can customize dietary and exercise advice by taking into account parents' preferences and allergy information. For example, the support unit can suggest nutritionally balanced recipes based on parents' favorite ingredients and dishes. The support unit can also provide allergen-free meal plans by taking into account parents' allergy information. Furthermore, the support unit can suggest appropriate exercise programs by taking into account parents' exercise preferences and physical fitness levels. This allows for more effective support by providing dietary and exercise advice tailored to individual needs.

[0096] When estimating the emotions of the parents, the analysis unit can also analyze facial expressions using facial expression recognition technology. For example, the analysis unit can analyze facial expressions such as the parents' smiles and wrinkles between the eyebrows to estimate changes in emotions. The analysis unit can also analyze the parents' eye movements and blinking frequency to estimate changes in emotions. Furthermore, the analysis unit can analyze changes in skin color such as redness or paleness of the parents' faces to estimate changes in emotions. As a result, using facial expression recognition technology can more accurately detect changes in emotions.

[0097] When detecting specific keywords in everyday conversations, the analysis unit can also determine changes in physical condition by taking into account the emotional nuances of the keywords. For example, if the keyword "painful" is uttered in a sad tone, the analysis unit can determine that this strongly suggests a deterioration in physical condition. The analysis unit can also determine that if the keyword "tired" is frequently used, this indicates chronic fatigue. Furthermore, the analysis unit can determine that an increase in positive keywords such as "happy" and "fun" indicates an improvement in mood. In this way, by taking into account the emotional nuances of keywords, changes in physical condition can be determined more accurately.

[0098] When analyzing the content of a conversation, the analysis unit can detect changes in physical condition or mood by analyzing not only the tone and speaking speed of the parents' voices, but also the strength and rhythm of their voices. For example, the analysis unit may determine that a sudden change in the volume or volume of the parents' voices indicates a change in emotion. The analysis unit may also determine that an irregular speech rhythm of the parents indicates stress or anxiety. Furthermore, the analysis unit may determine that an inconsistent strength or volume of the parents' voices indicates a change in physical condition. Thus, by analyzing the strength and rhythm of the voices, changes in physical condition or mood can be detected more accurately.

[0099] When estimating the parents' emotions, the analysis unit learns patterns of emotional changes and can grasp long-term emotional trends. For example, the analysis unit analyzes past conversation data to learn patterns of emotional changes in the parents. The analysis unit can also analyze whether emotional changes are related to seasons or weather, and grasp long-term emotional trends. Furthermore, the analysis unit can analyze whether emotional changes are related to specific events or occurrences, and grasp long-term emotional trends. In this way, by learning patterns of emotional changes, long-term emotional trends can be grasped more accurately.

[0100] When analyzing the content of a conversation, the analysis unit can compare it not only with the parents' past health data but also with the health data of other family members to detect changes in physical condition. For example, the analysis unit can compare the health data of the parents with the health data of the child to determine whether genetic factors are affecting the physical condition. The analysis unit can also compare the health data of the parents with the health data of siblings to determine whether common lifestyle habits are affecting the physical condition. Furthermore, the analysis unit can compare the health data of the parents with the health data of the spouse to determine whether common environmental factors are affecting the physical condition. This allows for more accurate detection of changes in physical condition by comparing it with the health data of other family members.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The analysis unit analyzes the conversation content and detects changes in physical condition or mood. The analysis unit uses generative AI to analyze the conversation content and detect changes in physical condition or mood. For example, it can detect changes in body temperature, heart rate, and emotions from the conversation content. It can also analyze changes in the tone, speed, and content of the conversation to estimate changes in physical condition or mood. Step 2: The communication unit summarizes the information detected by the analysis unit and transmits it to the child. The communication unit uses generative AI to summarize the information and transmit it to the child. For example, it may report that both parents are in good health and that there are no particular concerns. It can also adjust the level of detail in the summary depending on the importance and type of information. Step 3: The coordination unit contacts welfare services or hospitals based on the information from the analysis unit. The coordination unit uses generative AI to contact the appropriate welfare services or hospitals. For example, if the person's health deteriorates, the coordination unit may contact welfare services or hospitals. It can also determine the priority of contacts based on the urgency and importance of the contact. Step 4: The support department provides specific information and advice on health and lifestyle. The support department uses generative AI to provide information and advice on health and lifestyle. For example, it can provide advice on proper diet and exercise. It can also support parents in living a healthy lifestyle.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] 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 engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] 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.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] 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.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] 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.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] 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.

[0131] 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.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 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.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0147] 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.

[0148] 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.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0158] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0159] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0160] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0163] 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.

[0164] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0165] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0166] 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.

[0167] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0168] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0169] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0171] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0172] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0173] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0174] [Explanation of symbols]

[0175] 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. An analysis unit that analyzes the content of conversations and detects changes in physical condition and mood; a communication unit that summarizes the information detected by the analysis unit and communicates the summary to the child; a linking unit that contacts welfare services and hospitals based on the information from the analysis unit; A support department that provides specific information and advice on health and lifestyles. A system characterized by:

2. The analysis unit Analyzing everyday conversations to detect changes in physical condition and mood 2. The system of claim 1.

3. The transmission unit is Summarize the parents' situation and communicate it to the child 2. The system of claim 1.

4. The linking unit is Contacting social services or hospitals if your health deteriorates 2. The system of claim 1.

5. The support portion is Providing dietary and exercise advice 2. The system of claim 1.

6. The analysis unit Estimate the emotions of parents and detect changes in their physical condition and mood based on the estimated emotions of parents 2. The system of claim 1.

7. The analysis unit Detecting specific keywords in everyday conversations and determining changes in physical condition based on their frequency and context 2. The system of claim 1.

8. The analysis unit When analyzing conversations, the system analyzes the tone of the parents' voices and the rate at which they speak to detect changes in their physical condition or mood.

2. The system of claim 1.

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