system

Abstract

The system according to this embodiment aims to enable family members to confidently monitor the health and living conditions of elderly people who live separately from them. [Solution] The system according to this embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit collects information by having regular conversations with the elderly. The analysis unit analyzes the information collected by the collection unit. The provision unit provides the family with the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to manage the health of elderly people living alone and to grasp their living conditions, and it is difficult for their families to watch over them with confidence.

[0005] The system according to the embodiment aims to enable the family members to confidently grasp the health management and living conditions of the elderly living alone.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects information by regularly conversing with the elderly. The analysis unit analyzes the information collected by the collection unit. The provision unit provides the analysis result obtained by the analysis unit to the family members. [Effects of the Invention]

[0007] The system according to this embodiment allows family members to confidently monitor the health and living conditions of elderly relatives who live separately from them. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The avatar telephone service system according to an embodiment of the present invention is an avatar telephone service for elderly people who live separately from their children. This avatar telephone service system regularly converses with the elderly person to check on their health and provide support for their daily life. For example, the avatar telephone service system asks the elderly person, "What did you eat today?" to confirm the details of their meal. Next, the avatar telephone service system organizes the information obtained from the conversation and communicates it to the family in real time via a messaging app. For example, the avatar telephone service system communicates information such as, "Mom ate a lot of vegetables today," to the family. This allows the family to understand the elderly person's health status and take necessary actions. In addition to health information, the avatar telephone service system also communicates information on necessary food items and daily necessities, which the family can purchase and arrange online. For example, the avatar telephone service system communicates information such as, "Mom is running out of milk," to the family, who can purchase milk online and have it delivered to the elderly person's home. In this way, the avatar telephone service system aims to manage health and living conditions, and further improve the quality of life for the elderly person while they feel the presence of their family, without feeling lonely. This allows the avatar telephone service system to efficiently manage the health and living conditions of elderly individuals and to communicate this information with their families.

[0029] The avatar telephone service system according to this embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit collects information by regularly conversing with elderly individuals. The collection unit, for example, uses an AI agent to converse with elderly individuals and provide health checks and support for their daily lives. For example, the collection unit can ask the elderly individual, "What did you eat today?" to confirm the details of their meals. The collection unit can also ask, for example, "How have you been feeling lately?" to confirm their health status. Furthermore, the collection unit can ask, for example, "How has the quality of your sleep been lately?" to confirm their sleep status. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit organizes the collected information to understand the individual's health status and living situation. For example, the analysis unit can evaluate nutritional balance based on the collected meal details. Furthermore, the analysis unit can evaluate health risks based on the collected health status information. Furthermore, the analysis unit can evaluate sleep quality based on the collected sleep status information. The provision unit provides the analysis results obtained by the analysis unit to the family. The service provider can communicate with family members, for example, through a messaging app. The service provider can communicate information to family members, for example, such as, "Mom ate a lot of vegetables today." The service provider can also communicate information to family members, for example, such as, "Mom is in good health." Furthermore, the service provider can also communicate information to family members, for example, such as, "Mom is getting good sleep." In this way, the avatar telephone service system according to the embodiment can efficiently manage the health status and living conditions of elderly people and communicate this information to their families.

[0030] The data collection unit gathers information by regularly conversing with elderly individuals. For example, the unit uses AI agents to converse with elderly individuals, checking on their health and providing support for their daily lives. Specifically, the AI ​​agents utilize natural language processing technology to interact with elderly individuals and analyze their responses to questions in real time. For example, they can ask elderly individuals, "What did you eat today?" to confirm the details of their meals. In this case, the AI ​​agent records the elderly individual's response as text data, gaining a detailed understanding of the type and quantity of food, as well as the balance of nutrients. The data collection unit can also check the elderly individual's health status by asking, for example, "How have you been feeling lately?" From the elderly individual's responses, it can detect changes in their physical condition or the presence of specific symptoms and, if necessary, encourage them to visit a medical institution. Furthermore, the data collection unit can check their sleep patterns by asking, for example, "How has the quality of your sleep been lately?" By collecting information on the elderly individual's sleep patterns and sleep quality, it is possible to detect early signs of sleep disorders. The data collection unit centrally manages this information and stores it in a database to support long-term health management. Furthermore, the data collection unit uses speech recognition technology to accurately transcribe the speech of elderly individuals into text, improving data accuracy. This allows the data collection unit to gather basic data necessary to understand the health and living conditions of elderly individuals in detail and provide appropriate support.

[0031] The analysis department analyzes the information collected by the data collection department. For example, the analysis department organizes the collected information to understand health status and living conditions. Specifically, it can evaluate nutritional balance based on collected dietary information. Using AI-based data analysis technology, it automatically classifies the nutrients in the diet and evaluates whether a balanced diet is being consumed. The analysis department can also evaluate health risks based on collected health status information. For example, it can analyze fluctuations in vital signs such as blood pressure, weight, and body temperature, and issue early warnings if abnormal values ​​are detected. Furthermore, the analysis department can evaluate sleep quality based on collected sleep status information. By analyzing sleep depth, frequency of interruptions, and total sleep time, it can suggest improvement measures if sleep quality is declining. The analysis department comprehensively evaluates this information to gain a comprehensive understanding of the health status and living conditions of elderly individuals. By comparing it with past data, it can also understand changes and trends in health status and use this information for long-term health management. In addition, the analysis department can use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue early warnings. This allows the analysis department to conduct a detailed analysis of the health and living conditions of elderly individuals and provide information to enable them to take appropriate measures.

[0032] The service provider provides families with the analysis results obtained by the analysis department. The service provider communicates with families, for example, through messaging apps. Specifically, it can send notifications to smartphones and tablets used by family members, allowing for real-time information sharing. For example, it can share information such as, "Mom ate a lot of vegetables today." This allows families to understand the elderly person's diet and confirm whether they are eating a nutritionally balanced meal. The service provider can also share information such as, "Mom is in good health." This allows families to monitor the elderly person's health with peace of mind. Furthermore, the service provider can share information such as, "Mom's sleep quality is good." This allows families to understand the elderly person's sleep patterns and support lifestyle improvements as needed. The service provider regularly updates this information to provide families with the latest status. The service provider can also collect feedback from families and use it to improve the system. For example, it can adjust the content and frequency of notifications according to family requests. This allows the service provider to efficiently manage and communicate the elderly person's health and living situation to families.

[0033] The voice collection unit can converse with the elderly by mimicking the voices of family members and caregivers. The voice collection unit can mimic the voices of family members and caregivers, for example, using speech synthesis technology. The voice collection unit can, for example, record the voices of family members, extract the characteristics of those voices, and mimic them. The voice collection unit can also, for example, record the voices of caregivers, extract the characteristics of those voices, and mimic them. Furthermore, the voice collection unit can generate the voices of family members and caregivers in real time, for example, using speech synthesis technology. This allows the voice collection unit to create a sense of familiarity with the elderly and enhance the effectiveness of the conversation. Some or all of the above processing in the voice collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the voice collection unit can input the characteristics of the voices of family members and caregivers into a generative AI, and the generative AI can generate speech based on those characteristics.

[0034] The analysis department can organize the collected information and understand the health status and living conditions. For example, the analysis department can store the collected information in a database and classify and prioritize the data. For example, the analysis department can classify the collected dietary information by nutrient and evaluate the nutritional balance. Furthermore, the analysis department can apply algorithms to assess health risks based on the collected health status information. In addition, the analysis department can set criteria for evaluating sleep quality based on the collected sleep status information. This allows the analysis department to accurately understand the health status and living conditions of elderly people. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input the collected information into AI, and the AI ​​can analyze the health status and living conditions based on that information.

[0035] The service provider can share analysis results with family members via a messaging app. For example, the service provider can use a messaging app to share analysis results with family members. For example, the service provider can share information such as, "Mom ate a lot of vegetables today," via a messaging app. The service provider can also share information such as, "Mom is feeling well," via a messaging app. Furthermore, the service provider can share information such as, "Mom is sleeping well," via SMS. This makes it easier for family members to understand the health and living conditions of elderly individuals. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input analysis results into an AI, which can then generate an appropriate message based on those results and share it with family members via a messaging app.

[0036] The supply unit can share information about necessary groceries and daily necessities with the family, allowing the family to purchase and arrange them online. For example, the supply unit can share information such as, "Mom is running low on milk." It can also share information such as, "Mom is running low on toilet paper." Furthermore, it can share information such as, "Mom needs detergent." In addition, it can share information such as, "Mom is running low on food." This streamlines the management of daily necessities for the elderly. Some or all of the above processing in the supply unit may be performed using AI, for example, or not. For example, the supply unit can input information about necessary groceries and daily necessities into the AI, which can then generate appropriate messages based on that information and share them with the family.

[0037] The data collection unit can regularly converse with elderly individuals to check on their health and provide support for their daily lives. For example, the data collection unit can converse with elderly individuals daily to check on their health status. For example, the data collection unit can ask questions such as, "What did you eat today?" to confirm the details of their meals. It can also ask questions such as, "How have you been feeling lately?" to check on their health status. Furthermore, it can ask questions such as, "How has the quality of your sleep been lately?" to check on their sleep patterns. In this way, the data collection unit can continuously monitor the health status and living conditions of elderly individuals. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input a schedule for regularly conversing with elderly individuals into the AI, and the AI ​​can conduct conversations based on that schedule.

[0038] The data collection unit can analyze the elderly person's past conversation history and select the optimal timing and content for conversation. For example, if the elderly person has preferred to converse at a specific time in the past, the data collection unit will start a conversation at that time. For example, the data collection unit can prioritize topics that the elderly person has shown interest in in the past. The data collection unit can also reduce stress by avoiding topics that the elderly person has avoided in the past. This allows the data collection unit to conduct conversations at the optimal time for the elderly person. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the elderly person's past conversation history into an AI, which can then use that history to select the optimal timing and content for conversation.

[0039] The data collection unit can customize the questions asked during a conversation based on the elderly person's current health status and living situation. For example, if the elderly person has recently been unwell, the data collection unit can increase the number of health-related questions. If the elderly person has recently started a new hobby, the data collection unit can ask questions about that hobby. Also, if the elderly person has recently been eating less, the data collection unit can increase the number of questions about their diet. This allows the data collection unit to ask appropriate questions tailored to the elderly person's situation. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the elderly person's current health status and living situation into the AI, which can then customize the questions based on that data.

[0040] The data collection unit can prioritize topics that are highly relevant to the elderly person based on their geographical location during a conversation. For example, the data collection unit can provide topics related to the weather or events in the area where the elderly person lives. For example, it can provide topics related to places the elderly person frequently visits. Furthermore, the data collection unit can provide topics related to news or events in the area where the elderly person lives. In this way, the data collection unit provides appropriate topics according to the elderly person's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the elderly person's geographical location information into AI, and the AI ​​can select highly relevant topics based on that information.

[0041] The data collection unit can analyze the social media activity of elderly individuals during conversations and provide relevant topics. For example, the data collection unit can incorporate topics that elderly individuals have shown interest in on social media into the conversation. For example, the data collection unit can provide topics related to accounts that elderly individuals follow on social media. The data collection unit can also provide topics related to content that elderly individuals have shared on social media. This ensures that the data collection unit provides appropriate topics based on the elderly individual's social media activity. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the elderly individual's social media activity into an AI, which can then select relevant topics based on that data.

[0042] The analysis unit can improve the accuracy of its analysis by referring to past data when analyzing collected information. For example, the analysis unit can refer to the past health data of elderly people to analyze their current health status. For example, the analysis unit can refer to the past lifestyle patterns of elderly people to analyze their current living situation. Furthermore, the analysis unit can refer to the past conversation history of elderly people to analyze their current emotional state. In this way, the analysis unit improves the accuracy of its analysis by referring to past data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past data into AI, and the AI ​​can analyze current information based on that data.

[0043] The analysis unit can apply different analysis algorithms to each category of information when analyzing the collected information. For example, the analysis unit can apply a health-specific analysis algorithm to health information. For example, it can apply a lifestyle-specific analysis algorithm to lifestyle information. Furthermore, the analysis unit can apply an emotion-specific analysis algorithm to emotion information. This allows the analysis unit to perform appropriate analysis for each category of information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input different analysis algorithms for each category of information into the AI, and the AI ​​can analyze the information based on those algorithms.

[0044] The analysis department can prioritize the analysis of collected information based on when the information was submitted. For example, it may prioritize the analysis of recently collected information. For example, it may prioritize the analysis of information that is collected regularly. It may also prioritize the analysis of information that is of high urgency. This allows the analysis department to conduct appropriate analysis based on when the information was submitted. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input the submission dates of the information into the AI, and the AI ​​can determine the analysis priority based on that information.

[0045] The analysis unit can improve the accuracy of its analysis by referring to relevant literature and data when analyzing collected information. For example, when analyzing health information, the analysis unit can refer to the latest medical literature. For example, when analyzing lifestyle information, the analysis unit can refer to relevant statistical data. Furthermore, when analyzing emotional information, the analysis unit can refer to psychological research data. In this way, the analysis unit improves the accuracy of its analysis by referring to relevant literature and data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input relevant literature and data into AI, and the AI ​​can improve the accuracy of its analysis based on that information.

[0046] The information provider can adjust the level of detail based on the importance of the information being provided. For example, the provider can provide important health information in detail. For example, it can provide lifestyle information concisely. Furthermore, it can provide urgent information quickly. This enables the provider to provide appropriate information according to its importance. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can input the importance of the information into the AI, and the AI ​​can adjust the level of detail based on that information.

[0047] The information delivery unit can apply different delivery methods depending on the category of information being provided. For example, health information may be provided in the form of a detailed report. For example, lifestyle information may be provided in the form of a concise message. Furthermore, for example, emotional information may be provided in a friendly tone. This enables the information delivery unit to provide appropriate information according to the category of information. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or not using AI. For example, the information delivery unit can input the category of information into the AI, and the AI ​​can apply a delivery method based on that information.

[0048] The information provider can prioritize information based on when it is submitted. For example, the provider may prioritize recently collected information. For example, the provider may prioritize information that is collected regularly. The provider may also prioritize information that is urgent. This enables the provider to provide appropriate information based on when it is submitted. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the provider can input the submission dates of information into the AI, and the AI ​​can determine the priority of information based on that information.

[0049] The information provider can adjust the order of the information based on its relevance. For example, the provider may prioritize providing information related to the health status of elderly people. For example, the provider may prioritize providing information related to the living situation of elderly people. The provider may also prioritize providing information related to the emotional state of elderly people. This enables the provider to provide appropriate information according to its relevance. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can input the relevance of the information into the AI, and the AI ​​can adjust the order of the information based on that information.

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

[0051] The information gathering function can customize the content of conversations based on the hobbies and interests of elderly individuals. For example, if an elderly person is interested in gardening, the information gathering function can ask, "How is your garden doing these days?" If the elderly person enjoys reading, the information gathering function can ask, "What book have you read recently?" Furthermore, if the elderly person likes to travel, the information gathering function can ask, "Where would you like to go to visit recently?" This allows the information gathering function to engage in conversations tailored to the hobbies and interests of elderly individuals, improving the quality of the conversations.

[0052] The analysis department can predict the lifestyle patterns of elderly individuals based on the collected information and provide lifestyle support based on the prediction results. For example, the analysis department can predict that elderly individuals eat breakfast at a specific time every morning and provide health advice tailored to that time. It can also predict that elderly individuals go shopping on a specific day each week and provide a shopping list the day before. Furthermore, the analysis department can predict that elderly individuals visit the hospital on a specific day each month and provide a reminder the day before to encourage preparation for the visit. This enables the analysis department to provide appropriate support based on the lifestyle patterns of elderly individuals.

[0053] The service provider can provide families with regular reports on the elderly person's living situation. For example, the service provider can provide families with a weekly report on "Mom's diet and health status this week." They can also provide families with a monthly report on "Mom's activity level and quality of life this month." Furthermore, they can provide families with a report after a specific event, such as "Mom's recent medical visit results and doctor's advice." This makes it easier for families to regularly understand the elderly person's living situation.

[0054] The analysis department can predict health risks for the elderly based on the collected information and propose preventive measures based on the prediction results. For example, the analysis department can predict the risk of nutritional deficiencies from the elderly's diet and propose a nutritionally balanced diet. It can also predict the risk of insufficient exercise from the elderly's exercise level and propose moderate exercise. Furthermore, the analysis department can predict the risk of sleep disorders from the elderly's sleep patterns and provide advice on how to get quality sleep. In this way, the analysis department can provide appropriate preventive measures based on the health risks of the elderly.

[0055] The analysis department can evaluate the quality of life of elderly individuals based on the collected information and make suggestions for lifestyle improvements based on the evaluation results. For example, the analysis department can evaluate the nutritional balance of elderly individuals' diets and suggest balanced meals. It can also evaluate exercise habits based on their activity levels and suggest appropriate exercise. Furthermore, the analysis department can evaluate the quality of sleep based on their sleep patterns and provide advice for getting quality sleep. This enables the analysis department to make appropriate suggestions for improving the quality of life of elderly individuals.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The data collection unit collects information by regularly conversing with elderly individuals. For example, they use an AI agent to converse with elderly individuals, checking on their health and providing support for their daily lives. The data collection unit asks elderly individuals questions such as, "What did you eat today?", "How have you been feeling lately?", and "How has the quality of your sleep been lately?" to check on their diet, health status, and sleep patterns. Step 2: The analysis department analyzes the information collected by the collection department. For example, it organizes the collected information to understand health status and lifestyle. It evaluates nutritional balance based on collected dietary information, assesses health risks based on health status information, and evaluates sleep quality based on sleep status information. Step 3: The service provider provides the family with the analysis results obtained by the analysis provider. For example, they communicate with the family via a messaging app and share information such as, "Mom ate lots of vegetables today," "Mom is feeling well," and "Mom's sleep quality is good."

[0058] (Example of form 2) The avatar telephone service system according to an embodiment of the present invention is an avatar telephone service for elderly people who live separately from their children. This avatar telephone service system regularly converses with the elderly person to check on their health and provide support for their daily life. For example, the avatar telephone service system asks the elderly person, "What did you eat today?" to confirm the details of their meal. Next, the avatar telephone service system organizes the information obtained from the conversation and communicates it to the family in real time via a messaging app. For example, the avatar telephone service system communicates information such as, "Mom ate a lot of vegetables today," to the family. This allows the family to understand the elderly person's health status and take necessary actions. In addition to health information, the avatar telephone service system also communicates information on necessary food items and daily necessities, which the family can purchase and arrange online. For example, the avatar telephone service system communicates information such as, "Mom is running out of milk," to the family, who can purchase milk online and have it delivered to the elderly person's home. In this way, the avatar telephone service system aims to manage health and living conditions, and further improve the quality of life for the elderly person while they feel the presence of their family, without feeling lonely. This allows the avatar telephone service system to efficiently manage the health and living conditions of elderly individuals and to communicate this information with their families.

[0059] The avatar telephone service system according to this embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit collects information by regularly conversing with elderly individuals. The collection unit, for example, uses an AI agent to converse with elderly individuals and provide health checks and support for their daily lives. For example, the collection unit can ask the elderly individual, "What did you eat today?" to confirm the details of their meals. The collection unit can also ask, for example, "How have you been feeling lately?" to confirm their health status. Furthermore, the collection unit can ask, for example, "How has the quality of your sleep been lately?" to confirm their sleep status. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit organizes the collected information to understand the individual's health status and living situation. For example, the analysis unit can evaluate nutritional balance based on the collected meal details. Furthermore, the analysis unit can evaluate health risks based on the collected health status information. Furthermore, the analysis unit can evaluate sleep quality based on the collected sleep status information. The provision unit provides the analysis results obtained by the analysis unit to the family. The service provider can communicate with family members, for example, through a messaging app. The service provider can communicate information to family members, for example, such as, "Mom ate a lot of vegetables today." The service provider can also communicate information to family members, for example, such as, "Mom is in good health." Furthermore, the service provider can also communicate information to family members, for example, such as, "Mom is getting good sleep." In this way, the avatar telephone service system according to the embodiment can efficiently manage the health status and living conditions of elderly people and communicate this information to their families.

[0060] The data collection unit gathers information by regularly conversing with elderly individuals. For example, the unit uses AI agents to converse with elderly individuals, checking on their health and providing support for their daily lives. Specifically, the AI ​​agents utilize natural language processing technology to interact with elderly individuals and analyze their responses to questions in real time. For example, they can ask elderly individuals, "What did you eat today?" to confirm the details of their meals. In this case, the AI ​​agent records the elderly individual's response as text data, gaining a detailed understanding of the type and quantity of food, as well as the balance of nutrients. The data collection unit can also check the elderly individual's health status by asking, for example, "How have you been feeling lately?" From the elderly individual's responses, it can detect changes in their physical condition or the presence of specific symptoms and, if necessary, encourage them to visit a medical institution. Furthermore, the data collection unit can check their sleep patterns by asking, for example, "How has the quality of your sleep been lately?" By collecting information on the elderly individual's sleep patterns and sleep quality, it is possible to detect early signs of sleep disorders. The data collection unit centrally manages this information and stores it in a database to support long-term health management. Furthermore, the data collection unit uses speech recognition technology to accurately transcribe the speech of elderly individuals into text, improving data accuracy. This allows the data collection unit to gather basic data necessary to understand the health and living conditions of elderly individuals in detail and provide appropriate support.

[0061] The analysis department analyzes the information collected by the data collection department. For example, the analysis department organizes the collected information to understand health status and living conditions. Specifically, it can evaluate nutritional balance based on collected dietary information. Using AI-based data analysis technology, it automatically classifies the nutrients in the diet and evaluates whether a balanced diet is being consumed. The analysis department can also evaluate health risks based on collected health status information. For example, it can analyze fluctuations in vital signs such as blood pressure, weight, and body temperature, and issue early warnings if abnormal values ​​are detected. Furthermore, the analysis department can evaluate sleep quality based on collected sleep status information. By analyzing sleep depth, frequency of interruptions, and total sleep time, it can suggest improvement measures if sleep quality is declining. The analysis department comprehensively evaluates this information to gain a comprehensive understanding of the health status and living conditions of elderly individuals. By comparing it with past data, it can also understand changes and trends in health status and use this information for long-term health management. In addition, the analysis department can use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue early warnings. This allows the analysis department to conduct a detailed analysis of the health and living conditions of elderly individuals and provide information to enable them to take appropriate measures.

[0062] The service provider provides families with the analysis results obtained by the analysis department. The service provider communicates with families, for example, through messaging apps. Specifically, it can send notifications to smartphones and tablets used by family members, allowing for real-time information sharing. For example, it can share information such as, "Mom ate a lot of vegetables today." This allows families to understand the elderly person's diet and confirm whether they are eating a nutritionally balanced meal. The service provider can also share information such as, "Mom is in good health." This allows families to monitor the elderly person's health with peace of mind. Furthermore, the service provider can share information such as, "Mom's sleep quality is good." This allows families to understand the elderly person's sleep patterns and support lifestyle improvements as needed. The service provider regularly updates this information to provide families with the latest status. The service provider can also collect feedback from families and use it to improve the system. For example, it can adjust the content and frequency of notifications according to family requests. This allows the service provider to efficiently manage and communicate the elderly person's health and living situation to families.

[0063] The voice collection unit can converse with the elderly by mimicking the voices of family members and caregivers. The voice collection unit can mimic the voices of family members and caregivers, for example, using speech synthesis technology. The voice collection unit can, for example, record the voices of family members, extract the characteristics of those voices, and mimic them. The voice collection unit can also, for example, record the voices of caregivers, extract the characteristics of those voices, and mimic them. Furthermore, the voice collection unit can generate the voices of family members and caregivers in real time, for example, using speech synthesis technology. This allows the voice collection unit to create a sense of familiarity with the elderly and enhance the effectiveness of the conversation. Some or all of the above processing in the voice collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the voice collection unit can input the characteristics of the voices of family members and caregivers into a generative AI, and the generative AI can generate speech based on those characteristics.

[0064] The analysis department can organize the collected information and understand the health status and living conditions. For example, the analysis department can store the collected information in a database and classify and prioritize the data. For example, the analysis department can classify the collected dietary information by nutrient and evaluate the nutritional balance. Furthermore, the analysis department can apply algorithms to assess health risks based on the collected health status information. In addition, the analysis department can set criteria for evaluating sleep quality based on the collected sleep status information. This allows the analysis department to accurately understand the health status and living conditions of elderly people. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input the collected information into AI, and the AI ​​can analyze the health status and living conditions based on that information.

[0065] The service provider can share analysis results with family members via a messaging app. For example, the service provider can use a messaging app to share analysis results with family members. For example, the service provider can share information such as, "Mom ate a lot of vegetables today," via a messaging app. The service provider can also share information such as, "Mom is feeling well," via a messaging app. Furthermore, the service provider can share information such as, "Mom is sleeping well," via SMS. This makes it easier for family members to understand the health and living conditions of elderly individuals. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input analysis results into an AI, which can then generate an appropriate message based on those results and share it with family members via a messaging app.

[0066] The supply unit can share information about necessary groceries and daily necessities with the family, allowing the family to purchase and arrange them online. For example, the supply unit can share information such as, "Mom is running low on milk." It can also share information such as, "Mom is running low on toilet paper." Furthermore, it can share information such as, "Mom needs detergent." In addition, it can share information such as, "Mom is running low on food." This streamlines the management of daily necessities for the elderly. Some or all of the above processing in the supply unit may be performed using AI, for example, or not. For example, the supply unit can input information about necessary groceries and daily necessities into the AI, which can then generate appropriate messages based on that information and share them with the family.

[0067] The data collection unit can regularly converse with elderly individuals to check on their health and provide support for their daily lives. For example, the data collection unit can converse with elderly individuals daily to check on their health status. For example, the data collection unit can ask questions such as, "What did you eat today?" to confirm the details of their meals. It can also ask questions such as, "How have you been feeling lately?" to check on their health status. Furthermore, it can ask questions such as, "How has the quality of your sleep been lately?" to check on their sleep patterns. In this way, the data collection unit can continuously monitor the health status and living conditions of elderly individuals. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input a schedule for regularly conversing with elderly individuals into the AI, and the AI ​​can conduct conversations based on that schedule.

[0068] The data collection unit can estimate the emotions of elderly individuals and adjust the content and tone of conversation based on the estimated emotions. For example, if an elderly person is feeling lonely, the AI ​​agent can speak in a gentle tone and offer words of encouragement. If an elderly person is feeling energetic, the AI ​​agent can speak in a cheerful tone and offer pleasant topics. Furthermore, if an elderly person is tired, the AI ​​agent can speak in a calm tone and offer relaxing topics. This enables the data collection unit to conduct appropriate conversations according to the emotions of elderly individuals. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit can input data for estimating the emotions of elderly individuals into an AI, which can then estimate the emotions based on that data and adjust the content and tone of conversation.

[0069] The data collection unit can analyze the elderly person's past conversation history and select the optimal timing and content for conversation. For example, if the elderly person has preferred to converse at a specific time in the past, the data collection unit will start a conversation at that time. For example, the data collection unit can prioritize topics that the elderly person has shown interest in in the past. The data collection unit can also reduce stress by avoiding topics that the elderly person has avoided in the past. This allows the data collection unit to conduct conversations at the optimal time for the elderly person. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the elderly person's past conversation history into an AI, which can then use that history to select the optimal timing and content for conversation.

[0070] The data collection unit can customize the questions asked during a conversation based on the elderly person's current health status and living situation. For example, if the elderly person has recently been unwell, the data collection unit can increase the number of health-related questions. If the elderly person has recently started a new hobby, the data collection unit can ask questions about that hobby. Also, if the elderly person has recently been eating less, the data collection unit can increase the number of questions about their diet. This allows the data collection unit to ask appropriate questions tailored to the elderly person's situation. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the elderly person's current health status and living situation into the AI, which can then customize the questions based on that data.

[0071] The data collection unit can estimate the emotions of elderly individuals and adjust the frequency of conversations based on the estimated emotions. For example, if an elderly person is feeling lonely, the data collection unit can increase the frequency of conversations. For example, if an elderly person is busy, the data collection unit can decrease the frequency of conversations. The data collection unit can also converse at an appropriate frequency if an elderly person is relaxed. This allows the data collection unit to provide an appropriate conversation frequency according to the emotions of the elderly person. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input data for estimating the emotions of elderly individuals into an AI, which can then estimate the emotions based on that data and adjust the frequency of conversations.

[0072] The data collection unit can prioritize topics that are highly relevant to the elderly person based on their geographical location during a conversation. For example, the data collection unit can provide topics related to the weather or events in the area where the elderly person lives. For example, it can provide topics related to places the elderly person frequently visits. Furthermore, the data collection unit can provide topics related to news or events in the area where the elderly person lives. In this way, the data collection unit provides appropriate topics according to the elderly person's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the elderly person's geographical location information into AI, and the AI ​​can select highly relevant topics based on that information.

[0073] The data collection unit can analyze the social media activity of elderly individuals during conversations and provide relevant topics. For example, the data collection unit can incorporate topics that elderly individuals have shown interest in on social media into the conversation. For example, the data collection unit can provide topics related to accounts that elderly individuals follow on social media. The data collection unit can also provide topics related to content that elderly individuals have shared on social media. This ensures that the data collection unit provides appropriate topics based on the elderly individual's social media activity. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the elderly individual's social media activity into an AI, which can then select relevant topics based on that data.

[0074] The analysis unit can estimate the emotions of elderly individuals and adjust the presentation of the analysis results based on the estimated emotions. For example, if an elderly individual is feeling anxious, the analysis unit will use a reassuring presentation. If an elderly individual is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if an elderly individual is in a hurry, the analysis unit can provide concise analysis results. This ensures that the analysis unit provides appropriate analysis results tailored to the elderly individual's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input data for estimating the emotions of elderly individuals into an AI, which then estimates the emotions based on that data and adjusts the presentation of the analysis results.

[0075] The analysis unit can improve the accuracy of its analysis by referring to past data when analyzing collected information. For example, the analysis unit can refer to the past health data of elderly people to analyze their current health status. For example, the analysis unit can refer to the past lifestyle patterns of elderly people to analyze their current living situation. Furthermore, the analysis unit can refer to the past conversation history of elderly people to analyze their current emotional state. In this way, the analysis unit improves the accuracy of its analysis by referring to past data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past data into AI, and the AI ​​can analyze current information based on that data.

[0076] The analysis unit can apply different analysis algorithms to each category of information when analyzing the collected information. For example, the analysis unit can apply a health-specific analysis algorithm to health information. For example, it can apply a lifestyle-specific analysis algorithm to lifestyle information. Furthermore, the analysis unit can apply an emotion-specific analysis algorithm to emotion information. This allows the analysis unit to perform appropriate analysis for each category of information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input different analysis algorithms for each category of information into the AI, and the AI ​​can analyze the information based on those algorithms.

[0077] The analysis unit can estimate the emotions of elderly individuals and prioritize analysis results based on the estimated emotions. For example, if an elderly individual is feeling anxious, the analysis unit may prioritize analyzing health information. For example, if an elderly individual is relaxed, the analysis unit may prioritize analyzing lifestyle information. Furthermore, if an elderly individual is in a hurry, the analysis unit may prioritize analyzing important information. This allows the analysis unit to determine an appropriate priority of analysis results according to the elderly individual's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input data for estimating the emotions of elderly individuals into an AI, which can then estimate the emotions based on that data and determine the priority of analysis results.

[0078] The analysis department can prioritize the analysis of collected information based on when the information was submitted. For example, it may prioritize the analysis of recently collected information. For example, it may prioritize the analysis of information that is collected regularly. It may also prioritize the analysis of information that is of high urgency. This allows the analysis department to conduct appropriate analysis based on when the information was submitted. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input the submission dates of the information into the AI, and the AI ​​can determine the analysis priority based on that information.

[0079] The analysis unit can improve the accuracy of its analysis by referring to relevant literature and data when analyzing collected information. For example, when analyzing health information, the analysis unit can refer to the latest medical literature. For example, when analyzing lifestyle information, the analysis unit can refer to relevant statistical data. Furthermore, when analyzing emotional information, the analysis unit can refer to psychological research data. In this way, the analysis unit improves the accuracy of its analysis by referring to relevant literature and data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input relevant literature and data into AI, and the AI ​​can improve the accuracy of its analysis based on that information.

[0080] The information provider can estimate the emotions of elderly individuals and adjust the way the information is presented based on the estimated emotions. For example, if an elderly person is feeling anxious, the information provider can provide information in a way that provides reassurance. For example, if an elderly person is relaxed, the information provider can provide detailed information. Also, if an elderly person is in a hurry, the information provider can provide concise information. This enables the information provider to provide appropriate information according to the emotions of elderly individuals. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input data for estimating the emotions of elderly individuals into an AI, the AI ​​can estimate emotions based on that data, and adjust the way the information is presented.

[0081] The information provider can adjust the level of detail based on the importance of the information being provided. For example, the provider can provide important health information in detail. For example, it can provide lifestyle information concisely. Furthermore, it can provide urgent information quickly. This enables the provider to provide appropriate information according to its importance. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can input the importance of the information into the AI, and the AI ​​can adjust the level of detail based on that information.

[0082] The information delivery unit can apply different delivery methods depending on the category of information being provided. For example, health information may be provided in the form of a detailed report. For example, lifestyle information may be provided in the form of a concise message. Furthermore, for example, emotional information may be provided in a friendly tone. This enables the information delivery unit to provide appropriate information according to the category of information. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or not using AI. For example, the information delivery unit can input the category of information into the AI, and the AI ​​can apply a delivery method based on that information.

[0083] The information provider can estimate the emotions of elderly individuals and determine the priority of information to provide based on the estimated emotions. For example, if an elderly individual is feeling anxious, the information provider can prioritize providing health information. For example, if an elderly individual is relaxed, the information provider can prioritize providing lifestyle information. Furthermore, if an elderly individual is in a hurry, the information provider can prioritize providing important information. This allows the information provider to determine an appropriate priority for providing information according to the emotions of the elderly individual. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI, or not using AI. For example, the information provider can input data for estimating the emotions of elderly individuals into an AI, which can then estimate the emotions based on that data and determine the priority of information.

[0084] The information provider can prioritize information based on when it is submitted. For example, the provider may prioritize recently collected information. For example, the provider may prioritize information that is collected regularly. The provider may also prioritize information that is urgent. This enables the provider to provide appropriate information based on when it is submitted. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the provider can input the submission dates of information into the AI, and the AI ​​can determine the priority of information based on that information.

[0085] The information provider can adjust the order of the information based on its relevance. For example, the provider may prioritize providing information related to the health status of elderly people. For example, the provider may prioritize providing information related to the living situation of elderly people. The provider may also prioritize providing information related to the emotional state of elderly people. This enables the provider to provide appropriate information according to its relevance. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can input the relevance of the information into the AI, and the AI ​​can adjust the order of the information based on that information.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The information gathering function can customize the content of conversations based on the hobbies and interests of elderly individuals. For example, if an elderly person is interested in gardening, the information gathering function can ask, "How is your garden doing these days?" If the elderly person enjoys reading, the information gathering function can ask, "What book have you read recently?" Furthermore, if the elderly person likes to travel, the information gathering function can ask, "Where would you like to go to visit recently?" This allows the information gathering function to engage in conversations tailored to the hobbies and interests of elderly individuals, improving the quality of the conversations.

[0088] The analysis department can predict the lifestyle patterns of elderly individuals based on the collected information and provide lifestyle support based on the prediction results. For example, the analysis department can predict that elderly individuals eat breakfast at a specific time every morning and provide health advice tailored to that time. It can also predict that elderly individuals go shopping on a specific day each week and provide a shopping list the day before. Furthermore, the analysis department can predict that elderly individuals visit the hospital on a specific day each month and provide a reminder the day before to encourage preparation for the visit. This enables the analysis department to provide appropriate support based on the lifestyle patterns of elderly individuals.

[0089] The service provider can provide families with regular reports on the elderly person's living situation. For example, the service provider can provide families with a weekly report on "Mom's diet and health status this week." They can also provide families with a monthly report on "Mom's activity level and quality of life this month." Furthermore, they can provide families with a report after a specific event, such as "Mom's recent medical visit results and doctor's advice." This makes it easier for families to regularly understand the elderly person's living situation.

[0090] The data collection unit can estimate the emotions of elderly individuals and adjust the content of the conversation based on those estimates. For example, if an elderly person is feeling sad, the unit can ask, "Have you had anything on your mind lately?" and offer words of comfort. If an elderly person is feeling happy, the unit can ask, "Has anything good happened to you recently?" and offer words of empathy. Furthermore, if an elderly person is feeling angry, the unit can ask, "Has anything bothered you?" and suggest solutions. This allows the data collection unit to engage in appropriate conversations tailored to the emotions of elderly individuals.

[0091] The analysis department can predict health risks for the elderly based on the collected information and propose preventive measures based on the prediction results. For example, the analysis department can predict the risk of nutritional deficiencies from the elderly's diet and propose a nutritionally balanced diet. It can also predict the risk of insufficient exercise from the elderly's exercise level and propose moderate exercise. Furthermore, the analysis department can predict the risk of sleep disorders from the elderly's sleep patterns and provide advice on how to get quality sleep. In this way, the analysis department can provide appropriate preventive measures based on the health risks of the elderly.

[0092] The information provider can estimate the emotions of elderly individuals and adjust the format of the information provided based on those estimates. For example, if an elderly person is feeling anxious, the provider can offer information in reassuring, gentle language. If the elderly person is relaxed, the provider can offer detailed information. Furthermore, if the elderly person is in a hurry, the provider can offer concise information. This enables the provider to deliver information appropriately according to the emotions of elderly individuals.

[0093] The data collection unit can estimate the emotions of elderly individuals and adjust the frequency of conversation based on those estimates. For example, if an elderly person is feeling lonely, the unit can increase the frequency of conversation. Conversely, if an elderly person is busy, the unit can decrease the frequency of conversation. Furthermore, if an elderly person is relaxed, the unit can engage in conversation at a moderate frequency. This allows the data collection unit to provide an appropriate conversation frequency that aligns with the emotions of the elderly person.

[0094] The analysis department can evaluate the quality of life of elderly individuals based on the collected information and make suggestions for lifestyle improvements based on the evaluation results. For example, the analysis department can evaluate the nutritional balance of elderly individuals' diets and suggest balanced meals. It can also evaluate exercise habits based on their activity levels and suggest appropriate exercise. Furthermore, the analysis department can evaluate the quality of sleep based on their sleep patterns and provide advice for getting quality sleep. This enables the analysis department to make appropriate suggestions for improving the quality of life of elderly individuals.

[0095] The information provider can estimate the emotions of elderly individuals and prioritize the information to be provided based on those estimated emotions. For example, if an elderly person is feeling anxious, the provider can prioritize providing health information. If an elderly person is relaxed, the provider can prioritize providing lifestyle information. Furthermore, if an elderly person is in a hurry, the provider can prioritize providing important information. This allows the provider to determine the appropriate information priorities based on the emotions of the elderly person.

[0096] The data collection unit can estimate the emotions of elderly individuals and adjust the tone of conversation based on those estimates. For example, if an elderly person is feeling lonely, the unit can speak in a gentle tone. If the elderly person is energetic, the unit can speak in a cheerful tone. Furthermore, if the elderly person is tired, the unit can speak in a calm tone. This allows the data collection unit to use an appropriate conversation tone that matches the emotions of the elderly person.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The data collection unit collects information by regularly conversing with elderly individuals. For example, they use an AI agent to converse with elderly individuals, checking on their health and providing support for their daily lives. The data collection unit asks elderly individuals questions such as, "What did you eat today?", "How have you been feeling lately?", and "How has the quality of your sleep been lately?" to check on their diet, health status, and sleep patterns. Step 2: The analysis department analyzes the information collected by the collection department. For example, it organizes the collected information to understand health status and lifestyle. It evaluates nutritional balance based on collected dietary information, assesses health risks based on health status information, and evaluates sleep quality based on sleep status information. Step 3: The service provider provides the family with the analysis results obtained by the analysis provider. For example, they communicate with the family via a messaging app and share information such as, "Mom ate lots of vegetables today," "Mom is feeling well," and "Mom's sleep quality is good."

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0101] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0102] Each of the multiple elements described above, including the collection unit, analysis unit, and provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit converses with the elderly person using the microphone 38B and control unit 46A of the smart device 14 and collects information. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and organizes the collected information to understand their health status and living situation. The provision unit is implemented, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12, and communicates with family members through a messaging application. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0107] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0109] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0110] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0111] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0112] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0113] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0114] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0117] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements described above, including the collection unit, analysis unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit converses with the elderly person using the microphone 238 and control unit 46A of the smart glasses 214 and collects information. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and organizes the collected information to understand their health status and living situation. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12, and communicates with family members through a messaging app. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the collection unit, analysis unit, and provision unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit converses with the elderly person using the microphone 238 and control unit 46A of the headset terminal 314 and collects information. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and organizes the collected information to understand their health status and living situation. The provision unit is implemented, for example, by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12, and communicates with family members through a messaging application. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0143] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0144] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements described above, including the collection unit, analysis unit, and provision unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit converses with the elderly using the microphone 238 and control unit 46A of the robot 414 and collects information. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and organizes the collected information to understand their health status and living situation. The provision unit is implemented, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12, and communicates with family members through a messaging application. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0152] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0154] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0155] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0156] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0160] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0161] 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.

[0162] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0163] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0164] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0165] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0167] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0168] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0169] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0170] (Note 1) The collection department collects information by regularly conversing with elderly people, An analysis unit analyzes the information collected by the aforementioned collection unit, The system includes a provisioning unit that provides the analysis results obtained by the analysis unit to the family. A system characterized by the following features. (Note 2) The aforementioned collection unit is They converse with the elderly by imitating the voices of family members and caregivers. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is The collected information is organized to understand the health status and living conditions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Share the analysis results with your family via a messaging app. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Information on necessary groceries and daily necessities is shared with the family, who then purchase and arrange them online. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is Regularly converse with elderly individuals to check on their health and provide support for their daily lives. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the emotions of elderly people and adjusts the content and tone of conversation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the past conversation history of elderly individuals to select the optimal timing and content for conversations. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During conversations, customize the questions based on the elderly person's current health condition and living situation. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system estimates the emotions of elderly individuals and adjusts the frequency of conversations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During conversations, prioritize topics that are highly relevant based on the elderly person's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During conversations, analyze the social media activity of older adults and provide relevant topics. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is We estimate the emotions of elderly people and adjust the way the analysis results are presented based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is When analyzing collected information, historical data is referenced to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is When analyzing the collected information, different analysis algorithms are applied to each category of information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is The system estimates the emotions of elderly individuals and prioritizes the analysis results based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is When analyzing the collected information, we prioritize the analysis based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is When analyzing collected information, we improve the accuracy of the analysis by referring to relevant literature and data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, We estimate the emotions of elderly people and adjust the way information is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, Adjust the level of detail of the information based on its importance. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, Different delivery methods will be applied depending on the category of information to be provided. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, The system estimates the emotions of older adults and prioritizes the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, Prioritize information based on when it is submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, The order of information is adjusted based on the relevance of the information provided. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection department collects information by regularly conversing with elderly people, An analysis unit analyzes the information collected by the aforementioned collection unit, The system includes a provisioning unit that provides the analysis results obtained by the analysis unit to the family. A system characterized by the following features.

2. The aforementioned collection unit is They converse with the elderly by imitating the voices of family members and caregivers. The system according to feature 1.

3. The aforementioned analysis unit is The collected information is organized to understand the health status and living conditions. The system according to feature 1.

4. The aforementioned supply unit is, Share the analysis results with your family via a messaging app. The system according to feature 1.

5. The aforementioned supply unit is, Information on necessary groceries and daily necessities is shared with the family, who then purchase and arrange them online. The system according to feature 1.

6. The aforementioned collection unit is Regularly converse with elderly individuals to check on their health and provide support for their daily lives. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the emotions of elderly people and adjusts the content and tone of conversation based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the past conversation history of elderly individuals to select the optimal timing and content for conversations. The system according to feature 1.

Images