system

Abstract

The system according to the embodiment aims to efficiently grasp the health and psychological state of elderly people and to alleviate the labor shortage in local governments. [Solution] The system according to the embodiment includes an AI call unit, a collection unit, and an analysis unit. The AI ​​call unit asks a subject questions about their health condition. The collection unit collects the speech content and voice data collected by the AI ​​call unit. The analysis unit analyzes the voice data collected by the collection unit and infers the subject's health condition or psychological state.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult to efficiently grasp the health and psychological state of the elderly, and labor shortages in local governments are an issue.

[0005] The system according to the embodiment aims to efficiently grasp the health and psychological state of elderly people and to alleviate the labor shortage in local governments. [Means for solving the problem]

[0006] The system according to the embodiment includes an AI call unit, a collection unit, and an analysis unit. The AI ​​call unit asks the subject questions about their health condition. The collection unit collects the speech content and voice data collected by the AI ​​call unit. The analysis unit analyzes the voice data collected by the collection unit and infers the subject's health condition or psychological state. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently grasp the health and psychological state of elderly people and alleviate the labor shortages of local governments. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The health check system of an embodiment of the present invention automates the elderly's health check process using AI calls and further analyzes the elderly's speech using AI to estimate their health and psychological state. This health check system begins by calling the elderly and asking questions about their health. For example, the AI ​​call system might ask about their daily routine. When the elderly responds, the speech content and audio data are collected. Next, the collected audio data is analyzed by AI. The AI ​​analyzes not only the content of the speech but also the tone of voice, speaking speed, and pauses. This allows the elderly's health and psychological state to be estimated. For example, a hoarse voice or slow speech may indicate poor health or psychological issues. Finally, based on the AI's estimation, a notification is sent to a local government official. The official then follows up with the elderly as needed. For example, if poor health is suspected, they can contact a medical institution or arrange for a visit. This system can alleviate local government manpower shortages and enable early detection and response to elderly loneliness and health issues. This will enable the health confirmation system to automatically check the health and psychological state of elderly people, thereby alleviating labor shortages in local governments.

[0029] A health confirmation system according to an embodiment includes an AI call unit, a collection unit, and an analysis unit. The AI ​​call unit asks the subject questions about their health condition. For example, the AI ​​call unit can ask questions about their daily routine and physical condition. The collection unit collects the speech content and voice data collected by the AI ​​call unit. For example, the collection unit can record the subject's responses and save them as voice data. The analysis unit analyzes the voice data collected by the collection unit to infer their health condition or psychological state. For example, the analysis unit can analyze the content of the voice data to infer their health condition. The analysis unit can also analyze the tone and speaking rate of the voice data to infer their psychological state. This allows the health confirmation system to automatically check the health and psychological state of elderly people and alleviate labor shortages in local governments.

[0030] The health confirmation system is equipped with a notification unit that sends notifications to local government officials based on the analysis results. The notification unit sends notifications to local government officials based on the analysis results. For example, the notification unit can send the analysis results to the officials by email or SMS. The notification unit can also send notifications through a dedicated app. This allows local government officials to be notified of the analysis results, enabling a prompt response.

[0031] The health confirmation system includes a voice analysis unit that analyzes the elderly person's voice tone and speaking speed. The voice analysis unit analyzes the elderly person's voice tone and speaking speed. For example, the voice analysis unit can analyze the pitch and volume of the voice to evaluate the voice tone. The voice analysis unit can also analyze the number of utterances per unit time to evaluate the speaking speed. This makes it possible to more accurately estimate the health and psychological state of the elderly person by analyzing the voice tone and speaking speed.

[0032] The health confirmation system includes a psychological analysis unit that estimates the psychological state of the elderly person. The psychological analysis unit estimates the psychological state of the elderly person. For example, the psychological analysis unit can analyze the content of voice data to evaluate the stress level and emotional state. The psychological analysis unit can also analyze the tone and speaking rate of the voice data to estimate the psychological state. This allows for more appropriate responses by estimating the psychological state of the elderly person.

[0033] The AI ​​call unit can analyze the elderly person's past response history and select the most appropriate questioning method. For example, if the elderly person has provided detailed answers in the past, the AI ​​call unit can ask more detailed questions to collect more in-depth information. Also, if the elderly person has provided short answers in the past, the AI ​​call unit can ask concise questions to quickly collect information. Furthermore, if the elderly person has responded well to a particular question in the past, the AI ​​call unit can prioritize that question. This allows the AI ​​call unit to select a more appropriate questioning method by analyzing past response history.

[0034] The AI ​​call unit can set the optimal call timing based on the elderly person's lifestyle rhythm. For example, if the elderly person is a morning person, the AI ​​call unit will make a call in the morning. Also, if the elderly person is a night owl, the AI ​​call unit can make a call in the evening. Furthermore, if the elderly person is active during the day, the AI ​​call unit can make a call during the day. This allows for more effective information collection by setting the call timing based on the elderly person's lifestyle rhythm.

[0035] The AI ​​call unit can ask questions specific to the area by taking into account the elderly person's geographic location information. For example, if the elderly person lives in a cold region, the AI ​​call unit can ask about heating usage and cold weather precautions. If the elderly person lives in an urban area, the AI ​​call unit can also ask about transportation and shopping habits. Furthermore, if the elderly person lives in a rural area, the AI ​​call unit can ask about farm work status and local events. This allows for more appropriate questions to be asked by taking into account the elderly person's geographic location information.

[0036] The AI ​​call unit can analyze the social media activity of elderly people and ask relevant questions. For example, if an elderly person posts about health on social media, the AI ​​call unit can ask questions based on the content of that post. In addition, if an elderly person interacts with friends on social media, the AI ​​call unit can ask questions based on those interactions. Furthermore, if an elderly person posts about their hobbies on social media, the AI ​​call unit can ask questions based on those hobbies. This makes it possible to ask more relevant questions by analyzing the social media activity of elderly people.

[0037] The collection unit can collect not only voice data but also background sounds and environmental sounds during collection. For example, the collection unit collects background sounds from the environment in which the elderly person is speaking to understand the living environment. The collection unit can also collect environmental sounds from when the elderly person is speaking to infer the elderly person's health condition. Furthermore, the collection unit can collect sounds from around the elderly person when they are speaking to infer the elderly person's sense of loneliness. In this way, by collecting background sounds and environmental sounds, more detailed information can be obtained.

[0038] The collection unit can check the consistency of the elderly person's answers when collecting the data, and reconfirm any abnormalities. For example, if the elderly person's answers are inconsistent, the collection unit can ask the same questions again to confirm. Furthermore, if the elderly person's answers are inconsistent, the collection unit can ask detailed questions to confirm. Furthermore, if the elderly person's answers are unclear, the collection unit can ask specific questions to confirm. In this way, by checking the consistency of the answers, more accurate information can be obtained.

[0039] The collection unit can select the optimal collection method by taking into consideration the device information of the elderly person when collecting data. For example, if the elderly person uses a smartphone, the collection unit can prioritize collecting voice data. Also, if the elderly person uses a landline phone, the collection unit can prioritize collecting call content. Furthermore, if the elderly person uses a tablet, the collection unit can collect data through video calls. This allows a more appropriate collection method to be selected by taking into consideration the device information of the elderly person.

[0040] The collection unit can collect relevant data by referring to the elderly person's past medical history at the time of collection. For example, the collection unit collects data related to a specific health condition based on the elderly person's past medical history. The collection unit can also collect data related to a specific symptom based on the elderly person's past medical history. Furthermore, the collection unit can collect data related to a specific treatment based on the elderly person's past medical history. In this way, by referring to the past medical history, more relevant data can be collected.

[0041] During analysis, the analysis unit can detect abnormalities by comparing with past data. For example, the analysis unit can detect abnormal changes by comparing with past voice data of the elderly person. The analysis unit can also detect abnormal changes by comparing with the content of past utterances of the elderly person. Furthermore, the analysis unit can also detect abnormal changes by comparing with the elderly person's past health conditions. This allows for early detection of abnormalities by comparing with past data.

[0042] During the analysis, the analysis unit can analyze collected background sounds and environmental sounds in addition to the voice data. For example, the analysis unit analyzes the voice data and background sounds of the elderly person to understand their living environment. The analysis unit can also analyze the voice data and environmental sounds of the elderly person to infer their health condition. Furthermore, the analysis unit can analyze the voice data of the elderly person and surrounding sounds to infer their sense of loneliness. In this way, by analyzing background sounds and environmental sounds, more detailed information can be obtained.

[0043] The analysis unit can take into account the geographical location information of the elderly person during analysis. For example, if the elderly person lives in a cold region, the analysis unit analyzes the health condition related to the cold. Furthermore, if the elderly person lives in an urban area, the analysis unit can also analyze the health condition related to urban life. Furthermore, if the elderly person lives in a rural area, the analysis unit can analyze the health condition related to agricultural work. This allows for more appropriate analysis by taking into account the geographical location information of the elderly person.

[0044] During the analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to the elderly. For example, the analysis unit can improve the accuracy of the analysis by referring to the latest research on the health status of the elderly. The analysis unit can also improve the accuracy of the analysis by referring to the latest research on the psychological state of the elderly. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to the latest research on the living environment of the elderly. In this way, the accuracy of the analysis is improved by referring to related literature.

[0045] At the time of notification, the notification unit can select the optimal notification method by referring to the past response history of the local government official. For example, the notification unit selects the optimal notification method based on cases in which the official responded quickly in the past. The notification unit can also select the optimal notification method based on cases in which the official responded in detail in the past. Furthermore, the notification unit can also select the optimal notification method based on cases in which the official responded in a specific way in the past. In this way, by referring to the past response history, a more appropriate notification method can be selected.

[0046] The notification unit can set the priority of the notification according to the level of urgency when making a notification. For example, if the health condition of the elderly person is urgent, the notification unit will give the highest priority to the notification. In addition, if the health condition of the elderly person is important, the notification unit can also give the notification with priority. Furthermore, if the health condition of the elderly person is normal, the notification unit can also give the notification with normal priority. In this way, by setting the priority of the notification according to the level of urgency, a quick response is possible.

[0047] When sending a notification, the notification unit can select the optimal notification method by taking into account the geographical location information of the local government official. For example, if the official is nearby, the notification unit sends a notification so that a prompt response can be made. In addition, if the official is far away, the notification unit can also send a notification including detailed information. Furthermore, if the official is on the move, the notification unit can update the location information in real time and select the optimal notification method. This allows for a quicker response by taking into account the official's geographical location information.

[0048] When sending a notification, the notification unit can select the optimal notification method by taking into consideration the device information of the local government official. For example, if the official is using a smartphone, the notification unit can send a push notification. Furthermore, if the official is using a PC, the notification unit can also send an email notification. Furthermore, if the official is using a tablet, the notification unit can also send an app notification. This makes it possible to select a more appropriate notification method by taking into consideration the official's device information.

[0049] During voice analysis, the voice analysis unit can analyze not only the tone of voice and speaking speed, but also the strength and rhythm of the voice. For example, the voice analysis unit analyzes the tone of the voice of an elderly person to infer their emotional state. The voice analysis unit can also analyze the speaking speed of an elderly person to infer their health condition. Furthermore, the voice analysis unit can analyze the strength and rhythm of the voice of an elderly person to infer their psychological state. In this way, by analyzing the tone of voice, speaking speed, strength and rhythm of the voice, more detailed information can be obtained.

[0050] During voice analysis, the voice analysis unit can detect abnormalities by comparing with past voice data. For example, the voice analysis unit can detect abnormal changes by comparing with past voice data of an elderly person. The voice analysis unit can also detect abnormal changes by comparing with the content of past utterances of the elderly person. Furthermore, the voice analysis unit can also detect abnormal changes by comparing with the elderly person's past health conditions. This allows for early detection of abnormalities by comparing with past voice data.

[0051] The voice analysis unit can perform voice analysis while taking into account the geographical location information of the elderly person. For example, if the elderly person lives in a cold region, the voice analysis unit analyzes health conditions related to the cold. In addition, if the elderly person lives in an urban area, the voice analysis unit can also analyze health conditions related to urban life. Furthermore, if the elderly person lives in a rural area, the voice analysis unit can analyze health conditions related to farm work. This allows for more appropriate analysis by taking into account the geographical location information of the elderly person.

[0052] During voice analysis, the voice analysis unit can improve the accuracy of the analysis by referring to literature related to the elderly. For example, the voice analysis unit can improve the accuracy of the analysis by referring to the latest research on the health status of the elderly. The voice analysis unit can also improve the accuracy of the analysis by referring to the latest research on the psychological state of the elderly. Furthermore, the voice analysis unit can improve the accuracy of the analysis by referring to the latest research on the living environment of the elderly. In this way, by referring to related literature, the accuracy of the analysis is improved.

[0053] During the psychological analysis, the psychological analysis unit can analyze not only the content of the speech but also the tone of voice, speaking speed, and pauses. For example, the psychological analysis unit analyzes the content of the speech of an elderly person to infer their emotional state. The psychological analysis unit can also analyze the tone of voice of an elderly person to infer their psychological state. Furthermore, the psychological analysis unit can analyze the speaking speed and pauses of an elderly person to infer their health condition. In this way, by analyzing not only the content of the speech but also the tone of voice, speaking speed, and pauses, more detailed information can be obtained.

[0054] During the psychological analysis, the psychological analysis unit can detect abnormalities by comparing with past psychological data. For example, the psychological analysis unit can detect abnormal changes by comparing with the elderly person's past psychological data. The psychological analysis unit can also detect abnormal changes by comparing with the elderly person's past speech content. Furthermore, the psychological analysis unit can also detect abnormal changes by comparing with the elderly person's past health condition. This allows for early detection of abnormalities by comparing with past psychological data.

[0055] The psychological analysis unit can take into account the geographical location information of the elderly person when conducting the psychological analysis. For example, if the elderly person lives in a cold region, the psychological analysis unit analyzes the psychological state related to the cold. In addition, if the elderly person lives in an urban area, the psychological analysis unit can also analyze the psychological state related to urban life. Furthermore, if the elderly person lives in a rural area, the psychological analysis unit can also analyze the psychological state related to farm work. This allows for more appropriate analysis by taking into account the geographical location information of the elderly person.

[0056] During the psychological analysis, the psychological analysis unit can improve the accuracy of the analysis by referring to literature related to the elderly. For example, the psychological analysis unit can improve the accuracy of the analysis by referring to the latest research on the psychological state of the elderly. The psychological analysis unit can also improve the accuracy of the analysis by referring to the latest research on the health state of the elderly. Furthermore, the psychological analysis unit can improve the accuracy of the analysis by referring to the latest research on the living environment of the elderly. In this way, by referring to related literature, the accuracy of the analysis is improved.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The health confirmation system can further include an environmental monitoring unit that monitors the elderly person's living environment. The environmental monitoring unit, for example, measures the temperature and humidity in the elderly person's residence to confirm whether an appropriate environment is being maintained. The environmental monitoring unit can also monitor the lighting and sound levels in the residence to evaluate whether a comfortable living environment is being maintained. Furthermore, the environmental monitoring unit can measure the air quality in the residence to identify factors that may affect health. This allows for comprehensive monitoring of the elderly person's living environment, enabling more appropriate health management.

[0059] The health confirmation system can further include an activity monitoring unit that monitors the elderly person's activity level. The activity monitoring unit, for example, measures the elderly person's number of steps and distance traveled to evaluate the elderly person's daily activity level. The activity monitoring unit can also record the elderly person's sitting and standing time to evaluate their activity balance. Furthermore, the activity monitoring unit can monitor the elderly person's sleep patterns and evaluate their sleep quality. This allows for comprehensive monitoring of the elderly person's activity level, enabling more appropriate health management.

[0060] The health confirmation system can further include a dietary monitoring unit that monitors the dietary content of the elderly person. The dietary monitoring unit, for example, records the content and calories of the meals consumed by the elderly person and evaluates the nutritional balance. The dietary monitoring unit can also record the amount of water consumed by the elderly person and check whether they are properly hydrated. Furthermore, the dietary monitoring unit can record the frequency and time of meals of the elderly person and evaluate their eating patterns. This allows for comprehensive monitoring of the dietary content of the elderly person, enabling more appropriate health management.

[0061] The health confirmation system can further include a medication monitoring unit that monitors the elderly person's medication status. The medication monitoring unit, for example, checks whether the elderly person is taking their prescribed medication properly. The medication monitoring unit can also send a reminder if the elderly person forgets to take their medication. Furthermore, the medication monitoring unit can also issue a warning if the elderly person is taking too much medication. This allows for comprehensive monitoring of the elderly person's medication status, enabling more appropriate health management.

[0062] The health confirmation system may further include an interaction monitoring unit that monitors the social interactions of the elderly. The interaction monitoring unit may, for example, record the extent to which the elderly interacts with family and friends and assess the risk of social isolation. The interaction monitoring unit may also check whether the elderly participates in local events and activities. Furthermore, the interaction monitoring unit may record whether the elderly interacts online and assess the risk of the digital divide. This allows for comprehensive monitoring of the social interactions of the elderly, enabling more appropriate health management.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The AI ​​caller asks the subject questions about their health, such as their daily routine and physical condition. Step 2: The collection unit collects the speech content and voice data collected by the AI ​​call unit. For example, the subject's responses can be recorded and saved as voice data. Step 3: The analysis unit analyzes the voice data collected by the collection unit and infers the subject's health or psychological state. For example, the content of the voice data can be analyzed to infer the subject's health state. The tone and speaking rate of the voice data can also be analyzed to infer the subject's psychological state.

[0065] (Example 2) The health check system of an embodiment of the present invention automates the elderly's health check process using AI calls and further analyzes the elderly's speech using AI to estimate their health and psychological state. This health check system begins by calling the elderly and asking questions about their health. For example, the AI ​​call system might ask about their daily routine. When the elderly responds, the speech content and audio data are collected. Next, the collected audio data is analyzed by AI. The AI ​​analyzes not only the content of the speech but also the tone of voice, speaking speed, and pauses. This allows the elderly's health and psychological state to be estimated. For example, a hoarse voice or slow speech may indicate poor health or psychological issues. Finally, based on the AI's estimation, a notification is sent to a local government official. The official then follows up with the elderly as needed. For example, if poor health is suspected, they can contact a medical institution or arrange for a visit. This system can alleviate local government manpower shortages and enable early detection and response to elderly loneliness and health issues. This will enable the health confirmation system to automatically check the health and psychological state of elderly people, thereby alleviating labor shortages in local governments.

[0066] A health confirmation system according to an embodiment includes an AI call unit, a collection unit, and an analysis unit. The AI ​​call unit asks the subject questions about their health condition. For example, the AI ​​call unit can ask questions about their daily routine and physical condition. The collection unit collects the speech content and voice data collected by the AI ​​call unit. For example, the collection unit can record the subject's responses and save them as voice data. The analysis unit analyzes the voice data collected by the collection unit to infer their health condition or psychological state. For example, the analysis unit can analyze the content of the voice data to infer their health condition. The analysis unit can also analyze the tone and speaking rate of the voice data to infer their psychological state. This allows the health confirmation system to automatically check the health and psychological state of elderly people and alleviate labor shortages in local governments.

[0067] The health confirmation system is equipped with a notification unit that sends notifications to local government officials based on the analysis results. The notification unit sends notifications to local government officials based on the analysis results. For example, the notification unit can send the analysis results to the officials by email or SMS. The notification unit can also send notifications through a dedicated app. This allows local government officials to be notified of the analysis results, enabling a prompt response.

[0068] The health confirmation system includes a voice analysis unit that analyzes the elderly person's voice tone and speaking speed. The voice analysis unit analyzes the elderly person's voice tone and speaking speed. For example, the voice analysis unit can analyze the pitch and volume of the voice to evaluate the voice tone. The voice analysis unit can also analyze the number of utterances per unit time to evaluate the speaking speed. This makes it possible to more accurately estimate the health and psychological state of the elderly person by analyzing the voice tone and speaking speed.

[0069] The health confirmation system includes a psychological analysis unit that estimates the psychological state of the elderly person. The psychological analysis unit estimates the psychological state of the elderly person. For example, the psychological analysis unit can analyze the content of voice data to evaluate the stress level and emotional state. The psychological analysis unit can also analyze the tone and speaking rate of the voice data to estimate the psychological state. This allows for more appropriate responses by estimating the psychological state of the elderly person.

[0070] The AI ​​call unit can estimate the emotions of the elderly person and adjust the content and order of questions based on the estimated emotions. For example, if the elderly person is feeling anxious, the AI ​​call unit will first ask questions to relax them and gradually move on to questions about their health. Also, if the elderly person is relaxed, the AI ​​call unit can first ask specific questions about their health to collect more detailed information. Furthermore, if the elderly person is in a hurry, the AI ​​call unit can prioritize important questions and collect the necessary information in a short amount of time. This makes it possible to collect information more effectively by adjusting the content and order of questions according to the elderly person's emotions.

[0071] The AI ​​call unit can analyze the elderly person's past response history and select the most appropriate questioning method. For example, if the elderly person has provided detailed answers in the past, the AI ​​call unit can ask more detailed questions to collect more in-depth information. Also, if the elderly person has provided short answers in the past, the AI ​​call unit can ask concise questions to quickly collect information. Furthermore, if the elderly person has responded well to a particular question in the past, the AI ​​call unit can prioritize that question. This allows the AI ​​call unit to select a more appropriate questioning method by analyzing past response history.

[0072] The AI ​​call unit can set the optimal call timing based on the elderly person's lifestyle rhythm. For example, if the elderly person is a morning person, the AI ​​call unit will make a call in the morning. Also, if the elderly person is a night owl, the AI ​​call unit can make a call in the evening. Furthermore, if the elderly person is active during the day, the AI ​​call unit can make a call during the day. This allows for more effective information collection by setting the call timing based on the elderly person's lifestyle rhythm.

[0073] The AI ​​call unit can estimate the emotions of the elderly person and adjust the frequency of calls based on the estimated emotions. For example, if the elderly person feels lonely, the AI ​​call unit will increase the frequency of calls to communicate with them. In addition, if the elderly person feels stressed, the AI ​​call unit can reduce the frequency of calls to ease the burden on them. Furthermore, if the elderly person feels relaxed, the AI ​​call unit can make calls at a normal frequency. This allows for more appropriate communication by adjusting the frequency of calls according to the elderly person's emotions.

[0074] The AI ​​call unit can ask questions specific to the area by taking into account the elderly person's geographic location information. For example, if the elderly person lives in a cold region, the AI ​​call unit can ask about heating usage and cold weather precautions. If the elderly person lives in an urban area, the AI ​​call unit can also ask about transportation and shopping habits. Furthermore, if the elderly person lives in a rural area, the AI ​​call unit can ask about farm work status and local events. This allows for more appropriate questions to be asked by taking into account the elderly person's geographic location information.

[0075] The AI ​​call unit can analyze the social media activity of elderly people and ask relevant questions. For example, if an elderly person posts about health on social media, the AI ​​call unit can ask questions based on the content of that post. In addition, if an elderly person interacts with friends on social media, the AI ​​call unit can ask questions based on those interactions. Furthermore, if an elderly person posts about their hobbies on social media, the AI ​​call unit can ask questions based on those hobbies. This makes it possible to ask more relevant questions by analyzing the social media activity of elderly people.

[0076] The collection unit can estimate the emotions of the elderly person and adjust the type of data to be collected based on the estimated emotions. For example, if the elderly person is feeling anxious, the collection unit prioritizes collecting data that will give them a sense of security. In addition, if the elderly person is relaxed, the collection unit can also collect data related to their detailed health condition. Furthermore, if the elderly person is in a hurry, the collection unit can prioritize collecting important data. This allows for more appropriate data collection by adjusting the type of data to be collected according to the emotions of the elderly person.

[0077] The collection unit can collect not only voice data but also background sounds and environmental sounds during collection. For example, the collection unit collects background sounds from the environment in which the elderly person is speaking to understand the living environment. The collection unit can also collect environmental sounds from when the elderly person is speaking to infer the elderly person's health condition. Furthermore, the collection unit can collect sounds from around the elderly person when they are speaking to infer the elderly person's sense of loneliness. In this way, by collecting background sounds and environmental sounds, more detailed information can be obtained.

[0078] The collection unit can check the consistency of the elderly person's answers when collecting the data, and reconfirm any abnormalities. For example, if the elderly person's answers are inconsistent, the collection unit can ask the same questions again to confirm. Furthermore, if the elderly person's answers are inconsistent, the collection unit can ask detailed questions to confirm. Furthermore, if the elderly person's answers are unclear, the collection unit can ask specific questions to confirm. In this way, by checking the consistency of the answers, more accurate information can be obtained.

[0079] The collection unit can estimate the emotions of the elderly person and determine the priority of collected data based on the estimated emotions. For example, if the elderly person is feeling anxious, the collection unit can prioritize collecting data that gives a sense of security. Also, if the elderly person is relaxed, the collection unit can prioritize collecting data related to detailed health conditions. Furthermore, if the elderly person is in a hurry, the collection unit can prioritize collecting important data. In this way, by determining the priority of collected data according to the emotions of the elderly person, more important data can be collected preferentially.

[0080] The collection unit can select the optimal collection method by taking into consideration the device information of the elderly person when collecting data. For example, if the elderly person uses a smartphone, the collection unit can prioritize collecting voice data. Also, if the elderly person uses a landline phone, the collection unit can prioritize collecting call content. Furthermore, if the elderly person uses a tablet, the collection unit can collect data through video calls. This allows a more appropriate collection method to be selected by taking into consideration the device information of the elderly person.

[0081] The collection unit can collect relevant data by referring to the elderly person's past medical history at the time of collection. For example, the collection unit collects data related to a specific health condition based on the elderly person's past medical history. The collection unit can also collect data related to a specific symptom based on the elderly person's past medical history. Furthermore, the collection unit can collect data related to a specific treatment based on the elderly person's past medical history. In this way, by referring to the past medical history, more relevant data can be collected.

[0082] The analysis unit can estimate the emotions of the elderly person and adjust the analysis algorithm based on the estimated emotions. For example, if the elderly person feels anxious, the analysis unit uses an analysis algorithm to provide a sense of security. If the elderly person feels relaxed, the analysis unit can also use a detailed analysis algorithm. Furthermore, if the elderly person is in a hurry, the analysis unit can also use a quick analysis algorithm. This allows for more accurate analysis by adjusting the analysis algorithm according to the elderly person's emotions.

[0083] During analysis, the analysis unit can detect abnormalities by comparing with past data. For example, the analysis unit can detect abnormal changes by comparing with past voice data of the elderly person. The analysis unit can also detect abnormal changes by comparing with the content of past utterances of the elderly person. Furthermore, the analysis unit can also detect abnormal changes by comparing with the elderly person's past health conditions. This allows for early detection of abnormalities by comparing with past data.

[0084] During the analysis, the analysis unit can analyze collected background sounds and environmental sounds in addition to the voice data. For example, the analysis unit analyzes the voice data and background sounds of the elderly person to understand their living environment. The analysis unit can also analyze the voice data and environmental sounds of the elderly person to infer their health condition. Furthermore, the analysis unit can analyze the voice data of the elderly person and surrounding sounds to infer their sense of loneliness. In this way, by analyzing background sounds and environmental sounds, more detailed information can be obtained.

[0085] The analysis unit can estimate the emotions of the elderly person and adjust the display method of the analysis results based on the estimated emotions. For example, if the elderly person is feeling anxious, the analysis unit provides a display method that gives a sense of security. Furthermore, if the elderly person is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the elderly person is in a hurry, the analysis unit can also provide a display method that focuses on the main points. In this way, by adjusting the display method of the analysis results according to the emotions of the elderly person, it is possible to provide more appropriate information.

[0086] The analysis unit can take into account the geographical location information of the elderly person during analysis. For example, if the elderly person lives in a cold region, the analysis unit analyzes the health condition related to the cold. Furthermore, if the elderly person lives in an urban area, the analysis unit can also analyze the health condition related to urban life. Furthermore, if the elderly person lives in a rural area, the analysis unit can analyze the health condition related to agricultural work. This allows for more appropriate analysis by taking into account the geographical location information of the elderly person.

[0087] During the analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to the elderly. For example, the analysis unit can improve the accuracy of the analysis by referring to the latest research on the health status of the elderly. The analysis unit can also improve the accuracy of the analysis by referring to the latest research on the psychological state of the elderly. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to the latest research on the living environment of the elderly. In this way, the accuracy of the analysis is improved by referring to related literature.

[0088] The notification unit can estimate the elderly person's emotions and adjust the content and timing of the notification based on the estimated emotions. For example, if the elderly person is feeling anxious, the notification unit can provide a notification with content that gives a sense of security. Furthermore, if the elderly person is relaxed, the notification unit can also provide a notification that includes detailed information. Furthermore, if the elderly person is in a hurry, the notification unit can also provide a notification that focuses on the main points. This makes it possible to provide more appropriate notifications by adjusting the content and timing of the notification according to the elderly person's emotions.

[0089] At the time of notification, the notification unit can select the optimal notification method by referring to the past response history of the local government official. For example, the notification unit selects the optimal notification method based on cases in which the official responded quickly in the past. The notification unit can also select the optimal notification method based on cases in which the official responded in detail in the past. Furthermore, the notification unit can also select the optimal notification method based on cases in which the official responded in a specific way in the past. In this way, by referring to the past response history, a more appropriate notification method can be selected.

[0090] The notification unit can set the priority of the notification according to the level of urgency when making a notification. For example, if the health condition of the elderly person is urgent, the notification unit will give the highest priority to the notification. In addition, if the health condition of the elderly person is important, the notification unit can also give the notification with priority. Furthermore, if the health condition of the elderly person is normal, the notification unit can also give the notification with normal priority. In this way, by setting the priority of the notification according to the level of urgency, a quick response is possible.

[0091] The notification unit can estimate the elderly person's emotions and adjust the frequency of notifications based on the estimated emotions. For example, if the elderly person feels lonely, the notification unit can increase the frequency of notifications to communicate with them. Also, if the elderly person feels stressed, the notification unit can reduce the frequency of notifications to reduce the burden on them. Furthermore, if the elderly person feels relaxed, the notification unit can notify them at a normal frequency. In this way, more appropriate communication is possible by adjusting the frequency of notifications according to the elderly person's emotions.

[0092] When sending a notification, the notification unit can select the optimal notification method by taking into account the geographical location information of the local government official. For example, if the official is nearby, the notification unit sends a notification so that a prompt response can be made. In addition, if the official is far away, the notification unit can also send a notification including detailed information. Furthermore, if the official is on the move, the notification unit can update the location information in real time and select the optimal notification method. This allows for a quicker response by taking into account the official's geographical location information.

[0093] When sending a notification, the notification unit can select the optimal notification method by taking into consideration the device information of the local government official. For example, if the official is using a smartphone, the notification unit can send a push notification. Furthermore, if the official is using a PC, the notification unit can also send an email notification. Furthermore, if the official is using a tablet, the notification unit can also send an app notification. This makes it possible to select a more appropriate notification method by taking into consideration the official's device information.

[0094] The voice analysis unit can estimate the emotion of the elderly person and adjust the voice analysis algorithm based on the estimated emotion. For example, if the elderly person feels anxious, the voice analysis unit uses a voice analysis algorithm that provides a sense of security. Furthermore, if the elderly person feels relaxed, the voice analysis unit can use a detailed voice analysis algorithm. Furthermore, if the elderly person is in a hurry, the voice analysis unit can use a quick voice analysis algorithm. In this way, by adjusting the voice analysis algorithm according to the emotion of the elderly person, more accurate analysis is possible.

[0095] During voice analysis, the voice analysis unit can analyze not only the tone of voice and speaking speed, but also the strength and rhythm of the voice. For example, the voice analysis unit analyzes the tone of the voice of an elderly person to infer their emotional state. The voice analysis unit can also analyze the speaking speed of an elderly person to infer their health condition. Furthermore, the voice analysis unit can analyze the strength and rhythm of the voice of an elderly person to infer their psychological state. In this way, by analyzing the tone of voice, speaking speed, strength and rhythm of the voice, more detailed information can be obtained.

[0096] During voice analysis, the voice analysis unit can detect abnormalities by comparing with past voice data. For example, the voice analysis unit can detect abnormal changes by comparing with past voice data of an elderly person. The voice analysis unit can also detect abnormal changes by comparing with the content of past utterances of the elderly person. Furthermore, the voice analysis unit can also detect abnormal changes by comparing with the elderly person's past health conditions. This allows for early detection of abnormalities by comparing with past voice data.

[0097] The voice analysis unit can estimate the emotions of the elderly person and adjust the method for displaying the results of the voice analysis based on the estimated emotions. For example, if the elderly person is feeling anxious, the voice analysis unit can provide a display method that gives a sense of security. Furthermore, if the elderly person is relaxed, the voice analysis unit can also provide a display method that includes detailed information. Furthermore, if the elderly person is in a hurry, the voice analysis unit can also provide a display method that focuses on the main points. In this way, by adjusting the method for displaying the results of the voice analysis according to the emotions of the elderly person, it is possible to provide more appropriate information.

[0098] The voice analysis unit can perform voice analysis while taking into account the geographical location information of the elderly person. For example, if the elderly person lives in a cold region, the voice analysis unit analyzes health conditions related to the cold. In addition, if the elderly person lives in an urban area, the voice analysis unit can also analyze health conditions related to urban life. Furthermore, if the elderly person lives in a rural area, the voice analysis unit can analyze health conditions related to farm work. This allows for more appropriate analysis by taking into account the geographical location information of the elderly person.

[0099] During voice analysis, the voice analysis unit can improve the accuracy of the analysis by referring to literature related to the elderly. For example, the voice analysis unit can improve the accuracy of the analysis by referring to the latest research on the health status of the elderly. The voice analysis unit can also improve the accuracy of the analysis by referring to the latest research on the psychological state of the elderly. Furthermore, the voice analysis unit can improve the accuracy of the analysis by referring to the latest research on the living environment of the elderly. In this way, by referring to related literature, the accuracy of the analysis is improved.

[0100] The psychology analysis unit can estimate the emotions of the elderly person and adjust the psychology analysis algorithm based on the estimated emotions. For example, if the elderly person feels anxious, the psychology analysis unit uses a psychology analysis algorithm to give a sense of security. Furthermore, if the elderly person is relaxed, the psychology analysis unit can use a detailed psychology analysis algorithm. Furthermore, if the elderly person is in a hurry, the psychology analysis unit can use a quick psychology analysis algorithm. In this way, by adjusting the psychology analysis algorithm according to the emotions of the elderly person, more accurate analysis is possible.

[0101] During the psychological analysis, the psychological analysis unit can analyze not only the content of the speech but also the tone of voice, speaking speed, and pauses. For example, the psychological analysis unit analyzes the content of the speech of an elderly person to infer their emotional state. The psychological analysis unit can also analyze the tone of voice of an elderly person to infer their psychological state. Furthermore, the psychological analysis unit can analyze the speaking speed and pauses of an elderly person to infer their health condition. In this way, by analyzing not only the content of the speech but also the tone of voice, speaking speed, and pauses, more detailed information can be obtained.

[0102] During the psychological analysis, the psychological analysis unit can detect abnormalities by comparing with past psychological data. For example, the psychological analysis unit can detect abnormal changes by comparing with the elderly person's past psychological data. The psychological analysis unit can also detect abnormal changes by comparing with the elderly person's past speech content. Furthermore, the psychological analysis unit can also detect abnormal changes by comparing with the elderly person's past health condition. This allows for early detection of abnormalities by comparing with past psychological data.

[0103] The psychological analysis unit can estimate the emotions of the elderly person and adjust the method for displaying the results of the psychological analysis based on the estimated emotions. For example, if the elderly person is feeling anxious, the psychological analysis unit provides a display method that gives a sense of security. Furthermore, if the elderly person is relaxed, the psychological analysis unit can also provide a display method that includes detailed information. Furthermore, if the elderly person is in a hurry, the psychological analysis unit can also provide a display method that focuses on the main points. In this way, by adjusting the method for displaying the results of the psychological analysis according to the emotions of the elderly person, it is possible to provide more appropriate information.

[0104] The psychological analysis unit can take into account the geographical location information of the elderly person when conducting the psychological analysis. For example, if the elderly person lives in a cold region, the psychological analysis unit analyzes the psychological state related to the cold. In addition, if the elderly person lives in an urban area, the psychological analysis unit can also analyze the psychological state related to urban life. Furthermore, if the elderly person lives in a rural area, the psychological analysis unit can also analyze the psychological state related to farm work. This allows for more appropriate analysis by taking into account the geographical location information of the elderly person.

[0105] During the psychological analysis, the psychological analysis unit can improve the accuracy of the analysis by referring to literature related to the elderly. For example, the psychological analysis unit can improve the accuracy of the analysis by referring to the latest research on the psychological state of the elderly. The psychological analysis unit can also improve the accuracy of the analysis by referring to the latest research on the health state of the elderly. Furthermore, the psychological analysis unit can improve the accuracy of the analysis by referring to the latest research on the living environment of the elderly. In this way, by referring to related literature, the accuracy of the analysis is improved. === Hard Collateral 1-1 === Each of the multiple elements, including the AI ​​call unit, collection unit, analysis unit, notification unit, voice analysis unit, and psychological analysis unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the AI ​​call unit is implemented by the control unit 46A of the smart device 14 and asks the subject questions about their health condition. The collection unit is implemented by the control unit 46A of the smart device 14 and collects speech content and voice data. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected voice data to infer the health condition or psychological state. The notification unit is implemented by the specific processing unit 290 of the data processing device 12 and sends a notification to a local government official based on the analysis results. The voice analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the tone of voice and speaking rate. The psychological analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and infers the psychological state of the elderly person. === Hard Collateral 1-2 === Each of the multiple elements, including the AI ​​call unit, collection unit, analysis unit, notification unit, voice analysis unit, and psychological analysis unit, described above, is implemented, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the AI ​​call unit is implemented by the control unit 46A of the smart glasses 214 and asks the subject questions about their health condition. The collection unit is implemented by the control unit 46A of the smart glasses 214 and collects speech content and voice data. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected voice data to infer the subject's health condition or psychological state. The notification unit is implemented by the specific processing unit 290 of the data processing device 12 and sends a notification to a local government official based on the analysis results. The voice analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the tone of voice and speaking rate. The psychological analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and infers the elderly person's psychological state. === Hard Collateral 1-3 === Each of the multiple elements, including the AI ​​call unit, collection unit, analysis unit, notification unit, voice analysis unit, and psychological analysis unit, is implemented, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the AI ​​call unit is implemented by the control unit 46A of the headset-type terminal 314 and asks the subject questions about their health condition. The collection unit is implemented by the control unit 46A of the headset-type terminal 314 and collects speech content and voice data. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected voice data to infer the subject's health condition or psychological state. The notification unit is implemented by the specific processing unit 290 of the data processing device 12 and sends a notification to a local government official based on the analysis results. The voice analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the tone of voice and speaking rate. The psychological analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and infers the elderly person's psychological state. === Hard Collateral 1-4 === Each of the multiple elements, including the AI ​​call unit, collection unit, analysis unit, notification unit, voice analysis unit, and psychological analysis unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the AI ​​call unit is realized by the control unit 46A of the robot 414 and asks the subject questions about their health condition. The collection unit is realized by the control unit 46A of the robot 414 and collects speech content and voice data. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected voice data to infer the health condition or psychological state. The notification unit is realized by the specific processing unit 290 of the data processing device 12 and sends a notification to a local government official based on the analysis results. The voice analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the tone of voice and speaking rate. The psychological analysis unit is realized by the specific processing unit 290 of the data processing device 12 and infers the psychological state of the elderly person.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The health confirmation system can further include an environmental monitoring unit that monitors the elderly person's living environment. The environmental monitoring unit, for example, measures the temperature and humidity in the elderly person's residence to confirm whether an appropriate environment is being maintained. The environmental monitoring unit can also monitor the lighting and sound levels in the residence to evaluate whether a comfortable living environment is being maintained. Furthermore, the environmental monitoring unit can measure the air quality in the residence to identify factors that may affect health. This allows for comprehensive monitoring of the elderly person's living environment, enabling more appropriate health management.

[0108] The health confirmation system can further include an activity monitoring unit that monitors the elderly person's activity level. The activity monitoring unit, for example, measures the elderly person's number of steps and distance traveled to evaluate the elderly person's daily activity level. The activity monitoring unit can also record the elderly person's sitting and standing time to evaluate their activity balance. Furthermore, the activity monitoring unit can monitor the elderly person's sleep patterns and evaluate their sleep quality. This allows for comprehensive monitoring of the elderly person's activity level, enabling more appropriate health management.

[0109] The health confirmation system can further include a dietary monitoring unit that monitors the dietary content of the elderly person. The dietary monitoring unit, for example, records the content and calories of the meals consumed by the elderly person and evaluates the nutritional balance. The dietary monitoring unit can also record the amount of water consumed by the elderly person and check whether they are properly hydrated. Furthermore, the dietary monitoring unit can record the frequency and time of meals of the elderly person and evaluate their eating patterns. This allows for comprehensive monitoring of the dietary content of the elderly person, enabling more appropriate health management.

[0110] The health confirmation system can further include a medication monitoring unit that monitors the elderly person's medication status. The medication monitoring unit, for example, checks whether the elderly person is taking their prescribed medication properly. The medication monitoring unit can also send a reminder if the elderly person forgets to take their medication. Furthermore, the medication monitoring unit can also issue a warning if the elderly person is taking too much medication. This allows for comprehensive monitoring of the elderly person's medication status, enabling more appropriate health management.

[0111] The health confirmation system may further include an interaction monitoring unit that monitors the social interactions of the elderly. The interaction monitoring unit may, for example, record the extent to which the elderly interacts with family and friends and assess the risk of social isolation. The interaction monitoring unit may also check whether the elderly participates in local events and activities. Furthermore, the interaction monitoring unit may record whether the elderly interacts online and assess the risk of the digital divide. This allows for comprehensive monitoring of the social interactions of the elderly, enabling more appropriate health management.

[0112] The AI ​​call unit can estimate the emotions of the elderly person and personalize the content of the call based on the estimated emotions. For example, if the elderly person feels lonely, it can offer words of encouragement or fun topics to talk about. If the elderly person feels stressed, it can also offer topics to help them relax. Furthermore, if the elderly person feels relaxed, it can ask specific questions about their health condition and collect more detailed information. This allows for more effective information collection by personalizing the content of the call according to the elderly person's emotions.

[0113] The analysis unit can estimate the emotions of the elderly person and adjust the notification method of the analysis results based on the estimated emotions. For example, if the elderly person is feeling anxious, a notification method that gives a sense of security can be provided. If the elderly person is relaxed, a notification method that includes detailed information can be provided. Furthermore, if the elderly person is in a hurry, a notification method that focuses on the main points can be provided. In this way, by adjusting the notification method of the analysis results according to the elderly person's emotions, more appropriate information can be provided.

[0114] The collection unit can estimate the emotions of the elderly person and determine the priority of data to be collected based on the estimated emotions. For example, if the elderly person is feeling anxious, data that gives a sense of security can be collected preferentially. Also, if the elderly person is relaxed, data related to detailed health conditions can be collected preferentially. Furthermore, if the elderly person is in a hurry, important data can be collected preferentially. In this way, by determining the priority of collected data according to the emotions of the elderly person, more important data can be collected preferentially.

[0115] The notification unit can estimate the elderly person's emotions and adjust the content and timing of the notification based on the estimated emotions. For example, if the elderly person is feeling anxious, the notification unit can send a notification with content that gives a sense of security. If the elderly person is relaxed, the notification unit can send a notification with detailed information. Furthermore, if the elderly person is in a hurry, the notification unit can send a notification that focuses on the main points. This makes it possible to adjust the content and timing of the notification according to the elderly person's emotions, thereby enabling more appropriate notifications.

[0116] The psychological analysis unit can estimate the emotions of the elderly person and adjust the way in which the results of the psychological analysis are displayed based on the estimated emotions. For example, if the elderly person is feeling anxious, a display method that gives a sense of security can be provided. If the elderly person is relaxed, a display method that includes detailed information can be provided. Furthermore, if the elderly person is in a hurry, a display method that focuses on the main points can be provided. In this way, by adjusting the way in which the results of the psychological analysis are displayed according to the emotions of the elderly person, it is possible to provide more appropriate information.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The AI ​​caller asks the subject questions about their health, such as their daily routine and physical condition. Step 2: The collection unit collects the speech content and voice data collected by the AI ​​call unit. For example, the subject's responses can be recorded and saved as voice data. Step 3: The analysis unit analyzes the voice data collected by the collection unit and infers the subject's health or psychological state. For example, the content of the voice data can be analyzed to infer the subject's health state. The tone and speaking rate of the voice data can also be analyzed to infer the subject's psychological state.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0121] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0126] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

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

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

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

[0130] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0133] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0137] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0142] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

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

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

[0146] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0153] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0158] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0159] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0161] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0162] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0163] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0164] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0165] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0166] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0167] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0168] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0170] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0173] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0174] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0175] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0176] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0177] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0178] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0179] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0180] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0181] 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.

[0182] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0183] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0184] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0185] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0186] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0187] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0188] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0189] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0190] [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An AI call unit that asks the subject questions about their health status; a collection unit that collects the speech content and voice data collected by the AI ​​call unit; an analysis unit that analyzes the voice data collected by the collection unit and infers a health state or a psychological state; Equipped with A system characterized by:

2. Equipped with a notification section that sends notifications to local government officials based on the analysis results The system of claim 1 .

3. Equipped with a voice analysis unit that analyzes the tone of voice and speaking speed of elderly people The system of claim 1 .

4. Equipped with a psychological analysis unit that estimates the psychological state of the elderly The system of claim 1 .

5. The AI ​​call unit Estimate the emotions of the elderly person and adjust the content and order of questions based on the estimated emotions. The system of claim 1 .

6. The AI ​​call unit Analyzing the elderly person's past response history and selecting the most appropriate question method The system of claim 1 .

7. The AI ​​call unit Set optimal call timing based on the elderly person's daily rhythm The system of claim 1 .

8. The AI ​​call unit Estimate the elderly person's emotions and adjust the frequency of calls based on the estimated emotions The system of claim 1 .

9. The AI ​​call unit Considering seniors' geographic location to ask location-specific questions The system of claim 1 .

10. The AI ​​call unit Analyze the social media activity of seniors and ask relevant questions The system of claim 1 .

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