System

Abstract

An object of a system according to an embodiment is to grasp the health condition of an elderly person in real time, detect an abnormality, and quickly take measures.SOLUTION: A system includes an interaction unit, a measurement unit, and a countermeasure unit. The interaction unit listens to the elderly person and makes a response. The measurement unit grasps the health condition of the elderly person in real time based on the information obtained by the interaction unit. The countermeasure unit detects an abnormality based on the data obtained by the measurement unit and automatically takes a countermeasure.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that it is difficult to grasp the health status of elderly people in real time, detect abnormalities, and take prompt measures.

[0005] The system according to the embodiment aims to grasp the health status of elderly people in real time, detect abnormalities, and take prompt measures. [Means for solving the problem]

[0006] The system according to the embodiment includes a dialogue unit, a measurement unit, and a countermeasure unit. The dialogue unit listens to what the elderly person says and responds. The measurement unit grasps the elderly person's health condition in real time based on information obtained by the dialogue unit. The countermeasure unit detects abnormalities based on data obtained by the measurement unit and automatically takes countermeasures. [Effects of the Invention]

[0007] The system according to the embodiment can grasp the health condition of elderly people in real time, detect abnormalities, and take prompt measures. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention supports the daily lives of bedridden elderly people by linking a conversational robot utilizing AI technology with a wearable measuring device. This system includes a dialogue unit that listens to the elderly person and provides appropriate responses; a measurement unit that monitors the elderly person's health status in real time based on information obtained by the dialogue unit; and a countermeasure unit that detects abnormalities based on the data obtained by the measurement unit and automatically takes necessary measures. This allows the system to listen to the elderly person's conversation, monitor their health status in real time, and automatically take necessary measures in the event of an abnormality. For example, the conversational robot alleviates feelings of loneliness through dialogue with the elderly person. Next, the wearable measuring device monitors the elderly person's health status in real time. For example, data such as heart rate, blood pressure, and body temperature are measured and analyzed by AI. Furthermore, in the event of an emergency, the wearable measuring device automatically takes measures. For example, if the heart rate suddenly increases, the AI ​​detects the abnormality and sends a notification to emergency contacts. It can also call an ambulance if necessary. This ensures the safety of the elderly person.

[0029] An elderly support system according to an embodiment includes a dialogue unit, a measurement unit, and a countermeasure unit. The dialogue unit listens to the elderly and provides an appropriate response. For example, if the elderly says, "The weather is nice today," the dialogue unit responds with, "Yes, it's sunny today." The dialogue unit can also provide appropriate advice when the elderly asks a health-related question. The dialogue unit can also estimate the elderly's emotions and adjust the tone and content of the response based on the estimated emotions. The measurement unit monitors the elderly's health status in real time based on information obtained by the dialogue unit. For example, the measurement unit measures data such as heart rate, blood pressure, and body temperature, and analyzes it using AI. The measurement unit can also analyze the measurement data in real time to detect abnormalities. The measurement unit can also store the measurement data in the cloud and share it with medical professionals. The countermeasure unit detects abnormalities based on the data obtained by the measurement unit and automatically takes necessary countermeasures. For example, if the heart rate suddenly increases, the countermeasure unit detects the abnormality and sends a notification to an emergency contact. The countermeasure unit can also call an ambulance if necessary. Furthermore, the countermeasure unit can estimate the emotions of the elderly person and determine the priority of countermeasures based on the estimated emotions. As a result, the elderly support system according to the embodiment can listen to the elderly person, grasp their health condition in real time, and automatically take countermeasures when an abnormality occurs.

[0030] The dialogue unit can analyze the elderly person's past conversation history and provide dialogue content that is optimal for each elderly person. For example, the dialogue unit can re-provide topics about hobbies that the elderly person has talked about in the past. The dialogue unit can also re-provide topics about family that the elderly person has talked about in the past. The dialogue unit can also re-provide topics about health that the elderly person has talked about in the past. This allows for better communication by providing optimal dialogue content for the elderly. Some or all of the above-mentioned processing in the dialogue unit may be performed using AI, for example, or may be performed without using AI. For example, the dialogue unit can input the elderly person's past conversation data into a generation AI and cause the generation AI to generate optimal dialogue content.

[0031] The dialogue unit can provide related information and news depending on the topic of the elderly. For example, if the elderly talks about the weather, the dialogue unit can provide the latest weather forecast. Furthermore, if the elderly talks about sports, the dialogue unit can provide the latest sports news. Furthermore, if the elderly talks about health, the dialogue unit can provide the latest health information. This enriches the dialogue by providing information related to the topic of the elderly. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input information related to the topic of the elderly to the generation AI and cause the generation AI to generate related information.

[0032] The dialogue unit can analyze the tone and speed of the elderly person's voice and estimate changes in their health condition. For example, if the elderly person's voice is hoarse, the dialogue unit estimates poor health. The dialogue unit can also estimate fatigue if the elderly person's voice speed is slow. The dialogue unit can also estimate depression if the elderly person's voice tone is low. This makes it possible to detect abnormalities early by estimating the elderly person's health condition from changes in their voice. Some or all of the above-mentioned processing in the dialogue unit may be performed using AI, for example, or may be performed without using AI. For example, the dialogue unit can input the elderly person's voice data into the generation AI and cause the generation AI to estimate changes in their health condition.

[0033] The dialogue unit can introduce local events and services taking into account the geographical location information of the elderly person. The dialogue unit, for example, provides event information in the area where the elderly person lives. The dialogue unit can also provide medical service information in the area where the elderly person lives. The dialogue unit can also provide welfare service information in the area where the elderly person lives. This makes it possible to provide information based on the geographical location information of the elderly person. Some or all of the above-mentioned processing in the dialogue unit may be performed using AI, for example, or may be performed without using AI. For example, the dialogue unit can input the geographical location information of the elderly person to the generation AI and cause the generation AI to execute information on local events and services.

[0034] The dialogue unit can analyze the social media activity of the elderly person and provide related topics. For example, the dialogue unit can provide topics related to photos shared by the elderly person on social media. The dialogue unit can also provide topics related to comments made by the elderly person on social media. The dialogue unit can also provide topics related to accounts followed by the elderly person on social media. This makes it possible to provide topics based on the elderly person's social media activity. Some or all of the above-described processing in the dialogue unit may be performed using AI, for example, or may be performed without using AI. For example, the dialogue unit can input the elderly person's social media data into a generation AI and cause the generation AI to generate related topics.

[0035] The dialogue unit can customize the dialogue content by reflecting the elderly person's past feedback. For example, the dialogue unit can preferentially provide topics that the elderly person has previously preferred. The dialogue unit can also avoid topics that the elderly person has previously avoided. The dialogue unit can also adjust the tone and content of the dialogue based on the feedback provided by the elderly person in the past. This makes it possible to customize the dialogue content based on the elderly person's past feedback. Some or all of the above-mentioned processing in the dialogue unit may be performed using AI, for example, or may be performed without using AI. For example, the dialogue unit can input the elderly person's past feedback data into the generation AI and cause the generation AI to customize the dialogue content.

[0036] The measurement unit can analyze the measurement data in real time and detect abnormalities early. The measurement unit, for example, detects sudden fluctuations in heart rate in real time. The measurement unit can also detect abnormal increases in blood pressure in real time. The measurement unit can also detect sudden fluctuations in body temperature in real time. This enables abnormality detection in real time. Some or all of the above-mentioned processing in the measurement unit may be performed using AI, for example, or may be performed without using AI. For example, the measurement unit can input measurement data acquired in real time to a generation AI and cause the generation AI to perform early abnormality detection.

[0037] The measurement unit can store the measurement data in the cloud and share it with medical professionals. For example, the measurement unit can store heart rate data in the cloud and share it with medical professionals. The measurement unit can also store blood pressure data in the cloud and share it with medical professionals. The measurement unit can also store body temperature data in the cloud and share it with medical professionals. This enables the measurement data to be stored in the cloud and shared. Some or all of the above-mentioned processing in the measurement unit may be performed using AI, for example, or may be performed without using AI. For example, the measurement unit can input the measurement data to be stored in the cloud to a generation AI and have the generation AI store and share the data.

[0038] The measurement unit can propose an optimal health management plan for each elderly person based on the measurement data. The measurement unit can propose an exercise plan based on heart rate data, for example. The measurement unit can also propose a meal plan based on blood pressure data. The measurement unit can also propose a rest plan based on body temperature data. This makes it possible to propose an optimal health management plan for each elderly person. Some or all of the above-mentioned processing in the measurement unit may be performed using AI, for example, or may be performed without using AI. For example, the measurement unit can input the measurement data to a generation AI and have the generation AI execute the proposal of a health management plan.

[0039] The measurement unit can geographically map the measurement data and analyze health trends by region. The measurement unit can, for example, map heart rate data by region and analyze health trends. The measurement unit can also map blood pressure data by region and analyze health trends. The measurement unit can also map body temperature data by region and analyze health trends. This makes it possible to analyze health trends by region. Some or all of the above-mentioned processing in the measurement unit can be performed using AI, for example, or can be performed without using AI. For example, the measurement unit can input the measurement data to be geographically mapped to a generation AI and have the generation AI perform an analysis of health trends.

[0040] The measurement unit can integrate the measurement data with other health data to evaluate the overall health condition. For example, the measurement unit can integrate heart rate data and dietary data to evaluate the health condition. The measurement unit can also integrate blood pressure data and exercise data to evaluate the health condition. The measurement unit can also integrate body temperature data and sleep data to evaluate the health condition. This makes it possible to evaluate the overall health condition. Some or all of the above-mentioned processing in the measurement unit may be performed using, for example, AI, or may be performed without using AI. For example, the measurement unit can input other health data into the generation AI and cause the generation AI to perform an evaluation of the overall health condition.

[0041] The measurement unit can predict changes in health status and suggest preventive measures based on the measurement data. The measurement unit can predict changes in health status and suggest preventive measures based on, for example, heart rate data. The measurement unit can also predict changes in health status and suggest preventive measures based on blood pressure data. The measurement unit can also predict changes in health status and suggest preventive measures based on body temperature data. This makes it possible to predict changes in health status and suggest preventive measures. Some or all of the above-described processing in the measurement unit may be performed using, for example, AI, or may be performed without using AI. For example, the measurement unit can input the measurement data to a generation AI and cause the generation AI to predict changes in health status and suggest preventive measures.

[0042] When an abnormality is detected, the countermeasure unit can select the optimal countermeasure by referring to past data. For example, when the countermeasure unit detects an abnormal heart rate, it selects the optimal countermeasure by referring to past data. Furthermore, when the countermeasure unit detects an abnormal blood pressure, it can also select the optimal countermeasure by referring to past data. Furthermore, when the countermeasure unit detects an abnormal body temperature, it can also select the optimal countermeasure by referring to past data. This makes it possible to select the optimal countermeasure based on past data. Some or all of the above-mentioned processing in the countermeasure unit may be performed using, for example, AI, or may be performed without using AI. For example, the countermeasure unit can input past data into the generation AI and have the generation AI select the optimal countermeasure.

[0043] The countermeasure unit can send a notification to a medical professional in real time when an abnormality is detected. For example, when the countermeasure unit detects an abnormal heart rate, it sends a notification to a medical professional in real time. Furthermore, when the countermeasure unit detects an abnormal blood pressure, it can also send a notification to a medical professional in real time. Furthermore, when the countermeasure unit detects an abnormal body temperature, it can also send a notification to a medical professional in real time. This makes it possible to notify a medical professional in real time when an abnormality is detected. Some or all of the above-mentioned processing in the countermeasure unit may be performed using, for example, AI, or may be performed without using AI. For example, when an abnormality is detected, the countermeasure unit can generate notification content using a generation AI and send it to a medical professional in real time.

[0044] The countermeasure unit can automatically initiate a call to the emergency contact when an abnormality is detected. For example, when the countermeasure unit detects an abnormal heart rate, it can automatically initiate a call to the emergency contact. Furthermore, when the countermeasure unit detects an abnormal blood pressure, it can also automatically initiate a call to the emergency contact. Furthermore, when the countermeasure unit detects an abnormal body temperature, it can also automatically initiate a call to the emergency contact. This makes it possible to automatically call the emergency contact when an abnormality is detected. Some or all of the above-mentioned processing in the countermeasure unit may be performed using, for example, AI, or may be performed without using AI. For example, when an abnormality is detected, the countermeasure unit can generate call content using a generation AI and automatically initiate a call to the emergency contact.

[0045] When an abnormality is detected, the countermeasure unit can select the most appropriate medical institution by taking geographical information into consideration. For example, when an abnormality in heart rate is detected, the countermeasure unit selects the nearest medical institution. Furthermore, when an abnormality in blood pressure is detected, the countermeasure unit can also select the nearest medical institution. Furthermore, when an abnormality in body temperature is detected, the countermeasure unit can also select the nearest medical institution. This makes it possible to select the most appropriate medical institution when an abnormality is detected. Some or all of the above-mentioned processing in the countermeasure unit may be performed using AI, for example, or may be performed without using AI. For example, the countermeasure unit can input geographical information into the generation AI and cause the generation AI to select the most appropriate medical institution.

[0046] The countermeasure unit can send a notification to family members via social media when an abnormality is detected. For example, when the countermeasure unit detects an abnormal heart rate, it can send a notification to family members via social media. Furthermore, when the countermeasure unit detects an abnormal blood pressure, it can also send a notification to family members via social media. Furthermore, when the countermeasure unit detects an abnormal body temperature, it can also send a notification to family members via social media. This makes it possible to notify family members when an abnormality is detected. Some or all of the above-mentioned processing in the countermeasure unit may be performed using, for example, AI, or may be performed without using AI. For example, when an abnormality is detected, the countermeasure unit can generate notification content using a generation AI and send a notification to family members via social media.

[0047] When an abnormality is detected, the countermeasure unit can customize the countermeasure method by reflecting past feedback. For example, when the countermeasure unit detects an abnormal heart rate, it customizes the countermeasure method based on past feedback. Furthermore, when the countermeasure unit detects an abnormal blood pressure, it can also customize the countermeasure method based on past feedback. Furthermore, when the countermeasure unit detects an abnormal body temperature, it can also customize the countermeasure method based on past feedback. This makes it possible to customize the countermeasure method based on past feedback. Some or all of the above-mentioned processing in the countermeasure unit may be performed using, for example, AI, or may be performed without using AI. For example, the countermeasure unit can input past feedback data into the generation AI and have the generation AI customize the countermeasure method.

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

[0049] The dialogue unit can analyze the tone and speed of the elderly person's voice to estimate changes in their health condition. For example, if the elderly person's voice is hoarse, it can estimate poor health. The dialogue unit can also estimate fatigue if the elderly person's voice speed is slow. The dialogue unit can also estimate depression if the elderly person's voice tone is low. This makes it possible to detect abnormalities early by estimating the elderly person's health condition from changes in their voice. Some or all of the above-mentioned processing in the dialogue unit may be performed using AI, for example, or may be performed without using AI. For example, the dialogue unit can input the elderly person's voice data into the generation AI and have the generation AI estimate changes in their health condition.

[0050] The dialogue unit can introduce local events and services taking into account the geographical location information of the elderly person. For example, it can provide event information in the area where the elderly person lives. The dialogue unit can also provide medical service information in the area where the elderly person lives. The dialogue unit can also provide welfare service information in the area where the elderly person lives. This makes it possible to provide information based on the geographical location information of the elderly person. Some or all of the above-mentioned processing in the dialogue unit may be performed using AI, for example, or may be performed without using AI. For example, the dialogue unit can input the geographical location information of the elderly person to the generation AI and cause the generation AI to execute information on local events and services.

[0051] The dialogue unit can analyze the social media activity of the elderly person and provide related topics. For example, the dialogue unit can provide topics related to photos shared by the elderly person on social media. The dialogue unit can also provide topics related to comments made by the elderly person on social media. The dialogue unit can also provide topics related to accounts followed by the elderly person on social media. This makes it possible to provide topics based on the elderly person's social media activity. Some or all of the above-described processing in the dialogue unit may be performed using AI, for example, or may be performed without using AI. For example, the dialogue unit can input the elderly person's social media data into the generation AI and cause the generation AI to generate related topics.

[0052] The dialogue unit can customize the dialogue content by reflecting the elderly person's past feedback. For example, the dialogue unit can prioritize topics that the elderly person has previously preferred. The dialogue unit can also avoid topics that the elderly person has previously avoided. The dialogue unit can also adjust the tone and content of the dialogue based on the feedback provided by the elderly person in the past. This makes it possible to customize the dialogue content based on the elderly person's past feedback. Some or all of the above-mentioned processing in the dialogue unit may be performed using AI, for example, or may be performed without using AI. For example, the dialogue unit can input the elderly person's past feedback data into the generation AI and cause the generation AI to customize the dialogue content.

[0053] The measurement unit can analyze the measurement data in real time and detect abnormalities early. For example, it can detect sudden fluctuations in heart rate in real time. The measurement unit can also detect abnormal increases in blood pressure in real time. The measurement unit can also detect sudden fluctuations in body temperature in real time. This enables abnormality detection in real time. Some or all of the above-mentioned processing in the measurement unit may be performed using AI, for example, or may be performed without using AI. For example, the measurement unit can input measurement data acquired in real time to a generation AI and cause the generation AI to perform early abnormality detection.

[0054] The measurement unit can store the measurement data in the cloud and share it with medical professionals. For example, heart rate data can be stored in the cloud and shared with medical professionals. The measurement unit can also store blood pressure data in the cloud and share it with medical professionals. The measurement unit can also store body temperature data in the cloud and share it with medical professionals. This enables the measurement data to be stored in the cloud and shared. Some or all of the above-described processing in the measurement unit may be performed using AI, for example, or may be performed without using AI. For example, the measurement unit can input the measurement data to be stored in the cloud to a generation AI and have the generation AI store and share the data.

[0055] The measurement unit can propose an optimal health management plan for each elderly person based on the measurement data. For example, an exercise plan can be proposed based on heart rate data. The measurement unit can also propose a meal plan based on blood pressure data. The measurement unit can also propose a rest plan based on body temperature data. This makes it possible to propose an optimal health management plan for each elderly person. Some or all of the above-mentioned processing in the measurement unit may be performed using AI, for example, or may be performed without using AI. For example, the measurement unit can input the measurement data to a generation AI and have the generation AI execute the proposal of a health management plan.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The dialogue unit listens to what the elderly person says and responds appropriately. For example, if the elderly person says, "The weather is nice today," the dialogue unit responds, "Yes, it's sunny today." The dialogue unit can also provide appropriate advice if the elderly person asks a health-related question. Furthermore, the dialogue unit can estimate the elderly person's emotions and adjust the tone and content of the response based on the estimated emotions. Step 2: The measurement unit uses the information obtained by the dialogue unit to grasp the elderly person's health condition in real time. For example, data such as heart rate, blood pressure, and body temperature are measured and analyzed by AI. The measurement unit can also analyze the measurement data in real time to detect abnormalities. Furthermore, the measurement unit can store the measurement data in the cloud and share it with medical professionals. Step 3: The countermeasures unit detects abnormalities based on the data obtained by the measurement unit and automatically takes necessary measures. For example, if the heart rate suddenly rises, the unit detects the abnormality and sends a notification to emergency contacts. It can also call an ambulance if necessary. Furthermore, the countermeasures unit can estimate the elderly person's emotions and prioritize countermeasures based on the estimated emotions.

[0058] (Example 2) A system according to an embodiment of the present invention supports the daily lives of bedridden elderly people by linking a conversational robot utilizing AI technology with a wearable measuring device. This system includes a dialogue unit that listens to the elderly person and provides appropriate responses; a measurement unit that monitors the elderly person's health status in real time based on information obtained by the dialogue unit; and a countermeasure unit that detects abnormalities based on the data obtained by the measurement unit and automatically takes necessary measures. This allows the system to listen to the elderly person's conversation, monitor their health status in real time, and automatically take necessary measures in the event of an abnormality. For example, the conversational robot alleviates feelings of loneliness through dialogue with the elderly person. Next, the wearable measuring device monitors the elderly person's health status in real time. For example, data such as heart rate, blood pressure, and body temperature are measured and analyzed by AI. Furthermore, in the event of an emergency, the wearable measuring device automatically takes measures. For example, if the heart rate suddenly increases, the AI ​​detects the abnormality and sends a notification to emergency contacts. It can also call an ambulance if necessary. This ensures the safety of the elderly person.

[0059] An elderly support system according to an embodiment includes a dialogue unit, a measurement unit, and a countermeasure unit. The dialogue unit listens to the elderly and provides an appropriate response. For example, if the elderly says, "The weather is nice today," the dialogue unit responds with, "Yes, it's sunny today." The dialogue unit can also provide appropriate advice when the elderly asks a health-related question. The dialogue unit can also estimate the elderly's emotions and adjust the tone and content of the response based on the estimated emotions. The measurement unit monitors the elderly's health status in real time based on information obtained by the dialogue unit. For example, the measurement unit measures data such as heart rate, blood pressure, and body temperature, and analyzes it using AI. The measurement unit can also analyze the measurement data in real time to detect abnormalities. The measurement unit can also store the measurement data in the cloud and share it with medical professionals. The countermeasure unit detects abnormalities based on the data obtained by the measurement unit and automatically takes necessary countermeasures. For example, if the heart rate suddenly increases, the countermeasure unit detects the abnormality and sends a notification to an emergency contact. The countermeasure unit can also call an ambulance if necessary. Furthermore, the countermeasure unit can estimate the emotions of the elderly person and determine the priority of countermeasures based on the estimated emotions. As a result, the elderly support system according to the embodiment can listen to the elderly person, grasp their health condition in real time, and automatically take countermeasures when an abnormality occurs.

[0060] The dialogue unit can estimate the elderly person's emotions and adjust the tone and content of the response based on the estimated elderly person's emotions. For example, if the elderly person is sad, the AI ​​can provide encouraging words in a gentle tone. If the elderly person is happy, the AI ​​can respond with empathetic words in a bright tone. If the elderly person is anxious, the AI ​​can provide reassuring words in a calm tone. This enables an appropriate response according to the elderly person's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the elderly person's voice data into the generation AI and have the generation AI perform emotion estimation.

[0061] The dialogue unit can analyze the elderly person's past conversation history and provide dialogue content that is optimal for each elderly person. For example, the dialogue unit can re-provide topics about hobbies that the elderly person has talked about in the past. The dialogue unit can also re-provide topics about family that the elderly person has talked about in the past. The dialogue unit can also re-provide topics about health that the elderly person has talked about in the past. This allows for better communication by providing optimal dialogue content for the elderly. Some or all of the above-mentioned processing in the dialogue unit may be performed using AI, for example, or may be performed without using AI. For example, the dialogue unit can input the elderly person's past conversation data into a generation AI and cause the generation AI to generate optimal dialogue content.

[0062] The dialogue unit can provide related information and news depending on the topic of the elderly. For example, if the elderly talks about the weather, the dialogue unit can provide the latest weather forecast. Furthermore, if the elderly talks about sports, the dialogue unit can provide the latest sports news. Furthermore, if the elderly talks about health, the dialogue unit can provide the latest health information. This enriches the dialogue by providing information related to the topic of the elderly. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input information related to the topic of the elderly to the generation AI and cause the generation AI to generate related information.

[0063] The dialogue unit can analyze the tone and speed of the elderly person's voice and estimate changes in their health condition. For example, if the elderly person's voice is hoarse, the dialogue unit estimates poor health. The dialogue unit can also estimate fatigue if the elderly person's voice speed is slow. The dialogue unit can also estimate depression if the elderly person's voice tone is low. This makes it possible to detect abnormalities early by estimating the elderly person's health condition from changes in their voice. Some or all of the above-mentioned processing in the dialogue unit may be performed using AI, for example, or may be performed without using AI. For example, the dialogue unit can input the elderly person's voice data into the generation AI and cause the generation AI to estimate changes in their health condition.

[0064] The dialogue unit can estimate the elderly person's emotions and adjust the frequency of dialogue based on the estimated elderly person's emotions. For example, the dialogue unit can increase the frequency of dialogue when the elderly person feels lonely. The dialogue unit can also decrease the frequency of dialogue when the elderly person feels tired. The dialogue unit can also maintain a moderate frequency of dialogue when the elderly person feels relaxed. This enables more appropriate support by adjusting the frequency of dialogue according to the elderly person's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the dialogue unit can be performed using AI, for example, or without AI. For example, the dialogue unit can input the elderly person's voice data into the generation AI and have the generation AI perform emotion estimation.

[0065] The dialogue unit can introduce local events and services taking into account the geographical location information of the elderly person. The dialogue unit, for example, provides event information in the area where the elderly person lives. The dialogue unit can also provide medical service information in the area where the elderly person lives. The dialogue unit can also provide welfare service information in the area where the elderly person lives. This makes it possible to provide information based on the geographical location information of the elderly person. Some or all of the above-mentioned processing in the dialogue unit may be performed using AI, for example, or may be performed without using AI. For example, the dialogue unit can input the geographical location information of the elderly person to the generation AI and cause the generation AI to execute information on local events and services.

[0066] The dialogue unit can analyze the social media activity of the elderly person and provide related topics. For example, the dialogue unit can provide topics related to photos shared by the elderly person on social media. The dialogue unit can also provide topics related to comments made by the elderly person on social media. The dialogue unit can also provide topics related to accounts followed by the elderly person on social media. This makes it possible to provide topics based on the elderly person's social media activity. Some or all of the above-described processing in the dialogue unit may be performed using AI, for example, or may be performed without using AI. For example, the dialogue unit can input the elderly person's social media data into a generation AI and cause the generation AI to generate related topics.

[0067] The dialogue unit can customize the dialogue content by reflecting the elderly person's past feedback. For example, the dialogue unit can preferentially provide topics that the elderly person has previously preferred. The dialogue unit can also avoid topics that the elderly person has previously avoided. The dialogue unit can also adjust the tone and content of the dialogue based on the feedback provided by the elderly person in the past. This makes it possible to customize the dialogue content based on the elderly person's past feedback. Some or all of the above-mentioned processing in the dialogue unit may be performed using AI, for example, or may be performed without using AI. For example, the dialogue unit can input the elderly person's past feedback data into the generation AI and cause the generation AI to customize the dialogue content.

[0068] The measurement unit can estimate the elderly person's emotions and adjust the analysis method of the measurement data based on the estimated elderly person's emotions. For example, if the elderly person is feeling stressed, the measurement unit can analyze heart rate fluctuations in detail. If the elderly person is relaxed, the measurement unit can also use a normal analysis method. If the elderly person is feeling anxious, the measurement unit can also analyze blood pressure fluctuations in detail. This enables the analysis of the measurement data according to the elderly person's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the measurement unit can be performed using AI, for example, or without AI. For example, the measurement unit can input the elderly person's emotion data into the generation AI and cause the generation AI to adjust the analysis method of the measurement data.

[0069] The measurement unit can analyze the measurement data in real time and detect abnormalities early. The measurement unit, for example, detects sudden fluctuations in heart rate in real time. The measurement unit can also detect abnormal increases in blood pressure in real time. The measurement unit can also detect sudden fluctuations in body temperature in real time. This enables abnormality detection in real time. Some or all of the above-mentioned processing in the measurement unit may be performed using AI, for example, or may be performed without using AI. For example, the measurement unit can input measurement data acquired in real time to a generation AI and cause the generation AI to perform early abnormality detection.

[0070] The measurement unit can store the measurement data in the cloud and share it with medical professionals. For example, the measurement unit can store heart rate data in the cloud and share it with medical professionals. The measurement unit can also store blood pressure data in the cloud and share it with medical professionals. The measurement unit can also store body temperature data in the cloud and share it with medical professionals. This enables the measurement data to be stored in the cloud and shared. Some or all of the above-mentioned processing in the measurement unit may be performed using AI, for example, or may be performed without using AI. For example, the measurement unit can input the measurement data to be stored in the cloud to a generation AI and have the generation AI store and share the data.

[0071] The measurement unit can propose an optimal health management plan for each elderly person based on the measurement data. The measurement unit can propose an exercise plan based on heart rate data, for example. The measurement unit can also propose a meal plan based on blood pressure data. The measurement unit can also propose a rest plan based on body temperature data. This makes it possible to propose an optimal health management plan for each elderly person. Some or all of the above-mentioned processing in the measurement unit may be performed using AI, for example, or may be performed without using AI. For example, the measurement unit can input the measurement data to a generation AI and have the generation AI execute the proposal of a health management plan.

[0072] The measurement unit can estimate the elderly person's emotions and adjust the display method of the measurement data based on the estimated elderly person's emotions. For example, if the elderly person is feeling stressed, the measurement unit can provide a simple display method. If the elderly person is feeling relaxed, the measurement unit can also provide a detailed display method. If the elderly person is feeling anxious, the measurement unit can also provide a display method that gives a sense of security. This makes it possible to display the measurement data according to the elderly person's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the measurement unit can be performed using AI, for example, or without AI. For example, the measurement unit can input the elderly person's emotion data into the generation AI and have the generation AI adjust the display method.

[0073] The measurement unit can geographically map the measurement data and analyze health trends by region. The measurement unit can, for example, map heart rate data by region and analyze health trends. The measurement unit can also map blood pressure data by region and analyze health trends. The measurement unit can also map body temperature data by region and analyze health trends. This makes it possible to analyze health trends by region. Some or all of the above-mentioned processing in the measurement unit can be performed using AI, for example, or can be performed without using AI. For example, the measurement unit can input the measurement data to be geographically mapped to a generation AI and have the generation AI perform an analysis of health trends.

[0074] The measurement unit can integrate the measurement data with other health data to evaluate the overall health condition. For example, the measurement unit can integrate heart rate data and dietary data to evaluate the health condition. The measurement unit can also integrate blood pressure data and exercise data to evaluate the health condition. The measurement unit can also integrate body temperature data and sleep data to evaluate the health condition. This makes it possible to evaluate the overall health condition. Some or all of the above-mentioned processing in the measurement unit may be performed using, for example, AI, or may be performed without using AI. For example, the measurement unit can input other health data into the generation AI and cause the generation AI to perform an evaluation of the overall health condition.

[0075] The measurement unit can predict changes in health status and suggest preventive measures based on the measurement data. The measurement unit can predict changes in health status and suggest preventive measures based on, for example, heart rate data. The measurement unit can also predict changes in health status and suggest preventive measures based on blood pressure data. The measurement unit can also predict changes in health status and suggest preventive measures based on body temperature data. This makes it possible to predict changes in health status and suggest preventive measures. Some or all of the above-described processing in the measurement unit may be performed using, for example, AI, or may be performed without using AI. For example, the measurement unit can input the measurement data to a generation AI and cause the generation AI to predict changes in health status and suggest preventive measures.

[0076] The countermeasure unit can estimate the elderly person's emotions and prioritize countermeasures based on the estimated elderly person's emotions. For example, if the elderly person is feeling anxious, the countermeasure unit can prioritize countermeasures that provide a sense of security. Furthermore, if the elderly person is feeling stressed, the countermeasure unit can prioritize countermeasures that relax the elderly person. Furthermore, if the elderly person is feeling lonely, the countermeasure unit can prioritize countermeasures that increase dialogue. This makes it possible to prioritize countermeasures according to the elderly person's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the countermeasure unit may be performed using, for example, AI, or may be performed without using AI. For example, the countermeasure unit can input the elderly person's emotion data into the generation AI and have the generation AI determine the priority of countermeasures.

[0077] When an abnormality is detected, the countermeasure unit can select the optimal countermeasure by referring to past data. For example, when the countermeasure unit detects an abnormal heart rate, it selects the optimal countermeasure by referring to past data. Furthermore, when the countermeasure unit detects an abnormal blood pressure, it can also select the optimal countermeasure by referring to past data. Furthermore, when the countermeasure unit detects an abnormal body temperature, it can also select the optimal countermeasure by referring to past data. This makes it possible to select the optimal countermeasure based on past data. Some or all of the above-mentioned processing in the countermeasure unit may be performed using, for example, AI, or may be performed without using AI. For example, the countermeasure unit can input past data into the generation AI and have the generation AI select the optimal countermeasure.

[0078] The countermeasure unit can send a notification to a medical professional in real time when an abnormality is detected. For example, when the countermeasure unit detects an abnormal heart rate, it sends a notification to a medical professional in real time. Furthermore, when the countermeasure unit detects an abnormal blood pressure, it can also send a notification to a medical professional in real time. Furthermore, when the countermeasure unit detects an abnormal body temperature, it can also send a notification to a medical professional in real time. This makes it possible to notify a medical professional in real time when an abnormality is detected. Some or all of the above-mentioned processing in the countermeasure unit may be performed using, for example, AI, or may be performed without using AI. For example, when an abnormality is detected, the countermeasure unit can generate notification content using a generation AI and send it to a medical professional in real time.

[0079] The countermeasure unit can automatically initiate a call to the emergency contact when an abnormality is detected. For example, when the countermeasure unit detects an abnormal heart rate, it can automatically initiate a call to the emergency contact. Furthermore, when the countermeasure unit detects an abnormal blood pressure, it can also automatically initiate a call to the emergency contact. Furthermore, when the countermeasure unit detects an abnormal body temperature, it can also automatically initiate a call to the emergency contact. This makes it possible to automatically call the emergency contact when an abnormality is detected. Some or all of the above-mentioned processing in the countermeasure unit may be performed using, for example, AI, or may be performed without using AI. For example, when an abnormality is detected, the countermeasure unit can generate call content using a generation AI and automatically initiate a call to the emergency contact.

[0080] The countermeasure unit can estimate the elderly person's emotions and adjust the content of the countermeasure based on the estimated elderly person's emotions. For example, if the elderly person feels anxious, the countermeasure unit can provide a countermeasure that gives a sense of security. Furthermore, if the elderly person feels stressed, the countermeasure unit can provide a countermeasure that relaxes the elderly person. Furthermore, if the elderly person feels lonely, the countermeasure unit can provide a countermeasure to increase dialogue. This makes it possible to adjust the content of the countermeasure according to the elderly person's emotions. Emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the countermeasure unit may be performed using, for example, AI, or may be performed without using AI. For example, the countermeasure unit can input the elderly person's emotion data into the generation AI and have the generation AI adjust the content of the countermeasure.

[0081] When an abnormality is detected, the countermeasure unit can select the most appropriate medical institution by taking geographical information into consideration. For example, when an abnormality in heart rate is detected, the countermeasure unit selects the nearest medical institution. Furthermore, when an abnormality in blood pressure is detected, the countermeasure unit can also select the nearest medical institution. Furthermore, when an abnormality in body temperature is detected, the countermeasure unit can also select the nearest medical institution. This makes it possible to select the most appropriate medical institution when an abnormality is detected. Some or all of the above-mentioned processing in the countermeasure unit may be performed using AI, for example, or may be performed without using AI. For example, the countermeasure unit can input geographical information into the generation AI and cause the generation AI to select the most appropriate medical institution.

[0082] The countermeasure unit can send a notification to family members via social media when an abnormality is detected. For example, when the countermeasure unit detects an abnormal heart rate, it can send a notification to family members via social media. Furthermore, when the countermeasure unit detects an abnormal blood pressure, it can also send a notification to family members via social media. Furthermore, when the countermeasure unit detects an abnormal body temperature, it can also send a notification to family members via social media. This makes it possible to notify family members when an abnormality is detected. Some or all of the above-mentioned processing in the countermeasure unit may be performed using, for example, AI, or may be performed without using AI. For example, when an abnormality is detected, the countermeasure unit can generate notification content using a generation AI and send a notification to family members via social media.

[0083] When an abnormality is detected, the countermeasure unit can customize the countermeasure method by reflecting past feedback. For example, when the countermeasure unit detects an abnormal heart rate, it customizes the countermeasure method based on past feedback. Furthermore, when the countermeasure unit detects an abnormal blood pressure, it can also customize the countermeasure method based on past feedback. Furthermore, when the countermeasure unit detects an abnormal body temperature, it can also customize the countermeasure method based on past feedback. This makes it possible to customize the countermeasure method based on past feedback. Some or all of the above-mentioned processing in the countermeasure unit may be performed using, for example, AI, or may be performed without using AI. For example, the countermeasure unit can input past feedback data into the generation AI and have the generation AI customize the countermeasure method. === Hard Collateral 1-1 === Each of the multiple elements, including the dialogue unit, measurement unit, and countermeasure unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the dialogue unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the dialogue unit listens to the elderly person's speech using the microphone 38B of the smart device 14, and the control unit 46A provides an appropriate response. The measurement unit, for example, uses the camera 42 or sensors of the smart device 14 to grasp the elderly person's health condition in real time, and the specific processing unit 290 of the data processing device 12 performs analysis. The countermeasure unit, for example, is realized by the specific processing unit 290 of the data processing device 12, detects abnormalities, and automatically takes necessary countermeasures. For example, if the heart rate suddenly increases, the data processing device 12 detects the abnormality and sends a notification to an emergency contact. === Hard Collateral 1-2 === Each of the multiple elements, including the dialogue unit, measurement unit, and countermeasure unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the dialogue unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the dialogue unit listens to the elderly person's speech using the microphone 238 of the smart glasses 214, and the control unit 46A provides an appropriate response. The measurement unit, for example, uses the camera 42 or a sensor of the smart glasses 214 to grasp the elderly person's health condition in real time, and the specific processing unit 290 of the data processing device 12 performs analysis. The countermeasure unit, for example, is realized by the specific processing unit 290 of the data processing device 12, detects abnormalities, and automatically takes necessary countermeasures. For example, if the heart rate suddenly increases, the data processing device 12 detects the abnormality and sends a notification to an emergency contact. === Hard Collateral 1-3 === Each of the multiple elements, including the dialogue unit, measurement unit, and countermeasure unit, described above, is implemented, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the dialogue unit is implemented by the control unit 46A of the headset-type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the dialogue unit listens to what the elderly person says using the microphone 238 of the headset-type terminal 314, and the control unit 46A provides an appropriate response. The measurement unit, for example, uses the camera 42 or sensors of the headset-type terminal 314 to grasp the elderly person's health condition in real time, and the specific processing unit 290 of the data processing device 12 performs analysis. The countermeasure unit, for example, is implemented by the specific processing unit 290 of the data processing device 12, and detects abnormalities and automatically takes necessary countermeasures. For example, if the heart rate suddenly increases, the data processing device 12 detects the abnormality and sends a notification to an emergency contact. === Hard Collateral 1-4 === Each of the multiple elements, including the dialogue unit, measurement unit, and countermeasure unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the dialogue unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the dialogue unit listens to the elderly person's speech using the microphone 238 of the robot 414, and the control unit 46A provides an appropriate response. The measurement unit, for example, uses the camera 42 or sensors of the robot 414 to grasp the elderly person's health condition in real time, and the specific processing unit 290 of the data processing device 12 performs analysis. The countermeasure unit, for example, is realized by the specific processing unit 290 of the data processing device 12, and detects abnormalities and automatically takes necessary countermeasures. For example, if the heart rate suddenly increases, the data processing device 12 detects the abnormality and sends a notification to an emergency contact.

[0084] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0085] The dialogue unit can analyze the tone and speed of the elderly person's voice to estimate changes in their health condition. For example, if the elderly person's voice is hoarse, it can estimate poor health. The dialogue unit can also estimate fatigue if the elderly person's voice speed is slow. The dialogue unit can also estimate depression if the elderly person's voice tone is low. This makes it possible to detect abnormalities early by estimating the elderly person's health condition from changes in their voice. Some or all of the above-mentioned processing in the dialogue unit may be performed using AI, for example, or may be performed without using AI. For example, the dialogue unit can input the elderly person's voice data into the generation AI and have the generation AI estimate changes in their health condition.

[0086] The dialogue unit can estimate the elderly person's emotions and adjust the frequency of dialogue based on the estimated elderly person's emotions. For example, if the elderly person feels lonely, the dialogue unit can increase the frequency of dialogue. If the elderly person feels tired, the dialogue unit can also decrease the frequency of dialogue. If the elderly person feels relaxed, the dialogue unit can maintain a moderate frequency of dialogue. This enables more appropriate support by adjusting the frequency of dialogue according to the elderly person's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the elderly person's voice data into the generation AI and have the generation AI perform emotion estimation.

[0087] The dialogue unit can introduce local events and services taking into account the geographical location information of the elderly person. For example, it can provide event information in the area where the elderly person lives. The dialogue unit can also provide medical service information in the area where the elderly person lives. The dialogue unit can also provide welfare service information in the area where the elderly person lives. This makes it possible to provide information based on the geographical location information of the elderly person. Some or all of the above-mentioned processing in the dialogue unit may be performed using AI, for example, or may be performed without using AI. For example, the dialogue unit can input the geographical location information of the elderly person to the generation AI and cause the generation AI to execute information on local events and services.

[0088] The dialogue unit can analyze the social media activity of the elderly person and provide related topics. For example, the dialogue unit can provide topics related to photos shared by the elderly person on social media. The dialogue unit can also provide topics related to comments made by the elderly person on social media. The dialogue unit can also provide topics related to accounts followed by the elderly person on social media. This makes it possible to provide topics based on the elderly person's social media activity. Some or all of the above-described processing in the dialogue unit may be performed using AI, for example, or may be performed without using AI. For example, the dialogue unit can input the elderly person's social media data into the generation AI and cause the generation AI to generate related topics.

[0089] The dialogue unit can customize the dialogue content by reflecting the elderly person's past feedback. For example, the dialogue unit can prioritize topics that the elderly person has previously preferred. The dialogue unit can also avoid topics that the elderly person has previously avoided. The dialogue unit can also adjust the tone and content of the dialogue based on the feedback provided by the elderly person in the past. This makes it possible to customize the dialogue content based on the elderly person's past feedback. Some or all of the above-mentioned processing in the dialogue unit may be performed using AI, for example, or may be performed without using AI. For example, the dialogue unit can input the elderly person's past feedback data into the generation AI and cause the generation AI to customize the dialogue content.

[0090] The measurement unit can estimate the elderly person's emotions and adjust the analysis method of the measurement data based on the estimated elderly person's emotions. For example, if the elderly person is feeling stressed, the measurement unit can analyze heart rate fluctuations in detail. If the elderly person is relaxed, the measurement unit can also use a normal analysis method. If the elderly person is feeling anxious, the measurement unit can also analyze blood pressure fluctuations in detail. This enables analysis of the measurement data according to the elderly person's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the measurement unit can be performed using AI, for example, or without AI. For example, the measurement unit can input the elderly person's emotion data into the generation AI and have the generation AI adjust the analysis method of the measurement data.

[0091] The measurement unit can analyze the measurement data in real time and detect abnormalities early. For example, it can detect sudden fluctuations in heart rate in real time. The measurement unit can also detect abnormal increases in blood pressure in real time. The measurement unit can also detect sudden fluctuations in body temperature in real time. This enables abnormality detection in real time. Some or all of the above-mentioned processing in the measurement unit may be performed using AI, for example, or may be performed without using AI. For example, the measurement unit can input measurement data acquired in real time to a generation AI and cause the generation AI to perform early abnormality detection.

[0092] The measurement unit can store the measurement data in the cloud and share it with medical professionals. For example, heart rate data can be stored in the cloud and shared with medical professionals. The measurement unit can also store blood pressure data in the cloud and share it with medical professionals. The measurement unit can also store body temperature data in the cloud and share it with medical professionals. This enables the measurement data to be stored in the cloud and shared. Some or all of the above-described processing in the measurement unit may be performed using AI, for example, or may be performed without using AI. For example, the measurement unit can input the measurement data to be stored in the cloud to a generation AI and have the generation AI store and share the data.

[0093] The measurement unit can propose an optimal health management plan for each elderly person based on the measurement data. For example, an exercise plan can be proposed based on heart rate data. The measurement unit can also propose a meal plan based on blood pressure data. The measurement unit can also propose a rest plan based on body temperature data. This makes it possible to propose an optimal health management plan for each elderly person. Some or all of the above-mentioned processing in the measurement unit may be performed using AI, for example, or may be performed without using AI. For example, the measurement unit can input the measurement data to a generation AI and have the generation AI execute the proposal of a health management plan.

[0094] The countermeasure unit can estimate the elderly person's emotions and prioritize countermeasures based on the estimated elderly person's emotions. For example, if the elderly person feels anxious, the countermeasure unit can prioritize countermeasures that provide a sense of security. If the elderly person feels stressed, the countermeasure unit can prioritize countermeasures that relax the elderly person. If the elderly person feels lonely, the countermeasure unit can prioritize countermeasures that increase dialogue. This makes it possible to prioritize countermeasures according to the elderly person's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the countermeasure unit may be performed using, for example, AI, or without AI. For example, the countermeasure unit can input the elderly person's emotion data into the generation AI and have the generation AI determine the priority of countermeasures.

[0095] The processing flow of the second embodiment will be briefly explained below.

[0096] Step 1: The dialogue unit listens to what the elderly person says and responds appropriately. For example, if the elderly person says, "The weather is nice today," the dialogue unit responds, "Yes, it's sunny today." The dialogue unit can also provide appropriate advice if the elderly person asks a health-related question. Furthermore, the dialogue unit can estimate the elderly person's emotions and adjust the tone and content of the response based on the estimated emotions. Step 2: The measurement unit uses the information obtained by the dialogue unit to grasp the elderly person's health condition in real time. For example, data such as heart rate, blood pressure, and body temperature are measured and analyzed by AI. The measurement unit can also analyze the measurement data in real time to detect abnormalities. Furthermore, the measurement unit can store the measurement data in the cloud and share it with medical professionals. Step 3: The countermeasures unit detects abnormalities based on the data obtained by the measurement unit and automatically takes necessary measures. For example, if the heart rate suddenly rises, the unit detects the abnormality and sends a notification to emergency contacts. It can also call an ambulance if necessary. Furthermore, the countermeasures unit can estimate the elderly person's emotions and prioritize countermeasures based on the estimated emotions.

[0097] 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.

[0098] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] 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.

[0100] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

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

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

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

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

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

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

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

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

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

[0111] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. 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.

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

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

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

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

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] 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.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0148] 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.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0167] 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.

[0168] [Explanation of symbols]

[0169] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A dialogue section where participants listen to and respond to the elderly, a measurement unit that grasps the health condition of the elderly person in real time based on the information obtained by the dialogue unit; a countermeasure unit that detects an abnormality based on the data obtained by the measurement unit and automatically takes countermeasures. A system characterized by:

2. The dialogue unit Estimate the emotions of the elderly and adjust the tone and content of responses based on the estimated emotions of the elderly.

2. The system of claim 1.

3. The dialogue unit Analyzing the conversation history of elderly people and providing dialogue content for each individual elderly person 2. The system of claim 1.

4. The dialogue unit Providing relevant information and news on topics related to seniors 2. The system of claim 1.

5. The dialogue unit Analyzing the tone and speed of an elderly person's voice to predict changes in their health 2. The system of claim 1.

6. The dialogue unit Estimating the emotions of the elderly and adjusting the frequency of interactions based on the estimated emotions of the elderly 2. The system of claim 1.

7. The dialogue unit Considering seniors' geographic location, connecting them to local events and services 2. The system of claim 1.

8. The dialogue unit Analyzing social media activity among seniors and providing relevant topics 2. The system of claim 1.

Images