System

Abstract

An object of a system according to an embodiment is to efficiently perform situation confirmation and abnormality detection of an elderly person.SOLUTION: A system according to an embodiment includes an automatic call system, a generation AI, a robot, a sensor, and a camera. The automated phone call system makes a phone call to the target person. The generation AI generates a conversation according to a situation of a target person called by the automatic call system. The robot performs a conversation generated by the generative AI. The sensor and the camera collect biological information and behavior patterns.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 does not adequately check the status of elderly people or detect abnormalities, so there is room for improvement.

[0005] The system according to the embodiment aims to efficiently check the status of elderly people and detect abnormalities. [Means for solving the problem]

[0006] The system according to the embodiment includes an automatic call system, a generation AI, a robot, a sensor, and a camera. The automatic call system makes a call to a target person. The generation AI generates a conversation according to the situation of the target person who is called by the automatic call system. The robot carries out the conversation generated by the generation AI. The sensor and camera collect biometric information and behavioral patterns. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently check the status of elderly people and detect abnormalities. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The elderly safety system according to an embodiment of the present invention is a system that uses AI technology to ensure the safety and security of the elderly. This system consists of two elements: an automated call system that uses telephone lines, and an AI monitoring and alert system that uses robots. As a result, the elderly safety system can ensure the safety and security of the elderly and support their daily lives.

[0029] An elderly safety system according to an embodiment includes an automatic call system, a generation AI, a robot, sensors, and a camera. The automatic call system calls a target. For example, it periodically calls the target to check their status. The generation AI generates a conversation tailored to the target's status when the automatic call system calls them. For example, the generation AI uses a text generation AI (e.g., LLM) to generate a conversation tailored to the target's status. The generation AI can also use a multimodal generation AI to generate a conversation tailored to the target's status. The robot engages in a conversation generated by the generation AI. For example, the robot plays back the conversation generated by the generation AI as audio and interacts with the target. The sensors and cameras collect biometric information and behavioral patterns. For example, the sensors collect biometric information such as heart rate and body temperature. The cameras capture and analyze the target's behavioral patterns. This allows the elderly safety system to ensure the safety and security of the elderly and support their daily lives. For example, if the generation AI detects an abnormality, it automatically notifies relevant parties and emergency services. This allows for early detection of elderly health problems and emergencies and prompt response.

[0030] The generation AI can automatically convert the contents of a call into text and compare it with the conversation history to detect anomalies. For example, the generation AI can convert the contents of a call into text in real time and compare it with past conversation history to detect anomalies. For example, it can detect anomalies if there is a sudden change in what the user says. The generation AI can also convert the contents of a call into text and compare it with the conversation history to detect anomalies. For example, it can detect anomalies if the user's statements are inconsistent. This makes it possible to automatically detect anomalies in the content of a call.

[0031] The automatic calling system can add a video calling function and also collect visual information to detect abnormalities. For example, the automatic calling system can add a video calling function and analyze the user's facial expressions and movements to detect abnormalities. For example, an abnormality can be detected when the user's facial expression is different from usual. The automatic calling system can also add a video calling function and collect visual information to detect abnormalities. For example, an abnormality can be detected when the user's movements are different from usual. This makes it possible to detect abnormalities based on visual information.

[0032] The automated call system can be multilingual and can accommodate elderly people who speak different languages. The automated call system, for example, can make call content multilingual and can accommodate elderly people who speak different languages. For example, it can accommodate multiple languages ​​such as English, Spanish, and Chinese. The automated call system can also be multilingual and can accommodate elderly people who speak different languages. For example, it can translate call content in real time and can accommodate elderly people who speak different languages. This makes it possible to accommodate elderly people who speak different languages.

[0033] The robot can monitor the elderly person's living environment and make suggestions to adjust the temperature, humidity, and lighting. For example, the robot can monitor the elderly person's living environment and suggest adjustments if the temperature or humidity is not appropriate. For example, if the room is cold, it can suggest turning on the heater. The robot can also monitor the living environment and suggest adjustments if the lighting is not appropriate. For example, if the room is dark, it can suggest turning on the lights. In this way, it makes suggestions to keep the elderly person's living environment comfortable.

[0034] Robots can be designed to resemble pets, which can strengthen emotional ties. Robots can be designed to resemble pets, which can make it easier for the elderly to feel an emotional connection. For example, robots in the shape of dogs or cats can be developed. Robots can also be designed to resemble pets, which can strengthen emotional ties. For example, robots can be designed to act as if they are snuggling up to the elderly. This can make it easier for the elderly to feel an emotional connection.

[0035] The robot can automatically set up video calls with the elderly's family and friends to maintain social connections. For example, the robot can set up regular video calls with family. The robot can also automatically set up video calls with the elderly's family and friends to maintain social connections. For example, the robot can set up video calls for specific events or anniversaries. This makes it possible to automatically set up video calls to maintain social connections for the elderly.

[0036] Generative AI can analyze the dietary content and nutritional balance of elderly people and evaluate their health condition. For example, generative AI can analyze the dietary content of elderly people and evaluate their nutritional balance. For example, it can analyze photos of meals and calculate nutrients. Generative AI can also analyze the dietary content and nutritional balance of elderly people and evaluate their health condition. For example, it can evaluate nutritional balance based on food records. This makes it possible to analyze the dietary content and nutritional balance of elderly people and evaluate their health condition.

[0037] The generating AI can analyze the sleep patterns of elderly people and make suggestions to improve their sleep quality. The generating AI can, for example, analyze the sleep patterns of elderly people and make suggestions to improve their sleep quality. For example, it can analyze the sleep time and the percentage of deep sleep. The generating AI can also analyze the sleep patterns of elderly people and make suggestions to improve their sleep quality. For example, it can suggest improvements to the sleeping environment (a quiet environment and an appropriate temperature). This makes suggestions to improve the sleep quality of elderly people.

[0038] Generative AI stores biometric information in the cloud, allowing medical professionals to access it remotely for evaluation. For example, generative AI stores the biometric information of an elderly person in the cloud, allowing medical professionals to access it remotely for evaluation. For example, heart rate and body temperature data is uploaded to the cloud. Generative AI can also store biometric information in the cloud, allowing medical professionals to access it remotely for evaluation. For example, medical professionals can remotely check the biometric information and evaluate the health condition. This allows medical professionals to evaluate the biometric information remotely.

[0039] The generation AI can analyze the exercise patterns of elderly people and suggest appropriate exercise programs. The generation AI can, for example, analyze the exercise patterns of elderly people and suggest appropriate exercise programs. For example, it can customize an exercise program by analyzing walking speed and exercise frequency. The generation AI can also analyze the exercise patterns of elderly people and suggest appropriate exercise programs. For example, it can suggest exercise programs according to individual health conditions. This makes it possible to analyze the exercise patterns of elderly people and suggest appropriate exercise programs.

[0040] When a generative AI detects an anomaly, it can compare it with past data to identify the pattern of the anomaly and propose preventive measures. For example, when a generative AI detects an anomaly, it can compare it with past data to identify the pattern of the anomaly. For example, it can propose preventive measures based on past abnormal data. When a generative AI detects an anomaly, it can compare it with past data to identify the pattern of the anomaly and propose preventive measures. For example, it can predict the occurrence of an anomaly under certain conditions and propose preventive measures. This makes it possible to identify the pattern of the anomaly and propose preventive measures.

[0041] The generation AI can automatically send a detailed situation report to an emergency contact when an abnormality is detected. For example, the generation AI can automatically send a detailed situation report to an emergency contact when an abnormality is detected. For example, it can send a report that includes the type of abnormality and the time of occurrence. The generation AI can also automatically send a detailed situation report to an emergency contact when an abnormality is detected. For example, it can send a report that includes the location where the abnormality occurred and how to respond. This makes it possible to send a detailed situation report to an emergency contact when an abnormality is detected.

[0042] The generative AI can link the anomaly detection system with smart home devices to detect abnormalities within the home. For example, the generative AI can link the anomaly detection system with smart home devices to detect abnormalities within the home (gas leaks, fires, etc.). For example, it can link with gas leak sensors and fire alarms. The generative AI can also link the anomaly detection system with smart home devices to detect abnormalities within the home. For example, it can link with water leak sensors and door sensors. This makes it possible to detect abnormalities within the home as well.

[0043] When an abnormality is detected, the generating AI can automatically notify neighbors and the community, encouraging a prompt response. When an abnormality is detected, the generating AI can automatically notify neighbors and the community. For example, if an abnormality occurs, it can send an email or message to neighbors. When an abnormality is detected, the generating AI can also automatically notify neighbors and the community, encouraging a prompt response. For example, if an abnormality occurs, it can post a notification on the community bulletin board. This allows neighbors and the community to be notified when an abnormality is detected, encouraging a prompt response.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The elderly safety system can further include a health management unit. The health management unit analyzes the dietary content and nutritional balance of the elderly person to evaluate their health condition. For example, it analyzes photos of meals to calculate nutrients and suggest balanced meals. The health management unit can also evaluate nutritional balance based on dietary records and suggest supplements to compensate for necessary nutrients. This makes it possible to support the elderly in maintaining their health and maintaining a balanced nutritional state.

[0046] The elderly safety system can further include an exercise support unit. The exercise support unit analyzes the elderly's exercise patterns and suggests appropriate exercise programs. For example, it analyzes walking speed and exercise frequency to customize an exercise program according to each individual's health condition. The exercise support unit can also monitor exercise progress and adjust the program as necessary. This supports the elderly's exercise habits and promotes health maintenance.

[0047] The elderly safety system can further include a sleep management unit. The sleep management unit analyzes the sleep patterns of the elderly and makes suggestions to improve the quality of their sleep. For example, it analyzes the sleep time and the percentage of deep sleep and suggests an appropriate sleeping environment (a quiet environment and an appropriate temperature). The sleep management unit can also suggest relaxation methods (meditation and deep breathing) to improve the quality of sleep. This improves the quality of sleep of the elderly and improves their health.

[0048] The elderly safety system may further include a communication support unit that automatically sets up video calls with the elderly's family and friends to maintain social connections. For example, the communication support unit may set up regular video calls with family members, or video calls for specific events or anniversaries. The communication support unit may also record the contents of video calls and play them back later. This helps the elderly maintain social connections and reduce feelings of loneliness.

[0049] The elderly safety system may further include an environmental monitoring unit. The environmental monitoring unit monitors the elderly's living environment and makes suggestions to adjust temperature, humidity, and lighting. For example, if the room is cold, it may suggest turning on the heater, and if the room is dark, it may suggest turning on the lights. The environmental monitoring unit may also monitor air quality and suggest using an air purifier as needed. This makes suggestions to maintain a comfortable living environment for the elderly.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The automated call system calls the target person, for example, periodically calling the target person to check on their status. Step 2: The generation AI generates a conversation appropriate to the situation of the person called by the automated calling system. For example, the generation AI uses a text generation AI (e.g., LLM) to generate a conversation appropriate to the person's situation. The generation AI can also use a multimodal generation AI to generate a conversation appropriate to the person's situation. Step 3: The robot engages in the conversation generated by the generation AI. For example, the robot plays back the conversation generated by the generation AI in audio and converses with the target person. Step 4: The sensors and cameras collect biometric information and behavioral patterns. For example, the sensors collect biometric information such as heart rate and body temperature. The cameras capture and analyze the subject's behavioral patterns.

[0052] (Example 2) The elderly safety system according to an embodiment of the present invention is a system that uses AI technology to ensure the safety and security of the elderly. This system consists of two elements: an automated call system that uses telephone lines, and an AI monitoring and alert system that uses robots. As a result, the elderly safety system can ensure the safety and security of the elderly and support their daily lives.

[0053] An elderly safety system according to an embodiment includes an automatic call system, a generation AI, a robot, sensors, and a camera. The automatic call system calls a target. For example, it periodically calls the target to check their status. The generation AI generates a conversation tailored to the target's status when the automatic call system calls them. For example, the generation AI uses a text generation AI (e.g., LLM) to generate a conversation tailored to the target's status. The generation AI can also use a multimodal generation AI to generate a conversation tailored to the target's status. The robot engages in a conversation generated by the generation AI. For example, the robot plays back the conversation generated by the generation AI as audio and interacts with the target. The sensors and cameras collect biometric information and behavioral patterns. For example, the sensors collect biometric information such as heart rate and body temperature. The cameras capture and analyze the target's behavioral patterns. This allows the elderly safety system to ensure the safety and security of the elderly and support their daily lives. For example, if the generation AI detects an abnormality, it automatically notifies relevant parties and emergency services. This allows for early detection of elderly health problems and emergencies and prompt response.

[0054] The generation AI can analyze changes in the user's voice tone and speaking style to estimate their emotional state and adjust the content of the conversation. For example, the generation AI can analyze changes in the user's voice tone and speaking style in real time to estimate their emotional state. For example, if the user is tired, it can generate conversation content that helps them relax. The generation AI can also estimate the user's emotional state based on changes in the user's voice tone and speaking style and adjust the content of the conversation. For example, if the user is excited, it can generate conversation content that helps them calm down. This makes it possible to provide conversation content that matches the user's emotional state.

[0055] The generation AI can automatically convert the contents of a call into text and compare it with the conversation history to detect anomalies. For example, the generation AI can convert the contents of a call into text in real time and compare it with past conversation history to detect anomalies. For example, it can detect anomalies if there is a sudden change in what the user says. The generation AI can also convert the contents of a call into text and compare it with the conversation history to detect anomalies. For example, it can detect anomalies if the user's statements are inconsistent. This makes it possible to automatically detect anomalies in the content of a call.

[0056] The generation AI can use the emotion estimation function to generate conversations to help the user relax when they are feeling stressed or anxious. For example, the generation AI can use the emotion estimation function to generate conversations to help the user relax when they are feeling stressed or anxious. For example, it can provide topics that will help the user relax (hobbies or favorite music). The generation AI can also use the emotion estimation function to generate conversations to help the user relax when they are feeling stressed or anxious. For example, it can provide an environment that will help the user relax (a quiet place or the sounds of nature). This can provide conversations that will reduce the user's stress and anxiety.

[0057] The automatic calling system can add a video calling function and also collect visual information to detect abnormalities. For example, the automatic calling system can add a video calling function and analyze the user's facial expressions and movements to detect abnormalities. For example, an abnormality can be detected when the user's facial expression is different from usual. The automatic calling system can also add a video calling function and collect visual information to detect abnormalities. For example, an abnormality can be detected when the user's movements are different from usual. This makes it possible to detect abnormalities based on visual information.

[0058] The automated call system can be multilingual and can accommodate elderly people who speak different languages. The automated call system, for example, can make call content multilingual and can accommodate elderly people who speak different languages. For example, it can accommodate multiple languages ​​such as English, Spanish, and Chinese. The automated call system can also be multilingual and can accommodate elderly people who speak different languages. For example, it can translate call content in real time and can accommodate elderly people who speak different languages. This makes it possible to accommodate elderly people who speak different languages.

[0059] The generation AI can use the emotion estimation function to provide the user with the most relaxing music or entertainment during a call. The generation AI can, for example, use the emotion estimation function to provide the user with the most relaxing music during a call. For example, it can play classical music or nature sounds depending on the user's emotional state. The generation AI can also use the emotion estimation function to provide the user with the most relaxing entertainment during a call. For example, it can play the user's favorite movie or video depending on the user's emotional state. This allows the user to be provided with music or entertainment that helps them relax.

[0060] The robot can analyze the facial expressions and movements of the elderly person in real time, estimate their emotional state, and adjust the content of the conversation accordingly. For example, the robot can analyze the facial expressions of the elderly person in real time to estimate their emotional state. For example, if the elderly person looks sad, the robot can offer words of encouragement. The robot can also analyze the movements of the elderly person in real time to estimate their emotional state. For example, if the elderly person looks tired, the robot can suggest that they take a break. This makes it possible to provide conversation content that suits the elderly person's emotional state.

[0061] The robot can monitor the elderly person's living environment and make suggestions to adjust the temperature, humidity, and lighting. For example, the robot can monitor the elderly person's living environment and suggest adjustments if the temperature or humidity is not appropriate. For example, if the room is cold, it can suggest turning on the heater. The robot can also monitor the living environment and suggest adjustments if the lighting is not appropriate. For example, if the room is dark, it can suggest turning on the lights. In this way, it makes suggestions to keep the elderly person's living environment comfortable.

[0062] The robot can use the emotion estimation function to suggest entertainment or activities when the elderly person feels lonely. For example, the robot can use the emotion estimation function to suggest entertainment such as movies or music when the elderly person feels lonely. For example, it can suggest watching a favorite movie together. The robot can also use the emotion estimation function to suggest activities when the elderly person feels lonely. For example, it can suggest going for a walk or exercising. This makes it possible to suggest entertainment or activities to help reduce the elderly person's sense of loneliness.

[0063] Robots can be designed to resemble pets, which can strengthen emotional ties. Robots can be designed to resemble pets, which can make it easier for the elderly to feel an emotional connection. For example, robots in the shape of dogs or cats can be developed. Robots can also be designed to resemble pets, which can strengthen emotional ties. For example, robots can be designed to act as if they are snuggling up to the elderly. This can make it easier for the elderly to feel an emotional connection.

[0064] The robot can automatically set up video calls with the elderly's family and friends to maintain social connections. For example, the robot can set up regular video calls with family. The robot can also automatically set up video calls with the elderly's family and friends to maintain social connections. For example, the robot can set up video calls for specific events or anniversaries. This makes it possible to automatically set up video calls to maintain social connections for the elderly.

[0065] The robot can use its emotion estimation function to suggest hobbies and activities that the elderly would enjoy most and support them in carrying them out. For example, the robot can use its emotion estimation function to suggest hobbies and activities that the elderly would enjoy most. For example, it can suggest hobbies such as gardening and cooking. The robot can also use its emotion estimation function to suggest hobbies and activities that the elderly would enjoy most and support them in carrying them out. For example, it can help prepare the tools for a hobby. This allows the robot to suggest hobbies and activities that the elderly would enjoy and support them in carrying them out.

[0066] Generative AI can analyze the dietary content and nutritional balance of elderly people and evaluate their health condition. For example, generative AI can analyze the dietary content of elderly people and evaluate their nutritional balance. For example, it can analyze photos of meals and calculate nutrients. Generative AI can also analyze the dietary content and nutritional balance of elderly people and evaluate their health condition. For example, it can evaluate nutritional balance based on food records. This makes it possible to analyze the dietary content and nutritional balance of elderly people and evaluate their health condition.

[0067] The generating AI can analyze the sleep patterns of elderly people and make suggestions to improve their sleep quality. The generating AI can, for example, analyze the sleep patterns of elderly people and make suggestions to improve their sleep quality. For example, it can analyze the sleep time and the percentage of deep sleep. The generating AI can also analyze the sleep patterns of elderly people and make suggestions to improve their sleep quality. For example, it can suggest improvements to the sleeping environment (a quiet environment and an appropriate temperature). This makes suggestions to improve the sleep quality of elderly people.

[0068] The generation AI can use the emotion estimation function to analyze the stress level of the elderly and provide advice for stress reduction. The generation AI, for example, uses the emotion estimation function to analyze the stress level of the elderly. For example, it evaluates the stress level by analyzing facial expressions and tone of voice. The generation AI can also use the emotion estimation function to analyze the stress level of the elderly and provide advice for stress reduction. For example, it can suggest relaxing activities (yoga or meditation). This allows the generation AI to analyze the stress level of the elderly and provide advice for stress reduction.

[0069] Generative AI stores biometric information in the cloud, allowing medical professionals to access it remotely for evaluation. For example, generative AI stores the biometric information of an elderly person in the cloud, allowing medical professionals to access it remotely for evaluation. For example, heart rate and body temperature data is uploaded to the cloud. Generative AI can also store biometric information in the cloud, allowing medical professionals to access it remotely for evaluation. For example, medical professionals can remotely check the biometric information and evaluate the health condition. This allows medical professionals to evaluate the biometric information remotely.

[0070] The generation AI can analyze the exercise patterns of elderly people and suggest appropriate exercise programs. The generation AI can, for example, analyze the exercise patterns of elderly people and suggest appropriate exercise programs. For example, it can customize an exercise program by analyzing walking speed and exercise frequency. The generation AI can also analyze the exercise patterns of elderly people and suggest appropriate exercise programs. For example, it can suggest exercise programs according to individual health conditions. This makes it possible to analyze the exercise patterns of elderly people and suggest appropriate exercise programs.

[0071] The generative AI can use its emotion estimation function to suggest environmental settings that will help the elderly to relax the most. For example, it can suggest music that has a relaxing effect. The generative AI can also use its emotion estimation function to suggest environmental settings that will help the elderly to relax the most. For example, it can suggest adjustments to lighting or selection of fragrances. In this way, it can suggest environmental settings that will help the elderly to relax.

[0072] When a generative AI detects an anomaly, it can compare it with past data to identify the pattern of the anomaly and propose preventive measures. For example, when a generative AI detects an anomaly, it can compare it with past data to identify the pattern of the anomaly. For example, it can propose preventive measures based on past abnormal data. When a generative AI detects an anomaly, it can compare it with past data to identify the pattern of the anomaly and propose preventive measures. For example, it can predict the occurrence of an anomaly under certain conditions and propose preventive measures. This makes it possible to identify the pattern of the anomaly and propose preventive measures.

[0073] The generation AI can automatically send a detailed situation report to an emergency contact when an abnormality is detected. For example, the generation AI can automatically send a detailed situation report to an emergency contact when an abnormality is detected. For example, it can send a report that includes the type of abnormality and the time of occurrence. The generation AI can also automatically send a detailed situation report to an emergency contact when an abnormality is detected. For example, it can send a report that includes the location where the abnormality occurred and how to respond. This makes it possible to send a detailed situation report to an emergency contact when an abnormality is detected.

[0074] The generation AI can use the emotion estimation function to evaluate the emotional state of the elderly when an anomaly is detected and propose an appropriate response. The generation AI can, for example, use the emotion estimation function to evaluate the emotional state of the elderly when an anomaly is detected. For example, if an elderly person feels anxious when an abnormality occurs, it can propose a response to reassure them. The generation AI can also use the emotion estimation function to evaluate the emotional state of the elderly when an anomaly is detected and propose an appropriate response. For example, if an elderly person is in a state of panic when an abnormality occurs, it can propose a response to calm them down. This makes it possible to evaluate the emotional state of the elderly when an anomaly is detected and propose an appropriate response.

[0075] The generative AI can link the anomaly detection system with smart home devices to detect abnormalities within the home. For example, the generative AI can link the anomaly detection system with smart home devices to detect abnormalities within the home (gas leaks, fires, etc.). For example, it can link with gas leak sensors and fire alarms. The generative AI can also link the anomaly detection system with smart home devices to detect abnormalities within the home. For example, it can link with water leak sensors and door sensors. This makes it possible to detect abnormalities within the home as well.

[0076] When an abnormality is detected, the generating AI can automatically notify neighbors and the community, encouraging a prompt response. When an abnormality is detected, the generating AI can automatically notify neighbors and the community. For example, if an abnormality occurs, it can send an email or message to neighbors. When an abnormality is detected, the generating AI can also automatically notify neighbors and the community, encouraging a prompt response. For example, if an abnormality occurs, it can post a notification on the community bulletin board. This allows neighbors and the community to be notified when an abnormality is detected, encouraging a prompt response.

[0077] The generation AI can use the emotion estimation function to provide a message or music that will reassure the elderly after an abnormality is detected. For example, the generation AI can use the emotion estimation function to provide a message that will reassure the elderly after an abnormality is detected. For example, it can send a message informing that the abnormality has been resolved. The generation AI can also use the emotion estimation function to provide music that will reassure the elderly after an abnormality is detected. For example, it can play music that has a relaxing effect. In this way, it can provide a message or music that will reassure the elderly after an abnormality is detected.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The elderly safety system can further include a health management unit. The health management unit analyzes the dietary content and nutritional balance of the elderly person to evaluate their health condition. For example, it analyzes photos of meals to calculate nutrients and suggest balanced meals. The health management unit can also evaluate nutritional balance based on dietary records and suggest supplements to compensate for necessary nutrients. This makes it possible to support the elderly in maintaining their health and maintaining a balanced nutritional state.

[0080] The elderly safety system can further include an exercise support unit. The exercise support unit analyzes the elderly's exercise patterns and suggests appropriate exercise programs. For example, it analyzes walking speed and exercise frequency to customize an exercise program according to each individual's health condition. The exercise support unit can also monitor exercise progress and adjust the program as necessary. This supports the elderly's exercise habits and promotes health maintenance.

[0081] The elderly safety system can further include a sleep management unit. The sleep management unit analyzes the sleep patterns of the elderly and makes suggestions to improve the quality of their sleep. For example, it analyzes the sleep time and the percentage of deep sleep and suggests an appropriate sleeping environment (a quiet environment and an appropriate temperature). The sleep management unit can also suggest relaxation methods (meditation and deep breathing) to improve the quality of sleep. This improves the quality of sleep of the elderly and improves their health.

[0082] The elderly safety system may further include a communication support unit that automatically sets up video calls with the elderly's family and friends to maintain social connections. For example, the communication support unit may set up regular video calls with family members, or video calls for specific events or anniversaries. The communication support unit may also record the contents of video calls and play them back later. This helps the elderly maintain social connections and reduce feelings of loneliness.

[0083] The elderly safety system may further include an environmental monitoring unit. The environmental monitoring unit monitors the elderly's living environment and makes suggestions to adjust temperature, humidity, and lighting. For example, if the room is cold, it may suggest turning on the heater, and if the room is dark, it may suggest turning on the lights. The environmental monitoring unit may also monitor air quality and suggest using an air purifier as needed. This makes suggestions to maintain a comfortable living environment for the elderly.

[0084] The Elderly Safety System can also use its emotion estimation function to suggest environmental settings that will help the elderly feel most relaxed. For example, it can suggest relaxing music, adjust lighting, and select fragrances. If the elderly person feels stressed, the emotion estimation function can also suggest relaxing activities (yoga or meditation). This provides an environment where the elderly can relax and reduce stress.

[0085] The Elderly Safety System can also use its emotion estimation function to suggest entertainment and activities when seniors feel lonely. For example, it can suggest entertainment such as movies and music, and activities such as walks and exercise. It can also use its emotion estimation function to suggest hobbies and activities that seniors enjoy most and support them in carrying them out. This reduces seniors' feelings of loneliness and improves their quality of life.

[0086] The Elderly Safety System can also use its emotion estimation function to evaluate the emotional state of the elderly when an abnormality is detected and propose appropriate responses. For example, if an elderly person feels anxious when an abnormality occurs, it can propose responses to reassure them, and if they are in a state of panic, it can propose responses to calm them. The emotion estimation function can also be used to provide messages or music to reassure the elderly after an abnormality is detected. This allows the system to evaluate the emotional state of the elderly when an abnormality is detected and propose appropriate responses.

[0087] The Elderly Safety System can also use its emotion estimation function to generate conversations to relax elderly people who are feeling stressed or anxious. For example, it can provide topics that help users relax (hobbies or favorite music) and a relaxing environment (quiet places or natural sounds). It can also use the emotion estimation function to provide music or entertainment that the elderly find most relaxing during a call. This provides conversations that reduce the elderly's stress and anxiety.

[0088] The Elderly Safety System can also use its emotion estimation function to suggest environmental settings that will help the elderly feel most relaxed. For example, it can suggest relaxing music, adjust lighting, and select fragrances. If the elderly person feels stressed, the emotion estimation function can also suggest relaxing activities (yoga or meditation). This provides an environment where the elderly can relax and reduce stress.

[0089] The processing flow of the second embodiment will be briefly explained below.

[0090] Step 1: The automated call system calls the target person, for example, periodically calling the target person to check on their status. Step 2: The generation AI generates a conversation appropriate to the situation of the person called by the automated calling system. For example, the generation AI uses a text generation AI (e.g., LLM) to generate a conversation appropriate to the person's situation. The generation AI can also use a multimodal generation AI to generate a conversation appropriate to the person's situation. Step 3: The robot engages in the conversation generated by the generation AI. For example, the robot plays back the conversation generated by the generation AI in audio and converses with the target person. Step 4: The sensors and cameras collect biometric information and behavioral patterns. For example, the sensors collect biometric information such as heart rate and body temperature. The cameras capture and analyze the subject's behavioral patterns.

[0091] 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.

[0092] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0093] 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.

[0094] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0095] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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).

[0100] 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.

[0101] 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.

[0102] 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.

[0103] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0104] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0110] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0119] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0125] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0135] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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."

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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. [Explanation of symbols]

[0158] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An automated calling system that calls the target person; A generation AI that generates a conversation according to the situation of a person who is called by the automatic calling system; a robot that carries out a conversation generated by the generation AI; Equipped with sensors and cameras that collect biometric information and behavioral patterns A system characterized by:

2. The generated AI is Analyzes changes in the user's tone of voice and speaking style to estimate their emotional state and adjust the content of the conversation.

2. The system of claim 1.

3. The automated call system Add video chat functionality and collect visual information to detect anomalies 2. The system of claim 1.

4. The robot Analyzing the facial expressions and movements of elderly people in real time, estimating their emotional state and adjusting the content of conversations accordingly 2. The system of claim 1.

5. The generated AI is Analyzing the dietary content and nutritional balance of elderly people to assess their health status 2. The system of claim 1.

6. The generated AI is When an anomaly is detected, it compares it with past data to identify patterns and suggest preventative measures.

2. The system of claim 1.

7. The generated AI is Evaluating the emotional state of the elderly when an abnormality is detected and suggesting appropriate responses 2. The system of claim 1.

8. The generated AI is Providing messages or music to reassure elderly people after detecting an abnormality 2. The system of claim 1.

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