System

Abstract

To provide a system capable of detecting anomalies early while preventing the decline of cognitive and physical functions in the elderly.SOLUTION: A system in an embodiment includes a conversation unit, a measurement unit, a guidance unit, a detection unit, and a notification unit. The conversation unit performs a conversation with a user based on the personal data of the user. The measurement unit measures the temperature and blood pressure of the user. The guidance unit provides training for users. The detection unit detects a specific symptom or behavior based on one or more pieces of information of the conversation results by the conversation unit, measurement results by the measurement unit, and guidance results by the guidance unit. The notification unit notifies detection results by the detection unit to a specific contact target.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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology does not adequately develop systems for preventing the decline of cognitive and physical functions in the elderly and for early detection of abnormalities, and there is room for improvement.

[0005] The system according to the embodiment aims to prevent the deterioration of cognitive and physical functions of the elderly and to detect abnormalities at an early stage. [Means for solving the problem]

[0006] The system according to the embodiment includes a conversation unit, a measurement unit, an instruction unit, a detection unit, and a notification unit. The conversation unit converses with the user based on the user's personal data. The measurement unit measures the user's body temperature and blood pressure. The instruction unit provides training instruction to the user. The detection unit detects a specific symptom or a specific behavior based on one or more pieces of information among the conversation result by the conversation unit, the measurement result by the measurement unit, and the instruction result by the instruction unit. The notification unit notifies a specific contact of the detection result by the detection unit. Effect of the Invention

[0007] The system according to the embodiment can prevent the decline of cognitive and physical functions in the elderly and detect abnormalities at an early stage. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the 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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The system according to the embodiment of the present invention is a system that provides AI robots to elderly people living alone in nursing homes or at home. This system has learned the user's personal data, and will have conversations and ask questions that are unique to that person in order to prevent the decline of cognitive and physical functions. It also measures body temperature and blood pressure, and provides training guidance. Furthermore, if it recognizes any worrying symptoms or behavior, it has the function of transferring the data to registered facilities, medical institutions, and relatives. It also issues alerts such as the time to take medication and the room temperature. For example, the AI ​​robot will have a conversation based on the user's personal data. For example, it will provide topics based on the user's hobbies and past experiences to maintain cognitive function. Next, the AI ​​robot will measure the user's body temperature and blood pressure. This allows the health condition to be constantly monitored, and any abnormalities can be detected early. The AI ​​robot will also provide training guidance to the user. For example, it will teach the user simple exercises and stretching methods to maintain physical function. Furthermore, the AI ​​robot will detect specific symptoms and behaviors based on the results of the conversation, measurement results, and training guidance. For example, if the body temperature rises suddenly, or if there is an abnormality in the content of the conversation, etc. This information will be notified to registered facilities, medical institutions, and relatives. This allows appropriate measures to be taken early. The AI ​​robot also issues alerts regarding the time to take medication and the room temperature. For example, when it is time to take medication, an alert is sent, urging the user to take their medicine. Also, if the room temperature is not appropriate, an alert is sent, urging the user to adjust the temperature to an appropriate level. In this way, the AI ​​robot helps maintain the cognitive and physical functions of the elderly, and by constantly monitoring their health condition, provides an environment in which they can live with peace of mind. In this way, the AI ​​robot system helps maintain the cognitive and physical functions of the elderly, and by constantly monitoring their health condition.

[0029] The AI ​​robot system according to the embodiment includes a conversation unit, a measurement unit, a guidance unit, a detection unit, and a notification unit. The conversation unit has a conversation with the user based on the user's personal data. For example, the conversation unit uses a generation AI to provide topics based on the user's hobbies and past experiences. The generation AI learns the user's personal data, for example, using deep learning technology, and generates appropriate conversation content. The measurement unit measures the user's body temperature and blood pressure. For example, the measurement unit uses AI to analyze data from a thermometer and a blood pressure monitor and monitor the user's health condition. The guidance unit provides training guidance for the user. For example, the guidance unit uses the generation AI to provide a training program suitable for the user. The generation AI suggests appropriate exercise and stretching methods based on the user's personal data. The detection unit detects a specific symptom or a specific behavior based on one or more pieces of information among the conversation result by the conversation unit, the measurement result by the measurement unit, and the guidance result by the guidance unit. For example, the detection unit uses AI to detect a sudden rise in body temperature or an abnormality in the conversation content. The notification unit notifies a specific contact of the detection result by the detection unit. For example, the notification unit uses AI to forward data to registered facilities, medical institutions, and relatives, allowing the AI ​​robot system to help maintain the user's cognitive and physical functions and constantly monitor their health.

[0030] The conversation unit has a conversation with the user based on the user's personal data. For example, the conversation unit uses the generation AI to provide topics based on the user's hobbies and past experiences. The generation AI learns the user's personal data using, for example, deep learning technology to generate appropriate conversation content. Specifically, the generation AI collects data such as the user's past conversation history, social media posts, and survey results, and identifies the user's interests and concerns based on these data. Furthermore, the generation AI uses natural language processing technology to analyze the user's remarks in real time and generate appropriate responses. For example, if the user talks about a recent trip, the generation AI can provide information about the travel destination and develop related topics. In addition, the generation AI can analyze the user's emotional state and select appropriate tones and expressions to realize a more natural and friendly conversation. This allows the conversation unit to deepen communication with the user and improve the user's satisfaction. Furthermore, the conversation unit can collect user feedback and use it as learning data for the generation AI to continuously improve the quality of the conversation. For example, if a user shows interest in a particular topic, the information can be recorded and brought up again in the next conversation. This enables the conversation unit to respond flexibly to the needs of the user, and provide a more personalized service.

[0031] The measurement unit measures the user's body temperature and blood pressure. For example, the measurement unit uses AI to analyze data from a thermometer or blood pressure monitor and monitor the user's health condition. Specifically, the measurement unit uses a non-contact thermometer or wearable device to measure the user's body temperature in real time. This allows the user to naturally collect health data in their daily lives. For the blood pressure monitor, a device that can be easily measured by simply wrapping it around the arm is used to record blood pressure regularly. The AI ​​analyzes these data and detects abnormal values ​​and trends. For example, if the body temperature rises suddenly or the blood pressure is abnormally high, the AI ​​immediately analyzes the data and issues a warning to the user. In addition, the measurement unit can monitor changes in the user's health condition over the long term based on past data. This allows the user to understand their health condition and take necessary measures. Furthermore, the measurement unit can identify factors that affect the health condition by taking into account the user's lifestyle and environmental factors. For example, it analyzes the effects of stress and lack of sleep on blood pressure and provides specific advice to the user. This allows the measurement unit to support the user's health management and promote a healthier life.

[0032] The instructor provides training guidance to the user. For example, the instructor uses the generation AI to provide a training program suitable for the user. The generation AI suggests appropriate exercise and stretching methods based on the user's personal data. Specifically, the generation AI creates an individually customized training program by considering the user's age, gender, physical fitness level, health condition, and other data. For example, a variety of programs can be provided according to the user's needs, from light stretching for beginners to high-intensity training for advanced users. In addition, the generation AI analyzes the user's training history and monitors the progress. This allows the user to provide specific advice and motivation to achieve their goals. For example, if a user continues training for a certain period of time, the results are evaluated and advice is given to move on to the next step. In addition, the generation AI can collect user feedback and continuously improve the content of the training program. This allows the instructor to maximize the user's training effect and support health maintenance and physical fitness improvement. In addition, the instructor can monitor the form and movements during training in real time and instruct the user to exercise in the correct form. This minimizes the risk of injury and allows training to be performed safely.

[0033] The detection unit detects a specific symptom or a specific behavior based on one or more pieces of information among the conversation result by the conversation unit, the measurement result by the measurement unit, and the instruction result by the instruction unit. For example, the detection unit uses AI to detect a sudden rise in body temperature or an abnormality in the content of the conversation. Specifically, the AI ​​analyzes the collected data and identifies abnormal patterns and trends. For example, if the user's body temperature rises suddenly, the AI ​​immediately analyzes the data and detects the possibility of a fever. In addition, if the user repeatedly brings up the same topic in the conversation content or if there is a change in the choice of words, the AI ​​can detect a decline in cognitive function or a change in mental state. Furthermore, the training results by the instruction unit are analyzed to monitor changes in the user's athletic ability and physical strength. For example, if a user complains of abnormal fatigue during training, the AI ​​can detect excessive exercise or a deterioration in health based on the data. This allows the detection unit to detect changes in the user's health condition and cognitive function at an early stage and provide information for taking appropriate measures. Furthermore, the detection unit can also perform long-term risk assessments and trend analysis by utilizing past data and statistical information. This allows a comprehensive assessment of the user's health condition and allows preventative measures to be taken.

[0034] The notification unit notifies a specific contact of the detection result by the detection unit. For example, the notification unit uses AI to transfer data to registered facilities, medical institutions, and relatives. Specifically, the notification unit receives abnormality detection information from the detection unit and promptly notifies the pre-set contacts. For example, if the user's body temperature rises suddenly, the notification unit transmits the information to a medical institution to encourage appropriate response. In addition, if an abnormality is detected in the user's cognitive function, the notification unit can contact relatives or a nursing facility to request early response. Furthermore, the notification unit can reliably transmit information using multiple communication means. For example, important information is reliably delivered by using not only smartphone notifications but also voice calls, SMS, emails, etc. in combination. As a result, the notification unit can quickly and reliably transmit the user's health condition and changes in cognitive function to related parties and support appropriate response. Furthermore, the notification unit can encrypt data and control access to protect the user's privacy. As a result, the notification unit can realize safe and reliable information transmission and support the user's health management.

[0035] The alert unit can issue an alert of the time to take medicine or the room temperature. For example, the alert unit issues a voice alert when it is time to take medicine, prompting the user to take the medicine. In addition, when the room temperature is not appropriate, the alert unit can issue a text message or an app notification to prompt the user to adjust the temperature to an appropriate temperature. In this way, by issuing an alert of the time to take medicine or the room temperature, it is possible to support the user's health management. A part or all of the above-mentioned processing in the alert unit may be performed, for example, using AI, or may be performed without using AI. For example, the alert unit can input data of the time to take medicine or the room temperature into AI and cause the AI ​​to issue an alert.

[0036] The instruction unit can provide training guidance to the user based on one or more pieces of information among the conversation result by the conversation unit and the measurement result by the measurement unit. The instruction unit provides training guidance to the user based on, for example, the conversation result by the conversation unit. For example, the conversation unit provides topics based on the user's hobbies and interests, and the instruction unit proposes an appropriate training program based on the results. The instruction unit can also provide training guidance to the user based on the measurement results by the measurement unit. For example, the measurement unit measures the user's body temperature and blood pressure, and the instruction unit proposes appropriate exercise and stretching methods based on the results. As a result, training guidance can be provided to the user by providing training guidance based on the conversation result and the measurement result. A part or all of the above-mentioned processing in the instruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction unit can input data from the conversation unit and the measurement unit into AI and have AI generate the content of the training guidance.

[0037] The conversation unit can have a conversation with the user using a generation AI that has learned the user's personal data. The conversation unit, for example, uses the generation AI to provide topics based on the user's hobbies and past experiences. The generation AI learns the user's personal data using deep learning technology and generates appropriate conversation content. For example, the generation AI provides topics related to the user's hobbies based on information about the user's hobbies. The generation AI can also provide topics related to past events based on the user's past experiences. This makes it possible to provide individually appropriate conversations by using the generation AI that has learned the user's personal data. The generation AI is realized using, for example, a text generation AI (e.g., LLM) or natural language processing technology. Part or all of the above-mentioned processing in the conversation unit is performed using the generation AI.

[0038] The instruction unit can provide training guidance to the user using a generation AI that has learned the user's personal data. The instruction unit, for example, uses the generation AI to provide a training program suitable for the user. The generation AI learns the user's personal data using deep learning technology and suggests appropriate exercise and stretching methods. For example, the generation AI generates an appropriate training plan based on the user's health condition and exercise history. The generation AI can also dynamically adjust the training plan based on the user's feedback. As a result, by using the generation AI that has learned the user's personal data, it is possible to provide individually suitable training guidance. The generation AI is realized, for example, using a text generation AI (e.g., LLM) or natural language processing technology. A part or all of the above-mentioned processing in the instruction unit is performed using the generation AI.

[0039] The measurement unit can measure the user's body temperature and blood pressure using a generation AI that has learned the user's personal data. The measurement unit, for example, uses the generation AI to analyze data from a thermometer and a blood pressure monitor to monitor the user's health condition. The generation AI learns the user's personal data using deep learning technology and provides an appropriate measurement method. For example, the generation AI suggests an appropriate measurement frequency and timing based on the user's health condition and past measurement data. The generation AI can also detect abnormal values ​​and issue an early warning based on the measurement results. As a result, by using the generation AI that has learned the user's personal data, it is possible to measure the body temperature and blood pressure that are appropriate for each individual. The generation AI is realized using, for example, a text generation AI (e.g., LLM) or natural language processing technology. A part or all of the above-mentioned processing in the measurement unit is performed using the generation AI.

[0040] The conversation unit can analyze the user's past conversation history and select the optimal way to proceed with the conversation. The conversation unit can, for example, use a generation AI to analyze the user's past conversation history. The generation AI can use deep learning technology to learn the user's past conversation data and select the optimal way to proceed with the conversation. For example, the generation AI can take up topics that the user liked in the past again. The generation AI can also avoid topics that the user avoided in the past. Furthermore, the generation AI can also dig deeper into topics that the user talked about at length in the past. This makes it possible to provide a conversation progress method that is suitable for the user by analyzing the past conversation history. The generation AI can be realized, for example, using a text generation AI (e.g., LLM) or natural language processing technology. Part or all of the above-mentioned processing in the conversation unit is performed using the generation AI.

[0041] The conversation unit can analyze the user's reaction in real time during the conversation and dynamically adjust the content of the conversation. The conversation unit can, for example, use a generation AI to analyze the user's reaction in real time during the conversation. The generation AI can analyze the user's reaction using facial expression recognition and voice analysis technology. For example, the generation AI can analyze the user's facial expression data and calculate the degree of interest or boredom. The generation AI can also analyze the user's voice data and calculate the degree of interest or boredom. This allows the conversation unit to dynamically adjust the content of the conversation based on the user's reaction. For example, if the user shows interest, the topic can be further explored. Also, if the user seems bored, the topic can be changed. Furthermore, if the user is confused, the topic can be simplified. The generation AI can be, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the conversation unit is performed using the generation AI.

[0042] The conversation unit can provide relevant information based on the user's hobbies and interests during the conversation. The conversation unit provides relevant information based on the user's hobbies and interests using, for example, a generation AI. The generation AI learns the user's personal data using deep learning technology and generates appropriate information. For example, if the user likes music, the generation AI provides information on new albums. Also, if the user likes gardening, the generation AI can provide information on seasonal flowers. Furthermore, if the user likes traveling, the generation AI can provide information on travel destinations. This improves the quality of the conversation by providing relevant information based on the user's hobbies and interests. The generation AI is realized using, for example, a text generation AI (e.g., LLM) or natural language processing technology. Part or all of the above-mentioned processing in the conversation unit is performed using the generation AI.

[0043] The conversation unit can provide topics related to the region during the conversation, taking into account the geographical location information of the user. The conversation unit provides topics related to the region, taking into account the geographical location information of the user, for example, using a generation AI. The generation AI learns the user's location information using deep learning technology and generates appropriate topics. For example, the generation AI provides event information for the region where the user lives. The generation AI can also provide weather information for the region where the user lives. Furthermore, the generation AI can also provide news for the region where the user lives. This improves the quality of the conversation by providing topics related to the region, taking into account the geographical location information of the user. The generation AI is realized, for example, using a text generation AI (e.g., LLM) or natural language processing technology. A part or all of the above-mentioned processing in the conversation unit is performed using the generation AI.

[0044] The measurement unit can analyze the user's past measurement data and perform early detection of abnormal values. The measurement unit can analyze the user's past measurement data, for example, using a generation AI. The generation AI can learn the user's past measurement data using deep learning technology and perform early detection of abnormal values. For example, the generation AI can analyze the user's past blood pressure data and detect abnormal increases. The generation AI can also analyze the user's past body temperature data and detect abnormal fluctuations. Furthermore, the generation AI can also comprehensively analyze the user's past measurement data and detect abnormalities early. This makes it possible to detect abnormal values ​​early by analyzing past measurement data. The generation AI can be realized, for example, using a text generation AI (for example, LLM) or natural language processing technology. A part or all of the above-mentioned processing in the measurement unit is performed using the generation AI.

[0045] The measurement unit can adjust the measurement method taking into account the user's physical condition and environmental conditions during measurement. The measurement unit adjusts the measurement method taking into account the user's physical condition and environmental conditions, for example, using a generation AI. The generation AI uses deep learning technology to learn the user's physical condition and environmental conditions and provide an appropriate measurement method. For example, the generation AI simplifies the measurement method when the user is in poor health. In addition, the generation AI can also perform detailed measurements when the user is relaxed. Furthermore, the generation AI can also adjust the measurement method when the user's environment is noisy. As a result, more accurate measurements are possible by adjusting the measurement method taking into account the user's physical condition and environmental conditions. The generation AI is realized, for example, using a text generation AI (e.g., LLM) or natural language processing technology. Part or all of the above-mentioned processing in the measurement unit is performed using the generation AI.

[0046] The measurement unit can correct the measurement result by referring to the lifestyle habit data of the user during measurement. The measurement unit refers to the lifestyle habit data of the user using, for example, a generation AI. The generation AI learns the lifestyle habit data of the user using deep learning technology and provides an appropriate measurement result. For example, the generation AI corrects the measurement result when the user has exercised. The generation AI can also correct the measurement result when the user has eaten. Furthermore, the generation AI can also correct the measurement result when the user is sleep deprived. This allows for more accurate measurement by correcting the measurement result by referring to the lifestyle habit data of the user. The generation AI is realized using, for example, a text generation AI (e.g., LLM) or natural language processing technology. A part or all of the above-mentioned processing in the measurement unit is performed using the generation AI.

[0047] The measurement unit can correct the environmental conditions by taking into account the geographical location information of the user during measurement. The measurement unit, for example, uses a generation AI to consider the geographical location information of the user. The generation AI uses deep learning technology to learn the user's location information and provide appropriate environmental conditions. For example, the generation AI corrects the measurement results when the user is at high altitude. The generation AI can also correct the measurement results when the user is in a humid place. Furthermore, the generation AI can also correct the measurement results when the user is in a cold area. This enables more accurate measurement by correcting the environmental conditions by taking into account the geographical location information of the user. The generation AI is realized, for example, using a text generation AI (e.g., LLM) or natural language processing technology. Part or all of the above-mentioned processing in the measurement unit is performed using the generation AI.

[0048] The instruction unit can analyze the user's past training history and provide an optimal training plan. The instruction unit can analyze the user's past training history, for example, using a generation AI. The generation AI can learn the user's past training data using deep learning technology and provide an optimal training plan. For example, the generation AI can propose an optimal plan based on the training the user has performed in the past. The generation AI can also propose effective training from the user's past training history. Furthermore, the generation AI can also analyze the user's past training history and provide a balanced plan. As a result, a training plan suitable for the user can be provided by analyzing the past training history. The generation AI can be realized, for example, using a text generation AI (for example, LLM) or natural language processing technology. A part or all of the above-mentioned processing in the instruction unit is performed using the generation AI.

[0049] The instructor can analyze the user's motion in real time during training and dynamically adjust the instruction content. The instructor can, for example, use a generation AI to analyze the user's motion in real time during training. The generation AI can analyze the user's motion using facial expression recognition and motion analysis technology. For example, the generation AI can analyze the user's motion data and evaluate the accuracy and smoothness. This allows the instructor to dynamically adjust the instruction content based on the user's motion. For example, if the user's motion is incorrect, a correction instruction can be issued. Also, if the user's motion is not smooth, it can be changed to a simpler motion. Furthermore, if the user's motion is accurate, it can proceed to the next step. The generation AI can be, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the instructor is performed using the generation AI.

[0050] The instructor can suggest related activities based on the user's hobbies and interests during training. The instructor uses, for example, a generation AI to suggest related activities based on the user's hobbies and interests. The generation AI learns the user's personal data using deep learning technology and generates appropriate activities. For example, if the user likes music, the generation AI suggests exercises that match the music. If the user likes gardening, the generation AI can also suggest stretches related to gardening. Furthermore, if the user likes traveling, the generation AI can also suggest exercises related to travel. This improves the quality of training by suggesting related activities based on the user's hobbies and interests. The generation AI is realized, for example, using a text generation AI (e.g., LLM) or natural language processing technology. Part or all of the above-mentioned processing in the instructor is performed using the generation AI.

[0051] The instruction unit can suggest activities related to the area taking into account the geographical location information of the user during training. The instruction unit, for example, uses a generation AI to suggest activities related to the area taking into account the geographical location information of the user. The generation AI learns the user's location information using deep learning technology and generates appropriate activities. For example, the generation AI suggests exercises that can be done in a park in the area where the user lives. The generation AI can also suggest exercises that match the climate of the area where the user lives. Furthermore, the generation AI can also suggest exercises related to events in the area where the user lives. As a result, the quality of training is improved by suggesting activities related to the area taking into account the geographical location information of the user. The generation AI is realized, for example, using a text generation AI (e.g., LLM) or natural language processing technology. A part or all of the above-mentioned processing in the instruction unit is performed using the generation AI.

[0052] The detection unit can analyze the user's past data and detect abnormalities early. The detection unit can analyze the user's past data using, for example, a generation AI. The generation AI can learn the user's past data using deep learning technology and detect abnormalities early. For example, the generation AI can analyze the user's past body temperature data and detect abnormal fluctuations early. The generation AI can also analyze the user's past blood pressure data and detect abnormal increases early. Furthermore, the generation AI can analyze the user's past conversation data and detect cognitive function decline early. This makes it possible to detect abnormalities early by analyzing past data. The generation AI can be realized using, for example, a text generation AI (e.g., LLM) or natural language processing technology. A part or all of the above-mentioned processing in the detection unit is performed using the generation AI.

[0053] The detection unit can adjust the detection method taking into account the user's physical condition and environmental conditions at the time of detection. The detection unit, for example, uses a generation AI to take into account the user's physical condition and environmental conditions. The generation AI uses deep learning technology to learn the user's physical condition and environmental conditions and provide an appropriate detection method. For example, the generation AI simplifies the detection method when the user is in poor health. The generation AI can also perform detailed detection when the user is relaxed. Furthermore, the generation AI can also adjust the detection method when the user's environment is noisy. As a result, more accurate detection is possible by adjusting the detection method taking into account the user's physical condition and environmental conditions. The generation AI is realized, for example, using a text generation AI (e.g., LLM) or natural language processing technology. A part or all of the above-mentioned processing in the detection unit is performed using the generation AI.

[0054] The detection unit can correct the abnormality detection result by referring to the lifestyle data of the user at the time of detection. The detection unit, for example, uses a generation AI to refer to the lifestyle data of the user. The generation AI uses deep learning technology to learn the lifestyle data of the user and provide an appropriate abnormality detection result. For example, the generation AI corrects the abnormality detection result when the user has exercised. The generation AI can also correct the abnormality detection result when the user has eaten. Furthermore, the generation AI can also correct the abnormality detection result when the user is sleep deprived. This allows for more accurate detection by correcting the abnormality detection result by referring to the lifestyle data of the user. The generation AI is realized, for example, using a text generation AI (for example, LLM) or natural language processing technology. A part or all of the above-mentioned processing in the detection unit is performed using the generation AI.

[0055] The detection unit can correct the environmental conditions in consideration of the geographical location information of the user at the time of detection. The detection unit, for example, uses a generation AI to consider the geographical location information of the user. The generation AI uses deep learning technology to learn the user's location information and provide appropriate environmental conditions. For example, the generation AI corrects the abnormality detection result when the user is at high altitude. The generation AI can also correct the abnormality detection result when the user is in a humid place. Furthermore, the generation AI can also correct the abnormality detection result when the user is in a cold place. This enables more accurate detection by correcting the environmental conditions in consideration of the geographical location information of the user. The generation AI is realized, for example, using a text generation AI (e.g., LLM) or natural language processing technology. A part or all of the above-mentioned processing in the detection unit is performed using the generation AI.

[0056] The notification unit can select the optimal notification method by referring to the user's past notification history at the time of notification. The notification unit, for example, uses a generation AI to refer to the user's past notification history. The generation AI uses deep learning technology to learn the user's past notification data and provide the optimal notification method. For example, the generation AI uses again a notification method that the user previously preferred. The generation AI can also avoid notification methods that the user previously avoided. Furthermore, the generation AI can also analyze the user's past notification history and select the optimal notification method. As a result, by referring to the past notification history, a notification method suitable for the user can be provided. The generation AI is realized, for example, using a text generation AI (for example, LLM) or natural language processing technology. A part or all of the above-mentioned processing in the notification unit is performed using the generation AI.

[0057] The notification unit can select the optimal notification method by considering the user's device information at the time of notification. The notification unit considers the user's device information, for example, using a generation AI. The generation AI learns the user's device information using deep learning technology and provides an appropriate notification method. For example, if the user is using a smartphone, the generation AI provides a notification method that matches the screen size. In addition, if the user is using a tablet, the generation AI can also provide a notification method optimized for a large screen. Furthermore, if the user is using a smartwatch, the generation AI can also provide a simple and highly visible notification method. This enables more appropriate notification by selecting the optimal notification method by considering the user's device information. The generation AI is realized, for example, using a text generation AI (e.g., LLM) or natural language processing technology. Part or all of the above-mentioned processing in the notification unit is performed using the generation AI.

[0058] The alert unit can select the optimal alert method by referring to the user's past alert history at the time of alert. The alert unit, for example, uses a generation AI to refer to the user's past alert history. The generation AI uses deep learning technology to learn the user's past alert data and provide the optimal alert method. For example, the generation AI uses again an alert method that the user preferred in the past. The generation AI can also avoid alert methods that the user avoided in the past. Furthermore, the generation AI can also analyze the user's past alert history and select the optimal alert method. As a result, by referring to the past alert history, an alert method suitable for the user can be provided. The generation AI is realized, for example, using a text generation AI (for example, LLM) or natural language processing technology. A part or all of the above-mentioned processing in the alert unit is performed using the generation AI.

[0059] The alert unit can select the optimal alert method by considering the user's device information at the time of alert. The alert unit considers the user's device information, for example, using a generation AI. The generation AI learns the user's device information using deep learning technology and provides an appropriate alert method. For example, if the user is using a smartphone, the generation AI provides an alert method that matches the screen size. In addition, if the user is using a tablet, the generation AI can also provide an alert method optimized for a large screen. Furthermore, if the user is using a smartwatch, the generation AI can also provide a concise and highly visible alert method. This enables more appropriate alerts by selecting the optimal alert method by considering the user's device information. The generation AI is realized, for example, using a text generation AI (e.g., LLM) or natural language processing technology. Part or all of the above-mentioned processing in the alert unit is performed using the generation AI.

[0060] The alert unit can select the optimal alert method by considering the user's device information at the time of alert. The alert unit considers the user's device information, for example, using a generation AI. The generation AI learns the user's device information using deep learning technology and provides an appropriate alert method. For example, if the user is using a smartphone, the generation AI provides an alert method that matches the screen size. In addition, if the user is using a tablet, the generation AI can also provide an alert method optimized for a large screen. Furthermore, if the user is using a smartwatch, the generation AI can also provide a concise and highly visible alert method. This enables more appropriate alerts by selecting the optimal alert method by considering the user's device information. The generation AI is realized, for example, using a text generation AI (e.g., LLM) or natural language processing technology. Part or all of the above-mentioned processing in the alert unit is performed using the generation AI.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The AI ​​robot system can further include a nutrition management unit that manages the user's diet. The nutrition management unit records the user's dietary content and analyzes nutritional balance. For example, when the user eats a meal, the meal content is recorded in a photo, and the AI ​​analyzes the content and evaluates the balance of nutrients. The nutrition management unit can also propose an appropriate meal plan based on the user's health condition and goals. For example, if the user wants to go on a diet, a meal plan that takes calorie restrictions into consideration is provided. Furthermore, the nutrition management unit can analyze the user's dietary history and improve the long-term nutritional balance. This can comprehensively support the user's health maintenance.

[0063] The AI ​​robot system may further include a sleep management unit that manages the user's sleep. The sleep management unit monitors the user's sleep patterns and provides advice to promote high-quality sleep. For example, a sensor may detect the user's movements while sleeping and analyze the depth of sleep and frequency of interruptions. The sleep management unit may also make suggestions to optimize the user's sleep environment. For example, the sleep management unit may encourage appropriate adjustments to room temperature and lighting. Furthermore, the sleep management unit may analyze the user's sleep history and improve the quality of long-term sleep. This provides comprehensive support for maintaining the user's health.

[0064] The AI ​​robot system may further include an exchange support unit that promotes social exchanges between users. The exchange support unit supports communication between the user and friends and family. For example, it provides an interface that allows easy video calls and sending and receiving of messages. The exchange support unit may also suggest local events and club activities based on the user's interests and hobbies. For example, if the user likes music, it may introduce nearby music events. Furthermore, the exchange support unit may provide regular opportunities for exchanges to prevent the user from becoming socially isolated. This may support the user's mental health.

[0065] The AI ​​robot system may further include a hobby support unit that supports the user's hobby activities. The hobby support unit provides information and activities related to the user's hobby. For example, if the user likes gardening, the hobby support unit provides information on how to grow seasonal flowers and plants. The hobby support unit may also suggest events and workshops related to the user's hobby. For example, if the user is interested in painting, the hobby support unit may introduce nearby painting classes. Furthermore, the hobby support unit may record the user's hobby activities and manage their progress. This may improve the quality of the user's life.

[0066] The process flow of the first embodiment will be briefly described below.

[0067] Step 1: The conversation unit engages in conversation with the user based on the user's personal data. For example, the conversation unit uses a generation AI to provide topics based on the user's hobbies and past experiences. The generation AI learns the user's personal data using deep learning technology and generates appropriate conversation content. Step 2: The measurement unit measures the user's body temperature and blood pressure. For example, the measurement unit uses AI to analyze data from the thermometer and blood pressure monitor and monitor the user's health condition. Step 3: The instructor provides training guidance to the user. For example, the instructor uses the generation AI to provide a training program suitable for the user. The generation AI suggests appropriate exercise and stretching methods based on the user's personal data. Step 4: The detection unit detects a specific symptom or a specific behavior based on one or more of the information of the conversation result by the conversation unit, the measurement result by the measurement unit, and the instruction result by the instruction unit. For example, the detection unit detects a sudden rise in body temperature or an abnormality in the content of the conversation using AI. Step 5: The notification unit notifies specific contacts of the detection results by the detection unit. For example, the notification unit uses AI to forward the data to registered facilities, medical institutions, and relatives.

[0068] (Example 2) The system according to the embodiment of the present invention is a system that provides AI robots to elderly people living alone in nursing homes or at home. This system has learned the user's personal data, and will have conversations and ask questions that are unique to that person in order to prevent the decline of cognitive and physical functions. It also measures body temperature and blood pressure, and provides training guidance. Furthermore, if it recognizes any worrying symptoms or behavior, it has the function of transferring the data to registered facilities, medical institutions, and relatives. It also issues alerts such as the time to take medication and the room temperature. For example, the AI ​​robot will have a conversation based on the user's personal data. For example, it will provide topics based on the user's hobbies and past experiences to maintain cognitive function. Next, the AI ​​robot will measure the user's body temperature and blood pressure. This allows the health condition to be constantly monitored, and any abnormalities can be detected early. The AI ​​robot will also provide training guidance to the user. For example, it will teach the user simple exercises and stretching methods to maintain physical function. Furthermore, the AI ​​robot will detect specific symptoms and behaviors based on the results of the conversation, measurement results, and training guidance. For example, if the body temperature rises suddenly, or if there is an abnormality in the content of the conversation, etc. This information will be notified to registered facilities, medical institutions, and relatives. This allows appropriate measures to be taken early. The AI ​​robot also issues alerts regarding the time to take medication and the room temperature. For example, when it is time to take medication, an alert is sent, urging the user to take their medicine. Also, if the room temperature is not appropriate, an alert is sent, urging the user to adjust the temperature to an appropriate level. In this way, the AI ​​robot helps maintain the cognitive and physical functions of the elderly, and by constantly monitoring their health condition, provides an environment in which they can live with peace of mind. In this way, the AI ​​robot system helps maintain the cognitive and physical functions of the elderly, and by constantly monitoring their health condition.

[0069] The AI ​​robot system according to the embodiment includes a conversation unit, a measurement unit, a guidance unit, a detection unit, and a notification unit. The conversation unit has a conversation with the user based on the user's personal data. For example, the conversation unit uses a generation AI to provide topics based on the user's hobbies and past experiences. The generation AI learns the user's personal data, for example, using deep learning technology, and generates appropriate conversation content. The measurement unit measures the user's body temperature and blood pressure. For example, the measurement unit uses AI to analyze data from a thermometer and a blood pressure monitor and monitor the user's health condition. The guidance unit provides training guidance for the user. For example, the guidance unit uses the generation AI to provide a training program suitable for the user. The generation AI suggests appropriate exercise and stretching methods based on the user's personal data. The detection unit detects a specific symptom or a specific behavior based on one or more pieces of information among the conversation result by the conversation unit, the measurement result by the measurement unit, and the guidance result by the guidance unit. For example, the detection unit uses AI to detect a sudden rise in body temperature or an abnormality in the conversation content. The notification unit notifies a specific contact of the detection result by the detection unit. For example, the notification unit uses AI to forward data to registered facilities, medical institutions, and relatives, allowing the AI ​​robot system to help maintain the user's cognitive and physical functions and constantly monitor their health.

[0070] The conversation unit has a conversation with the user based on the user's personal data. For example, the conversation unit uses the generation AI to provide topics based on the user's hobbies and past experiences. The generation AI learns the user's personal data using, for example, deep learning technology to generate appropriate conversation content. Specifically, the generation AI collects data such as the user's past conversation history, social media posts, and survey results, and identifies the user's interests and concerns based on these data. Furthermore, the generation AI uses natural language processing technology to analyze the user's remarks in real time and generate appropriate responses. For example, if the user talks about a recent trip, the generation AI can provide information about the travel destination and develop related topics. In addition, the generation AI can analyze the user's emotional state and select appropriate tones and expressions to realize a more natural and friendly conversation. This allows the conversation unit to deepen communication with the user and improve the user's satisfaction. Furthermore, the conversation unit can collect user feedback and use it as learning data for the generation AI to continuously improve the quality of the conversation. For example, if a user shows interest in a particular topic, the information can be recorded and brought up again in the next conversation. This enables the conversation unit to respond flexibly to the needs of the user, and provide a more personalized service.

[0071] The measurement unit measures the user's body temperature and blood pressure. For example, the measurement unit uses AI to analyze data from a thermometer or blood pressure monitor and monitor the user's health condition. Specifically, the measurement unit uses a non-contact thermometer or wearable device to measure the user's body temperature in real time. This allows the user to naturally collect health data in their daily lives. For the blood pressure monitor, a device that can be easily measured by simply wrapping it around the arm is used to record blood pressure regularly. The AI ​​analyzes these data and detects abnormal values ​​and trends. For example, if the body temperature rises suddenly or the blood pressure is abnormally high, the AI ​​immediately analyzes the data and issues a warning to the user. In addition, the measurement unit can monitor changes in the user's health condition over the long term based on past data. This allows the user to understand their health condition and take necessary measures. Furthermore, the measurement unit can identify factors that affect the health condition by taking into account the user's lifestyle and environmental factors. For example, it analyzes the effects of stress and lack of sleep on blood pressure and provides specific advice to the user. This allows the measurement unit to support the user's health management and promote a healthier life.

[0072] The instructor provides training guidance to the user. For example, the instructor uses the generation AI to provide a training program suitable for the user. The generation AI suggests appropriate exercise and stretching methods based on the user's personal data. Specifically, the generation AI creates an individually customized training program by considering the user's age, gender, physical fitness level, health condition, and other data. For example, a variety of programs can be provided according to the user's needs, from light stretching for beginners to high-intensity training for advanced users. In addition, the generation AI analyzes the user's training history and monitors the progress. This allows the user to provide specific advice and motivation to achieve their goals. For example, if a user continues training for a certain period of time, the results are evaluated and advice is given to move on to the next step. In addition, the generation AI can collect user feedback and continuously improve the content of the training program. This allows the instructor to maximize the user's training effect and support health maintenance and physical fitness improvement. In addition, the instructor can monitor the form and movements during training in real time and instruct the user to exercise in the correct form. This minimizes the risk of injury and allows training to be performed safely.

[0073] The detection unit detects a specific symptom or a specific behavior based on one or more pieces of information among the conversation result by the conversation unit, the measurement result by the measurement unit, and the instruction result by the instruction unit. For example, the detection unit uses AI to detect a sudden rise in body temperature or an abnormality in the content of the conversation. Specifically, the AI ​​analyzes the collected data and identifies abnormal patterns and trends. For example, if the user's body temperature rises suddenly, the AI ​​immediately analyzes the data and detects the possibility of a fever. In addition, if the user repeatedly brings up the same topic in the conversation content or if there is a change in the choice of words, the AI ​​can detect a decline in cognitive function or a change in mental state. Furthermore, the training results by the instruction unit are analyzed to monitor changes in the user's athletic ability and physical strength. For example, if a user complains of abnormal fatigue during training, the AI ​​can detect excessive exercise or a deterioration in health based on the data. This allows the detection unit to detect changes in the user's health condition and cognitive function at an early stage and provide information for taking appropriate measures. Furthermore, the detection unit can also perform long-term risk assessments and trend analysis by utilizing past data and statistical information. This allows a comprehensive assessment of the user's health condition and allows preventative measures to be taken.

[0074] The notification unit notifies a specific contact of the detection result by the detection unit. For example, the notification unit uses AI to transfer data to registered facilities, medical institutions, and relatives. Specifically, the notification unit receives abnormality detection information from the detection unit and promptly notifies the pre-set contacts. For example, if the user's body temperature rises suddenly, the notification unit transmits the information to a medical institution to encourage appropriate response. In addition, if an abnormality is detected in the user's cognitive function, the notification unit can contact relatives or a nursing facility to request early response. Furthermore, the notification unit can reliably transmit information using multiple communication means. For example, important information is reliably delivered by using not only smartphone notifications but also voice calls, SMS, emails, etc. in combination. As a result, the notification unit can quickly and reliably transmit the user's health condition and changes in cognitive function to related parties and support appropriate response. Furthermore, the notification unit can encrypt data and control access to protect the user's privacy. As a result, the notification unit can realize safe and reliable information transmission and support the user's health management.

[0075] The alert unit can issue an alert of the time to take medicine or the room temperature. For example, the alert unit issues a voice alert when it is time to take medicine, prompting the user to take the medicine. In addition, when the room temperature is not appropriate, the alert unit can issue a text message or an app notification to prompt the user to adjust the temperature to an appropriate temperature. In this way, by issuing an alert of the time to take medicine or the room temperature, it is possible to support the user's health management. A part or all of the above-mentioned processing in the alert unit may be performed, for example, using AI, or may be performed without using AI. For example, the alert unit can input data of the time to take medicine or the room temperature into AI and cause the AI ​​to issue an alert.

[0076] The instruction unit can provide training guidance to the user based on one or more pieces of information among the conversation result by the conversation unit and the measurement result by the measurement unit. The instruction unit provides training guidance to the user based on, for example, the conversation result by the conversation unit. For example, the conversation unit provides topics based on the user's hobbies and interests, and the instruction unit proposes an appropriate training program based on the results. The instruction unit can also provide training guidance to the user based on the measurement results by the measurement unit. For example, the measurement unit measures the user's body temperature and blood pressure, and the instruction unit proposes appropriate exercise and stretching methods based on the results. As a result, training guidance can be provided to the user by providing training guidance based on the conversation result and the measurement result. A part or all of the above-mentioned processing in the instruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction unit can input data from the conversation unit and the measurement unit into AI and have AI generate the content of the training guidance.

[0077] The conversation unit can have a conversation with the user using a generation AI that has learned the user's personal data. The conversation unit, for example, uses the generation AI to provide topics based on the user's hobbies and past experiences. The generation AI learns the user's personal data using deep learning technology and generates appropriate conversation content. For example, the generation AI provides topics related to the user's hobbies based on information about the user's hobbies. The generation AI can also provide topics related to past events based on the user's past experiences. This makes it possible to provide individually appropriate conversations by using the generation AI that has learned the user's personal data. The generation AI is realized using, for example, a text generation AI (e.g., LLM) or natural language processing technology. Part or all of the above-mentioned processing in the conversation unit is performed using the generation AI.

[0078] The instruction unit can provide training guidance to the user using a generation AI that has learned the user's personal data. The instruction unit, for example, uses the generation AI to provide a training program suitable for the user. The generation AI learns the user's personal data using deep learning technology and suggests appropriate exercise and stretching methods. For example, the generation AI generates an appropriate training plan based on the user's health condition and exercise history. The generation AI can also dynamically adjust the training plan based on the user's feedback. As a result, by using the generation AI that has learned the user's personal data, it is possible to provide individually suitable training guidance. The generation AI is realized, for example, using a text generation AI (e.g., LLM) or natural language processing technology. A part or all of the above-mentioned processing in the instruction unit is performed using the generation AI.

[0079] The measurement unit can measure the user's body temperature and blood pressure using a generation AI that has learned the user's personal data. The measurement unit, for example, uses the generation AI to analyze data from a thermometer and a blood pressure monitor to monitor the user's health condition. The generation AI learns the user's personal data using deep learning technology and provides an appropriate measurement method. For example, the generation AI suggests an appropriate measurement frequency and timing based on the user's health condition and past measurement data. The generation AI can also detect abnormal values ​​and issue an early warning based on the measurement results. As a result, by using the generation AI that has learned the user's personal data, it is possible to measure the body temperature and blood pressure that are appropriate for each individual. The generation AI is realized using, for example, a text generation AI (e.g., LLM) or natural language processing technology. A part or all of the above-mentioned processing in the measurement unit is performed using the generation AI.

[0080] The conversation unit can estimate the user's emotion and select a conversation topic based on the estimated user's emotion. The conversation unit estimates the user's emotion using, for example, a generation AI. The generation AI estimates the user's emotion using facial expression recognition and voice analysis technology. For example, the generation AI analyzes the user's facial expression data and calculates an emotion score. The generation AI can also analyze the user's voice data and calculate an emotion score. This allows the conversation unit to select an appropriate conversation topic based on the estimated user's emotion. For example, if the user is sad, a topic related to past happy memories can be provided. Also, if the user is excited, new information or news that attracts the user's interest can be provided. Furthermore, if the user is relaxed, a deeper topic related to hobbies and interests can be provided. The emotion estimation is realized using, for example, an emotion engine or a generation AI, using an emotion estimation function. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the conversation unit is performed using the generation AI.

[0081] The conversation unit can analyze the user's past conversation history and select the optimal way to proceed with the conversation. The conversation unit can, for example, use a generation AI to analyze the user's past conversation history. The generation AI can use deep learning technology to learn the user's past conversation data and select the optimal way to proceed with the conversation. For example, the generation AI can take up topics that the user liked in the past again. The generation AI can also avoid topics that the user avoided in the past. Furthermore, the generation AI can also dig deeper into topics that the user talked about at length in the past. This makes it possible to provide a conversation progress method that is suitable for the user by analyzing the past conversation history. The generation AI can be realized, for example, using a text generation AI (e.g., LLM) or natural language processing technology. Part or all of the above-mentioned processing in the conversation unit is performed using the generation AI.

[0082] The conversation unit can analyze the user's reaction in real time during the conversation and dynamically adjust the content of the conversation. The conversation unit can, for example, use a generation AI to analyze the user's reaction in real time during the conversation. The generation AI can analyze the user's reaction using facial expression recognition and voice analysis technology. For example, the generation AI can analyze the user's facial expression data and calculate the degree of interest or boredom. The generation AI can also analyze the user's voice data and calculate the degree of interest or boredom. This allows the conversation unit to dynamically adjust the content of the conversation based on the user's reaction. For example, if the user shows interest, the topic can be further explored. Also, if the user seems bored, the topic can be changed. Furthermore, if the user is confused, the topic can be simplified. The generation AI can be, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the conversation unit is performed using the generation AI.

[0083] The conversation unit can estimate the user's emotion and adjust the tempo of the conversation based on the estimated user's emotion. The conversation unit estimates the user's emotion using, for example, a generation AI. The generation AI estimates the user's emotion using facial expression recognition and voice analysis technology. For example, the generation AI analyzes the user's facial expression data and calculates an emotion score. The generation AI can also analyze the user's voice data and calculate an emotion score. This allows the conversation unit to adjust the tempo of the conversation based on the estimated user's emotion. For example, if the user is in a hurry, the tempo of the conversation can be made faster. Also, if the user is relaxed, the tempo of the conversation can be made slower. Furthermore, if the user is tired, the tempo of the conversation can be made gentler. The estimation of the emotion is realized using, for example, an emotion engine or a generation AI, using an emotion estimation function. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the conversation unit is performed using the generation AI.

[0084] The conversation unit can provide relevant information based on the user's hobbies and interests during the conversation. The conversation unit provides relevant information based on the user's hobbies and interests using, for example, a generation AI. The generation AI learns the user's personal data using deep learning technology and generates appropriate information. For example, if the user likes music, the generation AI provides information on new albums. Also, if the user likes gardening, the generation AI can provide information on seasonal flowers. Furthermore, if the user likes traveling, the generation AI can provide information on travel destinations. This improves the quality of the conversation by providing relevant information based on the user's hobbies and interests. The generation AI is realized using, for example, a text generation AI (e.g., LLM) or natural language processing technology. Part or all of the above-mentioned processing in the conversation unit is performed using the generation AI.

[0085] The conversation unit can provide topics related to the region during the conversation, taking into account the geographical location information of the user. The conversation unit provides topics related to the region, taking into account the geographical location information of the user, for example, using a generation AI. The generation AI learns the user's location information using deep learning technology and generates appropriate topics. For example, the generation AI provides event information for the region where the user lives. The generation AI can also provide weather information for the region where the user lives. Furthermore, the generation AI can also provide news for the region where the user lives. This improves the quality of the conversation by providing topics related to the region, taking into account the geographical location information of the user. The generation AI is realized, for example, using a text generation AI (e.g., LLM) or natural language processing technology. A part or all of the above-mentioned processing in the conversation unit is performed using the generation AI.

[0086] The measurement unit can estimate the user's emotion and adjust the frequency of measurement based on the estimated user's emotion. The measurement unit estimates the user's emotion using, for example, a generation AI. The generation AI estimates the user's emotion using facial expression recognition and voice analysis technology. For example, the generation AI analyzes the user's facial expression data and calculates an emotion score. The generation AI can also analyze the user's voice data and calculate an emotion score. This allows the measurement unit to adjust the frequency of measurement based on the estimated user's emotion. For example, if the user is feeling stressed, the frequency of measurement can be reduced. Also, if the user is relaxed, the frequency of measurement can be increased. Furthermore, if the user is in poor health, the frequency of measurement can be increased. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the measurement unit is performed using the generation AI.

[0087] The measurement unit can analyze the user's past measurement data and perform early detection of abnormal values. The measurement unit can analyze the user's past measurement data, for example, using a generation AI. The generation AI can learn the user's past measurement data using deep learning technology and perform early detection of abnormal values. For example, the generation AI can analyze the user's past blood pressure data and detect abnormal increases. The generation AI can also analyze the user's past body temperature data and detect abnormal fluctuations. Furthermore, the generation AI can also comprehensively analyze the user's past measurement data and detect abnormalities early. This makes it possible to detect abnormal values ​​early by analyzing past measurement data. The generation AI can be realized, for example, using a text generation AI (for example, LLM) or natural language processing technology. A part or all of the above-mentioned processing in the measurement unit is performed using the generation AI.

[0088] The measurement unit can adjust the measurement method taking into account the user's physical condition and environmental conditions during measurement. The measurement unit adjusts the measurement method taking into account the user's physical condition and environmental conditions, for example, using a generation AI. The generation AI uses deep learning technology to learn the user's physical condition and environmental conditions and provide an appropriate measurement method. For example, the generation AI simplifies the measurement method when the user is in poor health. In addition, the generation AI can also perform detailed measurements when the user is relaxed. Furthermore, the generation AI can also adjust the measurement method when the user's environment is noisy. As a result, more accurate measurements are possible by adjusting the measurement method taking into account the user's physical condition and environmental conditions. The generation AI is realized, for example, using a text generation AI (e.g., LLM) or natural language processing technology. Part or all of the above-mentioned processing in the measurement unit is performed using the generation AI.

[0089] The measurement unit can estimate the user's emotion and adjust the display method of the measurement result based on the estimated user's emotion. The measurement unit estimates the user's emotion, for example, using a generation AI. The generation AI estimates the user's emotion using facial expression recognition and voice analysis technology. For example, the generation AI analyzes the user's facial expression data and calculates an emotion score. The generation AI can also analyze the user's voice data and calculate an emotion score. This allows the measurement unit to adjust the display method of the measurement result based on the estimated user's emotion. For example, if the user is nervous, a simple and highly visible display method can be provided. Also, if the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the measurement unit is performed using the generation AI.

[0090] The measurement unit can correct the measurement result by referring to the lifestyle habit data of the user during measurement. The measurement unit refers to the lifestyle habit data of the user using, for example, a generation AI. The generation AI learns the lifestyle habit data of the user using deep learning technology and provides an appropriate measurement result. For example, the generation AI corrects the measurement result when the user has exercised. The generation AI can also correct the measurement result when the user has eaten. Furthermore, the generation AI can also correct the measurement result when the user is sleep deprived. This allows for more accurate measurement by correcting the measurement result by referring to the lifestyle habit data of the user. The generation AI is realized using, for example, a text generation AI (e.g., LLM) or natural language processing technology. A part or all of the above-mentioned processing in the measurement unit is performed using the generation AI.

[0091] The measurement unit can correct the environmental conditions by taking into account the geographical location information of the user during measurement. The measurement unit, for example, uses a generation AI to consider the geographical location information of the user. The generation AI uses deep learning technology to learn the user's location information and provide appropriate environmental conditions. For example, the generation AI corrects the measurement results when the user is at high altitude. The generation AI can also correct the measurement results when the user is in a humid place. Furthermore, the generation AI can also correct the measurement results when the user is in a cold area. This enables more accurate measurement by correcting the environmental conditions by taking into account the geographical location information of the user. The generation AI is realized, for example, using a text generation AI (e.g., LLM) or natural language processing technology. Part or all of the above-mentioned processing in the measurement unit is performed using the generation AI.

[0092] The coaching unit can estimate the user's emotion and adjust the training content based on the estimated user's emotion. The coaching unit estimates the user's emotion using, for example, a generation AI. The generation AI estimates the user's emotion using facial expression recognition and voice analysis technology. For example, the generation AI analyzes the user's facial expression data and calculates an emotion score. The generation AI can also analyze the user's voice data and calculate an emotion score. This allows the coaching unit to adjust the training content based on the estimated user's emotion. For example, if the user is tired, light stretching can be suggested. Also, if the user is energetic, a slightly harder training can be suggested. Furthermore, if the user is relaxed, yoga or meditation can be suggested. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the coaching unit is performed using the generation AI.

[0093] The instruction unit can analyze the user's past training history and provide an optimal training plan. The instruction unit can analyze the user's past training history, for example, using a generation AI. The generation AI can learn the user's past training data using deep learning technology and provide an optimal training plan. For example, the generation AI can propose an optimal plan based on the training the user has performed in the past. The generation AI can also propose effective training from the user's past training history. Furthermore, the generation AI can also analyze the user's past training history and provide a balanced plan. As a result, a training plan suitable for the user can be provided by analyzing the past training history. The generation AI can be realized, for example, using a text generation AI (for example, LLM) or natural language processing technology. A part or all of the above-mentioned processing in the instruction unit is performed using the generation AI.

[0094] The instructor can analyze the user's motion in real time during training and dynamically adjust the instruction content. The instructor can, for example, use a generation AI to analyze the user's motion in real time during training. The generation AI can analyze the user's motion using facial expression recognition and motion analysis technology. For example, the generation AI can analyze the user's motion data and evaluate the accuracy and smoothness. This allows the instructor to dynamically adjust the instruction content based on the user's motion. For example, if the user's motion is incorrect, a correction instruction can be issued. Also, if the user's motion is not smooth, it can be changed to a simpler motion. Furthermore, if the user's motion is accurate, it can proceed to the next step. The generation AI can be, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the instructor is performed using the generation AI.

[0095] The instructor can estimate the user's emotion and adjust the training progress speed based on the estimated user's emotion. The instructor, for example, uses a generation AI to estimate the user's emotion. The generation AI estimates the user's emotion using facial expression recognition and voice analysis technology. For example, the generation AI analyzes the user's facial expression data and calculates an emotion score. The generation AI can also analyze the user's voice data and calculate an emotion score. This allows the instructor to adjust the training progress speed based on the estimated user's emotion. For example, if the user is in a hurry, the training progress speed is increased. Also, if the user is relaxed, the training progress speed can be slowed down. Furthermore, if the user is tired, the training progress speed can be gentled down. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the instructor is performed using the generation AI.

[0096] The instructor can suggest related activities based on the user's hobbies and interests during training. The instructor uses, for example, a generation AI to suggest related activities based on the user's hobbies and interests. The generation AI learns the user's personal data using deep learning technology and generates appropriate activities. For example, if the user likes music, the generation AI suggests exercises that match the music. If the user likes gardening, the generation AI can also suggest stretches related to gardening. Furthermore, if the user likes traveling, the generation AI can also suggest exercises related to travel. This improves the quality of training by suggesting related activities based on the user's hobbies and interests. The generation AI is realized, for example, using a text generation AI (e.g., LLM) or natural language processing technology. Part or all of the above-mentioned processing in the instructor is performed using the generation AI.

[0097] The instruction unit can suggest activities related to the area taking into account the geographical location information of the user during training. The instruction unit, for example, uses a generation AI to suggest activities related to the area taking into account the geographical location information of the user. The generation AI learns the user's location information using deep learning technology and generates appropriate activities. For example, the generation AI suggests exercises that can be done in a park in the area where the user lives. The generation AI can also suggest exercises that match the climate of the area where the user lives. Furthermore, the generation AI can also suggest exercises related to events in the area where the user lives. As a result, the quality of training is improved by suggesting activities related to the area taking into account the geographical location information of the user. The generation AI is realized, for example, using a text generation AI (e.g., LLM) or natural language processing technology. A part or all of the above-mentioned processing in the instruction unit is performed using the generation AI.

[0098] The detection unit can estimate the user's emotion and adjust the anomaly detection criteria based on the estimated user's emotion. The detection unit estimates the user's emotion using, for example, a generation AI. The generation AI estimates the user's emotion using facial expression recognition and voice analysis technology. For example, the generation AI analyzes the user's facial expression data and calculates an emotion score. The generation AI can also analyze the user's voice data and calculate an emotion score. This allows the detection unit to adjust the anomaly detection criteria based on the estimated user's emotion. For example, when the user is stressed, the anomaly detection criteria can be relaxed. Also, when the user is relaxed, the anomaly detection criteria can be tightened. Furthermore, when the user is in poor physical condition, the anomaly detection criteria can be relaxed. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the detection unit is performed using the generation AI.

[0099] The detection unit can analyze the user's past data and detect abnormalities early. The detection unit can analyze the user's past data using, for example, a generation AI. The generation AI can learn the user's past data using deep learning technology and detect abnormalities early. For example, the generation AI can analyze the user's past body temperature data and detect abnormal fluctuations early. The generation AI can also analyze the user's past blood pressure data and detect abnormal increases early. Furthermore, the generation AI can analyze the user's past conversation data and detect cognitive function decline early. This makes it possible to detect abnormalities early by analyzing past data. The generation AI can be realized using, for example, a text generation AI (e.g., LLM) or natural language processing technology. A part or all of the above-mentioned processing in the detection unit is performed using the generation AI.

[0100] The detection unit can adjust the detection method taking into account the user's physical condition and environmental conditions at the time of detection. The detection unit, for example, uses a generation AI to take into account the user's physical condition and environmental conditions. The generation AI uses deep learning technology to learn the user's physical condition and environmental conditions and provide an appropriate detection method. For example, the generation AI simplifies the detection method when the user is in poor health. The generation AI can also perform detailed detection when the user is relaxed. Furthermore, the generation AI can also adjust the detection method when the user's environment is noisy. As a result, more accurate detection is possible by adjusting the detection method taking into account the user's physical condition and environmental conditions. The generation AI is realized, for example, using a text generation AI (e.g., LLM) or natural language processing technology. A part or all of the above-mentioned processing in the detection unit is performed using the generation AI.

[0101] The detection unit can estimate the user's emotion and adjust the display method of the anomaly detection result based on the estimated user's emotion. The detection unit estimates the user's emotion using, for example, a generation AI. The generation AI estimates the user's emotion using facial expression recognition and voice analysis technology. For example, the generation AI analyzes the user's facial expression data and calculates an emotion score. The generation AI can also analyze the user's voice data and calculate an emotion score. This allows the detection unit to adjust the display method of the anomaly detection result based on the estimated user's emotion. For example, when the user is nervous, a simple and highly visible display method can be provided. Also, when the user is relaxed, a display method including detailed information can be provided. Furthermore, when the user is in a hurry, a display method that focuses on the main points can be provided. The emotion estimation is realized using, for example, an emotion engine or a generation AI using an emotion estimation function. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the detection unit is performed using the generation AI.

[0102] The detection unit can correct the abnormality detection result by referring to the lifestyle data of the user at the time of detection. The detection unit, for example, uses a generation AI to refer to the lifestyle data of the user. The generation AI uses deep learning technology to learn the lifestyle data of the user and provide an appropriate abnormality detection result. For example, the generation AI corrects the abnormality detection result when the user has exercised. The generation AI can also correct the abnormality detection result when the user has eaten. Furthermore, the generation AI can also correct the abnormality detection result when the user is sleep deprived. This allows for more accurate detection by correcting the abnormality detection result by referring to the lifestyle data of the user. The generation AI is realized, for example, using a text generation AI (for example, LLM) or natural language processing technology. A part or all of the above-mentioned processing in the detection unit is performed using the generation AI.

[0103] The detection unit can correct the environmental conditions in consideration of the geographical location information of the user at the time of detection. The detection unit, for example, uses a generation AI to consider the geographical location information of the user. The generation AI uses deep learning technology to learn the user's location information and provide appropriate environmental conditions. For example, the generation AI corrects the abnormality detection result when the user is at high altitude. The generation AI can also correct the abnormality detection result when the user is in a humid place. Furthermore, the generation AI can also correct the abnormality detection result when the user is in a cold place. This enables more accurate detection by correcting the environmental conditions in consideration of the geographical location information of the user. The generation AI is realized, for example, using a text generation AI (e.g., LLM) or natural language processing technology. A part or all of the above-mentioned processing in the detection unit is performed using the generation AI.

[0104] The notification unit can estimate the user's emotion and adjust the notification content based on the estimated user's emotion. The notification unit estimates the user's emotion using, for example, a generation AI. The generation AI estimates the user's emotion using facial expression recognition and voice analysis technology. For example, the generation AI analyzes the user's facial expression data and calculates an emotion score. The generation AI can also analyze the user's voice data and calculate an emotion score. This allows the notification unit to adjust the notification content based on the estimated user's emotion. For example, when the user is nervous, a simple notification content with high visibility is provided. When the user is relaxed, a notification content including detailed information can also be provided. Furthermore, when the user is in a hurry, a notification content that focuses on the main points can also be provided. The emotion estimation is realized using, for example, an emotion engine or a generation AI using an emotion estimation function. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the notification unit is performed using the generation AI.

[0105] The notification unit can select the optimal notification method by referring to the user's past notification history at the time of notification. The notification unit, for example, uses a generation AI to refer to the user's past notification history. The generation AI uses deep learning technology to learn the user's past notification data and provide the optimal notification method. For example, the generation AI uses again a notification method that the user previously preferred. The generation AI can also avoid notification methods that the user previously avoided. Furthermore, the generation AI can also analyze the user's past notification history and select the optimal notification method. As a result, by referring to the past notification history, a notification method suitable for the user can be provided. The generation AI is realized, for example, using a text generation AI (for example, LLM) or natural language processing technology. A part or all of the above-mentioned processing in the notification unit is performed using the generation AI.

[0106] The notification unit can estimate the user's emotion and adjust the timing of the notification based on the estimated user's emotion. The notification unit estimates the user's emotion using, for example, a generation AI. The generation AI estimates the user's emotion using facial expression recognition and voice analysis technology. For example, the generation AI analyzes the user's facial expression data and calculates an emotion score. The generation AI can also analyze the user's voice data and calculate an emotion score. This allows the notification unit to adjust the timing of the notification based on the estimated user's emotion. For example, if the user is nervous, the timing of the notification is delayed. Also, if the user is relaxed, the timing of the notification can be advanced. Furthermore, if the user is in a hurry, the timing of the notification can be adjusted. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the notification unit is performed using the generation AI.

[0107] The notification unit can select the optimal notification method by considering the user's device information at the time of notification. The notification unit considers the user's device information, for example, using a generation AI. The generation AI learns the user's device information using deep learning technology and provides an appropriate notification method. For example, if the user is using a smartphone, the generation AI provides a notification method that matches the screen size. In addition, if the user is using a tablet, the generation AI can also provide a notification method optimized for a large screen. Furthermore, if the user is using a smartwatch, the generation AI can also provide a simple and highly visible notification method. This enables more appropriate notification by selecting the optimal notification method by considering the user's device information. The generation AI is realized, for example, using a text generation AI (e.g., LLM) or natural language processing technology. Part or all of the above-mentioned processing in the notification unit is performed using the generation AI.

[0108] The alert unit can estimate the user's emotion and adjust the content of the alert based on the estimated user's emotion. The alert unit estimates the user's emotion using, for example, a generation AI. The generation AI estimates the user's emotion using facial expression recognition and voice analysis technology. For example, the generation AI analyzes the user's facial expression data and calculates an emotion score. The generation AI can also analyze the user's voice data and calculate an emotion score. This allows the alert unit to adjust the content of the alert based on the estimated user's emotion. For example, if the user is nervous, a simple and highly visible alert content can be provided. Also, if the user is relaxed, an alert content including detailed information can be provided. Furthermore, if the user is in a hurry, an alert content that focuses on the main points can be provided. The emotion estimation is realized using, for example, an emotion engine or a generation AI using an emotion estimation function. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the alert unit is performed using the generation AI.

[0109] The alert unit can select the optimal alert method by referring to the user's past alert history at the time of alert. The alert unit, for example, uses a generation AI to refer to the user's past alert history. The generation AI uses deep learning technology to learn the user's past alert data and provide the optimal alert method. For example, the generation AI uses again an alert method that the user preferred in the past. The generation AI can also avoid alert methods that the user avoided in the past. Furthermore, the generation AI can also analyze the user's past alert history and select the optimal alert method. As a result, by referring to the past alert history, an alert method suitable for the user can be provided. The generation AI is realized, for example, using a text generation AI (for example, LLM) or natural language processing technology. A part or all of the above-mentioned processing in the alert unit is performed using the generation AI.

[0110] The alert unit can estimate the user's emotion and adjust the timing of the alert based on the estimated user's emotion. The alert unit estimates the user's emotion using, for example, a generation AI. The generation AI estimates the user's emotion using facial expression recognition and voice analysis technology. For example, the generation AI analyzes the user's facial expression data and calculates an emotion score. The generation AI can also analyze the user's voice data and calculate an emotion score. This allows the alert unit to adjust the timing of the alert based on the estimated user's emotion. For example, if the user is nervous, the timing of the alert is delayed. Also, if the user is relaxed, the timing of the alert can be advanced. Furthermore, if the user is in a hurry, the timing of the alert can be adjusted. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the alert unit is performed using the generation AI.

[0111] The alert unit can select the optimal alert method by considering the user's device information at the time of alert. The alert unit considers the user's device information, for example, using a generation AI. The generation AI learns the user's device information using deep learning technology and provides an appropriate alert method. For example, if the user is using a smartphone, the generation AI provides an alert method that matches the screen size. In addition, if the user is using a tablet, the generation AI can also provide an alert method optimized for a large screen. Furthermore, if the user is using a smartwatch, the generation AI can also provide a concise and highly visible alert method. This enables more appropriate alerts by selecting the optimal alert method by considering the user's device information. The generation AI is realized, for example, using a text generation AI (e.g., LLM) or natural language processing technology. Part or all of the above-mentioned processing in the alert unit is performed using the generation AI.

[0112] The alert unit can select the optimal alert method by considering the user's device information at the time of alert. The alert unit considers the user's device information, for example, using a generation AI. The generation AI learns the user's device information using deep learning technology and provides an appropriate alert method. For example, if the user is using a smartphone, the generation AI provides an alert method that matches the screen size. In addition, if the user is using a tablet, the generation AI can also provide an alert method optimized for a large screen. Furthermore, if the user is using a smartwatch, the generation AI can also provide a concise and highly visible alert method. This enables more appropriate alerts by selecting the optimal alert method by considering the user's device information. The generation AI is realized, for example, using a text generation AI (e.g., LLM) or natural language processing technology. Part or all of the above-mentioned processing in the alert unit is performed using the generation AI.

[0113] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0114] The AI ​​robot system can further include a nutrition management unit that manages the user's diet. The nutrition management unit records the user's dietary content and analyzes nutritional balance. For example, when the user eats a meal, the meal content is recorded in a photo, and the AI ​​analyzes the content and evaluates the balance of nutrients. The nutrition management unit can also propose an appropriate meal plan based on the user's health condition and goals. For example, if the user wants to go on a diet, a meal plan that takes calorie restrictions into consideration is provided. Furthermore, the nutrition management unit can analyze the user's dietary history and improve the long-term nutritional balance. This can comprehensively support the user's health maintenance.

[0115] The AI ​​robot system may further include a sleep management unit that manages the user's sleep. The sleep management unit monitors the user's sleep patterns and provides advice to promote high-quality sleep. For example, a sensor may detect the user's movements while sleeping and analyze the depth of sleep and frequency of interruptions. The sleep management unit may also make suggestions to optimize the user's sleep environment. For example, the sleep management unit may encourage appropriate adjustments to room temperature and lighting. Furthermore, the sleep management unit may analyze the user's sleep history and improve the quality of long-term sleep. This provides comprehensive support for maintaining the user's health.

[0116] The AI ​​robot system may further include an exchange support unit that promotes social exchanges between users. The exchange support unit supports communication between the user and friends and family. For example, it provides an interface that allows easy video calls and sending and receiving of messages. The exchange support unit may also suggest local events and club activities based on the user's interests and hobbies. For example, if the user likes music, it may introduce nearby music events. Furthermore, the exchange support unit may provide regular opportunities for exchanges to prevent the user from becoming socially isolated. This may support the user's mental health.

[0117] The AI ​​robot system may further include a hobby support unit that supports the user's hobby activities. The hobby support unit provides information and activities related to the user's hobby. For example, if the user likes gardening, the hobby support unit provides information on how to grow seasonal flowers and plants. The hobby support unit may also suggest events and workshops related to the user's hobby. For example, if the user is interested in painting, the hobby support unit may introduce nearby painting classes. Furthermore, the hobby support unit may record the user's hobby activities and manage their progress. This may improve the quality of the user's life.

[0118] The AI ​​robot system may further include a music providing unit that estimates the user's emotions and selects music based on the estimated user's emotions. The music providing unit estimates the user's emotions and provides music that matches the emotions. For example, if the user is relaxed, calm music is played. Also, if the user wants to cheer up, up-tempo music can be provided. Furthermore, the music providing unit can learn the user's music preferences and past playback history, and individually suggest the most suitable music. This makes it possible to support the maintenance of mental health by providing music that matches the user's emotions.

[0119] The AI ​​robot system may further include a relaxation support unit that estimates the user's emotions and suggests a relaxation method based on the estimated user's emotions. The relaxation support unit estimates the user's emotions and suggests a relaxation method according to the emotions. For example, if the user is feeling stressed, it may suggest deep breathing or meditation. Also, if the user is relaxed, it may suggest light stretching or yoga. Furthermore, the relaxation support unit may learn the user's past relaxation history and suggest the optimal method for each individual. This makes it possible to support the maintenance of mental health by providing a relaxation method according to the user's emotions.

[0120] The AI ​​robot system can further include a reading support unit that estimates the user's emotions and suggests books that match the user's emotions based on the estimated user's emotions. The reading support unit estimates the user's emotions and suggests books that match the emotions. For example, if the user is relaxed, it can suggest a calming story. Also, if the user wants to cheer up, it can suggest a book that includes an encouraging message. Furthermore, the reading support unit can learn the user's reading history and interests and suggest the most suitable book for each user. This makes it possible to support the maintenance of mental health by providing a reading experience that matches the user's emotions.

[0121] The AI ​​robot system may further include an exercise support unit that estimates the user's emotions and suggests exercises based on the estimated user's emotions. The exercise support unit estimates the user's emotions and suggests exercises according to those emotions. For example, if the user is feeling stressed, it may suggest yoga or stretching that has a relaxing effect. Also, if the user wants to cheer up, it may suggest energetic exercises. Furthermore, the exercise support unit may learn the user's exercise history and health condition and suggest optimal exercises for each user. This makes it possible to support the maintenance of physical and mental health by providing exercises according to the user's emotions.

[0122] The AI ​​robot system may further include an activity support unit that estimates the user's emotions and suggests daily activities based on the estimated user's emotions. The activity support unit estimates the user's emotions and suggests activities according to the emotions. For example, if the user is feeling relaxed, it may suggest walking or gardening. Also, if the user wants to cheer up, it may suggest dancing or sports. Furthermore, the activity support unit may learn the user's past activity history and interests and suggest the most suitable activity for each user. This makes it possible to support the maintenance of mental and physical health by providing activities according to the user's emotions.

[0123] The AI ​​robot system may further include a news providing unit that estimates the user's emotions and provides news based on the estimated user's emotions. The news providing unit estimates the user's emotions and provides news that matches the emotions. For example, if the user is relaxed, it provides calm news and positive topics. Also, if the user is excited, it can provide interesting, up-to-date news. Furthermore, the news providing unit can learn the user's news preferences and past browsing history, and individually suggest optimal news. This can improve the quality of information provision by providing news according to the user's emotions.

[0124] The process flow of the second embodiment will be briefly described below.

[0125] Step 1: The conversation unit engages in conversation with the user based on the user's personal data. For example, the conversation unit uses a generation AI to provide topics based on the user's hobbies and past experiences. The generation AI learns the user's personal data using deep learning technology and generates appropriate conversation content. Step 2: The measurement unit measures the user's body temperature and blood pressure. For example, the measurement unit uses AI to analyze data from the thermometer and blood pressure monitor and monitor the user's health condition. Step 3: The instructor provides training guidance to the user. For example, the instructor uses the generation AI to provide a training program suitable for the user. The generation AI suggests appropriate exercise and stretching methods based on the user's personal data. Step 4: The detection unit detects a specific symptom or a specific behavior based on one or more of the information of the conversation result by the conversation unit, the measurement result by the measurement unit, and the instruction result by the instruction unit. For example, the detection unit detects a sudden rise in body temperature or an abnormality in the content of the conversation using AI. Step 5: The notification unit notifies specific contacts of the detection results by the detection unit. For example, the notification unit uses AI to forward the data to registered facilities, medical institutions, and relatives.

[0126] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0127] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0128] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0129] For example, the conversation 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 generation AI is realized by the specific processing unit 290 of the data processing device 12, and generates appropriate conversation content based on the user's personal data. The measurement unit measures body temperature and blood pressure using, for example, the camera 42 or a sensor of the smart device 14, and analyzes the measured values ​​by the control unit 46A or the specific processing unit 290 of the data processing device 12. The instruction unit is realized by, for example, the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and provides a training program suitable for the user. The detection unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and detects specific symptoms and behaviors based on the conversation results, measurement results, and instruction results. The notification unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and notifies the registered facility, medical institution, and relatives of the detection results. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0130] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0131] 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.

[0132] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0133] 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.

[0134] 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 the voice according to instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0136] 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 performed in a secure state.

[0137] Fig. 4 shows an example of 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.

[0138] 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.

[0139] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0140] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 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 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0141] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0142] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0143] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0144] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0145] For example, the conversation 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 generation AI is realized by the specific processing unit 290 of the data processing device 12, and generates appropriate conversation content based on the user's personal data. The measurement unit, for example, measures body temperature and blood pressure using the camera 42 or a sensor of the smart glasses 214, and analyzes the measured values ​​by the control unit 46A or the specific processing unit 290 of the data processing device 12. The instruction unit, for example, is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and provides a training program suitable for the user. The detection unit, for example, is realized by the specific processing unit 290 of the data processing device 12, and detects specific symptoms and behaviors based on the conversation results, measurement results, and instruction results. The notification unit, for example, is realized by the specific processing unit 290 of the data processing device 12, and notifies the registered facility, medical institution, and relatives of the detection results. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0146] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0147] 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.

[0148] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0149] 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.

[0150] 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 the voice according to instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0152] 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 performed in a secure state.

[0153] 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.

[0154] 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.

[0155] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0156] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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.

[0157] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0158] 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 voice indicating a user input for 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.

[0159] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0160] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0161] For example, the conversation unit is realized by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the generation AI is realized by the specific processing unit 290 of the data processing device 12, and generates appropriate conversation content based on the user's personal data. The measurement unit measures body temperature and blood pressure using the camera 42 or a sensor of the headset terminal 314, and analyzes the measured values ​​by the control unit 46A or the specific processing unit 290 of the data processing device 12. The instruction unit is realized by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12, and provides a training program suitable for the user. The detection unit is realized by the specific processing unit 290 of the data processing device 12, and detects specific symptoms and behaviors based on the conversation results, measurement results, and instruction results. The notification unit is realized by the specific processing unit 290 of the data processing device 12, and notifies the registered facility, medical institution, and relatives of the detection results. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0162] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0163] 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.

[0164] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0165] 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.

[0166] 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 the voice according to instructions from the processor 46.

[0167] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0168] 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 performed in a secure state.

[0169] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0170] 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.

[0171] 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.

[0172] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0173] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0174] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0175] 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 a voice indicating a user input for 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.

[0176] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0177] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0178] For example, the conversation 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 generation AI is realized by the specific processing unit 290 of the data processing device 12, and generates appropriate conversation content based on the user's personal data. The measurement unit measures body temperature and blood pressure using, for example, the camera 42 or a sensor of the robot 414, and analyzes the measured values ​​by the control unit 46A or the specific processing unit 290 of the data processing device 12. The instruction unit is realized by, for example, the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and provides a training program suitable for the user. The detection unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and detects specific symptoms and behaviors based on the conversation results, measurement results, and instruction results. The notification unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and notifies the registered facility, medical institution, and relatives of the detection results. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0179] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0180] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0181] These emotions are distributed in the three o'clock direction of emotion map 400 and usually 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.

[0182] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0183] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0184] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0185] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0186] 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.

[0187] 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 Universal Serial Bus (USB) 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.

[0188] In addition, 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 upon request from the data processing device 12.

[0189] 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.

[0190] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0191] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0192] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0193] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The above specific processes are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0194] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0195] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0196] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0197] (Appendix 1) A conversation unit that conducts a conversation with a user based on personal data of the user; A measurement unit for measuring a body temperature and a blood pressure of the user; A training unit that provides training guidance to the user; A detection unit that detects a specific symptom or a specific behavior based on one or more pieces of information among a conversation result by the conversation unit, a measurement result by the measurement unit, and a guidance result by the guidance unit; A notification unit that notifies a specific contact of the detection result by the detection unit. A system characterized by: (Appendix 2) Equipped with an alert unit that issues alerts regarding medication time or room temperature 2. The system of claim 1, (Appendix 3) The leadership team: providing training guidance to the user based on at least one of information on the conversation result by the conversation unit and the measurement result by the measurement unit; 2. The system of claim 1, (Appendix 4) The conversation unit includes: A conversation is held with the user using a generation AI that has learned the user's personal data. 2. The system of claim 1, (Appendix 5) The leadership team: A generation AI that has learned the user's personal data is used to provide training guidance to the user. 2. The system of claim 1, (Appendix 6) The measurement unit includes: Measure the user's body temperature and blood pressure using a generation AI that has learned the user's personal data. 2. The system of claim 1, (Appendix 7) The conversation unit includes: Estimate user emotions and select conversation topics based on the estimated user emotions 2. The system of claim 1, (Appendix 8) The conversation unit includes: Analyze the user's past conversation history and choose the best way to proceed with the conversation 2. The system of claim 1, (Appendix 9) The conversation unit includes: Analyze user responses in real time during a conversation and dynamically adjust the content of the conversation 2. The system of claim 1, (Appendix 10) The conversation unit includes: Estimate the user's emotions and adjust the tempo of the conversation based on the estimated user's emotions 2. The system of claim 1, (Appendix 11) The conversation unit includes: Providing relevant information based on the user's hobbies and interests during conversations 2. The system of claim 1, (Appendix 12) The conversation unit includes: Provide local topics during conversations by taking into account the user's geographic location 2. The system of claim 1, (Appendix 13) The measurement unit includes: Inferring a user's emotion and adjusting the frequency of measurements based on the estimated user's emotion 2. The system of claim 1, (Appendix 14) The measurement unit includes: Analyze the user's past measurement data and detect abnormal values ​​early 2. The system of claim 1, (Appendix 15) The measurement unit includes: Adjust the measurement method taking into account the user's physical condition and environmental conditions during measurement 2. The system of claim 1, (Appendix 16) The measurement unit includes: Inferring user emotion and adjusting the display method of measurement results based on the estimated user emotion 2. The system of claim 1, (Appendix 17) The measurement unit includes: The measurement results are corrected by referring to the user's lifestyle data during measurement. 2. The system of claim 1, (Appendix 18) The measurement unit includes: Compensate for environmental conditions by taking into account the user's geographic location during measurements 2. The system of claim 1, (Appendix 19) The leadership team: Estimate user emotions and adjust training content based on the estimated user emotions 2. The system of claim 1, (Appendix 20) The leadership team: Analyze the user's past training history and provide the optimal training plan 2. The system of claim 1, (Appendix 21) The leadership team: Analyze user movements in real time during training and dynamically adjust training content 2. The system of claim 1, (Appendix 22) The leadership team: Estimate the user's emotions and adjust the training progress speed based on the estimated user's emotions. 2. The system of claim 1, (Appendix 23) The leadership team: Suggest relevant activities during training based on the user's hobbies and interests 2. The system of claim 1, (Appendix 24) The leadership team: Considers the user's geographic location while working out to suggest locally relevant activities 2. The system of claim 1, (Appendix 25) The detection unit is Infer user sentiment and adjust anomaly detection criteria based on the inferred user sentiment 2. The system of claim 1, (Appendix 26) The detection unit is Analyze users' past data and detect anomalies early 2. The system of claim 1, (Appendix 27) The detection unit is Adjust the detection method taking into account the user's physical condition and environmental conditions during detection 2. The system of claim 1, (Appendix 28) The detection unit is Inferring user emotion and adjusting the display method of anomaly detection results based on the estimated user emotion 2. The system of claim 1, (Appendix 29) The detection unit is When detecting an anomaly, the system refers to the user's lifestyle data and corrects the anomaly detection result. 2. The system of claim 1, (Appendix 30) The detection unit is Compensate for environmental conditions during detection by taking into account the user's geographic location 2. The system of claim 1, (Appendix 31) The notification unit is Estimate user emotions and adjust notification content based on the estimated user emotions 2. The system of claim 1, (Appendix 32) The notification unit is When notifying a user, the system selects the optimal notification method by referring to the user's past notification history. 2. The system of claim 1, (Appendix 33) The notification unit is Infer user emotions and adjust notification timing based on the estimated user emotions 2. The system of claim 1, (Appendix 34) The notification unit is When notifying users, the optimal notification method is selected taking into account the user's device information. 2. The system of claim 1, (Appendix 35) The alert unit is Infer user emotions and adjust alert content based on the estimated user emotions 3. The system of claim 2, (Appendix 36) The alert unit is When an alert occurs, the system refers to the user's past alert history to select the optimal alert method. 3. The system of claim 2, (Appendix 37) The alert unit is Infer user emotions and adjust the timing of alerts based on the estimated user emotions 3. The system of claim 2, (Appendix 38) The alert unit is When an alert occurs, the optimal alert method is selected taking into account the user's device information. 3. The system of claim 2, (Appendix 39) The alert unit is When an alert occurs, the optimal alert method is selected taking into account the user's device information. 3. The system of claim 2, [Explanation of symbols]

[0198] 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 conversation unit that conducts a conversation with a user based on personal data of the user; A measurement unit for measuring a body temperature and a blood pressure of the user; A training unit that provides training guidance to the user; a detection unit that detects a specific symptom or a specific behavior based on one or more pieces of information among a conversation result by the conversation unit, a measurement result by the measurement unit, and a guidance result by the guidance unit; A notification unit that notifies a specific contact of the detection result by the detection unit. A system characterized by:

2. Equipped with an alert unit that issues alerts regarding medication time or room temperature 2. The system of claim 1 .

3. The leadership team: providing training guidance to the user based on at least one of the conversation result by the conversation unit and the measurement result by the measurement unit; 2. The system of claim 1 .

4. The conversation unit includes: A conversation is held with the user using a generation AI that has learned the user's personal data.

2. The system of claim 1 .

5. The leadership team: Providing training guidance to the user using a generation AI that has learned the user's personal data 2. The system of claim 1 .

6. The measurement unit includes: Measure the user's body temperature and blood pressure using a generation AI that has learned the user's personal data.

2. The system of claim 1 .

7. The conversation unit includes: Estimating a sentiment of the user and selecting a conversation topic based on the estimated sentiment of the user.

2. The system of claim 1 .

8. The conversation unit includes: Analyzing the user's past conversation history and selecting the optimal way to proceed with the conversation 2. The system of claim 1 .

9. The conversation unit includes: The user's reaction during the conversation is analyzed in real time, and the content of the conversation is dynamically adjusted.

2. The system of claim 1 .

Images