system

Abstract

To provide a system which acquires health information of a user, conducts health management, and reproduces a user according to generative AI through interactions with the user.SOLUTION: A system includes an acquisition part, a management part, an interactive part, and a reproduction part. The acquisition part acquires health information of a user. The management part manages the health of a user, based on the health information acquired by the acquisition part. The interactive part conducts interaction with a user. The reproduction part reproduces a user according to generative AI, based on interactions with the user.SELECTED DRAWING: Figure 1

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there are systems for acquiring user health information and performing health management, but there is a problem that reproducing the user by a generative AI through interaction with the user has not been sufficiently carried out.

[0005] The system according to the embodiment aims to acquire user health information, perform health management, and reproduce the user by a generative AI through interaction with the user.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an acquisition unit, a management unit, a dialogue unit, and a reproduction unit. The acquisition unit acquires the user's health information. The management unit manages the user's health based on the health information acquired by the acquisition unit. The dialogue unit engages in dialogue with the user. The reproduction unit reproduces the user using a generative AI based on the dialogue with the user. [Effects of the Invention]

[0007] The system according to this embodiment can acquire the user's health information, perform health management, and recreate the user through interaction with the user using a generated AI. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) A system according to an embodiment of the present invention is a system that provides an approach to recreating a deceased person using generative AI. This system uses a "cat-type robot" that provides physical and mental care to the user while they are alive. For physical care, the cat-type care robot acquires the user's health information (e.g., blood pressure, heart rate, calorie information, etc.) and manages their health based on this information. For mental care, it alleviates the user's loneliness through daily conversations. The content of the conversations is recorded, and the user's thoughts, catchphrases, values, messages, etc. are acquired. After the user dies, an AI capable of conversing in a manner reminiscent of the deceased is created based on this information. This AI uses generative AI to recreate the user and provides conversations to help remember the deceased. For example, the cat-type care robot is a system comprising an acquisition unit (which may include AI processing) that acquires the user's health information, a management unit (which may include AI processing) that manages the health based on the acquired health information, a dialogue unit (which includes AI processing or generative AI processing) that engages in dialogue with the user, and a recreation unit (which includes generative AI processing) that recreates the user using generative AI based on the dialogue with the user. Another feature is the inclusion of a care robot that includes an acquisition unit, a management unit, and a dialogue unit. This allows the system, which uses generative AI, to acquire, manage, and interact with the user's health information, and then reproduce it using the generative AI.

[0029] The system according to this embodiment comprises an acquisition unit, a management unit, an interaction unit, and a reproduction unit. The acquisition unit acquires the user's health information. The user's health information includes, but is not limited to, blood pressure, heart rate, and calorie information. For example, the acquisition unit measures the user's blood pressure using a blood pressure monitor. The acquisition unit can also measure the user's heart rate using a heart rate monitor. Furthermore, the acquisition unit can acquire the user's calorie information using a calorie meter. For example, the acquisition unit measures systolic and diastolic blood pressure using a blood pressure monitor and stores this as data. The heart rate monitor measures resting heart rate and exercise heart rate and stores this as data. The calorie meter measures calories consumed and calories consumed and stores this as data. The management unit manages the user's health based on the health information acquired by the acquisition unit. For example, the management unit monitors the user's health status. The management unit can also notify the user of abnormal values. Furthermore, the management unit can provide health advice based on the health information. For example, the management unit notifies the user if it detects an abnormal blood pressure reading. It also notifies the user if it detects an abnormal heart rate reading. It can also provide advice on appropriate diet and exercise based on calorie information. The dialogue unit engages in daily conversations with the user and records the content of those conversations. The dialogue unit can, for example, conduct voice conversations. It can also conduct text conversations. Furthermore, the dialogue unit can acquire the user's thoughts, catchphrases, values, and messages. For example, the dialogue unit uses speech recognition technology to save the user's conversation content as text data. In the case of text conversations, it saves the text entered by the user exactly as it is. The reproduction unit uses generative AI to recreate the user based on the information recorded by the dialogue unit. The reproduction unit can, for example, recreate the user's voice. It can also recreate the user's actions. Furthermore, it can recreate the user's facial expressions. For example, the reproduction unit uses generative AI to recreate the user's voice tone and speaking style. Action recreation can include the user's unique gestures and movements. Reproducing facial expressions can include the user's smile, surprise, and other expressions.As a result, the system according to this embodiment can acquire, manage, interact with, and reproduce the user's health information using a generative AI.

[0030] The acquisition unit acquires the user's health information. This information includes, but is not limited to, blood pressure, heart rate, and calorie information. For example, the acquisition unit measures the user's blood pressure using a blood pressure monitor. Specifically, the blood pressure monitor measures systolic and diastolic blood pressure and stores this data. Systolic blood pressure indicates the pressure when the heart contracts and pumps blood, and diastolic blood pressure indicates the pressure when the heart expands and receives blood. This allows for a detailed understanding of the user's blood pressure fluctuations. The acquisition unit can also measure the user's heart rate using a heart rate monitor. The heart rate monitor measures resting heart rate and exercise heart rate and stores this data. Resting heart rate indicates the heart rate when the user is at rest, and exercise heart rate indicates the heart rate when the user is exercising. This allows for detailed monitoring of the user's cardiac health. Furthermore, the acquisition unit can also acquire the user's calorie information using a calorie counter. The calorie counter measures calories consumed and calories taken in and stores this data. Calories burned represent the amount of energy the user expends through daily activities and exercise, while calories consumed represent the amount of energy the user takes in from food. This allows for a detailed understanding of the user's energy balance. The data acquisition unit centrally manages this data and provides basic information for comprehensively evaluating the user's health status.

[0031] The Management Department manages users' health based on health information acquired by the Acquisition Department. For example, the Management Department monitors users' health status. Specifically, it monitors collected blood pressure, heart rate, and calorie information in real time and immediately notifies users if abnormal values ​​are detected. For example, if blood pressure exceeds the normal range, the Management Department can issue a warning to the user and urge them to see a doctor. Similarly, it will also notify users if their heart rate is abnormally high or low. Furthermore, the Management Department can provide health advice based on health information. For example, based on calorie information, it can provide users with advice on appropriate diet and exercise. Specifically, if calorie intake exceeds calorie expenditure, the Management Department can suggest that the user revise their diet or increase their exercise. In addition, the Management Department can analyze users' health data over the long term to understand trends in their health status. This allows for early detection of changes in users' health status and the implementation of appropriate measures. The Management Department plays a crucial role in comprehensively managing users' health and supporting health maintenance and improvement.

[0032] The dialogue unit engages in daily conversations with the user and records their content. For example, the dialogue unit conducts voice conversations. Specifically, it uses speech recognition technology to save the user's conversation content as text data. Speech recognition technology can analyze the user's utterances in real time and convert them into text. This allows for accurate recording of the user's conversations. The dialogue unit can also conduct text-based conversations. In the case of text-based conversations, it saves the text entered by the user exactly as it is. This allows for an accurate understanding of the user's intentions and emotions. Furthermore, the dialogue unit can acquire the user's thoughts, catchphrases, values, and messages. For example, the dialogue unit can record the words and phrases the user uses daily, understanding the user's personality and characteristics. This allows the dialogue unit to make communication with the user more natural and approachable. Through conversations with the user, the dialogue unit understands the user's psychological state and emotional changes, providing important information for appropriate responses.

[0033] The reproduction unit uses generative AI to recreate the user based on the information recorded by the dialogue unit. For example, the reproduction unit can recreate the user's voice. Specifically, the generative AI learns the user's voice tone and speaking style and can generate the user's voice based on this. This allows for a faithful recreation of the user's voice. The reproduction unit can also recreate the user's actions. The generative AI learns the user's unique gestures and movements and can recreate the user's actions based on this. For example, it can recreate the user's common hand movements and postures. Furthermore, the reproduction unit can recreate the user's facial expressions. The generative AI learns the changes in the user's facial expressions and can recreate them based on this. For example, it can recreate the user's smile or surprised expression. In this way, the reproduction unit can comprehensively recreate the user's voice, actions, and facial expressions, enhancing the user's presence. The reproduction unit plays a crucial role in making communication with the user more natural and approachable by faithfully recreating the user's personality and characteristics using generative AI.

[0034] The system includes a care robot comprising an acquisition unit, a management unit, and a dialogue unit. The care robot can acquire, manage, and interact with the user's health information. The care robot has, for example, a mobility function, which allows it to move freely around the user. The care robot also has a dialogue function, which allows it to engage in voice and text dialogue with the user. Furthermore, the care robot has a monitoring function, which allows it to constantly monitor the user's health status. For example, the care robot can periodically measure the user's blood pressure and notify the user if an abnormal value is detected. Heart rate monitoring can be performed in a similar manner. By monitoring calorie information, the care robot can understand the user's diet and exercise habits and provide appropriate advice. Thus, the care robot can acquire, manage, and interact with the user's health information. Some or all of the above-described processes in the care robot may be performed using, for example, AI, or not using AI. For example, the care robot can use AI to analyze data and evaluate the user's health status in order to acquire the user's health information. Furthermore, care robots can use AI to analyze user emotions and generate appropriate responses in their conversational functions. In addition, care robots can use AI to detect abnormal values ​​in their monitoring functions and respond quickly.

[0035] The data acquisition unit can acquire health information, including the user's blood pressure, heart rate, and calorie information. For example, the data acquisition unit can measure the user's blood pressure using a blood pressure monitor. The blood pressure monitor measures systolic and diastolic blood pressure and stores this data. The data acquisition unit can also measure the user's heart rate using a heart rate monitor. The heart rate monitor measures resting heart rate and exercise heart rate and stores this data. Furthermore, the data acquisition unit can acquire the user's calorie information using a calorie counter. The calorie counter measures calories consumed and calories taken in and stores this data. This allows the data acquisition unit to acquire detailed health information about the user. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input data acquired from the blood pressure monitor into AI, which can analyze the data and detect abnormal values. Alternatively, the data acquisition unit can input data acquired from the heart rate monitor into AI, which can analyze the data and evaluate the user's health status. Furthermore, the data acquisition unit inputs the data acquired by the calorie meter into the AI, which then analyzes the data and provides appropriate advice.

[0036] The management unit can manage the user's health based on the acquired health information. For example, the management unit can monitor the user's health status. Based on the acquired health information, the management unit can continuously monitor the user's health status. The management unit can also notify the user of abnormal values. For example, if an abnormal blood pressure value is detected, the management unit will notify the user. Similarly, if an abnormal heart rate value is detected, the management unit will also notify the user. Furthermore, the management unit can provide health advice based on the health information. For example, based on calorie information, it can provide advice on appropriate diet and exercise. In this way, the management unit can manage the user's health based on the acquired health information. Some or all of the above processes in the management unit may be performed using AI, for example, or not using AI. For example, the management unit can input the acquired health information into AI, and the AI ​​can analyze the data and evaluate the user's health status. The management unit can also use AI to detect abnormal values. Furthermore, the management unit can use AI to provide health advice.

[0037] The dialogue unit can engage in daily conversations with the user and record their content. For example, the dialogue unit can conduct voice conversations. The dialogue unit uses speech recognition technology to save the user's conversation content as text data. The dialogue unit can also conduct text conversations. In the case of text conversations, the dialogue unit saves the text entered by the user as is. Furthermore, the dialogue unit can also acquire the user's thoughts, catchphrases, values, and messages. For example, the dialogue unit can analyze the user's conversation content and extract and save specific keywords and phrases. This allows the dialogue unit to record daily conversations with the user. Some or all of the above processing in the dialogue unit may be performed using AI, or not. For example, the dialogue unit can input data acquired using speech recognition technology into AI, and the AI ​​can analyze the data and extract specific keywords and phrases. Alternatively, the dialogue unit can input text conversation data into AI, and the AI ​​can analyze the data and extract the user's values ​​and messages.

[0038] The reproduction unit can reproduce the user using a generative AI based on the information recorded in the dialogue unit. For example, the reproduction unit can reproduce the user's voice. The reproduction unit can reproduce the user's voice tone and speaking style using a generative AI. The reproduction unit can also reproduce the user's actions. The reproduction unit can reproduce the user's unique gestures and movements using a generative AI. Furthermore, the reproduction unit can also reproduce the user's facial expressions. The reproduction unit can reproduce the user's smile, surprised expression, etc., using a generative AI. In this way, the reproduction unit can reproduce the user using a generative AI. Some or all of the above processing in the reproduction unit may be performed using a generative AI, or not. For example, the reproduction unit can input audio data recorded in the dialogue unit into a generative AI, and the generative AI can reproduce the voice. Also, the reproduction unit can input action data recorded in the dialogue unit into a generative AI, and the generative AI can reproduce the actions. Furthermore, the reproduction unit can input facial expression data recorded in the dialogue unit into a generative AI, and the generative AI can reproduce the facial expressions.

[0039] The data acquisition unit can analyze the user's past health data and select the optimal acquisition method. For example, the acquisition unit can analyze the user's past blood pressure data and acquire health information during the most stable time period. The acquisition unit can also be configured to avoid acquiring data after exercise based on the user's past heart rate data. Furthermore, the acquisition unit can prioritize acquiring data after meals, referencing the user's past calorie data. In this way, the acquisition unit can analyze the user's past health data and select the optimal acquisition method. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input past health data into AI, which can analyze the data and select the optimal acquisition method. The acquisition unit can also have AI adjust the acquisition timing based on past data. Furthermore, the acquisition unit can have AI customize the acquisition method based on past data.

[0040] The data acquisition unit can filter health information based on the user's current lifestyle and activity level. For example, if the user is exercising, the unit can adjust the timing to acquire health information after the exercise. It can also acquire health information while the user is resting, allowing them to relax. Furthermore, if the user is working, the unit can be configured to acquire health information during work breaks. This allows the data acquisition unit to filter health information based on the user's lifestyle and activity level. Some or all of the above processing in the data acquisition unit may be performed using AI, or without AI. For example, the data acquisition unit can input user activity data into the AI, which can analyze the data and adjust the acquisition timing. It can also input user lifestyle data into the AI, which can analyze the data and customize the acquisition method. Furthermore, it can input user activity level data into the AI, which can analyze the data and optimize the acquisition method.

[0041] The data acquisition unit can prioritize acquiring highly relevant information by considering the user's geographical location when acquiring health information. For example, if the user is at high altitude, the acquisition unit will prioritize acquiring oxygen concentration information. The acquisition unit can also acquire air quality information if the user is in an urban area. Furthermore, if the user is outdoors, the acquisition unit can acquire ultraviolet radiation level information. This allows the acquisition unit to acquire health information while considering the user's geographical location. Some or all of the above processing in the acquisition unit may be performed using AI, or without AI. For example, the acquisition unit can input the user's location data into AI, which can analyze the data and select highly relevant information. The acquisition unit can also customize the acquisition method based on the location information. Furthermore, the acquisition unit can adjust the acquisition timing based on the location information.

[0042] The data acquisition unit can analyze the user's social media activity and acquire relevant health information when acquiring health information. For example, if the user posts on social media indicating they are stressed, the data acquisition unit will prioritize acquiring heart rate. The data acquisition unit can also acquire post-exercise health information if the user posts about exercise. Furthermore, if the user posts about food, the data acquisition unit can acquire post-meal calorie information. In this way, the data acquisition unit can acquire health information by analyzing the user's social media activity. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input social media data into AI, which can analyze the data and select relevant health information. The data acquisition unit can also have AI customize the acquisition method based on social media activity data. Furthermore, the data acquisition unit can have AI adjust the acquisition timing based on social media data.

[0043] The management department can select an appropriate management method by referring to the user's past health data during health management. For example, the management department can propose an optimal exercise plan based on the user's past blood pressure data. It can also propose relaxation methods based on the user's past heart rate data. Furthermore, it can propose dietary management methods based on the user's past calorie data. In this way, the management department can select the optimal management method by referring to the user's past health data. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input past health data into AI, and the AI ​​can analyze the data and select the optimal management method. Furthermore, the management department can have the AI ​​customize the management method based on past data. Furthermore, the management department can have the AI ​​adjust the timing of management based on past data.

[0044] The management unit can customize health management methods based on the user's current lifestyle. For example, if the user is at work, the management unit can suggest short stretches. If the user is resting, the management unit can also suggest breathing exercises to help them relax. Furthermore, if the user is exercising, the management unit can suggest appropriate hydration methods. In this way, the management unit can customize health management methods based on the user's current lifestyle. Some or all of the above processes in the management unit may be performed using AI, for example, or not. For example, the management unit can input the user's lifestyle data into the AI, which can then analyze the data and customize the management methods. The management unit can also have the AI ​​adjust the management method based on the user's activity data. Furthermore, the management unit can have the AI ​​adjust the timing of management based on the user's lifestyle data.

[0045] The management department can select an appropriate management method when managing a user's health, taking into account the user's geographical location. For example, if the user is at high altitude, the management department may prioritize oxygen concentration management. If the user is in an urban area, the management department may also prioritize air quality management. Furthermore, if the user is outdoors, the management department may prioritize UV ​​protection. This allows the management department to select a health management method that considers the user's geographical location. Some or all of the above processing in the management department may be performed using AI, or not. For example, the management department can input the user's location data into the AI, which can analyze the data and select highly relevant information. The management department can also use the AI ​​to customize the management method based on the location information. Furthermore, the management department can use the AI ​​to adjust the timing of management based on the location information.

[0046] The management department can analyze a user's social media activity and propose management strategies during health management. For example, if a user posts about feeling stressed on social media, the management department can suggest ways to relax. If a user posts about exercise, the management department can also suggest an appropriate exercise plan. Furthermore, if a user posts about food, the management department can suggest a balanced meal plan. In this way, the management department can analyze a user's social media activity and propose health management strategies. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input social media data into AI, which can analyze the data and select relevant health information. The management department can also have AI customize management methods based on social media activity data. Furthermore, the management department can have AI adjust the timing of management based on social media data.

[0047] The dialogue unit can select an appropriate dialogue method by referring to the user's past dialogue history during a conversation. For example, the dialogue unit can select the optimal dialogue method based on the user's preferred dialogue style in the past. The dialogue unit can also prioritize specific topics in the conversation based on the user's past dialogue history. Furthermore, the dialogue unit can analyze the user's past dialogue history and select the most effective dialogue method. In this way, the dialogue unit can select the optimal dialogue method by referring to the user's past dialogue history. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or not using AI. For example, the dialogue unit can input past dialogue history into AI, and the AI ​​can analyze the data and select the optimal dialogue method. The dialogue unit can also have the AI ​​customize the dialogue method based on past data. Furthermore, the dialogue unit can have the AI ​​adjust the dialogue timing based on past data.

[0048] The dialogue unit can customize the content of the conversation based on the user's current feelings and situation. For example, if the user is sad, the dialogue unit can offer comforting content. If the user is happy, the dialogue unit can offer empathetic content. Furthermore, if the user is tired, the dialogue unit can offer relaxing content. In this way, the dialogue unit can customize the content of the conversation based on the user's current feelings and situation. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input user feelings data into AI, and the AI ​​can analyze the data to customize the content of the conversation. Furthermore, the dialogue unit can have AI adjust the conversation method based on user situation data. Furthermore, the dialogue unit can have AI adjust the timing of the conversation based on user feelings data.

[0049] The dialogue unit can select appropriate dialogue content during a conversation, taking into account the user's geographical location. For example, if the user is traveling, the dialogue unit can provide information about their travel destination. If the user is at home, the dialogue unit can also provide conversation to help them relax. Furthermore, if the user is at work, the dialogue unit can provide work-related conversation. In this way, the dialogue unit can select dialogue content considering the user's geographical location. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the user's location data into the AI, which can analyze the data and select highly relevant dialogue content. The dialogue unit can also have the AI ​​customize the dialogue method based on the location information. Furthermore, the dialogue unit can have the AI ​​adjust the timing of the dialogue based on the location information.

[0050] The dialogue unit can analyze the user's social media activity during a conversation and suggest conversation content. For example, if the user posts about feeling stressed on social media, the dialogue unit can suggest conversations to help them relax. It can also suggest conversations related to exercise if the user posts about exercise. Furthermore, if the user posts about food, it can suggest conversations related to food. In this way, the dialogue unit can analyze the user's social media activity and suggest conversation content. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input social media data into AI, which can analyze the data and select relevant conversation content. The dialogue unit can also have AI customize the conversation method based on social media activity data. Furthermore, the dialogue unit can have AI adjust the timing of conversations based on social media data.

[0051] The reproduction unit can select an appropriate reproduction method by referring to the user's past dialogue history during reproduction. For example, the reproduction unit can select the optimal reproduction method based on the user's preferred dialogue style in the past. The reproduction unit can also prioritize the reproduction of specific topics from the user's past dialogue history. Furthermore, the reproduction unit can analyze the user's past dialogue history and select the most effective reproduction method. This allows the reproduction unit to select the optimal reproduction method by referring to the user's past dialogue history. Some or all of the above processing in the reproduction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reproduction unit can input past dialogue history into a generative AI, which can analyze the data and select the optimal reproduction method. The reproduction unit can also have the generative AI customize the reproduction method based on past data. Furthermore, the reproduction unit can have the generative AI adjust the reproduction timing based on past data.

[0052] The reproduction unit can customize the content of the reproduction based on the user's current feelings and situation. For example, if the user is sad, the reproduction unit can reproduce comforting content. If the user is happy, the reproduction unit can also reproduce empathetic content. Furthermore, if the user is tired, the reproduction unit can reproduce relaxing content. In this way, the reproduction unit can customize the content of the reproduction based on the user's current feelings and situation. Some or all of the above processing in the reproduction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reproduction unit can input the user's feelings data into a generative AI, which can analyze the data and customize the content of the reproduction. The reproduction unit can also have the generative AI adjust the reproduction method based on the user's situation data. Furthermore, the reproduction unit can have the generative AI adjust the reproduction timing based on the user's feelings data.

[0053] The reproduction unit can select an appropriate reproduction method when reproducing, taking into account the user's geographical location information. For example, if the user is traveling, the reproduction unit can perform a reproduction that provides information about the travel destination. If the user is at home, the reproduction unit can also perform a reproduction to help them relax. Furthermore, if the user is at work, the reproduction unit can perform a reproduction related to work. In this way, the reproduction unit can select a reproduction method that takes into account the user's geographical location information. Some or all of the above processing in the reproduction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reproduction unit can input the user's location data into a generative AI, which can analyze the data and select a highly relevant reproduction method. The reproduction unit can also have the generative AI customize the reproduction method based on the location information. Furthermore, the reproduction unit can have the generative AI adjust the reproduction timing based on the location information.

[0054] The reproduction unit can analyze the user's social media activity during the reproduction process and suggest reproduction content. For example, if the user has posted about feeling stressed on social media, the reproduction unit can suggest a reproduction to help them relax. It can also suggest exercise-related reproductions if the user has posted about exercise. Furthermore, if the user has posted about food, it can suggest food-related reproductions. In this way, the reproduction unit can analyze the user's social media activity and suggest reproduction content. Some or all of the above processing in the reproduction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reproduction unit can input social media data into a generative AI, which can analyze the data and select relevant reproduction content. The reproduction unit can also use the social media activity data to customize the reproduction method using the generative AI. Furthermore, the reproduction unit can use the social media data to adjust the reproduction timing using the generative AI.

[0055] A care robot can select an appropriate action method by referring to the user's past health data during operation. For example, the care robot can suggest relaxation movements based on the user's past blood pressure data. It can also suggest appropriate exercise based on the user's past heart rate data. Furthermore, it can suggest a balanced diet based on the user's past calorie data. In this way, the care robot can select the optimal action method by referring to the user's past health data. Some or all of the above processes in a care robot may be performed using AI, for example, or not using AI. For example, the care robot can input past health data into AI, which can analyze the data and select the optimal action method. The care robot can also have AI customize its action method based on past data. Furthermore, the care robot can have AI adjust the timing of its actions based on past data.

[0056] A care robot can customize its actions based on the user's current lifestyle. For example, if the user is at work, the care robot can suggest short stretches. If the user is resting, the care robot can suggest breathing exercises to help them relax. Furthermore, if the user is exercising, the care robot can suggest appropriate hydration methods. This allows the care robot to customize its actions based on the user's current lifestyle. Some or all of the above processes in a care robot may be performed using AI, for example, or without AI. For example, the care robot can input user lifestyle data into AI, which can analyze the data and customize its actions. The care robot can also have AI adjust its actions based on user activity data. Furthermore, the care robot can have AI adjust its timing based on user lifestyle data.

[0057] A care robot can select an appropriate operating method while considering the user's geographical location. For example, if the user is at high altitude, the care robot may prioritize oxygen concentration management. It may also prioritize air quality management if the user is in an urban area. Furthermore, if the user is outdoors, it may prioritize UV ​​protection. This allows the care robot to select an operating method that considers the user's geographical location. Some or all of the above processing in a care robot may be performed using AI, for example, or without AI. For example, the care robot can input the user's location data into AI, which can analyze the data and select a highly relevant operating method. The AI ​​can also customize the operating method based on the location information. Furthermore, the AI ​​can adjust the timing of the care robot's actions based on the location information.

[0058] A care robot can analyze a user's social media activity during operation and suggest appropriate actions. For example, if a user posts about feeling stressed on social media, the care robot can suggest ways to relax. It can also suggest an appropriate exercise plan if the user posts about exercise. Furthermore, if the user posts about food, it can suggest a balanced meal plan. This allows the care robot to analyze the user's social media activity and suggest appropriate actions. Some or all of the above processing in the care robot may be performed using AI, for example, or without AI. For example, the care robot can input social media data into AI, which can analyze the data and select relevant actions. The AI ​​can also customize the robot's actions based on social media activity data. Furthermore, the AI ​​can adjust the timing of the robot's actions based on social media data.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The system comprises an acquisition unit, a management unit, an interaction unit, and a reproduction unit. Furthermore, the system may also include a data analysis unit that analyzes the user's past health data and selects the optimal acquisition method. For example, the data analysis unit analyzes the user's past blood pressure data and acquires health information during the most stable time period. It can also be configured to avoid acquiring data after exercise based on the user's past heart rate data. Additionally, it can prioritize acquiring data after meals based on the user's past calorie data. This allows the system to analyze the user's past health data and select the optimal acquisition method.

[0061] The system comprises an acquisition unit, a management unit, an interaction unit, and a reproduction unit. Furthermore, the system may also include a lifestyle analysis unit that adjusts the timing of health information acquisition based on the user's current lifestyle and activity level. For example, if the user is exercising, the lifestyle analysis unit may adjust the system to acquire health information after the exercise. If the user is resting, it may also acquire health information while the user is relaxed. Furthermore, if the user is working, it may be set to acquire health information during work breaks. This allows the system to filter health information based on the user's lifestyle and activity level.

[0062] The system comprises an acquisition unit, a management unit, an interaction unit, and a reproduction unit. Furthermore, the system may also include a location information analysis unit that prioritizes the acquisition of highly relevant information considering the user's geographical location. For example, if the user is at high altitude, the location information analysis unit prioritizes acquiring oxygen concentration data. If the user is in an urban area, it can also acquire air quality information. Furthermore, if the user is outdoors, it can acquire UV radiation level information. This allows the system to acquire health information while considering the user's geographical location.

[0063] The system comprises an acquisition unit, a management unit, an interaction unit, and a reproduction unit. Furthermore, the system may also include a social media analysis unit that analyzes the user's social media activity and acquires relevant health information. For example, the social media analysis unit prioritizes acquiring heart rate data if the user posts about feeling stressed on social media. It can also acquire post-exercise health information if the user posts about exercise. Additionally, it can acquire post-meal calorie information if the user posts about food. This allows the system to analyze the user's social media activity and acquire health information.

[0064] The system comprises an acquisition unit, a management unit, a dialogue unit, and a reproduction unit. Furthermore, the system may also include a dialogue history analysis unit that selects an appropriate dialogue method by referring to the user's past dialogue history. For example, the dialogue history analysis unit selects the optimal dialogue method based on the dialogue style the user has preferred in the past. It can also prioritize dialogue on specific topics based on the user's past dialogue history. Additionally, it can analyze the user's past dialogue history and select the most effective dialogue method. This allows the system to select the optimal dialogue method by referring to the user's past dialogue history.

[0065] The system comprises an acquisition unit, a management unit, an interaction unit, and a reproduction unit. Furthermore, the system may also include a location information reproduction unit that selects an appropriate reproduction method considering the user's geographical location. For example, if the user is traveling, the location information reproduction unit can perform a reproduction that provides information about the travel destination. If the user is at home, it can perform a reproduction to help them relax. Furthermore, if the user is at work, it can perform a reproduction related to work. This allows the system to select a reproduction method that takes the user's geographical location into consideration.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The acquisition unit acquires the user's health information. This information includes, for example, blood pressure, heart rate, and calorie information. The acquisition unit measures the user's blood pressure using a blood pressure monitor, measures the user's heart rate using a heart rate monitor, and acquires the user's calorie information using a calorie counter. This data is then stored. Step 2: The management unit manages the user's health based on the health information acquired by the acquisition unit. The management unit monitors the user's health status, notifies them of abnormal values, and provides health advice based on the health information. For example, it notifies the user if abnormal values ​​are detected in blood pressure or heart rate, and provides advice on appropriate diet and exercise based on calorie information. Step 3: The dialogue unit conducts daily conversations with the user and records the content. The dialogue unit engages in voice and text dialogues to acquire the user's thoughts, catchphrases, values, messages, etc. Using speech recognition technology, the content of the user's conversations is saved as text data, and in the case of text dialogues, the text entered by the user is saved as is. Step 4: The Reconstruction Unit uses generative AI to recreate the user based on the information recorded in the Dialogue Unit. The Reconstruction Unit recreates the user's voice, actions, and facial expressions. For example, it uses generative AI to recreate the user's voice tone and speaking style, recreates actions including unique gestures and movements, and recreates expressions such as smiles and surprise.

[0068] (Example of form 2) A system according to an embodiment of the present invention is a system that provides an approach to recreating a deceased person using generative AI. This system uses a "cat-type robot" that provides physical and mental care to the user while they are alive. For physical care, the cat-type care robot acquires the user's health information (e.g., blood pressure, heart rate, calorie information, etc.) and manages their health based on this information. For mental care, it alleviates the user's loneliness through daily conversations. The content of the conversations is recorded, and the user's thoughts, catchphrases, values, messages, etc. are acquired. After the user dies, an AI capable of conversing in a manner reminiscent of the deceased is created based on this information. This AI uses generative AI to recreate the user and provides conversations to help remember the deceased. For example, the cat-type care robot is a system comprising an acquisition unit (which may include AI processing) that acquires the user's health information, a management unit (which may include AI processing) that manages the health based on the acquired health information, a dialogue unit (which includes AI processing or generative AI processing) that engages in dialogue with the user, and a recreation unit (which includes generative AI processing) that recreates the user using generative AI based on the dialogue with the user. Another feature is the inclusion of a care robot that includes an acquisition unit, a management unit, and a dialogue unit. This allows the system, which uses generative AI, to acquire, manage, and interact with the user's health information, and then reproduce it using the generative AI.

[0069] The system according to this embodiment comprises an acquisition unit, a management unit, an interaction unit, and a reproduction unit. The acquisition unit acquires the user's health information. The user's health information includes, but is not limited to, blood pressure, heart rate, and calorie information. For example, the acquisition unit measures the user's blood pressure using a blood pressure monitor. The acquisition unit can also measure the user's heart rate using a heart rate monitor. Furthermore, the acquisition unit can acquire the user's calorie information using a calorie meter. For example, the acquisition unit measures systolic and diastolic blood pressure using a blood pressure monitor and stores this as data. The heart rate monitor measures resting heart rate and exercise heart rate and stores this as data. The calorie meter measures calories consumed and calories consumed and stores this as data. The management unit manages the user's health based on the health information acquired by the acquisition unit. For example, the management unit monitors the user's health status. The management unit can also notify the user of abnormal values. Furthermore, the management unit can provide health advice based on the health information. For example, the management unit notifies the user if it detects an abnormal blood pressure reading. It also notifies the user if it detects an abnormal heart rate reading. It can also provide advice on appropriate diet and exercise based on calorie information. The dialogue unit engages in daily conversations with the user and records the content of those conversations. The dialogue unit can, for example, conduct voice conversations. It can also conduct text conversations. Furthermore, the dialogue unit can acquire the user's thoughts, catchphrases, values, and messages. For example, the dialogue unit uses speech recognition technology to save the user's conversation content as text data. In the case of text conversations, it saves the text entered by the user exactly as it is. The reproduction unit uses generative AI to recreate the user based on the information recorded by the dialogue unit. The reproduction unit can, for example, recreate the user's voice. It can also recreate the user's actions. Furthermore, it can recreate the user's facial expressions. For example, the reproduction unit uses generative AI to recreate the user's voice tone and speaking style. Action recreation can include the user's unique gestures and movements. Reproducing facial expressions can include the user's smile, surprise, and other expressions.As a result, the system according to this embodiment can acquire, manage, interact with, and reproduce the user's health information using a generative AI.

[0070] The acquisition unit acquires the user's health information. This information includes, but is not limited to, blood pressure, heart rate, and calorie information. For example, the acquisition unit measures the user's blood pressure using a blood pressure monitor. Specifically, the blood pressure monitor measures systolic and diastolic blood pressure and stores this data. Systolic blood pressure indicates the pressure when the heart contracts and pumps blood, and diastolic blood pressure indicates the pressure when the heart expands and receives blood. This allows for a detailed understanding of the user's blood pressure fluctuations. The acquisition unit can also measure the user's heart rate using a heart rate monitor. The heart rate monitor measures resting heart rate and exercise heart rate and stores this data. Resting heart rate indicates the heart rate when the user is at rest, and exercise heart rate indicates the heart rate when the user is exercising. This allows for detailed monitoring of the user's cardiac health. Furthermore, the acquisition unit can also acquire the user's calorie information using a calorie counter. The calorie counter measures calories consumed and calories taken in and stores this data. Calories burned represent the amount of energy the user expends through daily activities and exercise, while calories consumed represent the amount of energy the user takes in from food. This allows for a detailed understanding of the user's energy balance. The data acquisition unit centrally manages this data and provides basic information for comprehensively evaluating the user's health status.

[0071] The Management Department manages users' health based on health information acquired by the Acquisition Department. For example, the Management Department monitors users' health status. Specifically, it monitors collected blood pressure, heart rate, and calorie information in real time and immediately notifies users if abnormal values ​​are detected. For example, if blood pressure exceeds the normal range, the Management Department can issue a warning to the user and urge them to see a doctor. Similarly, it will also notify users if their heart rate is abnormally high or low. Furthermore, the Management Department can provide health advice based on health information. For example, based on calorie information, it can provide users with advice on appropriate diet and exercise. Specifically, if calorie intake exceeds calorie expenditure, the Management Department can suggest that the user revise their diet or increase their exercise. In addition, the Management Department can analyze users' health data over the long term to understand trends in their health status. This allows for early detection of changes in users' health status and the implementation of appropriate measures. The Management Department plays a crucial role in comprehensively managing users' health and supporting health maintenance and improvement.

[0072] The dialogue unit engages in daily conversations with the user and records their content. For example, the dialogue unit conducts voice conversations. Specifically, it uses speech recognition technology to save the user's conversation content as text data. Speech recognition technology can analyze the user's utterances in real time and convert them into text. This allows for accurate recording of the user's conversations. The dialogue unit can also conduct text-based conversations. In the case of text-based conversations, it saves the text entered by the user exactly as it is. This allows for an accurate understanding of the user's intentions and emotions. Furthermore, the dialogue unit can acquire the user's thoughts, catchphrases, values, and messages. For example, the dialogue unit can record the words and phrases the user uses daily, understanding the user's personality and characteristics. This allows the dialogue unit to make communication with the user more natural and approachable. Through conversations with the user, the dialogue unit understands the user's psychological state and emotional changes, providing important information for appropriate responses.

[0073] The reproduction unit uses generative AI to recreate the user based on the information recorded by the dialogue unit. For example, the reproduction unit can recreate the user's voice. Specifically, the generative AI learns the user's voice tone and speaking style and can generate the user's voice based on this. This allows for a faithful recreation of the user's voice. The reproduction unit can also recreate the user's actions. The generative AI learns the user's unique gestures and movements and can recreate the user's actions based on this. For example, it can recreate the user's common hand movements and postures. Furthermore, the reproduction unit can recreate the user's facial expressions. The generative AI learns the changes in the user's facial expressions and can recreate them based on this. For example, it can recreate the user's smile or surprised expression. In this way, the reproduction unit can comprehensively recreate the user's voice, actions, and facial expressions, enhancing the user's presence. The reproduction unit plays a crucial role in making communication with the user more natural and approachable by faithfully recreating the user's personality and characteristics using generative AI.

[0074] The system includes a care robot comprising an acquisition unit, a management unit, and a dialogue unit. The care robot can acquire, manage, and interact with the user's health information. The care robot has, for example, a mobility function, which allows it to move freely around the user. The care robot also has a dialogue function, which allows it to engage in voice and text dialogue with the user. Furthermore, the care robot has a monitoring function, which allows it to constantly monitor the user's health status. For example, the care robot can periodically measure the user's blood pressure and notify the user if an abnormal value is detected. Heart rate monitoring can be performed in a similar manner. By monitoring calorie information, the care robot can understand the user's diet and exercise habits and provide appropriate advice. Thus, the care robot can acquire, manage, and interact with the user's health information. Some or all of the above-described processes in the care robot may be performed using, for example, AI, or not using AI. For example, the care robot can use AI to analyze data and evaluate the user's health status in order to acquire the user's health information. Furthermore, care robots can use AI to analyze user emotions and generate appropriate responses in their conversational functions. In addition, care robots can use AI to detect abnormal values ​​in their monitoring functions and respond quickly.

[0075] The data acquisition unit can acquire health information, including the user's blood pressure, heart rate, and calorie information. For example, the data acquisition unit can measure the user's blood pressure using a blood pressure monitor. The blood pressure monitor measures systolic and diastolic blood pressure and stores this data. The data acquisition unit can also measure the user's heart rate using a heart rate monitor. The heart rate monitor measures resting heart rate and exercise heart rate and stores this data. Furthermore, the data acquisition unit can acquire the user's calorie information using a calorie counter. The calorie counter measures calories consumed and calories taken in and stores this data. This allows the data acquisition unit to acquire detailed health information about the user. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input data acquired from the blood pressure monitor into AI, which can analyze the data and detect abnormal values. Alternatively, the data acquisition unit can input data acquired from the heart rate monitor into AI, which can analyze the data and evaluate the user's health status. Furthermore, the data acquisition unit inputs the data acquired by the calorie meter into the AI, which then analyzes the data and provides appropriate advice.

[0076] The management unit can manage the user's health based on the acquired health information. For example, the management unit can monitor the user's health status. Based on the acquired health information, the management unit can continuously monitor the user's health status. The management unit can also notify the user of abnormal values. For example, if an abnormal blood pressure value is detected, the management unit will notify the user. Similarly, if an abnormal heart rate value is detected, the management unit will also notify the user. Furthermore, the management unit can provide health advice based on the health information. For example, based on calorie information, it can provide advice on appropriate diet and exercise. In this way, the management unit can manage the user's health based on the acquired health information. Some or all of the above processes in the management unit may be performed using AI, for example, or not using AI. For example, the management unit can input the acquired health information into AI, and the AI ​​can analyze the data and evaluate the user's health status. The management unit can also use AI to detect abnormal values. Furthermore, the management unit can use AI to provide health advice.

[0077] The dialogue unit can engage in daily conversations with the user and record their content. For example, the dialogue unit can conduct voice conversations. The dialogue unit uses speech recognition technology to save the user's conversation content as text data. The dialogue unit can also conduct text conversations. In the case of text conversations, the dialogue unit saves the text entered by the user as is. Furthermore, the dialogue unit can also acquire the user's thoughts, catchphrases, values, and messages. For example, the dialogue unit can analyze the user's conversation content and extract and save specific keywords and phrases. This allows the dialogue unit to record daily conversations with the user. Some or all of the above processing in the dialogue unit may be performed using AI, or not. For example, the dialogue unit can input data acquired using speech recognition technology into AI, and the AI ​​can analyze the data and extract specific keywords and phrases. Alternatively, the dialogue unit can input text conversation data into AI, and the AI ​​can analyze the data and extract the user's values ​​and messages.

[0078] The reproduction unit can reproduce the user using a generative AI based on the information recorded in the dialogue unit. For example, the reproduction unit can reproduce the user's voice. The reproduction unit can reproduce the user's voice tone and speaking style using a generative AI. The reproduction unit can also reproduce the user's actions. The reproduction unit can reproduce the user's unique gestures and movements using a generative AI. Furthermore, the reproduction unit can also reproduce the user's facial expressions. The reproduction unit can reproduce the user's smile, surprised expression, etc., using a generative AI. In this way, the reproduction unit can reproduce the user using a generative AI. Some or all of the above processing in the reproduction unit may be performed using a generative AI, or not. For example, the reproduction unit can input audio data recorded in the dialogue unit into a generative AI, and the generative AI can reproduce the voice. Also, the reproduction unit can input action data recorded in the dialogue unit into a generative AI, and the generative AI can reproduce the actions. Furthermore, the reproduction unit can input facial expression data recorded in the dialogue unit into a generative AI, and the generative AI can reproduce the facial expressions.

[0079] The acquisition unit can estimate the user's emotions and adjust the timing of health information acquisition based on the estimated user emotions. For example, if the user is feeling stressed, the acquisition unit can adjust the timing to acquire health information during times when the user is relaxed. The acquisition unit can also set the acquisition unit to acquire health information at regular times when the user is relaxed. Furthermore, if the user is in a hurry, the acquisition unit can adjust the timing to acquire health information in a short amount of time. In this way, the acquisition unit can adjust the timing of health information acquisition based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI, or not using AI. For example, the acquisition unit can input the user's facial expression data into a generative AI, which can estimate emotions. The acquisition unit can also input the user's voice data into a generative AI, which can estimate emotions. Furthermore, the acquisition unit inputs the user's biometric data into a generating AI, which can then estimate emotions.

[0080] The data acquisition unit can analyze the user's past health data and select the optimal acquisition method. For example, the acquisition unit can analyze the user's past blood pressure data and acquire health information during the most stable time period. The acquisition unit can also be configured to avoid acquiring data after exercise based on the user's past heart rate data. Furthermore, the acquisition unit can prioritize acquiring data after meals, referencing the user's past calorie data. In this way, the acquisition unit can analyze the user's past health data and select the optimal acquisition method. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input past health data into AI, which can analyze the data and select the optimal acquisition method. The acquisition unit can also have AI adjust the acquisition timing based on past data. Furthermore, the acquisition unit can have AI customize the acquisition method based on past data.

[0081] The data acquisition unit can filter health information based on the user's current lifestyle and activity level. For example, if the user is exercising, the unit can adjust the timing to acquire health information after the exercise. It can also acquire health information while the user is resting, allowing them to relax. Furthermore, if the user is working, the unit can be configured to acquire health information during work breaks. This allows the data acquisition unit to filter health information based on the user's lifestyle and activity level. Some or all of the above processing in the data acquisition unit may be performed using AI, or without AI. For example, the data acquisition unit can input user activity data into the AI, which can analyze the data and adjust the acquisition timing. It can also input user lifestyle data into the AI, which can analyze the data and customize the acquisition method. Furthermore, it can input user activity level data into the AI, which can analyze the data and optimize the acquisition method.

[0082] The data acquisition unit can estimate the user's emotions and determine the priority of health information to acquire based on the estimated emotions. For example, if the user is stressed, the data acquisition unit may prioritize acquiring heart rate. It may also prioritize acquiring blood pressure if the user is relaxed. Furthermore, if the user is tired, it may prioritize acquiring calorie information. This allows the data acquisition unit to prioritize health information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the data acquisition unit may be performed using AI, or not. For example, the data acquisition unit can input user facial expression data into a generative AI, which can estimate emotions. The data acquisition unit can also input user voice data into a generative AI, which can estimate emotions. Furthermore, the data acquisition unit can input user biometric data into a generative AI, which can estimate emotions.

[0083] The data acquisition unit can prioritize acquiring highly relevant information by considering the user's geographical location when acquiring health information. For example, if the user is at high altitude, the acquisition unit will prioritize acquiring oxygen concentration information. The acquisition unit can also acquire air quality information if the user is in an urban area. Furthermore, if the user is outdoors, the acquisition unit can acquire ultraviolet radiation level information. This allows the acquisition unit to acquire health information while considering the user's geographical location. Some or all of the above processing in the acquisition unit may be performed using AI, or without AI. For example, the acquisition unit can input the user's location data into AI, which can analyze the data and select highly relevant information. The acquisition unit can also customize the acquisition method based on the location information. Furthermore, the acquisition unit can adjust the acquisition timing based on the location information.

[0084] The data acquisition unit can analyze the user's social media activity and acquire relevant health information when acquiring health information. For example, if the user posts on social media indicating they are stressed, the data acquisition unit will prioritize acquiring heart rate. The data acquisition unit can also acquire post-exercise health information if the user posts about exercise. Furthermore, if the user posts about food, the data acquisition unit can acquire post-meal calorie information. In this way, the data acquisition unit can acquire health information by analyzing the user's social media activity. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input social media data into AI, which can analyze the data and select relevant health information. The data acquisition unit can also have AI customize the acquisition method based on social media activity data. Furthermore, the data acquisition unit can have AI adjust the acquisition timing based on social media data.

[0085] The management unit can estimate the user's emotions and adjust health management methods based on the estimated emotions. For example, if the user is stressed, the management unit can suggest exercises to help them relax. If the user is relaxed, the management unit can also suggest regular health checkups. Furthermore, if the user is in a hurry, the management unit can suggest health management methods that can be done in a short amount of time. In this way, the management unit can adjust health management methods based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI, for example, or not using AI. For example, the management unit can input the user's facial expression data into a generative AI, and the generative AI can estimate emotions. The management unit can also input the user's voice data into a generative AI, and the generative AI can estimate emotions. Furthermore, the management unit can input the user's biometric data into a generative AI, and the generative AI can estimate emotions.

[0086] The management department can select an appropriate management method by referring to the user's past health data during health management. For example, the management department can propose an optimal exercise plan based on the user's past blood pressure data. It can also propose relaxation methods based on the user's past heart rate data. Furthermore, it can propose dietary management methods based on the user's past calorie data. In this way, the management department can select the optimal management method by referring to the user's past health data. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input past health data into AI, and the AI ​​can analyze the data and select the optimal management method. Furthermore, the management department can have the AI ​​customize the management method based on past data. Furthermore, the management department can have the AI ​​adjust the timing of management based on past data.

[0087] The management unit can customize health management methods based on the user's current lifestyle. For example, if the user is at work, the management unit can suggest short stretches. If the user is resting, the management unit can also suggest breathing exercises to help them relax. Furthermore, if the user is exercising, the management unit can suggest appropriate hydration methods. In this way, the management unit can customize health management methods based on the user's current lifestyle. Some or all of the above processes in the management unit may be performed using AI, for example, or not. For example, the management unit can input the user's lifestyle data into the AI, which can then analyze the data and customize the management methods. The management unit can also have the AI ​​adjust the management method based on the user's activity data. Furthermore, the management unit can have the AI ​​adjust the timing of management based on the user's lifestyle data.

[0088] The management unit can estimate the user's emotions and determine the priority of health management based on the estimated emotions. For example, if the user is stressed, the management unit will prioritize methods for relaxation. If the user is relaxed, the management unit may also prioritize regular health checkups. Furthermore, if the user is in a hurry, the management unit may prioritize health management methods that can be completed in a short time. In this way, the management unit can determine the priority of health management based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI, for example, or not using AI. For example, the management unit can input the user's facial expression data into a generative AI, and the generative AI can estimate emotions. The management unit can also input the user's voice data into a generative AI, and the generative AI can estimate emotions. Furthermore, the management unit can input the user's biometric data into a generative AI, and the generative AI can estimate emotions.

[0089] The management department can select an appropriate management method when managing a user's health, taking into account the user's geographical location. For example, if the user is at high altitude, the management department may prioritize oxygen concentration management. If the user is in an urban area, the management department may also prioritize air quality management. Furthermore, if the user is outdoors, the management department may prioritize UV ​​protection. This allows the management department to select a health management method that considers the user's geographical location. Some or all of the above processing in the management department may be performed using AI, or not. For example, the management department can input the user's location data into the AI, which can analyze the data and select highly relevant information. The management department can also use the AI ​​to customize the management method based on the location information. Furthermore, the management department can use the AI ​​to adjust the timing of management based on the location information.

[0090] The management department can analyze a user's social media activity and propose management strategies during health management. For example, if a user posts about feeling stressed on social media, the management department can suggest ways to relax. If a user posts about exercise, the management department can also suggest an appropriate exercise plan. Furthermore, if a user posts about food, the management department can suggest a balanced meal plan. In this way, the management department can analyze a user's social media activity and propose health management strategies. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input social media data into AI, which can analyze the data and select relevant health information. The management department can also have AI customize management methods based on social media activity data. Furthermore, the management department can have AI adjust the timing of management based on social media data.

[0091] The dialogue unit can estimate the user's emotions and adjust the way it expresses the dialogue based on those emotions. For example, if the user is stressed, the dialogue unit can use a calm tone. If the user is relaxed, it can use a bright tone. Furthermore, if the user is in a hurry, it can use a concise and rapid dialogue. In this way, the dialogue unit can adjust the way it expresses the dialogue based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the dialogue unit may be performed using AI, or not using AI. For example, the dialogue unit can input the user's facial expression data into the generative AI, which can estimate emotions. The dialogue unit can also input the user's voice data into the generative AI, which can estimate emotions. Furthermore, the dialogue unit can input the user's biometric data into the generative AI, which can estimate emotions.

[0092] The dialogue unit can select an appropriate dialogue method by referring to the user's past dialogue history during a conversation. For example, the dialogue unit can select the optimal dialogue method based on the user's preferred dialogue style in the past. The dialogue unit can also prioritize specific topics in the conversation based on the user's past dialogue history. Furthermore, the dialogue unit can analyze the user's past dialogue history and select the most effective dialogue method. In this way, the dialogue unit can select the optimal dialogue method by referring to the user's past dialogue history. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or not using AI. For example, the dialogue unit can input past dialogue history into AI, and the AI ​​can analyze the data and select the optimal dialogue method. The dialogue unit can also have the AI ​​customize the dialogue method based on past data. Furthermore, the dialogue unit can have the AI ​​adjust the dialogue timing based on past data.

[0093] The dialogue unit can customize the content of the conversation based on the user's current feelings and situation. For example, if the user is sad, the dialogue unit can offer comforting content. If the user is happy, the dialogue unit can offer empathetic content. Furthermore, if the user is tired, the dialogue unit can offer relaxing content. In this way, the dialogue unit can customize the content of the conversation based on the user's current feelings and situation. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input user feelings data into AI, and the AI ​​can analyze the data to customize the content of the conversation. Furthermore, the dialogue unit can have AI adjust the conversation method based on user situation data. Furthermore, the dialogue unit can have AI adjust the timing of the conversation based on user feelings data.

[0094] The dialogue unit can estimate the user's emotions and determine the priority of the dialogue based on the estimated emotions. For example, if the user is stressed, the dialogue unit will prioritize dialogue to help them relax. If the user is relaxed, the dialogue unit can also prioritize everyday dialogue. Furthermore, if the user is in a hurry, the dialogue unit can prioritize quick dialogue. In this way, the dialogue unit can determine the priority of the dialogue based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the dialogue unit may be performed using AI, or not using AI. For example, the dialogue unit can input the user's facial expression data into the generative AI, which can estimate emotions. The dialogue unit can also input the user's voice data into the generative AI, which can estimate emotions. Furthermore, the dialogue unit can input the user's biometric data into the generative AI, which can estimate emotions.

[0095] The dialogue unit can select appropriate dialogue content during a conversation, taking into account the user's geographical location. For example, if the user is traveling, the dialogue unit can provide information about their travel destination. If the user is at home, the dialogue unit can also provide conversation to help them relax. Furthermore, if the user is at work, the dialogue unit can provide work-related conversation. In this way, the dialogue unit can select dialogue content considering the user's geographical location. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the user's location data into the AI, which can analyze the data and select highly relevant dialogue content. The dialogue unit can also have the AI ​​customize the dialogue method based on the location information. Furthermore, the dialogue unit can have the AI ​​adjust the timing of the dialogue based on the location information.

[0096] The dialogue unit can analyze the user's social media activity during a conversation and suggest conversation content. For example, if the user posts about feeling stressed on social media, the dialogue unit can suggest conversations to help them relax. It can also suggest conversations related to exercise if the user posts about exercise. Furthermore, if the user posts about food, it can suggest conversations related to food. In this way, the dialogue unit can analyze the user's social media activity and suggest conversation content. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input social media data into AI, which can analyze the data and select relevant conversation content. The dialogue unit can also have AI customize the conversation method based on social media activity data. Furthermore, the dialogue unit can have AI adjust the timing of conversations based on social media data.

[0097] The reproduction unit can estimate the user's emotions and adjust the reproduction method based on the estimated emotions. For example, if the user is relaxed, the reproduction unit will reproduce in a calm tone. If the user is excited, the reproduction unit can also reproduce in a lively tone. Furthermore, if the user is sad, the reproduction unit can reproduce in a comforting tone. In this way, the reproduction unit can adjust the reproduction method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the reproduction unit may be performed using a generative AI, or not using a generative AI. For example, the reproduction unit can input the user's facial expression data into a generative AI, and the generative AI can estimate emotions. The reproduction unit can also input the user's voice data into a generative AI, and the generative AI can estimate emotions. Furthermore, the reproduction unit can input the user's biometric data into a generative AI, and the generative AI can estimate emotions.

[0098] The reproduction unit can select an appropriate reproduction method by referring to the user's past dialogue history during reproduction. For example, the reproduction unit can select the optimal reproduction method based on the user's preferred dialogue style in the past. The reproduction unit can also prioritize the reproduction of specific topics from the user's past dialogue history. Furthermore, the reproduction unit can analyze the user's past dialogue history and select the most effective reproduction method. This allows the reproduction unit to select the optimal reproduction method by referring to the user's past dialogue history. Some or all of the above processing in the reproduction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reproduction unit can input past dialogue history into a generative AI, which can analyze the data and select the optimal reproduction method. The reproduction unit can also have the generative AI customize the reproduction method based on past data. Furthermore, the reproduction unit can have the generative AI adjust the reproduction timing based on past data.

[0099] The reproduction unit can customize the content of the reproduction based on the user's current feelings and situation. For example, if the user is sad, the reproduction unit can reproduce comforting content. If the user is happy, the reproduction unit can also reproduce empathetic content. Furthermore, if the user is tired, the reproduction unit can reproduce relaxing content. In this way, the reproduction unit can customize the content of the reproduction based on the user's current feelings and situation. Some or all of the above processing in the reproduction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reproduction unit can input the user's feelings data into a generative AI, which can analyze the data and customize the content of the reproduction. The reproduction unit can also have the generative AI adjust the reproduction method based on the user's situation data. Furthermore, the reproduction unit can have the generative AI adjust the reproduction timing based on the user's feelings data.

[0100] The reproduction unit can estimate the user's emotions and determine the priority of reproductions based on the estimated user emotions. For example, if the user is stressed, the reproduction unit will prioritize reproductions that promote relaxation. It can also prioritize everyday reproductions if the user is relaxed. Furthermore, if the user is in a hurry, the reproduction unit can prioritize rapid reproductions. This allows the reproduction unit to determine the priority of reproductions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the reproduction unit may be performed using a generative AI, or not. For example, the reproduction unit can input user facial data into a generative AI, which can estimate emotions. It can also input user voice data into a generative AI, which can estimate emotions. Furthermore, it can input user biometric data into a generative AI, which can estimate emotions.

[0101] The reproduction unit can select an appropriate reproduction method when reproducing, taking into account the user's geographical location information. For example, if the user is traveling, the reproduction unit can perform a reproduction that provides information about the travel destination. If the user is at home, the reproduction unit can also perform a reproduction to help them relax. Furthermore, if the user is at work, the reproduction unit can perform a reproduction related to work. In this way, the reproduction unit can select a reproduction method that takes into account the user's geographical location information. Some or all of the above processing in the reproduction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reproduction unit can input the user's location data into a generative AI, which can analyze the data and select a highly relevant reproduction method. The reproduction unit can also have the generative AI customize the reproduction method based on the location information. Furthermore, the reproduction unit can have the generative AI adjust the reproduction timing based on the location information.

[0102] The reproduction unit can analyze the user's social media activity during the reproduction process and suggest reproduction content. For example, if the user has posted about feeling stressed on social media, the reproduction unit can suggest a reproduction to help them relax. It can also suggest exercise-related reproductions if the user has posted about exercise. Furthermore, if the user has posted about food, it can suggest food-related reproductions. In this way, the reproduction unit can analyze the user's social media activity and suggest reproduction content. Some or all of the above processing in the reproduction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reproduction unit can input social media data into a generative AI, which can analyze the data and select relevant reproduction content. The reproduction unit can also use the social media activity data to customize the reproduction method using the generative AI. Furthermore, the reproduction unit can use the social media data to adjust the reproduction timing using the generative AI.

[0103] A care robot can estimate the user's emotions and adjust its actions based on those emotions. For example, if the user is stressed, the care robot can perform actions to promote relaxation. If the user is relaxed, the care robot can also perform playful actions. Furthermore, if the user is in a hurry, the care robot can perform rapid actions. This allows the care robot to adjust its actions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in a care robot may be performed using AI, or not. For example, the care robot can input the user's facial expression data into a generative AI, which can then estimate the emotions. The care robot can also input the user's voice data into a generative AI, which can then estimate the emotions. Furthermore, the care robot can input the user's biometric data into a generative AI, which can then estimate the emotions.

[0104] A care robot can select an appropriate action method by referring to the user's past health data during operation. For example, the care robot can suggest relaxation movements based on the user's past blood pressure data. It can also suggest appropriate exercise based on the user's past heart rate data. Furthermore, it can suggest a balanced diet based on the user's past calorie data. In this way, the care robot can select the optimal action method by referring to the user's past health data. Some or all of the above processes in a care robot may be performed using AI, for example, or not using AI. For example, the care robot can input past health data into AI, which can analyze the data and select the optimal action method. The care robot can also have AI customize its action method based on past data. Furthermore, the care robot can have AI adjust the timing of its actions based on past data.

[0105] A care robot can customize its actions based on the user's current lifestyle. For example, if the user is at work, the care robot can suggest short stretches. If the user is resting, the care robot can suggest breathing exercises to help them relax. Furthermore, if the user is exercising, the care robot can suggest appropriate hydration methods. This allows the care robot to customize its actions based on the user's current lifestyle. Some or all of the above processes in a care robot may be performed using AI, for example, or without AI. For example, the care robot can input user lifestyle data into AI, which can analyze the data and customize its actions. The care robot can also have AI adjust its actions based on user activity data. Furthermore, the care robot can have AI adjust its timing based on user lifestyle data.

[0106] A care robot can estimate a user's emotions and determine the priority of its actions based on those emotions. For example, if the user is stressed, the care robot will prioritize actions that promote relaxation. If the user is relaxed, the care robot may also prioritize playful actions. Furthermore, if the user is in a hurry, the care robot may prioritize quick actions. This allows the care robot to determine action priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in a care robot may be performed using AI, or not. For example, the care robot can input the user's facial expression data into a generative AI, which can then estimate emotions. The care robot can also input the user's voice data into a generative AI, which can then estimate emotions. Furthermore, the care robot can input the user's biometric data into a generative AI, which can then estimate emotions.

[0107] A care robot can select an appropriate operating method while considering the user's geographical location. For example, if the user is at high altitude, the care robot may prioritize oxygen concentration management. It may also prioritize air quality management if the user is in an urban area. Furthermore, if the user is outdoors, it may prioritize UV ​​protection. This allows the care robot to select an operating method that considers the user's geographical location. Some or all of the above processing in a care robot may be performed using AI, for example, or without AI. For example, the care robot can input the user's location data into AI, which can analyze the data and select a highly relevant operating method. The AI ​​can also customize the operating method based on the location information. Furthermore, the AI ​​can adjust the timing of the care robot's actions based on the location information.

[0108] A care robot can analyze a user's social media activity during operation and suggest appropriate actions. For example, if a user posts about feeling stressed on social media, the care robot can suggest ways to relax. It can also suggest an appropriate exercise plan if the user posts about exercise. Furthermore, if the user posts about food, it can suggest a balanced meal plan. This allows the care robot to analyze the user's social media activity and suggest appropriate actions. Some or all of the above processing in the care robot may be performed using AI, for example, or without AI. For example, the care robot can input social media data into AI, which can analyze the data and select relevant actions. The AI ​​can also customize the robot's actions based on social media activity data. Furthermore, the AI ​​can adjust the timing of the robot's actions based on social media data.

[0109] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0110] The system comprises an acquisition unit that obtains the user's health information, a management unit that manages the health based on that information, a dialogue unit that interacts with the user, and a reproduction unit that recreates the user using generative AI. Furthermore, the system may also include an emotion management unit that estimates the user's emotions and adjusts the health management method based on the estimated emotions. For example, if the user is feeling stressed, the emotion management unit may suggest exercise to help them relax. If the user is relaxed, it may also suggest regular health checkups. Moreover, if the user is in a hurry, it may suggest health management methods that can be completed in a short amount of time. In this way, the system can adjust the health management method based on the user's emotions.

[0111] The system comprises an acquisition unit, a management unit, an interaction unit, and a reproduction unit. Furthermore, the system may also include a data analysis unit that analyzes the user's past health data and selects the optimal acquisition method. For example, the data analysis unit analyzes the user's past blood pressure data and acquires health information during the most stable time period. It can also be configured to avoid acquiring data after exercise based on the user's past heart rate data. Additionally, it can prioritize acquiring data after meals based on the user's past calorie data. This allows the system to analyze the user's past health data and select the optimal acquisition method.

[0112] The system comprises an acquisition unit, a management unit, an interaction unit, and a reproduction unit. Furthermore, the system may also include a lifestyle analysis unit that adjusts the timing of health information acquisition based on the user's current lifestyle and activity level. For example, if the user is exercising, the lifestyle analysis unit may adjust the system to acquire health information after the exercise. If the user is resting, it may also acquire health information while the user is relaxed. Furthermore, if the user is working, it may be set to acquire health information during work breaks. This allows the system to filter health information based on the user's lifestyle and activity level.

[0113] The system comprises an acquisition unit, a management unit, an interaction unit, and a reproduction unit. Furthermore, the system may include an emotion prioritization unit that estimates the user's emotions and determines the priority of health information to acquire based on those emotions. For example, the emotion prioritization unit might prioritize acquiring heart rate if the user is stressed. It could also prioritize acquiring blood pressure if the user is relaxed. Furthermore, it could prioritize acquiring calorie information if the user is tired. This allows the system to prioritize health information based on the user's emotions.

[0114] The system comprises an acquisition unit, a management unit, an interaction unit, and a reproduction unit. Furthermore, the system may also include a location information analysis unit that prioritizes the acquisition of highly relevant information considering the user's geographical location. For example, if the user is at high altitude, the location information analysis unit prioritizes acquiring oxygen concentration data. If the user is in an urban area, it can also acquire air quality information. Furthermore, if the user is outdoors, it can acquire UV radiation level information. This allows the system to acquire health information while considering the user's geographical location.

[0115] The system comprises an acquisition unit, a management unit, an interaction unit, and a reproduction unit. Furthermore, the system may also include a social media analysis unit that analyzes the user's social media activity and acquires relevant health information. For example, the social media analysis unit prioritizes acquiring heart rate data if the user posts about feeling stressed on social media. It can also acquire post-exercise health information if the user posts about exercise. Additionally, it can acquire post-meal calorie information if the user posts about food. This allows the system to analyze the user's social media activity and acquire health information.

[0116] The system comprises an acquisition unit, a management unit, a dialogue unit, and a reproduction unit. Furthermore, the system may also include an emotional dialogue unit that estimates the user's emotions and adjusts the dialogue's presentation based on those emotions. For example, the emotional dialogue unit might use a calm tone if the user is stressed, or a bright tone if the user is relaxed. It could also use a concise and rapid dialogue if the user is in a hurry. This allows the system to adjust the dialogue's presentation based on the user's emotions.

[0117] The system comprises an acquisition unit, a management unit, a dialogue unit, and a reproduction unit. Furthermore, the system may also include a dialogue history analysis unit that selects an appropriate dialogue method by referring to the user's past dialogue history. For example, the dialogue history analysis unit selects the optimal dialogue method based on the dialogue style the user has preferred in the past. It can also prioritize dialogue on specific topics based on the user's past dialogue history. Additionally, it can analyze the user's past dialogue history and select the most effective dialogue method. This allows the system to select the optimal dialogue method by referring to the user's past dialogue history.

[0118] The system comprises an acquisition unit, a management unit, an interaction unit, and a reproduction unit. Furthermore, the system may include an emotion reproduction unit that estimates the user's emotions and adjusts the reproduction method based on the estimated emotions. For example, the emotion reproduction unit might reproduce in a calm tone if the user is relaxed, in a lively tone if the user is excited, and in a comforting tone if the user is sad. This allows the system to adjust the reproduction method based on the user's emotions.

[0119] The system comprises an acquisition unit, a management unit, an interaction unit, and a reproduction unit. Furthermore, the system may also include a location information reproduction unit that selects an appropriate reproduction method considering the user's geographical location. For example, if the user is traveling, the location information reproduction unit can perform a reproduction that provides information about the travel destination. If the user is at home, it can perform a reproduction to help them relax. Furthermore, if the user is at work, it can perform a reproduction related to work. This allows the system to select a reproduction method that takes the user's geographical location into consideration.

[0120] The following briefly describes the processing flow for example form 2.

[0121] Step 1: The acquisition unit acquires the user's health information. This information includes, for example, blood pressure, heart rate, and calorie information. The acquisition unit measures the user's blood pressure using a blood pressure monitor, measures the user's heart rate using a heart rate monitor, and acquires the user's calorie information using a calorie counter. This data is then stored. Step 2: The management unit manages the user's health based on the health information acquired by the acquisition unit. The management unit monitors the user's health status, notifies them of abnormal values, and provides health advice based on the health information. For example, it notifies the user if abnormal values ​​are detected in blood pressure or heart rate, and provides advice on appropriate diet and exercise based on calorie information. Step 3: The dialogue unit conducts daily conversations with the user and records the content. The dialogue unit engages in voice and text dialogues to acquire the user's thoughts, catchphrases, values, messages, etc. Using speech recognition technology, the content of the user's conversations is saved as text data, and in the case of text dialogues, the text entered by the user is saved as is. Step 4: The Reconstruction Unit uses generative AI to recreate the user based on the information recorded in the Dialogue Unit. The Reconstruction Unit recreates the user's voice, actions, and facial expressions. For example, it uses generative AI to recreate the user's voice tone and speaking style, recreates actions including unique gestures and movements, and recreates expressions such as smiles and surprise.

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0124] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0125] For example, the acquisition unit can acquire the user's health information using the camera 42 and sensors of the smart device 14. The management unit can perform health management based on the health information acquired by the specific processing unit 290 of the data processing device 12. The dialogue unit can have daily conversations with the user using the control unit 46A of the smart device 14 and record the content of those conversations. The reproduction unit can reproduce the user using AI generated by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0126] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0127] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0128] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0129] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0130] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0132] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0133] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0134] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0135] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0136] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0137] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0138] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0140] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0141] For example, the acquisition unit can acquire the user's health information using the camera 42 and sensors of the smart glasses 214. The management unit can perform health management based on the health information acquired by the specific processing unit 290 of the data processing device 12. The dialogue unit can have daily conversations with the user using the control unit 46A of the smart glasses 214 and record the content of those conversations. The reproduction unit can reproduce the user using AI generated by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0142] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0143] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0144] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0145] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0146] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0148] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0149] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0150] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0151] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0152] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0154] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0156] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0157] For example, the acquisition unit can acquire the user's health information using the camera 42 and sensors of the headset terminal 314. The management unit can perform health management based on the health information acquired by the specific processing unit 290 of the data processing device 12. The dialogue unit can have daily conversations with the user using the control unit 46A of the headset terminal 314 and record the content of those conversations. The reproduction unit can reproduce the user using AI generated by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0158] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0159] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0160] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0161] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0162] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0163] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0164] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0165] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0166] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0167] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0168] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0169] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0170] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0171] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0172] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0173] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0174] For example, the acquisition unit can acquire the user's health information using the camera 42 and sensors of the robot 414. The management unit can perform health management based on the health information acquired by the specific processing unit 290 of the data processing device 12. The dialogue unit can have daily conversations with the user using the control unit 46A of the robot 414 and record the content of those conversations. The reproduction unit can reproduce the user using AI generated by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0175] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0176] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0177] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0178] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0179] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0180] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0181] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0182] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0183] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0184] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0185] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0186] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0187] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0188] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0189] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0190] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0191] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0192] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0193] (Note 1) An acquisition unit that acquires user health information, Based on the health information acquired by the acquisition unit, a management unit manages the user's health, A dialogue unit that interacts with the user, The system includes a reproduction unit that reproduces the user using a generative AI based on the interaction with the user. A system characterized by the following features. (Note 2) The care robot includes the acquisition unit, the management unit, and the dialogue unit. The system described in Appendix 1, characterized by the features described herein. (Note 3) The acquisition unit is, Obtain health information from the user, including blood pressure, heart rate, and calorie information. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned management department, Manage the user's health based on the acquired health information. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned dialogue unit, Engage in daily conversations with users and record the content of those conversations. The system described in Appendix 1, characterized by the features described herein. (Note 6) The reproduction unit is, Based on the information recorded in the dialogue section, a generative AI is used to recreate the user. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, The system estimates the user's emotions and adjusts the timing of health information acquisition based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, Analyze the user's past health data and select the appropriate method of data acquisition. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, When acquiring health information, filtering is performed based on the user's current lifestyle and activity level. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, It estimates the user's emotions and determines the priority of health information to acquire based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, When acquiring health information, the system prioritizes the acquisition of highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The acquisition unit is, When acquiring health information, the system analyzes the user's social media activity and retrieves relevant health information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned management department, It estimates the user's emotions and adjusts health management methods based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned management department, During health management, the system selects the appropriate management method by referring to the user's past health data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned management department, When managing health, the management methods are customized based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned management department, It estimates the user's emotions and determines health management priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned management department, When managing health information, select an appropriate management method that takes into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned management department, When managing health, we analyze users' social media activity and propose management methods. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned dialogue unit, It estimates the user's emotions and adjusts the way the dialogue is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned dialogue unit, During a conversation, the system selects the appropriate conversation method by referring to the user's past conversation history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned dialogue unit, During conversations, the dialogue content is customized based on the user's current feelings and situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned dialogue unit, It estimates the user's emotions and determines the priority of the conversation based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned dialogue unit, During the conversation, the system selects appropriate dialogue content while considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned dialogue unit, During conversations, the system analyzes the user's social media activity and suggests conversation topics. The system described in Appendix 1, characterized by the features described herein. (Note 25) The reproduction unit is, We estimate the user's emotions and adjust the reproduction method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The reproduction unit is, During reproduction, the system will refer to the user's past dialogue history to select the appropriate reproduction method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The reproduction unit is, During the reproduction process, the reproduction content is customized based on the user's current feelings and circumstances. The system described in Appendix 1, characterized by the features described herein. (Note 28) The reproduction unit is, The system estimates the user's emotions and determines the priority of reproduction based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The reproduction unit is, During reproduction, the appropriate reproduction method is selected, taking into account the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The reproduction unit is, During the reproduction process, we analyze the user's social media activity and suggest reproduction methods. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned care robot is It estimates the user's emotions and adjusts the care robot's actions based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned care robot is During operation, the system selects the appropriate action method by referring to the user's past health data. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned care robot is During operation, the means of operation are customized based on the user's current living situation. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned care robot is The system estimates the user's emotions and determines the priority of the care robot's actions based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned care robot is During operation, the system selects the appropriate method of operation, taking into account the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned care robot is During operation, the system analyzes the user's social media activity and suggests appropriate actions. The system described in Appendix 2, characterized by the features described herein. [Explanation of Symbols]

[0194] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An acquisition unit that acquires user health information, Based on the health information acquired by the acquisition unit, a management unit manages the user's health, A dialogue unit that interacts with the user, The system includes a reproduction unit that reproduces the user by having a generating AI learn the tone and manner of speaking of the user based on the interaction with the user, and generate the user's voice based on the learning, and by reproducing the user's actions, including the user's unique gestures and movements, from motion data captured by a camera and recorded by the dialogue unit, learning the changes in the user's facial expressions, and reproducing the user's facial expressions based on the learning of the changes in the user's facial expressions, The acquisition unit is characterized by estimating the user's emotions using a generating AI based on input of at least one of the user's facial expression data, voice data, and biometric data, and if the estimated emotions of the user indicate a stressed state, adjusting the system to acquire health information during times when the user is relaxed.

2. The care robot includes the acquisition unit, the management unit, and the dialogue unit. The system according to feature 1.

3. The acquisition unit is, The user's health information, including blood pressure, heart rate, and calorie information, is acquired. The system according to feature 1.

4. The aforementioned dialogue unit, Conduct daily conversations with the aforementioned user and record the content. The system according to feature 1.

5. The reproduction unit is, Based on the information recorded in the aforementioned dialogue unit, the user is recreated using a generation AI. The system according to feature 1.

6. The acquisition unit is, The system according to claim 1, characterized in that, if the estimated emotion of the user is in a relaxed state, it adjusts to acquire health information at regular intervals.

7. The acquisition unit is, By inputting the user's past health data into the generating AI, the appropriate acquisition method is selected. The system according to feature 1.

8. The acquisition unit is, By inputting the user's activity data into the generating AI, health information is obtained based on the user's current lifestyle and activity level. The system according to feature 1.

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