System

Abstract

An object of a system according to an embodiment is to analyze an emotional state of a user and generate and provide an appropriate conversation based on the emotional state.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects vital data. The collection unit collects environment data. The analysis unit analyzes an emotional state of the user based on the data collected by the collection unit. The generation unit generates a conversation based on the data analyzed by the analysis unit. The providing unit provides the conversation generated by the generating unit to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately analyze a user's emotional state in real time and generate appropriate conversations based on that analysis, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze the emotional state of the user and generate and provide appropriate conversations based on the analysis. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects vital data. The collection unit collects environmental data. The analysis unit analyzes the emotional state of the user based on the data collected by the collection unit. The generation unit generates a conversation based on the data analyzed by the analysis unit. The provision unit provides the conversation generated by the generation unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the emotional state of the user and generate and provide appropriate conversations based on the analysis. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A care robot system according to an embodiment of the present invention collects vital data and environmental data, analyzes a user's emotional state, and generates and provides a conversation. The care robot system includes a collection unit that collects vital data, a collection unit that collects environmental data, an analysis unit that analyzes a user's emotional state based on the collected data, a generation unit that generates a conversation based on the analyzed data, and a provision unit that provides the generated conversation to the user. For example, the care robot system collects vital data such as heart rate, body temperature, and blood pressure from a wearable device such as a smartwatch. The care robot system also collects environmental data such as room temperature, humidity, and illuminance from IoT products. These data are used in conjunction with an emotion recognition engine to analyze the user's emotional state. Next, the care robot system uses a generation AI to generate a conversation based on the user's emotional state, vital data, and environmental data. For example, if the user is feeling stressed, the system can provide advice on how to relax. It can also provide appropriate advice and solutions based on the user's health condition. The care robot system analyzes this data in real time and provides appropriate feedback to the user. For example, if the user is tired, the system can encourage them to take a break. It is also possible to adjust room temperature and illuminance based on environmental data. This allows the care robot system to provide appropriate care according to the user's health condition and emotional state. For example, if the user is feeling stressed, the robot can provide advice on how to relax, thereby reducing the user's stress. Providing appropriate advice according to the user's health condition also makes health management easier. In this way, by combining an emotion recognition engine, vital data, environmental data, and generative AI, it is possible to create a care robot that can provide highly accurate conversations and health solutions in real time.

[0029] The care robot system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects vital data. Examples of vital data include, but are not limited to, heart rate, blood pressure, and body temperature. The collection unit collects vital data from a wearable device such as a smart watch. The collection unit also collects environmental data from an IoT product. Examples of environmental data include, but are not limited to, room temperature, humidity, and illuminance. The collection unit also collects environmental data from a smart home device. The analysis unit analyzes a user's emotional state based on the collected vital data and environmental data. The analysis unit analyzes the emotional state using, for example, a machine learning algorithm. The analysis unit can also analyze the emotional state using data mining technology. The generation unit generates a conversation based on the user's emotional state, vital data, and environmental data using a generation AI. The generation unit generates the conversation using, for example, natural language generation technology. The generation unit can also generate the conversation using template-based generation. The providing unit provides the generated conversation to the user. The providing unit provides the conversation, for example, by using audio output. The providing unit can also provide the conversation by using text display. As a result, the care robot system according to the embodiment can collect vital data and environmental data, analyze the emotional state of the user, and generate and provide the conversation.

[0030] The collection unit can collect vital data from a smartwatch or other wearable device. For example, the collection unit collects vital data such as heart rate, body temperature, and blood pressure from the smartwatch. The collection unit can also collect vital data from other wearable devices. For example, the collection unit can collect vital data from a fitness tracker. Furthermore, the collection unit can collect user location information using the GPS function of the smartwatch. In this way, by collecting vital data from the wearable device, the user's health condition can be understood in real time. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input vital data acquired from the smartwatch to a generation AI and have the generation AI analyze the data.

[0031] The collection unit can collect environmental data from IoT products. For example, the collection unit collects environmental data such as room temperature, humidity, and illuminance from smart home devices. The collection unit can also collect environmental data from environmental sensors. For example, the collection unit can collect room temperature data from a temperature sensor. Furthermore, the collection unit can collect environmental data from a smart thermostat. In this way, by collecting environmental data from IoT products, the environmental conditions around the user can be understood. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input environmental data acquired from smart home devices to a generation AI and have the generation AI analyze the data.

[0032] The analysis unit can analyze the user's emotional state based on the collected vital data and environmental data. The analysis unit analyzes the emotional state using, for example, a machine learning algorithm. For example, the analysis unit uses a machine learning model that inputs collected data such as heart rate, body temperature, room temperature, and humidity and outputs the emotional state. The analysis unit can also analyze the emotional state using data mining technology. For example, the analysis unit clusters the collected data to identify the emotional state. Furthermore, the analysis unit can analyze the emotional state using an emotion recognition engine. For example, the analysis unit uses the emotion recognition engine to estimate the user's emotional state from the collected data. This enables more accurate emotion recognition by analyzing the user's emotional state based on the vital data and environmental data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the collected data to a generation AI and have the generation AI analyze the emotional state.

[0033] The generation unit can generate a conversation based on the user's emotional state, vital data, or environmental data. The generation unit generates the conversation using, for example, natural language generation technology. For example, the generation unit uses a natural language generation model that receives collected emotional state, vital data, and environmental data as input and outputs a conversation. The generation unit can also generate a conversation using template-based generation. For example, the generation unit generates a conversation based on a pre-prepared template. Furthermore, the generation unit can also generate a conversation using a generation AI. For example, the generation unit uses a generation AI to generate a conversation based on the user's emotional state, vital data, and environmental data. This allows for more appropriate conversation to be provided by generating a conversation based on the user's emotional state, vital data, and environmental data. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input collected data to a generation AI and have the generation AI generate a conversation.

[0034] The providing unit can provide the generated conversation to the user. The providing unit provides the conversation using, for example, audio output. For example, the providing unit outputs the generated conversation as audio using speech synthesis technology. The providing unit can also provide the conversation using text display. For example, the providing unit displays the generated conversation on a display. Furthermore, the providing unit can notify the user of the generated conversation to their smartphone. For example, the providing unit notifies the user of the generated conversation through a smartphone application. In this way, by providing the generated conversation to the user, appropriate feedback can be provided to the user. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the generated conversation to a generation AI and cause the generation AI to provide the conversation.

[0035] The providing unit can provide advice and solutions according to the user's health condition. The providing unit, for example, provides advice according to the user's health condition. For example, the providing unit provides advice regarding exercise and diet to the user based on the collected vital data. The providing unit can also provide solutions according to the user's health condition. For example, the providing unit provides solutions for health management to the user based on the collected vital data. Furthermore, the providing unit can also provide reminders according to the user's health condition. For example, the providing unit provides reminders to the user to exercise and rest regularly. This makes it possible to support the user's health management by providing advice and solutions according to the user's health condition. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input the collected vital data to a generation AI and cause the generation AI to provide advice and solutions.

[0036] The providing unit can adjust the room temperature or illuminance based on the environmental data. The providing unit, for example, adjusts the room temperature based on the environmental data. For example, the providing unit controls an air conditioner to adjust the room temperature based on the collected room temperature data. The providing unit can also adjust the illuminance based on the environmental data. For example, the providing unit controls lighting to adjust the illuminance based on the collected illuminance data. The providing unit can also adjust humidity based on the environmental data. For example, the providing unit controls a humidifier to adjust the humidity based on the collected humidity data. This makes it possible to maintain a comfortable environment for the user by adjusting the room temperature and illuminance based on the environmental data. Some or all of the above-described processing in the providing unit may be performed using, or without, AI. For example, the providing unit can input the collected environmental data to a generating AI and cause the generating AI to adjust the room temperature and illuminance.

[0037] When collecting vital data, the collection unit can change the type of data to be collected depending on the user's activity level. For example, when the user is exercising, the collection unit focuses on collecting heart rate and oxygen saturation. For example, the collection unit collects the user's heart rate and oxygen saturation in real time while exercising. Furthermore, when the user is resting, the collection unit can also focus on collecting body temperature and blood pressure. For example, the collection unit collects the user's body temperature and blood pressure at regular intervals while the user is resting. Furthermore, when the user is sleeping, the collection unit can also collect heart rate variability and respiration rate to evaluate sleep quality. For example, the collection unit collects the user's heart rate variability and respiration rate while sleeping to evaluate sleep quality. This enables more appropriate data collection by changing the type of data to be collected depending on the user's activity level. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input data for estimating the user's activity level into a generation AI and cause the generation AI to estimate the activity level and change the type of data.

[0038] When collecting vital data, the collection unit can detect abnormal values ​​by referring to the user's past health data. For example, the collection unit can detect abnormal heart rates by comparing the past heart rate data with the user's past heart rate data. For example, the collection unit can determine whether the current heart rate is outside the normal range based on the past heart rate data. The collection unit can also detect abnormal blood pressure by comparing the past blood pressure data with the user's past blood pressure data. For example, the collection unit can determine whether the current blood pressure is outside the normal range based on the past blood pressure data. Furthermore, the collection unit can detect abnormal body temperature by comparing the past body temperature data with the user's past body temperature data. For example, the collection unit can determine whether the current body temperature is outside the normal range based on the past body temperature data. In this way, by detecting abnormal values ​​by referring to the user's past health data, abnormal health conditions can be discovered early. Some or all of the above-mentioned processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input past health data into the generation AI and cause the generation AI to detect abnormal values.

[0039] The collection unit can set the optimal collection timing based on the user's daily rhythm when collecting vital data. For example, if the user has a habit of exercising every morning, the collection unit collects vital data before and after exercise. For example, the collection unit collects heart rate and body temperature before and after exercise to evaluate the effects of the exercise. Furthermore, if the user has time to relax at night, the collection unit can also collect vital data during that time. For example, the collection unit collects blood pressure and respiratory rate during relaxation time to evaluate the effectiveness of relaxation. Furthermore, if the user takes regular breaks, the collection unit can also collect vital data at those times. For example, the collection unit collects body temperature and heart rate during breaks to evaluate the effectiveness of the breaks. This enables more effective data collection by setting the optimal collection timing based on the user's daily rhythm. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input data for estimating the user's daily rhythm into the generation AI and have the generation AI set the optimal collection timing.

[0040] When collecting vital data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is at high altitude, the collection unit prioritizes collecting oxygen saturation. For example, the collection unit frequently collects the oxygen saturation of a user at high altitude to monitor the user's health condition at high altitude. The collection unit can also prioritize collecting body temperature when the user is in a cold region. For example, the collection unit frequently collects the body temperature of a user in a cold region to monitor the user's health condition in the cold region. Furthermore, the collection unit can prioritize collecting heart rate and stress level when the user is in an urban area. For example, the collection unit frequently collects the heart rate and stress level of a user in an urban area to monitor the user's health condition in the urban area. This enables more appropriate data collection by prioritizing the collection of highly relevant data by taking into account the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input the user's geographic location information into the generation AI and have the generation AI determine the prioritization of highly relevant data.

[0041] The collection unit can analyze the user's social media activity and collect related data when collecting vital data. For example, if a user posts on social media about feeling stressed, the collection unit can collect the user's heart rate and blood pressure. For example, the collection unit can frequently collect the heart rate and blood pressure of a stressed user. The collection unit can also collect the user's body temperature and respiratory rate when the user posts about being relaxed. For example, the collection unit can collect the user's body temperature and respiratory rate at regular intervals when the user is relaxing. Furthermore, the collection unit can collect the user's oxygen saturation and heart rate when the user posts about exercising. For example, the collection unit can collect the user's oxygen saturation and heart rate in real time while exercising to monitor their health. This enables more appropriate data collection by analyzing the user's social media activity and collecting related data. Some or all of the above-described processing by the collection unit can be performed using, or without, AI. For example, the collection unit can input the user's social media activity into a generation AI and cause the generation AI to collect related data.

[0042] The collection unit can customize the collection method by reflecting the user's past feedback when collecting vital data. For example, if the user has requested heart rate collection in the past, the collection unit increases the frequency of heart rate collection. For example, the collection unit adjusts the frequency of heart rate collection based on the user's preference. The collection unit can also increase the frequency of body temperature collection if the user has requested body temperature collection in the past. For example, the collection unit adjusts the frequency of body temperature collection based on the user's preference. Furthermore, the collection unit can also increase the frequency of blood pressure collection if the user has requested blood pressure collection in the past. For example, the collection unit adjusts the frequency of blood pressure collection based on the user's preference. This enables more appropriate data collection by customizing the collection method by reflecting the user's past feedback. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's past feedback into the generation AI and cause the generation AI to customize the collection method.

[0043] When collecting environmental data, the collection unit can change the type of data to be collected depending on the user's current activity. For example, when the user is exercising, the collection unit may focus on collecting temperature and humidity. For example, the collection unit may collect the user's temperature and humidity in real time while exercising. The collection unit may also focus on collecting illuminance and sound environment when the user is resting. For example, the collection unit may collect the user's illuminance and sound environment at regular intervals while the user is resting. Furthermore, the collection unit may focus on collecting air quality and temperature when the user is sleeping. For example, the collection unit may collect the user's air quality and temperature while sleeping to evaluate the user's sleep quality. This enables more appropriate data collection by changing the type of data to be collected depending on the user's current activity. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit may input data for estimating the user's current activity into a generation AI and cause the generation AI to estimate the activity and change the type of data.

[0044] When collecting environmental data, the collection unit can detect abnormal values ​​by referring to past environmental data. For example, the collection unit can detect abnormal temperatures by comparing the data with past room temperature data. For example, the collection unit can determine whether the current room temperature deviates from a normal range based on the past room temperature data. The collection unit can also detect abnormal humidity by comparing the data with past humidity data. For example, the collection unit can determine whether the current humidity deviates from a normal range based on the past humidity data. Furthermore, the collection unit can detect abnormal illuminance by comparing the data with past illuminance data. For example, the collection unit can determine whether the current illuminance deviates from a normal range based on the past illuminance data. In this way, by detecting abnormal values ​​by referring to past environmental data, abnormal environmental conditions can be discovered early. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input past environmental data to a generation AI and cause the generation AI to detect abnormal values.

[0045] The collection unit can set the optimal collection timing based on the user's lifestyle when collecting environmental data. For example, if the user has a habit of exercising every morning, the collection unit collects environmental data before and after exercise. For example, the collection unit collects room temperature and humidity before and after exercise to evaluate the effects of the exercise. The collection unit can also collect environmental data during the user's relaxation time if the user has time to relax at night. For example, the collection unit collects illuminance and sound environment during relaxation time to evaluate the effects of relaxation. Furthermore, if the user takes regular breaks, the collection unit can also collect environmental data at those times. For example, the collection unit collects air quality and temperature during breaks to evaluate the effects of the breaks. This enables more effective data collection by setting the optimal collection timing based on the user's lifestyle. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input data for estimating the user's lifestyle into the generation AI and have the generation AI set the optimal collection timing.

[0046] When collecting environmental data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, if the user is at a high altitude, the collection unit prioritizes collecting air quality and temperature. For example, the collection unit frequently collects air quality and temperature of the user at a high altitude to monitor the environmental conditions at the high altitude. The collection unit can also prioritize collecting room temperature and humidity if the user is in a cold area. For example, the collection unit frequently collects room temperature and humidity of the user in a cold area to monitor the environmental conditions in the cold area. Furthermore, the collection unit can prioritize collecting sound environment and air quality if the user is in an urban area. For example, the collection unit frequently collects sound environment and air quality of the user in an urban area to monitor the environmental conditions in the urban area. This enables more appropriate data collection by prioritizing the collection of highly relevant data by taking into account the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input the user's geographic location information into the generation AI and have the generation AI determine the prioritization of highly relevant data.

[0047] The collection unit can analyze the user's social media activity and collect related data when collecting environmental data. For example, if a user posts on social media about feeling stressed, the collection unit can collect room temperature and humidity. For example, the collection unit can frequently collect the room temperature and humidity of the stressed user. The collection unit can also collect illuminance and sound environment if the user posts about being relaxed. For example, the collection unit can collect the illuminance and sound environment of the relaxed user at regular intervals. Furthermore, the collection unit can collect air quality and temperature if the user posts about exercising. For example, the collection unit can collect the air quality and temperature of the exercising user in real time to monitor the environmental condition. This enables more appropriate data collection by analyzing the user's social media activity and collecting related data. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's social media activity into the generation AI and cause the generation AI to collect related data.

[0048] The collection unit can customize the collection method by reflecting the user's past feedback when collecting environmental data. For example, if the user has previously requested that room temperature be collected, the collection unit increases the frequency of room temperature collection. For example, the collection unit adjusts the frequency of room temperature collection based on the user's preference. The collection unit can also increase the frequency of humidity collection if the user has previously requested that humidity be collected. For example, the collection unit adjusts the frequency of humidity collection based on the user's preference. The collection unit can also increase the frequency of illuminance collection if the user has previously requested that illuminance be collected. For example, the collection unit adjusts the frequency of illuminance collection based on the user's preference. This enables more appropriate data collection by customizing the collection method by reflecting the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's past feedback into the generation AI and cause the generation AI to customize the collection method.

[0049] The analysis unit can improve the accuracy of the analysis by taking into account the correlation between vital data and environmental data during analysis. For example, when the room temperature is high, the analysis unit performs the analysis by taking into account an increase in heart rate. For example, the analysis unit performs the analysis by taking into account fluctuations in heart rate in a high room temperature environment. The analysis unit can also perform the analysis by taking into account fluctuations in respiratory rate in a low humidity environment. For example, the analysis unit performs the analysis by taking into account fluctuations in respiratory rate in a low humidity environment. Furthermore, the analysis unit can also perform the analysis by taking into account fluctuations in body temperature in a high illuminance environment. For example, the analysis unit performs the analysis by taking into account fluctuations in body temperature in a high illuminance environment. This improves the accuracy of the analysis by taking into account the correlation between vital data and environmental data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the correlation between vital data and environmental data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0050] During analysis, the analysis unit can detect abnormal values ​​by referring to the user's past data. The analysis unit, for example, compares the data with the user's past heart rate data to detect abnormal heart rates. For example, the analysis unit determines whether the current heart rate deviates from a normal range based on the past heart rate data. The analysis unit can also detect abnormal blood pressure by comparing the data with the user's past blood pressure data. For example, the analysis unit determines whether the current blood pressure deviates from a normal range based on the past blood pressure data. The analysis unit can also detect abnormal body temperature by comparing the data with the user's past body temperature data. For example, the analysis unit determines whether the current body temperature deviates from a normal range based on the past body temperature data. By detecting abnormal values ​​by referring to the user's past data, abnormal health conditions can be discovered early. Some or all of the above-described processing by the analysis unit may be performed using, or without, AI. For example, the analysis unit can input past data into a generation AI and cause the generation AI to detect abnormal values.

[0051] During analysis, the analysis unit can set the optimal analysis timing based on the user's lifestyle rhythm. For example, if the user has a habit of exercising every morning, the analysis unit analyzes the vital data after exercise. For example, the analysis unit analyzes the heart rate and body temperature after exercise to evaluate the effects of the exercise. Furthermore, if the user has time to relax at night, the analysis unit can also analyze the vital data during that time period. For example, the analysis unit analyzes the blood pressure and respiratory rate during the relaxation time period to evaluate the effects of the relaxation. Furthermore, if the user takes regular breaks, the analysis unit can also analyze the vital data at those times. For example, the analysis unit analyzes the body temperature and heart rate during breaks to evaluate the effects of the breaks. This enables more effective analysis by setting the optimal analysis timing based on the user's lifestyle rhythm. Some or all of the above-described processing by the analysis unit may be performed using, or without, AI. For example, the analysis unit can input data for estimating the user's lifestyle rhythm into the generation AI and have the generation AI set the optimal analysis timing.

[0052] The analysis unit can improve the accuracy of the analysis by taking into account the user's geographical location information during analysis. For example, when the user is at high altitude, the analysis unit takes into account fluctuations in oxygen saturation when performing the analysis. For example, the analysis unit takes into account fluctuations in oxygen saturation of a user at high altitude when performing the analysis. The analysis unit can also take into account fluctuations in body temperature when the user is in a cold region when performing the analysis. For example, the analysis unit takes into account fluctuations in body temperature of a user in a cold region when performing the analysis. Furthermore, the analysis unit can also take into account fluctuations in heart rate and stress level when the user is in an urban area when performing the analysis. For example, the analysis unit takes into account fluctuations in heart rate and stress level of a user in an urban area when performing the analysis. In this way, by taking into account the user's geographical location information, the accuracy of the analysis is improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's geographical location information into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0053] During analysis, the analysis unit can analyze the user's social media activity and analyze related data. For example, if a user posts on social media that they are feeling stressed, the analysis unit can analyze the heart rate and blood pressure data with an emphasis on the heart rate and blood pressure data of the user who is feeling stressed. Furthermore, if a user posts that they are relaxing, the analysis unit can also analyze the body temperature and respiratory rate data with an emphasis on the body temperature and respiratory rate data of the user who is relaxing. Furthermore, if a user posts that they are exercising, the analysis unit can analyze the oxygen saturation and heart rate data with an emphasis on the oxygen saturation and heart rate data of the user who is exercising. This allows for more appropriate analysis by analyzing the user's social media activity and analyzing related data. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's social media activity into a generation AI and cause the generation AI to analyze the related data.

[0054] During analysis, the analysis unit can customize the analysis algorithm by reflecting the user's past feedback. For example, if the user has requested heart rate analysis in the past, the analysis unit improves the accuracy of the heart rate analysis. For example, the analysis unit adjusts the accuracy of the heart rate analysis based on the user's preference. Furthermore, if the user has requested body temperature analysis in the past, the analysis unit can improve the accuracy of the body temperature analysis. For example, the analysis unit adjusts the accuracy of the body temperature analysis based on the user's preference. Furthermore, if the user has requested blood pressure analysis in the past, the analysis unit can improve the accuracy of the blood pressure analysis. For example, the analysis unit adjusts the accuracy of the blood pressure analysis based on the user's preference. This enables more appropriate analysis by customizing the analysis algorithm by reflecting the user's past feedback. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past feedback into the generation AI and cause the generation AI to customize the analysis algorithm.

[0055] The generation unit can adjust the content of the conversation during generation, taking into account the interrelationship between the vital data and the environmental data. For example, if the room temperature is high, the generation unit includes in the conversation advice on how to cool down. For example, the generation unit includes in the conversation specific methods for cooling down for a user in a high room temperature environment. The generation unit can also include in the conversation advice on humidifying for a low humidity environment. For example, the generation unit includes in the conversation specific methods for humidifying for a user in a low humidity environment. Furthermore, the generation unit can also include in the conversation advice on adjusting lighting for a high illuminance environment. For example, the generation unit includes in the conversation specific methods for adjusting lighting for a user in a high illuminance environment. This makes the content of the conversation more appropriate by taking into account the interrelationship between the vital data and the environmental data. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the interrelationship between the vital data and the environmental data into the generation AI and cause the generation AI to adjust the content of the conversation.

[0056] The generation unit can improve the accuracy of the conversation by referring to the user's past conversation history during generation. For example, if the user has previously requested advice on how to relax, the generation unit provides similar advice. For example, the generation unit includes specific methods for relaxing in the conversation for a user who previously requested advice on how to relax. The generation unit can also provide related information if the user has previously asked a health question. For example, the generation unit includes specific health-related information in the conversation for a user who previously asked a health question. Furthermore, if the user has previously talked about a specific topic, the generation unit can generate a conversation related to that topic. For example, the generation unit includes information related to the topic in the conversation for a user who previously talked about a specific topic. This improves the accuracy of the conversation by referring to the user's past conversation history. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the user's past conversation history into the generation AI and cause the generation AI to improve the accuracy of the conversation.

[0057] The generation unit can set optimal conversation timing based on the user's daily rhythm during generation. For example, if the user has a habit of exercising every morning, the generation unit generates a conversation for relaxing after exercise. For example, the generation unit includes specific ways for the user to relax in the conversation after exercise. Furthermore, if the user has time to relax at night, the generation unit can also generate a conversation for relaxing during that time period. For example, the generation unit includes specific ways for the user to relax in the conversation during relaxation time. Furthermore, if the user takes regular breaks, the generation unit can also generate a conversation for relaxing at that time. For example, the generation unit includes specific ways for the user to relax in the conversation during break time. This enables more effective conversation by setting optimal conversation timing based on the user's daily rhythm. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data for estimating the user's daily rhythm into the generation AI and cause the generation AI to set optimal conversation timing.

[0058] The generation unit can adjust the content of the conversation during generation, taking into account the user's geographical location information. For example, if the user is at high altitude, the generation unit generates a conversation about health management at high altitude. For example, the generation unit includes specific advice about health management at high altitude in the conversation for the user at high altitude. Furthermore, if the user is in a cold region, the generation unit can also generate a conversation about health management in cold regions. For example, the generation unit includes specific advice about health management in cold regions in the conversation for the user in a cold region. Furthermore, if the user is in an urban area, the generation unit can also generate a conversation about health management in urban areas. For example, the generation unit includes specific advice about health management in urban areas in the conversation for the user in an urban area. This makes the content of the conversation more appropriate by taking into account the user's geographical location information. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's geographical location information to the generation AI and cause the generation AI to adjust the content of the conversation.

[0059] The generation unit can analyze the user's social media activity and generate related conversations during generation. For example, if a user posts on social media that they are feeling stressed, the generation unit generates a conversation for stress reduction. For example, the generation unit includes specific advice for reducing stress in the conversation for the stressed user. Furthermore, if a user posts that they are relaxing, the generation unit can also generate a conversation for promoting relaxation. For example, the generation unit includes specific advice for promoting relaxation in the conversation for the relaxed user. Furthermore, if a user posts that they are exercising, the generation unit can also generate a conversation for improving the effectiveness of the exercise. For example, the generation unit includes specific advice for improving the effectiveness of the exercise in the conversation for the exercising user. In this way, by analyzing the user's social media activity and generating related conversations, more appropriate conversations can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's social media activity into the generation AI and cause the generation AI to generate related conversations.

[0060] The generation unit can customize the conversation generation algorithm by reflecting the user's past feedback during generation. For example, if a user has previously requested a conversation about relaxation, the generation unit generates a conversation about relaxation. For example, for a user who previously requested a conversation about relaxation, the generation unit includes specific methods for relaxing in the conversation. Furthermore, if a user has previously requested a conversation about health, the generation unit can also generate a conversation about health. For example, for a user who previously requested a conversation about health, the generation unit can include specific health-related information in the conversation. Furthermore, if a user has previously requested a conversation about a specific topic, the generation unit can generate a conversation related to that topic. For example, for a user who previously requested a conversation about a specific topic, the generation unit can include information related to that topic in the conversation. This allows the conversation generation algorithm to be customized by reflecting the user's past feedback, thereby providing more appropriate conversations. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the user's past feedback into the generation AI and cause the generation AI to customize the conversation generation algorithm.

[0061] The providing unit can adjust the content of the information to be provided by taking into account the correlation between the vital data and the environmental data when providing the information. For example, when the room temperature is high, the providing unit provides advice on how to cool the room. For example, the providing unit provides information on specific methods for cooling the room to a user in a high room temperature environment. The providing unit can also provide advice on humidifying the room when the humidity is low. For example, the providing unit provides information on specific methods for humidifying the room to a user in a low humidity environment. Furthermore, the providing unit can also provide advice on adjusting lighting when the illuminance is high. For example, the providing unit provides information on specific methods for adjusting lighting to a user in a high illuminance environment. This makes the content of the information to be provided more appropriate by taking into account the correlation between the vital data and the environmental data. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the correlation between the vital data and the environmental data into the generating AI and cause the generating AI to adjust the content of the information.

[0062] The providing unit can improve the accuracy of the information by referring to the user's past feedback when providing the information. For example, if the user has previously requested advice on how to relax, the providing unit provides the advice on how to relax. For example, the providing unit provides specific methods for relaxing to a user who has previously requested advice on how to relax. Furthermore, if the user has previously requested health advice, the providing unit can also provide health advice. For example, the providing unit provides specific health information to a user who has previously requested health advice. Furthermore, if the user has previously requested advice on a specific topic, the providing unit can also provide advice related to that topic. For example, the providing unit provides information related to a topic to a user who has previously requested advice on that topic. This improves the accuracy of the information provided by referring to the user's past feedback. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the user's past feedback to the generation AI and cause the generation AI to improve the accuracy of the information.

[0063] The providing unit can set the optimal timing for providing information based on the user's lifestyle rhythm when providing the information. For example, if the user has a habit of exercising every morning, the providing unit provides advice on how to relax after exercise. For example, the providing unit provides the user with information on specific methods for relaxing after exercise. Furthermore, if the user has time to relax at night, the providing unit can also provide advice on how to relax during that time period. For example, the providing unit provides the user with information on specific methods for relaxing during their relaxation time period. Furthermore, if the user takes regular breaks, the providing unit can also provide advice on how to relax at that time. For example, the providing unit provides the user with information on specific methods for relaxing during their break time. This enables more effective information provision by setting the optimal timing for providing information based on the user's lifestyle rhythm. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input data for estimating the user's lifestyle rhythm into the generating AI and cause the generating AI to set the optimal timing for providing information.

[0064] The providing unit can adjust the content of the information when providing the information, taking into account the user's geographical location information. For example, if the user is at high altitude, the providing unit provides information about health management at high altitude. For example, the providing unit provides specific advice about health management at high altitude to the user at high altitude. Furthermore, if the user is in a cold region, the providing unit can also provide information about health management in cold regions. For example, the providing unit provides specific advice about health management in cold regions to the user in a cold region. Furthermore, if the user is in an urban area, the providing unit can also provide information about health management in urban areas. For example, the providing unit provides specific advice about health management in urban areas to the user in an urban area. This makes the content of the information to be provided more appropriate by taking into account the user's geographical location information. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit may input the user's geographical location information to the generation AI and cause the generation AI to adjust the content of the information.

[0065] The providing unit can analyze the user's social media activity and provide related information at the time of providing the information. For example, if a user posts on social media that they are feeling stressed, the providing unit can provide information for stress reduction. For example, the providing unit can provide specific advice for stress reduction to a user who is feeling stressed. Furthermore, if a user posts that they are relaxing, the providing unit can also provide information for promoting relaxation. For example, the providing unit can provide specific advice for promoting relaxation to a user who is relaxing. Furthermore, if a user posts that they are exercising, the providing unit can also provide information for improving the effectiveness of the exercise. For example, the providing unit can provide specific advice for improving the effectiveness of the exercise to a user who is exercising. This enables more appropriate information to be provided by analyzing the user's social media activity and providing related information. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the user's social media activity into a generation AI and cause the generation AI to provide related information.

[0066] The providing unit can customize the information provision algorithm by reflecting the user's past feedback when providing information. For example, if the user previously requested information on relaxation, the providing unit provides the information on relaxation. For example, the providing unit provides specific methods for relaxation to a user who previously requested information on relaxation. Furthermore, if the user previously requested health information, the providing unit can also provide health information. For example, the providing unit provides specific health-related information to a user who previously requested health information. Furthermore, if the user previously requested information on a specific topic, the providing unit can also provide information related to that topic. For example, the providing unit provides information related to a user who previously requested information on a specific topic. This enables more appropriate information to be provided by customizing the information provision algorithm by reflecting the user's past feedback. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without AI. For example, the providing unit can input the user's past feedback to the generating AI and cause the generating AI to customize the information provision algorithm.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] The care robot system may further include an activity detection unit that detects the activity level of the user. The activity detection unit detects whether the user is exercising, resting, or sleeping, and provides the information to the analysis unit. For example, if the user is exercising, the activity detection unit may instruct the collection unit to focus on collecting data such as heart rate and oxygen saturation. If the user is resting, the activity detection unit may instruct the collection unit to focus on collecting data such as body temperature and blood pressure. Furthermore, if the user is sleeping, the activity detection unit may instruct the collection unit to collect data such as heart rate variability and respiratory rate. This makes it possible to collect data according to the user's activity level, thereby enabling more appropriate care to be provided.

[0069] The care robot system may further include a data reference unit that references the user's past health data. The data reference unit provides the collection unit with data such as the user's past heart rate, blood pressure, and body temperature to detect abnormal values. For example, if an abnormal heart rate is detected by comparing the data with past heart rate data, the data reference unit notifies the collection unit and instructs it to collect detailed data. A similar process can be used to detect an abnormal blood pressure by comparing the data with past blood pressure data. Furthermore, if an abnormal body temperature is detected by comparing the data with past body temperature data, the data reference unit can notify the collection unit and instruct it to collect detailed data. This allows the user's past health data to be referenced to detect abnormal values ​​early and provide appropriate care.

[0070] The care robot system can further include a rhythm learning unit that learns the user's daily rhythm. The rhythm learning unit sets the optimal data collection timing based on the user's daily rhythm. For example, if the user has the habit of exercising every morning, the rhythm learning unit instructs the collection unit to collect vital data before and after exercise. Also, if the user has time to relax at night, the collection unit can instruct the collection unit to collect vital data during that time period. Furthermore, if the user takes regular breaks, the collection unit can instruct the collection unit to collect vital data at those times. This allows the optimal data collection timing to be set based on the user's daily rhythm, enabling more effective data collection.

[0071] The care robot system may further include a geographical priority unit that prioritizes the collection of highly relevant data in consideration of the user's geographical location information. For example, the geographical priority unit may instruct the collection unit to prioritize collection of oxygen saturation when the user is in a high altitude area. Furthermore, the geographical priority unit may instruct the collection unit to prioritize collection of body temperature when the user is in a cold area. Furthermore, the geographical priority unit may instruct the collection unit to prioritize collection of heart rate and stress level when the user is in an urban area. This allows the collection of highly relevant data to be prioritized in consideration of the user's geographical location information, enabling more appropriate data collection.

[0072] The care robot system may further include a social analysis unit that analyzes the user's social media activity and collects related data. For example, if the user posts on social media that they are feeling stressed, the social analysis unit may instruct the collection unit to collect their heart rate and blood pressure. If the user posts on social media that they are relaxing, the social analysis unit may instruct the collection unit to collect their body temperature and respiratory rate. If the user posts on social media that they are exercising, the social analysis unit may instruct the collection unit to collect their oxygen saturation and heart rate. This allows the user's social media activity to be analyzed and related data to be collected, enabling more appropriate data collection.

[0073] The processing flow of the first embodiment will be briefly explained below.

[0074] Step 1: The collection unit collects vital data. The vital data includes, for example, heart rate, blood pressure, and body temperature. The collection unit collects the vital data from, for example, a wearable device such as a smart watch. The collection unit also collects environmental data from an IoT product. The environmental data includes, for example, room temperature, humidity, and illuminance. The collection unit collects the environmental data from, for example, a smart home device. Step 2: The analysis unit analyzes the user's emotional state based on the collected vital data and environmental data. The analysis unit analyzes the emotional state using, for example, machine learning algorithms or data mining techniques. Step 3: The generator uses a generation AI to generate a conversation based on the user's emotional state, vital data, and environmental data. The generator generates the conversation using, for example, natural language generation technology or template-based generation. Step 4: The providing unit provides the generated conversation to the user. The providing unit provides the conversation by using, for example, audio output or text display.

[0075] (Example 2) A care robot system according to an embodiment of the present invention collects vital data and environmental data, analyzes a user's emotional state, and generates and provides a conversation. The care robot system includes a collection unit that collects vital data, a collection unit that collects environmental data, an analysis unit that analyzes a user's emotional state based on the collected data, a generation unit that generates a conversation based on the analyzed data, and a provision unit that provides the generated conversation to the user. For example, the care robot system collects vital data such as heart rate, body temperature, and blood pressure from a wearable device such as a smartwatch. The care robot system also collects environmental data such as room temperature, humidity, and illuminance from IoT products. These data are used in conjunction with an emotion recognition engine to analyze the user's emotional state. Next, the care robot system uses a generation AI to generate a conversation based on the user's emotional state, vital data, and environmental data. For example, if the user is feeling stressed, the system can provide advice on how to relax. It can also provide appropriate advice and solutions based on the user's health condition. The care robot system analyzes this data in real time and provides appropriate feedback to the user. For example, if the user is tired, the system can encourage them to take a break. It is also possible to adjust room temperature and illuminance based on environmental data. This allows the care robot system to provide appropriate care according to the user's health condition and emotional state. For example, if the user is feeling stressed, the robot can provide advice on how to relax, thereby reducing the user's stress. Providing appropriate advice according to the user's health condition also makes health management easier. In this way, by combining an emotion recognition engine, vital data, environmental data, and generative AI, it is possible to create a care robot that can provide highly accurate conversations and health solutions in real time.

[0076] The care robot system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects vital data. Examples of vital data include, but are not limited to, heart rate, blood pressure, and body temperature. The collection unit collects vital data from a wearable device such as a smart watch. The collection unit also collects environmental data from an IoT product. Examples of environmental data include, but are not limited to, room temperature, humidity, and illuminance. The collection unit also collects environmental data from a smart home device. The analysis unit analyzes a user's emotional state based on the collected vital data and environmental data. The analysis unit analyzes the emotional state using, for example, a machine learning algorithm. The analysis unit can also analyze the emotional state using data mining technology. The generation unit generates a conversation based on the user's emotional state, vital data, and environmental data using a generation AI. The generation unit generates the conversation using, for example, natural language generation technology. The generation unit can also generate the conversation using template-based generation. The providing unit provides the generated conversation to the user. The providing unit provides the conversation, for example, by using audio output. The providing unit can also provide the conversation by using text display. As a result, the care robot system according to the embodiment can collect vital data and environmental data, analyze the emotional state of the user, and generate and provide the conversation.

[0077] The collection unit can collect vital data from a smartwatch or other wearable device. For example, the collection unit collects vital data such as heart rate, body temperature, and blood pressure from the smartwatch. The collection unit can also collect vital data from other wearable devices. For example, the collection unit can collect vital data from a fitness tracker. Furthermore, the collection unit can collect user location information using the GPS function of the smartwatch. In this way, by collecting vital data from the wearable device, the user's health condition can be understood in real time. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input vital data acquired from the smartwatch to a generation AI and have the generation AI analyze the data.

[0078] The collection unit can collect environmental data from IoT products. For example, the collection unit collects environmental data such as room temperature, humidity, and illuminance from smart home devices. The collection unit can also collect environmental data from environmental sensors. For example, the collection unit can collect room temperature data from a temperature sensor. Furthermore, the collection unit can collect environmental data from a smart thermostat. In this way, by collecting environmental data from IoT products, the environmental conditions around the user can be understood. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input environmental data acquired from smart home devices to a generation AI and have the generation AI analyze the data.

[0079] The analysis unit can analyze the user's emotional state based on the collected vital data and environmental data. The analysis unit analyzes the emotional state using, for example, a machine learning algorithm. For example, the analysis unit uses a machine learning model that inputs collected data such as heart rate, body temperature, room temperature, and humidity and outputs the emotional state. The analysis unit can also analyze the emotional state using data mining technology. For example, the analysis unit clusters the collected data to identify the emotional state. Furthermore, the analysis unit can analyze the emotional state using an emotion recognition engine. For example, the analysis unit uses the emotion recognition engine to estimate the user's emotional state from the collected data. This enables more accurate emotion recognition by analyzing the user's emotional state based on the vital data and environmental data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the collected data to a generation AI and have the generation AI analyze the emotional state.

[0080] The generation unit can generate a conversation based on the user's emotional state, vital data, or environmental data. The generation unit generates the conversation using, for example, natural language generation technology. For example, the generation unit uses a natural language generation model that receives collected emotional state, vital data, and environmental data as input and outputs a conversation. The generation unit can also generate a conversation using template-based generation. For example, the generation unit generates a conversation based on a pre-prepared template. Furthermore, the generation unit can also generate a conversation using a generation AI. For example, the generation unit uses a generation AI to generate a conversation based on the user's emotional state, vital data, and environmental data. This allows for more appropriate conversation to be provided by generating a conversation based on the user's emotional state, vital data, and environmental data. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input collected data to a generation AI and have the generation AI generate a conversation.

[0081] The providing unit can provide the generated conversation to the user. The providing unit provides the conversation using, for example, audio output. For example, the providing unit outputs the generated conversation as audio using speech synthesis technology. The providing unit can also provide the conversation using text display. For example, the providing unit displays the generated conversation on a display. Furthermore, the providing unit can notify the user of the generated conversation to their smartphone. For example, the providing unit notifies the user of the generated conversation through a smartphone application. In this way, by providing the generated conversation to the user, appropriate feedback can be provided to the user. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the generated conversation to a generation AI and cause the generation AI to provide the conversation.

[0082] The providing unit can provide advice and solutions according to the user's health condition. The providing unit, for example, provides advice according to the user's health condition. For example, the providing unit provides advice regarding exercise and diet to the user based on the collected vital data. The providing unit can also provide solutions according to the user's health condition. For example, the providing unit provides solutions for health management to the user based on the collected vital data. Furthermore, the providing unit can also provide reminders according to the user's health condition. For example, the providing unit provides reminders to the user to exercise and rest regularly. This makes it possible to support the user's health management by providing advice and solutions according to the user's health condition. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input the collected vital data to a generation AI and cause the generation AI to provide advice and solutions.

[0083] The providing unit can adjust the room temperature or illuminance based on the environmental data. The providing unit, for example, adjusts the room temperature based on the environmental data. For example, the providing unit controls an air conditioner to adjust the room temperature based on the collected room temperature data. The providing unit can also adjust the illuminance based on the environmental data. For example, the providing unit controls lighting to adjust the illuminance based on the collected illuminance data. The providing unit can also adjust humidity based on the environmental data. For example, the providing unit controls a humidifier to adjust the humidity based on the collected humidity data. This makes it possible to maintain a comfortable environment for the user by adjusting the room temperature and illuminance based on the environmental data. Some or all of the above-described processing in the providing unit may be performed using, or without, AI. For example, the providing unit can input the collected environmental data to a generating AI and cause the generating AI to adjust the room temperature and illuminance.

[0084] The collection unit can estimate the user's emotions and adjust the frequency of vital data collection based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit increases the collection frequency to acquire detailed vital data. For example, the collection unit frequently collects the heart rate and blood pressure of a stressed user. Furthermore, when the user is relaxed, the collection unit can reduce the collection frequency to reduce battery consumption. For example, the collection unit collects vital data of a relaxed user at regular intervals. Furthermore, when the user is exercising, the collection unit can increase the collection frequency to monitor the heart rate and blood pressure in real time. For example, the collection unit collects vital data of a user exercising in real time to monitor their health condition. This allows for more appropriate data collection by adjusting the frequency of vital data collection according to the user's emotions. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input data for estimating the user's emotions to a generation AI and cause the generation AI to estimate the emotions and adjust the collection frequency.

[0085] When collecting vital data, the collection unit can change the type of data to be collected depending on the user's activity level. For example, when the user is exercising, the collection unit focuses on collecting heart rate and oxygen saturation. For example, the collection unit collects the user's heart rate and oxygen saturation in real time while exercising. Furthermore, when the user is resting, the collection unit can also focus on collecting body temperature and blood pressure. For example, the collection unit collects the user's body temperature and blood pressure at regular intervals while the user is resting. Furthermore, when the user is sleeping, the collection unit can also collect heart rate variability and respiration rate to evaluate sleep quality. For example, the collection unit collects the user's heart rate variability and respiration rate while sleeping to evaluate sleep quality. This enables more appropriate data collection by changing the type of data to be collected depending on the user's activity level. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input data for estimating the user's activity level into a generation AI and cause the generation AI to estimate the activity level and change the type of data.

[0086] When collecting vital data, the collection unit can detect abnormal values ​​by referring to the user's past health data. For example, the collection unit can detect abnormal heart rates by comparing the past heart rate data with the user's past heart rate data. For example, the collection unit can determine whether the current heart rate is outside the normal range based on the past heart rate data. The collection unit can also detect abnormal blood pressure by comparing the past blood pressure data with the user's past blood pressure data. For example, the collection unit can determine whether the current blood pressure is outside the normal range based on the past blood pressure data. Furthermore, the collection unit can detect abnormal body temperature by comparing the past body temperature data with the user's past body temperature data. For example, the collection unit can determine whether the current body temperature is outside the normal range based on the past body temperature data. In this way, by detecting abnormal values ​​by referring to the user's past health data, abnormal health conditions can be discovered early. Some or all of the above-mentioned processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input past health data into the generation AI and cause the generation AI to detect abnormal values.

[0087] The collection unit can set the optimal collection timing based on the user's daily rhythm when collecting vital data. For example, if the user has a habit of exercising every morning, the collection unit collects vital data before and after exercise. For example, the collection unit collects heart rate and body temperature before and after exercise to evaluate the effects of the exercise. Furthermore, if the user has time to relax at night, the collection unit can also collect vital data during that time. For example, the collection unit collects blood pressure and respiratory rate during relaxation time to evaluate the effectiveness of relaxation. Furthermore, if the user takes regular breaks, the collection unit can also collect vital data at those times. For example, the collection unit collects body temperature and heart rate during breaks to evaluate the effectiveness of the breaks. This enables more effective data collection by setting the optimal collection timing based on the user's daily rhythm. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input data for estimating the user's daily rhythm into the generation AI and have the generation AI set the optimal collection timing.

[0088] The collection unit can estimate the user's emotions and prioritize the vital data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit prioritizes collecting heart rate and blood pressure. For example, the collection unit frequently collects the heart rate and blood pressure of a stressed user. The collection unit can also prioritize collecting body temperature and respiratory rate if the user is relaxed. For example, the collection unit collects the body temperature and respiratory rate of a relaxed user at regular intervals. Furthermore, the collection unit can prioritize collecting oxygen saturation and heart rate if the user is exercising. For example, the collection unit collects the oxygen saturation and heart rate of a user exercising in real time to monitor their health. This allows for the priority of vital data to be collected based on the user's emotions, thereby prioritizing the collection of more important data. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input data for estimating the user's emotions into a generation AI and have the generation AI perform emotion estimation and data prioritization.

[0089] When collecting vital data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is at high altitude, the collection unit prioritizes collecting oxygen saturation. For example, the collection unit frequently collects the oxygen saturation of a user at high altitude to monitor the user's health condition at high altitude. The collection unit can also prioritize collecting body temperature when the user is in a cold region. For example, the collection unit frequently collects the body temperature of a user in a cold region to monitor the user's health condition in the cold region. Furthermore, the collection unit can prioritize collecting heart rate and stress level when the user is in an urban area. For example, the collection unit frequently collects the heart rate and stress level of a user in an urban area to monitor the user's health condition in the urban area. This enables more appropriate data collection by prioritizing the collection of highly relevant data by taking into account the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input the user's geographic location information into the generation AI and have the generation AI determine the prioritization of highly relevant data.

[0090] The collection unit can analyze the user's social media activity and collect related data when collecting vital data. For example, if a user posts on social media about feeling stressed, the collection unit can collect the user's heart rate and blood pressure. For example, the collection unit can frequently collect the heart rate and blood pressure of a stressed user. The collection unit can also collect the user's body temperature and respiratory rate when the user posts about being relaxed. For example, the collection unit can collect the user's body temperature and respiratory rate at regular intervals when the user is relaxing. Furthermore, the collection unit can collect the user's oxygen saturation and heart rate when the user posts about exercising. For example, the collection unit can collect the user's oxygen saturation and heart rate in real time while exercising to monitor their health. This enables more appropriate data collection by analyzing the user's social media activity and collecting related data. Some or all of the above-described processing by the collection unit can be performed using, or without, AI. For example, the collection unit can input the user's social media activity into a generation AI and cause the generation AI to collect related data.

[0091] The collection unit can customize the collection method by reflecting the user's past feedback when collecting vital data. For example, if the user has requested heart rate collection in the past, the collection unit increases the frequency of heart rate collection. For example, the collection unit adjusts the frequency of heart rate collection based on the user's preference. The collection unit can also increase the frequency of body temperature collection if the user has requested body temperature collection in the past. For example, the collection unit adjusts the frequency of body temperature collection based on the user's preference. Furthermore, the collection unit can also increase the frequency of blood pressure collection if the user has requested blood pressure collection in the past. For example, the collection unit adjusts the frequency of blood pressure collection based on the user's preference. This enables more appropriate data collection by customizing the collection method by reflecting the user's past feedback. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's past feedback into the generation AI and cause the generation AI to customize the collection method.

[0092] The collection unit can estimate the user's emotions and adjust the frequency of environmental data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can increase the frequency of collecting room temperature and humidity. For example, the collection unit can frequently collect the room temperature and humidity of a stressed user. The collection unit can also reduce the frequency of collecting illuminance and sound environment if the user is relaxed. For example, the collection unit can collect the illuminance and sound environment of a relaxed user at regular intervals. Furthermore, the collection unit can increase the frequency of collecting air quality and temperature if the user is exercising. For example, the collection unit can collect the air quality and temperature of a user exercising in real time to monitor the environmental condition. This enables more appropriate data collection by adjusting the frequency of environmental data collection according to the user's emotions. Some or all of the above-mentioned processing in the collection unit can be performed using, or without, AI. For example, the collection unit can input data for estimating the user's emotions into the generation AI and have the generation AI estimate the emotions and adjust the collection frequency.

[0093] When collecting environmental data, the collection unit can change the type of data to be collected depending on the user's current activity. For example, when the user is exercising, the collection unit may focus on collecting temperature and humidity. For example, the collection unit may collect the user's temperature and humidity in real time while exercising. The collection unit may also focus on collecting illuminance and sound environment when the user is resting. For example, the collection unit may collect the user's illuminance and sound environment at regular intervals while the user is resting. Furthermore, the collection unit may focus on collecting air quality and temperature when the user is sleeping. For example, the collection unit may collect the user's air quality and temperature while sleeping to evaluate the user's sleep quality. This enables more appropriate data collection by changing the type of data to be collected depending on the user's current activity. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit may input data for estimating the user's current activity into a generation AI and cause the generation AI to estimate the activity and change the type of data.

[0094] When collecting environmental data, the collection unit can detect abnormal values ​​by referring to past environmental data. For example, the collection unit can detect abnormal temperatures by comparing the data with past room temperature data. For example, the collection unit can determine whether the current room temperature deviates from a normal range based on the past room temperature data. The collection unit can also detect abnormal humidity by comparing the data with past humidity data. For example, the collection unit can determine whether the current humidity deviates from a normal range based on the past humidity data. Furthermore, the collection unit can detect abnormal illuminance by comparing the data with past illuminance data. For example, the collection unit can determine whether the current illuminance deviates from a normal range based on the past illuminance data. In this way, by detecting abnormal values ​​by referring to past environmental data, abnormal environmental conditions can be discovered early. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input past environmental data to a generation AI and cause the generation AI to detect abnormal values.

[0095] The collection unit can set the optimal collection timing based on the user's lifestyle when collecting environmental data. For example, if the user has a habit of exercising every morning, the collection unit collects environmental data before and after exercise. For example, the collection unit collects room temperature and humidity before and after exercise to evaluate the effects of the exercise. The collection unit can also collect environmental data during the user's relaxation time if the user has time to relax at night. For example, the collection unit collects illuminance and sound environment during relaxation time to evaluate the effects of relaxation. Furthermore, if the user takes regular breaks, the collection unit can also collect environmental data at those times. For example, the collection unit collects air quality and temperature during breaks to evaluate the effects of the breaks. This enables more effective data collection by setting the optimal collection timing based on the user's lifestyle. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input data for estimating the user's lifestyle into the generation AI and have the generation AI set the optimal collection timing.

[0096] The collection unit can estimate the user's emotions and prioritize the environmental data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit prioritizes collecting room temperature and humidity. For example, the collection unit frequently collects the room temperature and humidity of a stressed user. The collection unit can also prioritize collecting illuminance and sound environment when the user is relaxed. For example, the collection unit collects the illuminance and sound environment of a relaxed user at regular intervals. Furthermore, the collection unit can prioritize collecting air quality and temperature when the user is exercising. For example, the collection unit collects the air quality and temperature of a user exercising in real time to monitor the environmental condition. This allows the collection of more important data to be prioritized by prioritizing the environmental data to be collected based on the user's emotions. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input data for estimating the user's emotions to a generation AI and have the generation AI perform emotion estimation and data prioritization.

[0097] When collecting environmental data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, if the user is at a high altitude, the collection unit prioritizes collecting air quality and temperature. For example, the collection unit frequently collects air quality and temperature of the user at a high altitude to monitor the environmental conditions at the high altitude. The collection unit can also prioritize collecting room temperature and humidity if the user is in a cold area. For example, the collection unit frequently collects room temperature and humidity of the user in a cold area to monitor the environmental conditions in the cold area. Furthermore, the collection unit can prioritize collecting sound environment and air quality if the user is in an urban area. For example, the collection unit frequently collects sound environment and air quality of the user in an urban area to monitor the environmental conditions in the urban area. This enables more appropriate data collection by prioritizing the collection of highly relevant data by taking into account the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input the user's geographic location information into the generation AI and have the generation AI determine the prioritization of highly relevant data.

[0098] The collection unit can analyze the user's social media activity and collect related data when collecting environmental data. For example, if a user posts on social media about feeling stressed, the collection unit can collect room temperature and humidity. For example, the collection unit can frequently collect the room temperature and humidity of the stressed user. The collection unit can also collect illuminance and sound environment if the user posts about being relaxed. For example, the collection unit can collect the illuminance and sound environment of the relaxed user at regular intervals. Furthermore, the collection unit can collect air quality and temperature if the user posts about exercising. For example, the collection unit can collect the air quality and temperature of the exercising user in real time to monitor the environmental condition. This enables more appropriate data collection by analyzing the user's social media activity and collecting related data. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's social media activity into the generation AI and cause the generation AI to collect related data.

[0099] The collection unit can customize the collection method by reflecting the user's past feedback when collecting environmental data. For example, if the user has previously requested that room temperature be collected, the collection unit increases the frequency of room temperature collection. For example, the collection unit adjusts the frequency of room temperature collection based on the user's preference. The collection unit can also increase the frequency of humidity collection if the user has previously requested that humidity be collected. For example, the collection unit adjusts the frequency of humidity collection based on the user's preference. The collection unit can also increase the frequency of illuminance collection if the user has previously requested that illuminance be collected. For example, the collection unit adjusts the frequency of illuminance collection based on the user's preference. This enables more appropriate data collection by customizing the collection method by reflecting the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's past feedback into the generation AI and cause the generation AI to customize the collection method.

[0100] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit may emphasize stress-related data in the analysis. For example, the analysis unit may emphasize the heart rate and blood pressure of the stressed user in the analysis. Furthermore, if the user is relaxed, the analysis unit may emphasize relaxation-related data in the analysis. For example, the analysis unit may emphasize the body temperature and respiratory rate of the relaxed user in the analysis. Furthermore, if the user is exercising, the analysis unit may emphasize exercise-related data in the analysis. For example, the analysis unit may emphasize the oxygen saturation and heart rate of the user during exercise in the analysis. This allows for more accurate analysis by adjusting the analysis algorithm according to the user's emotions. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit may input data for estimating the user's emotions into the generation AI and cause the generation AI to estimate the emotions and adjust the analysis algorithm.

[0101] The analysis unit can improve the accuracy of the analysis by taking into account the correlation between vital data and environmental data during analysis. For example, when the room temperature is high, the analysis unit performs the analysis by taking into account an increase in heart rate. For example, the analysis unit performs the analysis by taking into account fluctuations in heart rate in a high room temperature environment. The analysis unit can also perform the analysis by taking into account fluctuations in respiratory rate in a low humidity environment. For example, the analysis unit performs the analysis by taking into account fluctuations in respiratory rate in a low humidity environment. Furthermore, the analysis unit can also perform the analysis by taking into account fluctuations in body temperature in a high illuminance environment. For example, the analysis unit performs the analysis by taking into account fluctuations in body temperature in a high illuminance environment. This improves the accuracy of the analysis by taking into account the correlation between vital data and environmental data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the correlation between vital data and environmental data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0102] During analysis, the analysis unit can detect abnormal values ​​by referring to the user's past data. The analysis unit, for example, compares the data with the user's past heart rate data to detect abnormal heart rates. For example, the analysis unit determines whether the current heart rate deviates from a normal range based on the past heart rate data. The analysis unit can also detect abnormal blood pressure by comparing the data with the user's past blood pressure data. For example, the analysis unit determines whether the current blood pressure deviates from a normal range based on the past blood pressure data. The analysis unit can also detect abnormal body temperature by comparing the data with the user's past body temperature data. For example, the analysis unit determines whether the current body temperature deviates from a normal range based on the past body temperature data. By detecting abnormal values ​​by referring to the user's past data, abnormal health conditions can be discovered early. Some or all of the above-described processing by the analysis unit may be performed using, or without, AI. For example, the analysis unit can input past data into a generation AI and cause the generation AI to detect abnormal values.

[0103] During analysis, the analysis unit can set the optimal analysis timing based on the user's lifestyle rhythm. For example, if the user has a habit of exercising every morning, the analysis unit analyzes the vital data after exercise. For example, the analysis unit analyzes the heart rate and body temperature after exercise to evaluate the effects of the exercise. Furthermore, if the user has time to relax at night, the analysis unit can also analyze the vital data during that time period. For example, the analysis unit analyzes the blood pressure and respiratory rate during the relaxation time period to evaluate the effects of the relaxation. Furthermore, if the user takes regular breaks, the analysis unit can also analyze the vital data at those times. For example, the analysis unit analyzes the body temperature and heart rate during breaks to evaluate the effects of the breaks. This enables more effective analysis by setting the optimal analysis timing based on the user's lifestyle rhythm. Some or all of the above-described processing by the analysis unit may be performed using, or without, AI. For example, the analysis unit can input data for estimating the user's lifestyle rhythm into the generation AI and have the generation AI set the optimal analysis timing.

[0104] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, when the user is feeling stressed, the analysis unit provides a simple, highly visible display method. For example, the analysis unit concisely displays important information to a stressed user. The analysis unit can also provide a display method including detailed information when the user is relaxed. For example, the analysis unit displays detailed analysis results to a relaxed user. Furthermore, when the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. For example, the analysis unit concisely displays the main points to a rushed user. This allows for more appropriate information to be provided by adjusting the display method of the analysis results according to the user's emotions. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input data for estimating the user's emotions into a generation AI and have the generation AI estimate the emotions and adjust the display method.

[0105] The analysis unit can improve the accuracy of the analysis by taking into account the user's geographical location information during analysis. For example, when the user is at high altitude, the analysis unit takes into account fluctuations in oxygen saturation when performing the analysis. For example, the analysis unit takes into account fluctuations in oxygen saturation of a user at high altitude when performing the analysis. The analysis unit can also take into account fluctuations in body temperature when the user is in a cold region when performing the analysis. For example, the analysis unit takes into account fluctuations in body temperature of a user in a cold region when performing the analysis. Furthermore, the analysis unit can also take into account fluctuations in heart rate and stress level when the user is in an urban area when performing the analysis. For example, the analysis unit takes into account fluctuations in heart rate and stress level of a user in an urban area when performing the analysis. In this way, by taking into account the user's geographical location information, the accuracy of the analysis is improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's geographical location information into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0106] During analysis, the analysis unit can analyze the user's social media activity and analyze related data. For example, if a user posts on social media that they are feeling stressed, the analysis unit can analyze the heart rate and blood pressure data with an emphasis on the heart rate and blood pressure data of the user who is feeling stressed. Furthermore, if a user posts that they are relaxing, the analysis unit can also analyze the body temperature and respiratory rate data with an emphasis on the body temperature and respiratory rate data of the user who is relaxing. Furthermore, if a user posts that they are exercising, the analysis unit can analyze the oxygen saturation and heart rate data with an emphasis on the oxygen saturation and heart rate data of the user who is exercising. This allows for more appropriate analysis by analyzing the user's social media activity and analyzing related data. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's social media activity into a generation AI and cause the generation AI to analyze the related data.

[0107] During analysis, the analysis unit can customize the analysis algorithm by reflecting the user's past feedback. For example, if the user has requested heart rate analysis in the past, the analysis unit improves the accuracy of the heart rate analysis. For example, the analysis unit adjusts the accuracy of the heart rate analysis based on the user's preference. Furthermore, if the user has requested body temperature analysis in the past, the analysis unit can improve the accuracy of the body temperature analysis. For example, the analysis unit adjusts the accuracy of the body temperature analysis based on the user's preference. Furthermore, if the user has requested blood pressure analysis in the past, the analysis unit can improve the accuracy of the blood pressure analysis. For example, the analysis unit adjusts the accuracy of the blood pressure analysis based on the user's preference. This enables more appropriate analysis by customizing the analysis algorithm by reflecting the user's past feedback. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past feedback into the generation AI and cause the generation AI to customize the analysis algorithm.

[0108] The generation unit can estimate the user's emotions and adjust the tone of the conversation to be generated based on the estimated user's emotions. For example, if the user is feeling stressed, the generation unit generates conversation in a calm tone. For example, the generation unit generates conversation in a calm tone to encourage a stressed user to relax. The generation unit can also generate conversation in a bright tone if the user is relaxed. For example, the generation unit generates conversation in a cheerful tone for a relaxed user. Furthermore, the generation unit can generate conversation in an energetic tone if the user is excited. For example, the generation unit generates conversation in a lively tone for an excited user. This allows the tone of the conversation to be adjusted according to the user's emotions, thereby providing a more appropriate conversation. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input data for estimating the user's emotions into the generation AI and cause the generation AI to estimate the emotions and adjust the tone of the conversation.

[0109] The generation unit can adjust the content of the conversation during generation, taking into account the interrelationship between the vital data and the environmental data. For example, if the room temperature is high, the generation unit includes in the conversation advice on how to cool down. For example, the generation unit includes in the conversation specific methods for cooling down for a user in a high room temperature environment. The generation unit can also include in the conversation advice on humidifying for a low humidity environment. For example, the generation unit includes in the conversation specific methods for humidifying for a user in a low humidity environment. Furthermore, the generation unit can also include in the conversation advice on adjusting lighting for a high illuminance environment. For example, the generation unit includes in the conversation specific methods for adjusting lighting for a user in a high illuminance environment. This makes the content of the conversation more appropriate by taking into account the interrelationship between the vital data and the environmental data. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the interrelationship between the vital data and the environmental data into the generation AI and cause the generation AI to adjust the content of the conversation.

[0110] The generation unit can improve the accuracy of the conversation by referring to the user's past conversation history during generation. For example, if the user has previously requested advice on how to relax, the generation unit provides similar advice. For example, the generation unit includes specific methods for relaxing in the conversation for a user who previously requested advice on how to relax. The generation unit can also provide related information if the user has previously asked a health question. For example, the generation unit includes specific health-related information in the conversation for a user who previously asked a health question. Furthermore, if the user has previously talked about a specific topic, the generation unit can generate a conversation related to that topic. For example, the generation unit includes information related to the topic in the conversation for a user who previously talked about a specific topic. This improves the accuracy of the conversation by referring to the user's past conversation history. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the user's past conversation history into the generation AI and cause the generation AI to improve the accuracy of the conversation.

[0111] The generation unit can set optimal conversation timing based on the user's daily rhythm during generation. For example, if the user has a habit of exercising every morning, the generation unit generates a conversation for relaxing after exercise. For example, the generation unit includes specific ways for the user to relax in the conversation after exercise. Furthermore, if the user has time to relax at night, the generation unit can also generate a conversation for relaxing during that time period. For example, the generation unit includes specific ways for the user to relax in the conversation during relaxation time. Furthermore, if the user takes regular breaks, the generation unit can also generate a conversation for relaxing at that time. For example, the generation unit includes specific ways for the user to relax in the conversation during break time. This enables more effective conversation by setting optimal conversation timing based on the user's daily rhythm. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data for estimating the user's daily rhythm into the generation AI and cause the generation AI to set optimal conversation timing.

[0112] The generation unit can estimate the user's emotions and adjust the length of the conversation to be generated based on the estimated user emotions. For example, when the user is feeling stressed, the generation unit generates a short and to-the-point conversation. For example, the generation unit generates a conversation that succinctly conveys important information to a stressed user. The generation unit can also generate a longer conversation that includes detailed explanations when the user is relaxed. For example, the generation unit generates a conversation that includes detailed information for a relaxed user. Furthermore, when the user is excited, the generation unit can generate a conversation that adds visually stimulating effects. For example, the generation unit generates a conversation that adds visually stimulating effects for an excited user. This allows for adjusting the length of the conversation according to the user's emotions, thereby providing a more appropriate conversation. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input data for estimating the user's emotions into the generation AI and cause the generation AI to estimate the emotions and adjust the length of the conversation.

[0113] The generation unit can adjust the content of the conversation during generation, taking into account the user's geographical location information. For example, if the user is at high altitude, the generation unit generates a conversation about health management at high altitude. For example, the generation unit includes specific advice about health management at high altitude in the conversation for the user at high altitude. Furthermore, if the user is in a cold region, the generation unit can also generate a conversation about health management in cold regions. For example, the generation unit includes specific advice about health management in cold regions in the conversation for the user in a cold region. Furthermore, if the user is in an urban area, the generation unit can also generate a conversation about health management in urban areas. For example, the generation unit includes specific advice about health management in urban areas in the conversation for the user in an urban area. This makes the content of the conversation more appropriate by taking into account the user's geographical location information. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's geographical location information to the generation AI and cause the generation AI to adjust the content of the conversation.

[0114] The generation unit can analyze the user's social media activity and generate related conversations during generation. For example, if a user posts on social media that they are feeling stressed, the generation unit generates a conversation for stress reduction. For example, the generation unit includes specific advice for reducing stress in the conversation for the stressed user. Furthermore, if a user posts that they are relaxing, the generation unit can also generate a conversation for promoting relaxation. For example, the generation unit includes specific advice for promoting relaxation in the conversation for the relaxed user. Furthermore, if a user posts that they are exercising, the generation unit can also generate a conversation for improving the effectiveness of the exercise. For example, the generation unit includes specific advice for improving the effectiveness of the exercise in the conversation for the exercising user. In this way, by analyzing the user's social media activity and generating related conversations, more appropriate conversations can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's social media activity into the generation AI and cause the generation AI to generate related conversations.

[0115] The generation unit can customize the conversation generation algorithm by reflecting the user's past feedback during generation. For example, if a user has previously requested a conversation about relaxation, the generation unit generates a conversation about relaxation. For example, for a user who previously requested a conversation about relaxation, the generation unit includes specific methods for relaxing in the conversation. Furthermore, if a user has previously requested a conversation about health, the generation unit can also generate a conversation about health. For example, for a user who previously requested a conversation about health, the generation unit can include specific health-related information in the conversation. Furthermore, if a user has previously requested a conversation about a specific topic, the generation unit can generate a conversation related to that topic. For example, for a user who previously requested a conversation about a specific topic, the generation unit can include information related to that topic in the conversation. This allows the conversation generation algorithm to be customized by reflecting the user's past feedback, thereby providing more appropriate conversations. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the user's past feedback into the generation AI and cause the generation AI to customize the conversation generation algorithm.

[0116] The providing unit can estimate the user's emotions and adjust the tone of the information to be provided based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide information in a calm tone. For example, the providing unit can provide information to a stressed user in a calm tone to encourage relaxation. The providing unit can also provide information in a bright tone if the user is relaxed. For example, the providing unit can provide information to a relaxed user in a cheerful tone. Furthermore, if the user is excited, the providing unit can provide information in an energetic tone. For example, the providing unit can provide information to an excited user in a lively tone. This allows for more appropriate information to be provided by adjusting the tone of the information according to the user's emotions. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input data for estimating the user's emotions into the generating AI and cause the generating AI to estimate the emotions and adjust the tone of the information.

[0117] The providing unit can adjust the content of the information to be provided by taking into account the correlation between the vital data and the environmental data when providing the information. For example, when the room temperature is high, the providing unit provides advice on how to cool the room. For example, the providing unit provides information on specific methods for cooling the room to a user in a high room temperature environment. The providing unit can also provide advice on humidifying the room when the humidity is low. For example, the providing unit provides information on specific methods for humidifying the room to a user in a low humidity environment. Furthermore, the providing unit can also provide advice on adjusting lighting when the illuminance is high. For example, the providing unit provides information on specific methods for adjusting lighting to a user in a high illuminance environment. This makes the content of the information to be provided more appropriate by taking into account the correlation between the vital data and the environmental data. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the correlation between the vital data and the environmental data into the generating AI and cause the generating AI to adjust the content of the information.

[0118] The providing unit can improve the accuracy of the information by referring to the user's past feedback when providing the information. For example, if the user has previously requested advice on how to relax, the providing unit provides the advice on how to relax. For example, the providing unit provides specific methods for relaxing to a user who has previously requested advice on how to relax. Furthermore, if the user has previously requested health advice, the providing unit can also provide health advice. For example, the providing unit provides specific health information to a user who has previously requested health advice. Furthermore, if the user has previously requested advice on a specific topic, the providing unit can also provide advice related to that topic. For example, the providing unit provides information related to a topic to a user who has previously requested advice on that topic. This improves the accuracy of the information provided by referring to the user's past feedback. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the user's past feedback to the generation AI and cause the generation AI to improve the accuracy of the information.

[0119] The providing unit can set the optimal timing for providing information based on the user's lifestyle rhythm when providing the information. For example, if the user has a habit of exercising every morning, the providing unit provides advice on how to relax after exercise. For example, the providing unit provides the user with information on specific methods for relaxing after exercise. Furthermore, if the user has time to relax at night, the providing unit can also provide advice on how to relax during that time period. For example, the providing unit provides the user with information on specific methods for relaxing during their relaxation time period. Furthermore, if the user takes regular breaks, the providing unit can also provide advice on how to relax at that time. For example, the providing unit provides the user with information on specific methods for relaxing during their break time. This enables more effective information provision by setting the optimal timing for providing information based on the user's lifestyle rhythm. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input data for estimating the user's lifestyle rhythm into the generating AI and cause the generating AI to set the optimal timing for providing information.

[0120] The providing unit can estimate the user's emotions and adjust the length of the information to be provided based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide short, to-the-point information. For example, the providing unit can provide information that succinctly conveys important information to a user who is feeling stressed. The providing unit can also provide longer information including detailed explanations to a user who is relaxed. For example, the providing unit can provide information including detailed information to a relaxed user. Furthermore, the providing unit can also provide information with visually stimulating effects to a user who is excited. For example, the providing unit can provide information with visually stimulating effects to an excited user. This allows for more appropriate information to be provided by adjusting the length of the information according to the user's emotions. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input data for estimating the user's emotions to a generation AI and cause the generation AI to estimate the emotions and adjust the length of the information.

[0121] The providing unit can adjust the content of the information when providing the information, taking into account the user's geographical location information. For example, if the user is at high altitude, the providing unit provides information about health management at high altitude. For example, the providing unit provides specific advice about health management at high altitude to the user at high altitude. Furthermore, if the user is in a cold region, the providing unit can also provide information about health management in cold regions. For example, the providing unit provides specific advice about health management in cold regions to the user in a cold region. Furthermore, if the user is in an urban area, the providing unit can also provide information about health management in urban areas. For example, the providing unit provides specific advice about health management in urban areas to the user in an urban area. This makes the content of the information to be provided more appropriate by taking into account the user's geographical location information. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit may input the user's geographical location information to the generation AI and cause the generation AI to adjust the content of the information.

[0122] The providing unit can analyze the user's social media activity and provide related information at the time of providing the information. For example, if a user posts on social media that they are feeling stressed, the providing unit can provide information for stress reduction. For example, the providing unit can provide specific advice for stress reduction to a user who is feeling stressed. Furthermore, if a user posts that they are relaxing, the providing unit can also provide information for promoting relaxation. For example, the providing unit can provide specific advice for promoting relaxation to a user who is relaxing. Furthermore, if a user posts that they are exercising, the providing unit can also provide information for improving the effectiveness of the exercise. For example, the providing unit can provide specific advice for improving the effectiveness of the exercise to a user who is exercising. This enables more appropriate information to be provided by analyzing the user's social media activity and providing related information. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the user's social media activity into a generation AI and cause the generation AI to provide related information.

[0123] The providing unit can customize the information provision algorithm by reflecting the user's past feedback when providing information. For example, if the user previously requested information on relaxation, the providing unit provides the information on relaxation. For example, the providing unit provides specific methods for relaxation to a user who previously requested information on relaxation. Furthermore, if the user previously requested health information, the providing unit can also provide health information. For example, the providing unit provides specific health-related information to a user who previously requested health information. Furthermore, if the user previously requested information on a specific topic, the providing unit can also provide information related to that topic. For example, the providing unit provides information related to a user who previously requested information on a specific topic. This enables more appropriate information to be provided by customizing the information provision algorithm by reflecting the user's past feedback. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without AI. For example, the providing unit can input the user's past feedback to the generating AI and cause the generating AI to customize the information provision algorithm. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects vital data and environmental data using the camera 42 and microphone 38B of the smart device 14, and processes the data using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to identify the user's emotional state. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a conversation based on the analysis result. The provision unit provides the generated conversation to the user, for example, using the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects vital data and environmental data using the camera 42 and microphone 238 of the smart glasses 214, and processes the data using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to identify the user's emotional state. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a conversation based on the analysis result. The provision unit provides the generated conversation to the user using, for example, the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and provision unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects vital data and environmental data using the camera 42 and microphone 238 of the headset-type terminal 314, and processes the data using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to identify the user's emotional state. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a conversation based on the analysis results. The provision unit provides the generated conversation to the user using, for example, the display 343 of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects vital data and environmental data using the camera 42 and microphone 238 of the robot 414, and processes the data using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to identify the user's emotional state. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a conversation based on the analysis result. The provision unit provides the generated conversation to the user using, for example, the speaker 240 of the robot 414.

[0124] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0125] The care robot system may further include an activity detection unit that detects the activity level of the user. The activity detection unit detects whether the user is exercising, resting, or sleeping, and provides the information to the analysis unit. For example, if the user is exercising, the activity detection unit may instruct the collection unit to focus on collecting data such as heart rate and oxygen saturation. If the user is resting, the activity detection unit may instruct the collection unit to focus on collecting data such as body temperature and blood pressure. Furthermore, if the user is sleeping, the activity detection unit may instruct the collection unit to collect data such as heart rate variability and respiratory rate. This makes it possible to collect data according to the user's activity level, thereby enabling more appropriate care to be provided.

[0126] The care robot system may further include a data reference unit that references the user's past health data. The data reference unit provides the collection unit with data such as the user's past heart rate, blood pressure, and body temperature to detect abnormal values. For example, if an abnormal heart rate is detected by comparing the data with past heart rate data, the data reference unit notifies the collection unit and instructs it to collect detailed data. A similar process can be used to detect an abnormal blood pressure by comparing the data with past blood pressure data. Furthermore, if an abnormal body temperature is detected by comparing the data with past body temperature data, the data reference unit can notify the collection unit and instruct it to collect detailed data. This allows the user's past health data to be referenced to detect abnormal values ​​early and provide appropriate care.

[0127] The care robot system can further include a rhythm learning unit that learns the user's daily rhythm. The rhythm learning unit sets the optimal data collection timing based on the user's daily rhythm. For example, if the user has the habit of exercising every morning, the rhythm learning unit instructs the collection unit to collect vital data before and after exercise. Also, if the user has time to relax at night, the collection unit can instruct the collection unit to collect vital data during that time period. Furthermore, if the user takes regular breaks, the collection unit can instruct the collection unit to collect vital data at those times. This allows the optimal data collection timing to be set based on the user's daily rhythm, enabling more effective data collection.

[0128] The care robot system may further include an emotion prioritization unit that estimates the user's emotion and determines the priority of vital data to be collected based on the estimated emotion. For example, the emotion prioritization unit may instruct the collection unit to prioritize collecting heart rate and blood pressure when the user is feeling stressed. Furthermore, the emotion prioritization unit may instruct the collection unit to prioritize collecting body temperature and respiratory rate when the user is relaxed. Furthermore, the emotion prioritization unit may instruct the collection unit to prioritize collecting oxygen saturation and heart rate when the user is exercising. This allows the priority of vital data to be collected to be determined according to the user's emotion, and more important data to be collected preferentially.

[0129] The care robot system may further include an emotion analysis unit that estimates the user's emotion and adjusts the analysis algorithm based on the estimated emotion. For example, if the user is feeling stressed, the emotion analysis unit may instruct the analysis unit to prioritize stress-related data in the analysis. Also, if the user is relaxed, the emotion analysis unit may instruct the analysis unit to prioritize relaxation-related data in the analysis. Furthermore, if the user is exercising, the emotion analysis unit may instruct the analysis unit to prioritize exercise-related data in the analysis. This allows the analysis algorithm to be adjusted according to the user's emotion, enabling more accurate analysis.

[0130] The care robot system may further include an emotional tone unit that estimates the user's emotions and adjusts the tone of the conversation to be generated based on the estimated emotions. For example, the emotional tone unit may instruct the generation unit to generate conversation in a calm tone when the user is stressed. Alternatively, the emotional tone unit may instruct the generation unit to generate conversation in a cheerful tone when the user is relaxed. Furthermore, the emotional tone unit may instruct the generation unit to generate conversation in an energetic tone when the user is excited. This allows the tone of the conversation to be adjusted according to the user's emotions, making it possible to provide more appropriate conversation.

[0131] The care robot system may further include an emotion information unit that estimates the user's emotion and adjusts the tone of the information to be provided based on the estimated emotion. For example, if the user is feeling stressed, the emotion information unit may instruct the providing unit to provide information in a calm tone. If the user is relaxed, the emotion information unit may instruct the providing unit to provide information in a bright tone. If the user is excited, the emotion information unit may instruct the providing unit to provide information in an energetic tone. This allows the tone of the information to be adjusted according to the user's emotion, making it possible to provide more appropriate information.

[0132] The care robot system may further include an emotion lengthening unit that estimates the user's emotion and adjusts the length of information to be provided based on the estimated emotion. For example, if the user is feeling stressed, the emotion lengthening unit may instruct the providing unit to provide short, to-the-point information. Alternatively, if the user is relaxed, the emotion lengthening unit may instruct the providing unit to provide longer information including detailed explanations. Furthermore, if the user is excited, the providing unit may instruct the providing unit to provide information with visually stimulating effects. This allows the length of information to be adjusted according to the user's emotion, making it possible to provide more appropriate information.

[0133] The care robot system may further include a geographical priority unit that prioritizes the collection of highly relevant data in consideration of the user's geographical location information. For example, the geographical priority unit may instruct the collection unit to prioritize collection of oxygen saturation when the user is in a high altitude area. Furthermore, the geographical priority unit may instruct the collection unit to prioritize collection of body temperature when the user is in a cold area. Furthermore, the geographical priority unit may instruct the collection unit to prioritize collection of heart rate and stress level when the user is in an urban area. This allows the collection of highly relevant data to be prioritized in consideration of the user's geographical location information, enabling more appropriate data collection.

[0134] The care robot system may further include a social analysis unit that analyzes the user's social media activity and collects related data. For example, if the user posts on social media that they are feeling stressed, the social analysis unit may instruct the collection unit to collect their heart rate and blood pressure. If the user posts on social media that they are relaxing, the social analysis unit may instruct the collection unit to collect their body temperature and respiratory rate. If the user posts on social media that they are exercising, the social analysis unit may instruct the collection unit to collect their oxygen saturation and heart rate. This allows the user's social media activity to be analyzed and related data to be collected, enabling more appropriate data collection.

[0135] The processing flow of the second embodiment will be briefly explained below.

[0136] Step 1: The collection unit collects vital data. The vital data includes, for example, heart rate, blood pressure, and body temperature. The collection unit collects the vital data from, for example, a wearable device such as a smart watch. The collection unit also collects environmental data from an IoT product. The environmental data includes, for example, room temperature, humidity, and illuminance. The collection unit collects the environmental data from, for example, a smart home device. Step 2: The analysis unit analyzes the user's emotional state based on the collected vital data and environmental data. The analysis unit analyzes the emotional state using, for example, machine learning algorithms or data mining techniques. Step 3: The generator uses a generation AI to generate a conversation based on the user's emotional state, vital data, and environmental data. The generator generates the conversation using, for example, natural language generation technology or template-based generation. Step 4: The providing unit provides the generated conversation to the user. The providing unit provides the conversation by using, for example, audio output or text display.

[0137] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0142] 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.

[0143] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0144] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0148] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0149] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0151] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0153] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0155] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0158] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0159] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0160] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0161] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0163] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0164] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0165] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0166] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0167] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0168] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0169] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0171] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0174] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0175] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0176] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0177] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0178] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0179] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0180] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0181] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0182] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0183] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0184] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0185] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0186] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0187] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0188] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0189] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0190] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0191] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0192] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0193] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0194] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0195] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0196] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0197] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0198] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0199] 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.

[0200] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0201] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0202] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0203] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0204] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0205] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0206] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0207] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0208] [Explanation of symbols]

[0209] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects vital data; a collection unit that collects environmental data; an analysis unit that analyzes an emotional state of a user based on the data collected by the collection unit; a generation unit that generates a conversation based on the data analyzed by the analysis unit; a providing unit that provides the conversation generated by the generating unit to a user; Equipped with A system characterized by:

2. The collecting unit Collect vital data from a smartwatch or other wearable device 2. The system of claim 1.

3. The collecting unit Collecting environmental data from IoT products 2. The system of claim 1.

4. The analysis unit Analyze the user's emotional state based on collected vital and environmental data 2. The system of claim 1.

5. The generation unit Generate conversations based on the user's emotional state, vital data, or environmental data 2. The system of claim 1.

6. The providing unit Providing the generated conversation to the user 2. The system of claim 1.

7. The providing unit Providing advice and solutions tailored to the user's health condition 2. The system of claim 1.

8. The providing unit Adjusting room temperature or light levels based on environmental data 2. The system of claim 1.

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