System

Abstract

To provide a system capable of highly accurately generating conversation and controlling the operation of a robot in consideration of feelings and environmental data.SOLUTION: The system includes an emotion recognition unit, a speech generation unit, a vital data collection unit, a vital data analysis unit, an environmental data collection unit, an environmental data analysis unit, and a robot operation control unit. The emotion recognition unit recognizes an emotion of a user. The conversation generation unit generates a conversation on the basis of the emotion. The vital data collection unit collects vital data of a user through a wearable product or an IoT product. The vital data analyzer analyzes the vital data. The environmental data collector collects environmental data around a user through an IoT product. The environmental data is analyzed. The robot operation control unit controls the operation of the robot on the basis of the data analyzed by the emotion recognition unit, the vital data analysis unit, and the environmental data analysis unit.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 provide highly accurate conversation generation or robot motion control that takes into account emotions and environmental data, and there is room for improvement.

[0005] The system according to the embodiment aims to generate highly accurate conversations and control the movements of robots by taking into account emotions and environmental data. [Means for solving the problem]

[0006] The system according to the embodiment includes an emotion recognition unit, a conversation generation unit, a vital data collection unit, a vital data analysis unit, an environmental data collection unit, an environmental data analysis unit, and a robot operation control unit. The emotion recognition unit recognizes the user's emotions. The conversation generation unit generates a conversation based on the emotions recognized by the emotion recognition unit. The vital data collection unit collects vital data of the user through a wearable product or an IoT product. The vital data analysis unit analyzes the vital data collected by the vital data collection unit. The environmental data collection unit collects environmental data around the user through the IoT product. The environmental data analysis unit analyzes the environmental data collected by the environmental data collection unit. The robot operation control unit controls the robot's operation based on the data analyzed by the emotion recognition unit, vital data analysis unit, and environmental data analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate conversations and control the movements of a robot with high accuracy, taking into account emotions and environmental data. [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) The AI ​​robot system according to the embodiment of the present invention combines a generative AI with a robot to provide appropriate responses and actions based on the user's emotions, health status, and environmental data, thereby improving the user's happiness.

[0029] The AI ​​robot system according to the embodiment includes an emotion recognition unit, a conversation generation unit, a vital data collection unit, an environmental data collection unit, and a robot operation control unit. The emotion recognition unit recognizes a user's emotion. For example, the emotion recognition unit analyzes the user's facial expression using facial expression analysis technology to recognize the emotion. The emotion recognition unit also analyzes the user's tone of voice using voice analysis technology to recognize the emotion. The emotion recognition unit also analyzes the user's heart rate and body temperature using vital data analysis technology to recognize the emotion. The conversation generation unit generates a conversation based on the emotion recognized by the emotion recognition unit. For example, the conversation generation unit generates a conversation based on the user's emotion using natural language generation technology. The conversation generation unit also manages a conversation with the user using a dialogue management algorithm to generate an appropriate response. The conversation generation unit also generates a conversation based on the user's emotion using a generation AI. The vital data collection unit collects vital data of the user through a wearable product or an IoT product. For example, the vital data collection unit collects the user's heart rate using a heart rate sensor. The vital data collection unit collects the user's body temperature using a body temperature sensor. The vital data collection unit collects the user's blood pressure using a blood pressure sensor. The environmental data collection unit collects environmental data around the user through an IoT product. For example, the environmental data collection unit collects room temperature using a temperature sensor. The environmental data collection unit collects room humidity using a humidity sensor. The environmental data collection unit collects room illuminance using an illuminance sensor. The robot operation control unit controls the robot's operation based on the data analyzed by the emotion recognition unit, vital data analysis unit, and environmental data analysis unit. For example, if the user is tired, the robot operation control unit causes the robot to provide a massage. If the user feels lonely, the robot operation control unit causes the robot to play or talk with the user. The robot operation control unit adjusts the robot's operation according to the user's health condition. As a result, the AI ​​robot system according to the embodiment can respond and perform appropriate actions based on the user's emotions, health condition, and environmental data. For example, the AI ​​robot system generates conversations that correspond to the user's emotions, improving the user's happiness.The AI ​​robot system also provides advice based on the user's health condition to support the user's health, and makes appropriate suggestions based on the user's environmental data to optimize the user's living environment.

[0030] The emotion recognition unit can learn the user's past emotional data and identify individual emotional patterns. For example, the emotion recognition unit collects the user's past emotional data and identifies individual emotional patterns using a machine learning algorithm. For example, the emotion recognition unit learns what emotions the user feels in specific situations, improving the accuracy of emotion recognition. The emotion recognition unit also analyzes text data such as the user's diary and social media posts to extract past emotional patterns. This allows the unit to grasp fluctuations in the user's emotions and achieve more accurate emotion recognition. The emotion recognition unit also analyzes the user's past voice data and identifies emotional patterns from changes in voice tone and speaking style. For example, the emotion recognition unit learns the characteristics of the user's voice when they are feeling stressed, improving the accuracy of emotion recognition. In this way, learning the user's past emotional data improves the accuracy of emotion recognition.

[0031] The emotion recognition unit can analyze the user's gestures and posture and reflect them in emotion recognition. For example, the emotion recognition unit captures the user's gestures and posture with a camera and analyzes them using a machine learning algorithm. For example, if the user has their arms crossed, it determines that the user is nervous and generates conversation to relax the user. The emotion recognition unit also analyzes the user's movement data in real time and reflects this in emotion recognition. For example, if the user moves frequently, it determines that the user is restless and generates conversation to reassure the user. The emotion recognition unit also collects the user's posture data over a long period of time and identifies individual posture patterns. This allows for more accurate recognition of the user's emotional state and enables natural conversation. In this way, analyzing the user's gestures and posture improves the accuracy of emotion recognition.

[0032] The emotion recognition unit can accommodate different languages ​​and cultures and provide appropriate responses to global users. For example, the emotion recognition unit collects emotion data in different languages ​​and uses a machine learning algorithm to build a multilingual emotion recognition model. For example, emotion data from English, French, Chinese, and other languages ​​is trained. The emotion recognition unit also analyzes emotional expressions in different cultures and identifies culture-specific emotion patterns. This enables appropriate conversation generation according to the culture. For example, Japanese culture uses understated expressions. The emotion recognition unit also develops a multilingual conversation generation model to provide natural conversations that accommodate different languages ​​and cultures. For example, if the user speaks Spanish, the unit generates a conversation in Spanish. This allows the system to accommodate different languages ​​and cultures and provide appropriate responses to global users.

[0033] The emotion recognition unit can provide specialized support by specializing in specific fields such as education and medicine. For example, the emotion recognition unit develops emotion recognition and conversation generation models specialized for the education field to provide learning support tailored to the student's emotional state. For example, if a student is struggling to understand, the unit can offer words of encouragement. The emotion recognition unit can also develop emotion recognition and conversation generation models specialized for the medical field to provide medical support tailored to the patient's emotional state. For example, if a patient is feeling anxious, the unit can generate reassuring conversation. The emotion recognition unit can also develop emotion recognition and conversation generation models specialized for specific fields to provide specialized support. For example, in the business field, the unit can generate conversations for stress management and motivation improvement. This makes it possible to provide specialized support by specializing in specific fields such as education and medicine.

[0034] The vital data collection unit can provide an individualized health management plan by taking into account the user's lifestyle habits and past health data. The vital data collection unit, for example, collects the user's lifestyle habits data and combines it with the past health data to generate an individualized health management plan. For example, it proposes an optimal health management plan based on diet and exercise history. The vital data collection unit also analyzes the user's past health data and predicts specific health risks. This provides a preventive health management plan. For example, it predicts the risk of heart disease from past data and proposes an appropriate exercise plan. The vital data collection unit also integrates the user's lifestyle habits data and past health data to set individualized health goals. For example, it proposes specific goals for weight management and blood pressure management. This allows an individualized health management plan to be provided by taking into account the user's lifestyle habits and past health data.

[0035] When an abnormal value is detected, the vital data collection unit can immediately cooperate with a medical institution and take emergency measures. The vital data collection unit, for example, monitors vital data in real time and builds a system that automatically notifies a medical institution when an abnormal value is detected. For example, an emergency call is made when an abnormally high heart rate is detected. Furthermore, when an abnormal value is detected, the vital data collection unit immediately sends an alert to the user and encourages cooperation with a medical institution. For example, a warning is displayed when blood pressure reaches a dangerous level. Furthermore, when an abnormal value is detected, the vital data collection unit sets a protocol for cooperation with a medical institution to take emergency measures. For example, an ambulance is arranged and a doctor's appointment is automatically scheduled. This makes it possible to immediately cooperate with a medical institution when an abnormal value is detected and take emergency measures.

[0036] The vital data collection unit can be applied to the fields of sports and fitness to provide advice for improving performance. The vital data collection unit, for example, collects vital data in the fields of sports and fitness to build a system that provides advice for improving performance. For example, it monitors heart rate and calorie consumption during training. The vital data collection unit also analyzes the vital data and proposes individual training plans to athletes and fitness enthusiasts. For example, it provides an optimal combination of strength training and aerobic exercise. The vital data collection unit also analyzes the vital data in real time to provide instant feedback to improve sports and fitness performance. For example, it provides advice on adjusting form and pace during training. This allows the system to be applied to the fields of sports and fitness to provide advice for improving performance.

[0037] The vital data collection unit can link with smart home appliances to incorporate health management into daily life. For example, the vital data collection unit links a vital data collection device with smart home appliances to build a system that incorporates health management into daily life. For example, it links with a smart mirror or a smart scale. The vital data collection unit also analyzes the vital data collected through the smart home appliances and provides health management advice in daily life. For example, it suggests a daily activity plan based on morning body temperature and blood pressure. The vital data collection unit also links the vital data collection device with smart home appliances to provide reminders and alerts for health management. For example, it displays reminders for regular exercise and hydration. In this way, by linking with smart home appliances, health management can be incorporated into daily life.

[0038] The environmental data collection unit can learn the user's past environmental data and provide an individualized comfortable environment. For example, the environmental data collection unit collects the user's past environmental data and uses a machine learning algorithm to identify an individualized comfortable environment. For example, it learns the temperature and humidity patterns that the user finds comfortable. The environmental data collection unit also analyzes the user's past environmental data and predicts the level of comfort under specific environmental conditions. This allows it to suggest optimal environmental settings for the user. For example, it adjusts the environment according to specific seasons or time periods. The environmental data collection unit also builds a system that provides an individualized comfortable environment based on the user's past environmental data. For example, it suggests lighting settings and music that help the user relax. In this way, it is possible to provide an individualized comfortable environment by learning the user's past environmental data.

[0039] The environmental data collection unit can propose an optimal living environment by taking into account changes in the seasons and weather. The environmental data collection unit, for example, collects seasonal and weather data and reflects it in the analysis of environmental data. For example, it adjusts the air conditioning setting temperature in summer and proposes a heating setting temperature in winter. The environmental data collection unit also analyzes weather data in real time and makes proposals to optimize the user's living environment. For example, it adjusts indoor humidity on rainy days to provide a comfortable environment. The environmental data collection unit also analyzes environmental data by taking into account changes in the seasons and weather, and builds a system that proposes an optimal living environment for the user. For example, it proposes optimal lighting settings and the use of an air purifier for each season. In this way, it is possible to propose an optimal living environment by taking into account changes in the seasons and weather.

[0040] The environmental data collection unit can be applied to smart city management to improve the comfort of the entire city. For example, in smart city management, the environmental data collection unit collects environmental data and builds a system to improve the comfort of the entire city. For example, it monitors the temperature and humidity of the city and makes optimal environmental adjustments. The environmental data collection unit also analyzes the environmental data and applies it to smart city infrastructure management. For example, it analyzes traffic volume and noise levels and makes suggestions to improve the comfort of the city. In smart city management, the environmental data collection unit also implements projects to improve the comfort of the entire city based on the environmental data. For example, it optimizes the placement of green spaces and the environmental settings of public facilities. As a result, by applying this to smart city management, the comfort of the entire city can be improved.

[0041] The environmental data collection unit can be applied to the agricultural and industrial fields to enable efficient production management. For example, in the agricultural field, the environmental data collection unit uses an environmental data collection device to build a system for efficient production management. For example, the environmental data collection unit monitors soil humidity and temperature and proposes optimal irrigation plans. In the industrial field, the environmental data collection unit uses an environmental data collection device to optimize production processes. For example, the temperature and humidity inside a factory are monitored to improve product quality. In addition, the environmental data collection unit applies the environmental data collection device to the agricultural and industrial fields to implement projects for efficient production management. For example, production schedules are optimized based on environmental data. In this way, efficient production management can be achieved by applying the system to the agricultural and industrial fields.

[0042] The robot motion control unit can learn the user's past motion data and provide individual motion patterns. The robot motion control unit, for example, collects the user's past motion data and identifies individual motion patterns using a machine learning algorithm. For example, it learns the patterns when the user performs a specific motion and optimizes the robot's motion. The robot motion control unit also analyzes the user's past motion data and suggests the optimal robot motion under specific motion conditions. This allows for motion control tailored to the user. For example, it learns the motions the user makes when they are tired. The robot motion control unit also builds a system that provides individual motion patterns based on the user's past motion data. For example, it suggests a massage pattern that will relax the user. This allows for individual motion patterns to be provided by learning the user's past motion data.

[0043] The robot motion control unit can be applied to the fields of nursing care and rehabilitation to provide specialized support. For example, in the nursing care field, the robot motion control unit applies robot motion control to build a system that provides specialized support. For example, a nursing care robot supports the movement of elderly people. In the rehabilitation field, the robot motion control unit applies robot motion control to provide a rehabilitation program. For example, a rehabilitation robot supports the exercise of patients. In the nursing care and rehabilitation field, the robot motion control unit applies robot motion control to implement a project that provides specialized support. For example, a rehabilitation robot supports the recovery of patients. In this way, specialized support can be provided by applying it to the fields of nursing care and rehabilitation.

[0044] The robot movement control unit can be applied to the fields of entertainment and education to provide an interactive experience. For example, in the entertainment field, the robot movement control unit applies robot movement control to build a system that provides an interactive experience. For example, a robot dances or performs. In the education field, the robot movement control unit also applies robot movement control to provide an interactive learning experience. For example, an educational robot performs learning activities together with children. In the entertainment and education fields, the robot movement control unit also applies robot movement control to implement a project that provides an interactive experience. For example, a robot plays with children. In this way, by applying it to the fields of entertainment and education, an interactive experience can be provided.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The AI ​​robot system can also be equipped with a meal suggestion unit that proposes an appropriate meal plan based on the user's health condition. For example, it can analyze the user's vital data and propose a nutritionally balanced meal plan. If the user has specific health goals, it can also provide a meal plan that meets those goals. It can also take into account the user's allergy information and propose recipes using safe ingredients. This can support health management by providing an appropriate meal plan based on the user's health condition.

[0047] The AI ​​robot system can also be equipped with a schedule management unit that manages an appropriate schedule based on the user's lifestyle habits. For example, it can analyze the user's past behavioral data and propose an optimal schedule. It can also incorporate appropriate rest and exercise times into the schedule, taking into account the user's health condition and vital signs. It can also integrate the user's work and personal plans to provide a balanced schedule. This allows for efficient time management by managing an appropriate schedule based on the user's lifestyle habits.

[0048] The AI ​​robot system can also be equipped with a sleep management unit that performs appropriate sleep management based on the user's health condition. For example, it can analyze the user's vital data and suggest optimal sleep times. It can also monitor the user's sleep patterns and provide advice to promote high-quality sleep. It can also suggest a comfortable sleeping environment by taking into account the user's lifestyle habits and environmental data. This allows for appropriate sleep management based on the user's health condition, thereby supporting high-quality sleep.

[0049] The AI ​​robot system can also be equipped with a hydration suggestion unit that suggests appropriate hydration based on the user's health condition. For example, it can analyze the user's vital data and suggest appropriate amounts of water intake. It can also suggest optimal hydration timing by taking into account the user's activity level and environmental data. It can also suggest types and methods of hydration based on the user's health goals. This can support health management by suggesting appropriate hydration based on the user's health condition.

[0050] The AI ​​robot system can also include an exercise suggestion unit that suggests appropriate stretches and exercises based on the user's health condition. For example, it can analyze the user's vital data and suggest optimal stretches and exercises. It can also provide an individual exercise plan based on the user's activity level and health goals. It can also adjust the intensity and duration of exercises based on the user's health condition. This can support health management by suggesting appropriate stretches and exercises based on the user's health condition.

[0051] The AI ​​robot system can also be equipped with a reminder provider that provides appropriate reminders based on the user's health condition. For example, it can analyze the user's vital signs and remind them to have regular health checks or take their medicine. It can also provide reminders for exercise and hydration based on the user's lifestyle and health goals. It can also send reminders at appropriate times based on the user's schedule. This can support health management by providing appropriate reminders based on the user's health condition.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The emotion recognition unit recognizes the user's emotions. For example, emotions are recognized by analyzing the user's facial expressions using facial expression analysis technology, the user's tone of voice using voice analysis technology, and the user's heart rate and body temperature using vital data analysis technology. Step 2: The conversation generation unit generates a conversation based on the emotions recognized by the emotion recognition unit. For example, it uses natural language generation technology, dialogue management algorithms, and generation AI to generate a conversation based on the user's emotions and provide appropriate responses. Step 3: The vital data collection unit collects the user's vital data through wearable products and IoT products. For example, it collects the user's heart rate, body temperature, and blood pressure using a heart rate sensor, body temperature sensor, and blood pressure sensor. Step 4: The vital data analysis unit analyzes the vital data collected by the vital data collection unit, thereby understanding the user's health condition. Step 5: The environmental data collection unit collects environmental data around the user through the IoT product. For example, it collects the room temperature, humidity, and illuminance using a temperature sensor, humidity sensor, and illuminance sensor. Step 6: The environmental data analysis unit analyzes the environmental data collected by the environmental data collection unit, thereby understanding the environmental conditions around the user. Step 7: The robot behavior control unit controls the robot's behavior based on the data analyzed by the emotion recognition unit, vital data analysis unit, and environmental data analysis unit. For example, it may provide a massage if the user is tired, or play or talk with the user if they feel lonely. It may also adjust the robot's behavior according to the user's health condition.

[0054] (Example 2) The AI ​​robot system according to the embodiment of the present invention combines a generative AI with a robot to provide appropriate responses and actions based on the user's emotions, health status, and environmental data, thereby improving the user's happiness.

[0055] The AI ​​robot system according to the embodiment includes an emotion recognition unit, a conversation generation unit, a vital data collection unit, an environmental data collection unit, and a robot operation control unit. The emotion recognition unit recognizes a user's emotion. For example, the emotion recognition unit analyzes the user's facial expression using facial expression analysis technology to recognize the emotion. The emotion recognition unit also analyzes the user's tone of voice using voice analysis technology to recognize the emotion. The emotion recognition unit also analyzes the user's heart rate and body temperature using vital data analysis technology to recognize the emotion. The conversation generation unit generates a conversation based on the emotion recognized by the emotion recognition unit. For example, the conversation generation unit generates a conversation based on the user's emotion using natural language generation technology. The conversation generation unit also manages a conversation with the user using a dialogue management algorithm to generate an appropriate response. The conversation generation unit also generates a conversation based on the user's emotion using a generation AI. The vital data collection unit collects vital data of the user through a wearable product or an IoT product. For example, the vital data collection unit collects the user's heart rate using a heart rate sensor. The vital data collection unit collects the user's body temperature using a body temperature sensor. The vital data collection unit collects the user's blood pressure using a blood pressure sensor. The environmental data collection unit collects environmental data around the user through an IoT product. For example, the environmental data collection unit collects room temperature using a temperature sensor. The environmental data collection unit collects room humidity using a humidity sensor. The environmental data collection unit collects room illuminance using an illuminance sensor. The robot operation control unit controls the robot's operation based on the data analyzed by the emotion recognition unit, vital data analysis unit, and environmental data analysis unit. For example, if the user is tired, the robot operation control unit causes the robot to provide a massage. If the user feels lonely, the robot operation control unit causes the robot to play or talk with the user. The robot operation control unit adjusts the robot's operation according to the user's health condition. As a result, the AI ​​robot system according to the embodiment can respond and perform appropriate actions based on the user's emotions, health condition, and environmental data. For example, the AI ​​robot system generates conversations that correspond to the user's emotions, improving the user's happiness.The AI ​​robot system also provides advice based on the user's health condition to support the user's health, and makes appropriate suggestions based on the user's environmental data to optimize the user's living environment.

[0056] The emotion recognition unit can learn the user's past emotional data and identify individual emotional patterns. For example, the emotion recognition unit collects the user's past emotional data and identifies individual emotional patterns using a machine learning algorithm. For example, the emotion recognition unit learns what emotions the user feels in specific situations, improving the accuracy of emotion recognition. The emotion recognition unit also analyzes text data such as the user's diary and social media posts to extract past emotional patterns. This allows the unit to grasp fluctuations in the user's emotions and achieve more accurate emotion recognition. The emotion recognition unit also analyzes the user's past voice data and identifies emotional patterns from changes in voice tone and speaking style. For example, the emotion recognition unit learns the characteristics of the user's voice when they are feeling stressed, improving the accuracy of emotion recognition. In this way, learning the user's past emotional data improves the accuracy of emotion recognition.

[0057] The emotion recognition unit can analyze the user's gestures and posture and reflect them in emotion recognition. For example, the emotion recognition unit captures the user's gestures and posture with a camera and analyzes them using a machine learning algorithm. For example, if the user has their arms crossed, it determines that the user is nervous and generates conversation to relax the user. The emotion recognition unit also analyzes the user's movement data in real time and reflects this in emotion recognition. For example, if the user moves frequently, it determines that the user is restless and generates conversation to reassure the user. The emotion recognition unit also collects the user's posture data over a long period of time and identifies individual posture patterns. This allows for more accurate recognition of the user's emotional state and enables natural conversation. In this way, analyzing the user's gestures and posture improves the accuracy of emotion recognition.

[0058] The emotion recognition unit can automatically adjust the voice tone and speaking style based on the user's emotions. For example, the emotion recognition unit uses an emotion estimation function to automatically adjust the voice tone according to the user's emotional state. For example, if the user is sad, the emotion recognition unit speaks in a gentle tone. The emotion recognition unit also analyzes the user's emotional data and adjusts the speed and rhythm of speaking. For example, if the user is anxious, the emotion recognition unit reassures the user by speaking slowly. The emotion recognition unit also uses the emotion estimation function to automatically generate language based on the user's emotions. For example, if the user is angry, the emotion recognition unit selects calm and collected language. This makes it possible to generate a more empathetic response by adjusting the voice tone and speaking style based on the user's emotions.

[0059] The emotion recognition unit can accommodate different languages ​​and cultures and provide appropriate responses to global users. For example, the emotion recognition unit collects emotion data in different languages ​​and uses a machine learning algorithm to build a multilingual emotion recognition model. For example, emotion data from English, French, Chinese, and other languages ​​is trained. The emotion recognition unit also analyzes emotional expressions in different cultures and identifies culture-specific emotion patterns. This enables appropriate conversation generation according to the culture. For example, Japanese culture uses understated expressions. The emotion recognition unit also develops a multilingual conversation generation model to provide natural conversations that accommodate different languages ​​and cultures. For example, if the user speaks Spanish, the unit generates a conversation in Spanish. This allows the system to accommodate different languages ​​and cultures and provide appropriate responses to global users.

[0060] The emotion recognition unit can provide specialized support by specializing in specific fields such as education and medicine. For example, the emotion recognition unit develops emotion recognition and conversation generation models specialized for the education field to provide learning support tailored to the student's emotional state. For example, if a student is struggling to understand, the unit can offer words of encouragement. The emotion recognition unit can also develop emotion recognition and conversation generation models specialized for the medical field to provide medical support tailored to the patient's emotional state. For example, if a patient is feeling anxious, the unit can generate reassuring conversation. The emotion recognition unit can also develop emotion recognition and conversation generation models specialized for specific fields to provide specialized support. For example, in the business field, the unit can generate conversations for stress management and motivation improvement. This makes it possible to provide specialized support by specializing in specific fields such as education and medicine.

[0061] The emotion recognition unit can suggest music and video content based on the user's emotions, improving the entertainment experience. For example, the emotion recognition unit uses an emotion estimation function to suggest music that corresponds to the user's emotional state. For example, if the user wants to relax, music with a relaxing effect is played. The emotion recognition unit also analyzes the user's emotion data and suggests video content based on the emotion. For example, if the user is sad, a movie that will brighten the mood is recommended. The emotion recognition unit also uses the emotion estimation function to provide an entertainment experience based on the user's emotions. For example, if the user is excited, an action movie or a sporting event is suggested. In this way, the entertainment experience is improved by suggesting music and video content based on the user's emotions.

[0062] The vital data collection unit can provide an individualized health management plan by taking into account the user's lifestyle habits and past health data. The vital data collection unit, for example, collects the user's lifestyle habits data and combines it with the past health data to generate an individualized health management plan. For example, it proposes an optimal health management plan based on diet and exercise history. The vital data collection unit also analyzes the user's past health data and predicts specific health risks. This provides a preventive health management plan. For example, it predicts the risk of heart disease from past data and proposes an appropriate exercise plan. The vital data collection unit also integrates the user's lifestyle habits data and past health data to set individualized health goals. For example, it proposes specific goals for weight management and blood pressure management. This allows an individualized health management plan to be provided by taking into account the user's lifestyle habits and past health data.

[0063] When an abnormal value is detected, the vital data collection unit can immediately cooperate with a medical institution and take emergency measures. The vital data collection unit, for example, monitors vital data in real time and builds a system that automatically notifies a medical institution when an abnormal value is detected. For example, an emergency call is made when an abnormally high heart rate is detected. Furthermore, when an abnormal value is detected, the vital data collection unit immediately sends an alert to the user and encourages cooperation with a medical institution. For example, a warning is displayed when blood pressure reaches a dangerous level. Furthermore, when an abnormal value is detected, the vital data collection unit sets a protocol for cooperation with a medical institution to take emergency measures. For example, an ambulance is arranged and a doctor's appointment is automatically scheduled. This makes it possible to immediately cooperate with a medical institution when an abnormal value is detected and take emergency measures.

[0064] The vital data collection unit uses the emotion estimation function to analyze the correlation between the user's emotional state and vital data, thereby enabling stress management and mental health support. The vital data collection unit, for example, uses the emotion estimation function to analyze the correlation between the user's emotional state and vital data. For example, it identifies a pattern in which heart rate increases when stress is high. The vital data collection unit also integrates the user's emotional data and vital data to provide specific advice for stress management. For example, it suggests relaxation methods when stress is high. The vital data collection unit also uses the emotion estimation function to monitor the user's mental health state and provide expert support as needed. For example, it detects symptoms of depression from the emotional data and recommends counseling. As a result, the emotion estimation function can be used to analyze the correlation between the user's emotional state and vital data, enabling stress management and mental health support.

[0065] The vital data collection unit can be applied to the fields of sports and fitness to provide advice for improving performance. The vital data collection unit, for example, collects vital data in the fields of sports and fitness to build a system that provides advice for improving performance. For example, it monitors heart rate and calorie consumption during training. The vital data collection unit also analyzes the vital data and proposes individual training plans to athletes and fitness enthusiasts. For example, it provides an optimal combination of strength training and aerobic exercise. The vital data collection unit also analyzes the vital data in real time to provide instant feedback to improve sports and fitness performance. For example, it provides advice on adjusting form and pace during training. This allows the system to be applied to the fields of sports and fitness to provide advice for improving performance.

[0066] The vital data collection unit can link with smart home appliances to incorporate health management into daily life. For example, the vital data collection unit links a vital data collection device with smart home appliances to build a system that incorporates health management into daily life. For example, it links with a smart mirror or a smart scale. The vital data collection unit also analyzes the vital data collected through the smart home appliances and provides health management advice in daily life. For example, it suggests a daily activity plan based on morning body temperature and blood pressure. The vital data collection unit also links the vital data collection device with smart home appliances to provide reminders and alerts for health management. For example, it displays reminders for regular exercise and hydration. In this way, by linking with smart home appliances, health management can be incorporated into daily life.

[0067] The vital data collection unit can use the emotion estimation function to suggest a relaxation method according to the user's emotional state and support maintaining mental and physical balance. The vital data collection unit, for example, uses the emotion estimation function to suggest a relaxation method according to the user's emotional state. For example, if the user is feeling stressed, it can recommend deep breathing or meditation. The vital data collection unit also analyzes the user's emotional data and suggests relaxation music or aromatherapy according to the emotional state. For example, it can provide music or scents that have a relaxing effect. The vital data collection unit also uses the emotion estimation function to suggest relaxation exercises according to the user's emotional state. For example, it can provide specific yoga or stretching methods. In this way, the emotion estimation function can suggest relaxation methods according to the user's emotional state and support maintaining mental and physical balance.

[0068] The environmental data collection unit can learn the user's past environmental data and provide an individualized comfortable environment. For example, the environmental data collection unit collects the user's past environmental data and uses a machine learning algorithm to identify an individualized comfortable environment. For example, it learns the temperature and humidity patterns that the user finds comfortable. The environmental data collection unit also analyzes the user's past environmental data and predicts the level of comfort under specific environmental conditions. This allows it to suggest optimal environmental settings for the user. For example, it adjusts the environment according to specific seasons or time periods. The environmental data collection unit also builds a system that provides an individualized comfortable environment based on the user's past environmental data. For example, it suggests lighting settings and music that help the user relax. In this way, it is possible to provide an individualized comfortable environment by learning the user's past environmental data.

[0069] The environmental data collection unit can propose an optimal living environment by taking into account changes in the seasons and weather. The environmental data collection unit, for example, collects seasonal and weather data and reflects it in the analysis of environmental data. For example, it adjusts the air conditioning setting temperature in summer and proposes a heating setting temperature in winter. The environmental data collection unit also analyzes weather data in real time and makes proposals to optimize the user's living environment. For example, it adjusts indoor humidity on rainy days to provide a comfortable environment. The environmental data collection unit also analyzes environmental data by taking into account changes in the seasons and weather, and builds a system that proposes an optimal living environment for the user. For example, it proposes optimal lighting settings and the use of an air purifier for each season. In this way, it is possible to propose an optimal living environment by taking into account changes in the seasons and weather.

[0070] The environmental data collection unit uses the emotion estimation function to analyze the correlation between the user's emotional state and environmental data, and can adjust the environment according to the emotion. The environmental data collection unit, for example, uses the emotion estimation function to analyze the correlation between the user's emotional state and environmental data. For example, it identifies the environmental conditions when the user is relaxed and recreates that environment. The environmental data collection unit also integrates the user's emotional data and environmental data to adjust the environment according to the emotional state. For example, if the user is feeling stressed, it softens the lighting. The environmental data collection unit also uses the emotion estimation function to build a system that automatically adjusts environmental settings according to the user's emotional state. For example, if the user is tired, it plays music that has a relaxing effect. In this way, by using the emotion estimation function, it is possible to analyze the correlation between the user's emotional state and environmental data, and adjust the environment according to the emotion.

[0071] The environmental data collection unit can be applied to smart city management to improve the comfort of the entire city. For example, in smart city management, the environmental data collection unit collects environmental data and builds a system to improve the comfort of the entire city. For example, it monitors the temperature and humidity of the city and makes optimal environmental adjustments. The environmental data collection unit also analyzes the environmental data and applies it to smart city infrastructure management. For example, it analyzes traffic volume and noise levels and makes suggestions to improve the comfort of the city. In smart city management, the environmental data collection unit also implements projects to improve the comfort of the entire city based on the environmental data. For example, it optimizes the placement of green spaces and the environmental settings of public facilities. As a result, by applying this to smart city management, the comfort of the entire city can be improved.

[0072] The environmental data collection unit can be applied to the agricultural and industrial fields to enable efficient production management. For example, in the agricultural field, the environmental data collection unit uses an environmental data collection device to build a system for efficient production management. For example, the environmental data collection unit monitors soil humidity and temperature and proposes optimal irrigation plans. In the industrial field, the environmental data collection unit uses an environmental data collection device to optimize production processes. For example, the temperature and humidity inside a factory are monitored to improve product quality. In addition, the environmental data collection unit applies the environmental data collection device to the agricultural and industrial fields to implement projects for efficient production management. For example, production schedules are optimized based on environmental data. In this way, efficient production management can be achieved by applying the system to the agricultural and industrial fields.

[0073] The environmental data collection unit uses the emotion estimation function to suggest interior designs and lighting settings according to the user's emotional state, thereby improving the comfort of the living space. The environmental data collection unit, for example, uses the emotion estimation function to suggest interior designs according to the user's emotional state. For example, if the user wants to relax, it suggests interiors with calm colors. The environmental data collection unit also analyzes the user's emotional data and suggests lighting settings according to the emotional state. For example, if the user wants to concentrate, it sets bright lighting. The environmental data collection unit also uses the emotion estimation function to build a system that improves the comfort of the living space according to the user's emotional state. For example, if the user is tired, it suggests lighting and music that have a relaxing effect. In this way, by using the emotion estimation function, it is possible to suggest interior designs and lighting settings according to the user's emotional state, thereby improving the comfort of the living space.

[0074] The robot motion control unit can learn the user's past motion data and provide individual motion patterns. The robot motion control unit, for example, collects the user's past motion data and identifies individual motion patterns using a machine learning algorithm. For example, it learns the patterns when the user performs a specific motion and optimizes the robot's motion. The robot motion control unit also analyzes the user's past motion data and suggests the optimal robot motion under specific motion conditions. This allows for motion control tailored to the user. For example, it learns the motions the user makes when they are tired. The robot motion control unit also builds a system that provides individual motion patterns based on the user's past motion data. For example, it suggests a massage pattern that will relax the user. This allows for individual motion patterns to be provided by learning the user's past motion data.

[0075] The robot motion control unit can monitor the user's health condition and emotional state in real time and adjust the robot's motion. The robot motion control unit, for example, builds a system that monitors the user's health condition and emotional state in real time and adjusts the robot's motion. For example, if the user is tired, the robot provides a massage. The robot motion control unit also integrates the user's health data and emotional data to optimize the robot's motion. For example, if the user is feeling stressed, the robot performs a motion that has a relaxing effect. The robot motion control unit also develops a system that analyzes the user's health condition and emotional state in real time and dynamically adjusts the robot's motion. For example, if the user is feeling anxious, the robot performs a reassuring motion. In this way, the user's health condition and emotional state can be monitored in real time and the robot's motion can be appropriately adjusted.

[0076] The robot movement control unit can use the emotion estimation function to automatically adjust the robot's facial expressions and movements according to the user's emotions. The robot movement control unit, for example, uses the emotion estimation function to automatically adjust the robot's facial expressions according to the user's emotional state. For example, if the user is sad, the robot will make a gentle expression. The robot movement control unit also analyzes the user's emotional data and automatically generates robot movements according to the user's emotional state. For example, if the user is happy, the robot will move to share the user's joy. The robot movement control unit also uses the emotion estimation function to build a system that optimizes the robot's interaction according to the user's emotions. For example, if the user is angry, the robot will move to calm the user. In this way, the emotion estimation function can be used to automatically adjust the robot's facial expressions and movements according to the user's emotions.

[0077] The robot motion control unit can be applied to the fields of nursing care and rehabilitation to provide specialized support. For example, in the nursing care field, the robot motion control unit applies robot motion control to build a system that provides specialized support. For example, a nursing care robot supports the movement of elderly people. In the rehabilitation field, the robot motion control unit applies robot motion control to provide a rehabilitation program. For example, a rehabilitation robot supports the exercise of patients. In the nursing care and rehabilitation field, the robot motion control unit applies robot motion control to implement a project that provides specialized support. For example, a rehabilitation robot supports the recovery of patients. In this way, specialized support can be provided by applying it to the fields of nursing care and rehabilitation.

[0078] The robot movement control unit can be applied to the fields of entertainment and education to provide an interactive experience. For example, in the entertainment field, the robot movement control unit applies robot movement control to build a system that provides an interactive experience. For example, a robot dances or performs. In the education field, the robot movement control unit also applies robot movement control to provide an interactive learning experience. For example, an educational robot performs learning activities together with children. In the entertainment and education fields, the robot movement control unit also applies robot movement control to implement a project that provides an interactive experience. For example, a robot plays with children. In this way, by applying it to the fields of entertainment and education, an interactive experience can be provided.

[0079] The robot operation control unit uses the emotion estimation function to suggest robot actions according to the user's emotional state and provide emotional support. The robot operation control unit, for example, uses the emotion estimation function to build a system that suggests robot actions according to the user's emotional state. For example, if the user is sad, the robot performs a comforting action. The robot operation control unit also analyzes the user's emotional data and automatically generates robot actions according to the user's emotional state. For example, if the user is happy, the robot performs an action that shares the user's joy. The robot operation control unit also uses the emotion estimation function to develop a system that optimizes robot interaction according to the user's emotional state. For example, if the user is angry, the robot performs a calming action. In this way, by using the emotion estimation function, it is possible to suggest robot actions according to the user's emotional state and provide emotional support.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The AI ​​robot system can further include a music provider that selects appropriate music based on the user's emotions. For example, if the user wants to relax, it can play music that has a relaxing effect. If the user wants to concentrate, it can provide music that will help them concentrate. Furthermore, if the user is sad, it can suggest music that will brighten their mood. This allows for a richer entertainment experience by providing music based on the user's emotions.

[0082] The AI ​​robot system can also be equipped with a meal suggestion unit that proposes an appropriate meal plan based on the user's health condition. For example, it can analyze the user's vital data and propose a nutritionally balanced meal plan. If the user has specific health goals, it can also provide a meal plan that meets those goals. It can also take into account the user's allergy information and propose recipes using safe ingredients. This can support health management by providing an appropriate meal plan based on the user's health condition.

[0083] The AI ​​robot system can also include an exercise suggestion unit that suggests an appropriate exercise plan based on the user's emotions. For example, if the user is feeling stressed, the system can suggest relaxing yoga or stretching. If the user is feeling energetic, the system can suggest active exercise such as running or dancing. Furthermore, the system can adjust the intensity and duration of exercise depending on the user's emotional state. This allows the system to support the user's physical and mental health by providing an appropriate exercise plan based on the user's emotions.

[0084] The AI ​​robot system can also be equipped with a relaxation suggestion unit that suggests appropriate relaxation methods based on the user's emotions. For example, if the user is tense, it can suggest deep breathing or meditation. If the user is tired, it can also suggest aromatherapy or massage. It can also provide relaxation music or images according to the user's emotional state. This can help maintain a balance between mind and body by providing appropriate relaxation methods based on the user's emotions.

[0085] The AI ​​robot system can also be equipped with a learning suggestion unit that proposes an appropriate learning plan based on the user's emotions. For example, if the user wants to improve their concentration, the system can suggest a learning method to maintain their concentration. Also, if the user is feeling stressed, the system can suggest a method to study while relaxing. Furthermore, it can adjust the learning progress according to the user's emotional state and suggest appropriate breaks. This allows the system to support effective learning by providing an appropriate learning plan based on the user's emotions.

[0086] The AI ​​robot system can also be equipped with a schedule management unit that manages an appropriate schedule based on the user's lifestyle habits. For example, it can analyze the user's past behavioral data and propose an optimal schedule. It can also incorporate appropriate rest and exercise times into the schedule, taking into account the user's health condition and vital signs. It can also integrate the user's work and personal plans to provide a balanced schedule. This allows for efficient time management by managing an appropriate schedule based on the user's lifestyle habits.

[0087] The AI ​​robot system can also be equipped with a sleep management unit that performs appropriate sleep management based on the user's health condition. For example, it can analyze the user's vital data and suggest optimal sleep times. It can also monitor the user's sleep patterns and provide advice to promote high-quality sleep. It can also suggest a comfortable sleeping environment by taking into account the user's lifestyle habits and environmental data. This allows for appropriate sleep management based on the user's health condition, thereby supporting high-quality sleep.

[0088] The AI ​​robot system can also be equipped with a hydration suggestion unit that suggests appropriate hydration based on the user's health condition. For example, it can analyze the user's vital data and suggest appropriate amounts of water intake. It can also suggest optimal hydration timing by taking into account the user's activity level and environmental data. It can also suggest types and methods of hydration based on the user's health goals. This can support health management by suggesting appropriate hydration based on the user's health condition.

[0089] The AI ​​robot system can also include an exercise suggestion unit that suggests appropriate stretches and exercises based on the user's health condition. For example, it can analyze the user's vital data and suggest optimal stretches and exercises. It can also provide an individual exercise plan based on the user's activity level and health goals. It can also adjust the intensity and duration of exercises based on the user's health condition. This can support health management by suggesting appropriate stretches and exercises based on the user's health condition.

[0090] The AI ​​robot system can also be equipped with a reminder provider that provides appropriate reminders based on the user's health condition. For example, it can analyze the user's vital signs and remind them to have regular health checks or take their medicine. It can also provide reminders for exercise and hydration based on the user's lifestyle and health goals. It can also send reminders at appropriate times based on the user's schedule. This can support health management by providing appropriate reminders based on the user's health condition.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The emotion recognition unit recognizes the user's emotions. For example, emotions are recognized by analyzing the user's facial expressions using facial expression analysis technology, the user's tone of voice using voice analysis technology, and the user's heart rate and body temperature using vital data analysis technology. Step 2: The conversation generation unit generates a conversation based on the emotions recognized by the emotion recognition unit. For example, it uses natural language generation technology, dialogue management algorithms, and generation AI to generate a conversation based on the user's emotions and provide appropriate responses. Step 3: The vital data collection unit collects the user's vital data through wearable products and IoT products. For example, it collects the user's heart rate, body temperature, and blood pressure using a heart rate sensor, body temperature sensor, and blood pressure sensor. Step 4: The vital data analysis unit analyzes the vital data collected by the vital data collection unit, thereby understanding the user's health condition. Step 5: The environmental data collection unit collects environmental data around the user through the IoT product. For example, it collects the room temperature, humidity, and illuminance using a temperature sensor, humidity sensor, and illuminance sensor. Step 6: The environmental data analysis unit analyzes the environmental data collected by the environmental data collection unit, thereby understanding the environmental conditions around the user. Step 7: The robot behavior control unit controls the robot's behavior based on the data analyzed by the emotion recognition unit, vital data analysis unit, and environmental data analysis unit. For example, it may provide a massage if the user is tired, or play or talk with the user if they feel lonely. It may also adjust the robot's behavior according to the user's health condition.

[0093] 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.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0095] 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.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the 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 specific processing unit 290 using these models.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] In the robot 414, 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 robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0160] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an emotion recognition unit that recognizes the emotion of a user; a conversation generation unit that generates a conversation based on the emotion recognized by the emotion recognition unit; a vital data collection unit that collects vital data of users through wearable products and IoT products; a vital data analysis unit that analyzes the vital data collected by the vital data collection unit; an environmental data collection unit that collects environmental data around the user through IoT products; an environmental data analysis unit that analyzes the environmental data collected by the environmental data collection unit; a robot operation control unit that controls the operation of the robot based on the data analyzed by the emotion recognition unit, the vital data analysis unit, and the environmental data analysis unit. A system characterized by:

2. The emotion recognition unit Learning past emotion data of the user and identifying individual emotion patterns 2. The system of claim 1.

3. The emotion recognition unit Analyzing the user's gestures and postures and reflecting them in emotion recognition 2. The system of claim 1.

4. The emotion recognition unit Automatically adjusts voice tone and speaking style based on the user's emotions 2. The system of claim 1.

5. The emotion recognition unit Respond to different languages ​​and cultures and provide appropriate responses to global users 2. The system of claim 1.

6. The emotion recognition unit Specializing in specific fields such as education and healthcare, and providing specialized support 2. The system of claim 1.

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