system

Abstract

The system according to the embodiment aims to provide safety and security within the home. [Solution] A system according to an embodiment includes a moving unit, a dialogue unit, a detection unit, and a suggestion unit. The moving unit moves autonomously within the home. The dialogue unit recognizes the user's voice and movements and engages in dialogue. The detection unit detects abnormalities and issues an alert. The suggestion unit collects environmental data within the home and makes specific suggestions for maintaining the environment.

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 have not fully realized autonomous systems to provide safety and security within the home, and there is room for improvement.

[0005] The system according to the embodiment aims to provide safety and security within the home. [Means for solving the problem]

[0006] The system according to the embodiment includes a moving unit, a dialogue unit, a detection unit, and a suggestion unit. The moving unit moves autonomously within the home. The dialogue unit recognizes the user's voice and movements and engages in dialogue. The detection unit detects abnormalities and issues an alert. The suggestion unit collects environmental data within the home and makes specific suggestions for maintaining the environment. [Effects of the Invention]

[0007] The system according to the embodiment can provide safety and security within the home. [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-powered robot system according to an embodiment of the present invention autonomously navigates the home, interacts with users through communication, and issues alerts when abnormalities are detected. This AI-powered robot system is designed for target users such as elderly people, pet owners, and single-person households. Specifically, the robot autonomously navigates the home, recognizes the user's voice and movements, and engages in conversation. For example, when an elderly person speaks to the robot, the robot uses a generative AI to generate an appropriate response and continue the conversation. The robot also monitors pet movements and notifies the owner if an abnormality is detected. Furthermore, if the robot detects an abnormality, it issues an alert to family members or caregivers. To detect an abnormality, the generative AI learns the user's behavioral patterns and detects abnormal behavior. For example, an alert is issued if an elderly person remains motionless for an extended period of time or if a pet exhibits abnormal behavior. This robot is designed to provide safety and security within the home and supports the user's daily life. For example, the robot can remind the user to take their medication and manage their daily schedule. The robot also collects environmental data (such as temperature, humidity, and illuminance) within the home and makes suggestions to maintain a comfortable environment. In this way, the monitoring AI robot system equipped with generative AI aims to provide safety and security within the home through communication with the user and to support the user's daily life. In this way, the monitoring AI robot system can provide safety and security within the home and support the user's daily life.

[0029] The AI ​​monitoring robot system according to the embodiment includes a moving unit, a dialogue unit, a detection unit, and a suggestion unit. The moving unit autonomously moves within a home. The moving unit detects and avoids obstacles using, for example, an infrared sensor or an ultrasonic sensor. The moving unit detects and avoids obstacles using, for example, an infrared sensor. The moving unit can also detect and avoid obstacles using an ultrasonic sensor. Furthermore, the moving unit can combine an infrared sensor and an ultrasonic sensor to achieve more accurate obstacle detection and avoidance. The dialogue unit recognizes a user's voice and movements and engages in dialogue. The dialogue unit recognizes a user's voice using, for example, voice recognition technology, and generates an appropriate response using a generation AI. The dialogue unit recognizes a user's voice using, for example, voice recognition technology, and generates an appropriate response using a generation AI. The dialogue unit can also recognize a user's movements using a motion detection sensor and generate an appropriate response using a generation AI. Furthermore, the dialogue unit can combine voice recognition technology and a motion detection sensor to achieve more natural dialogue. The detection unit detects an abnormality and issues an alert. The detection unit detects abnormalities using, for example, a temperature sensor or a motion sensor, and the generation AI learns the user's behavioral patterns to detect the abnormalities. The detection unit detects abnormalities using, for example, a temperature sensor, and the generation AI learns the user's behavioral patterns to detect the abnormalities. The detection unit can also detect abnormalities using a motion sensor, and the generation AI can learn the user's behavioral patterns to detect the abnormalities. Furthermore, the detection unit can combine a temperature sensor and a motion sensor to perform more accurate abnormality detection. The suggestion unit collects environmental data within the home and makes suggestions to maintain a comfortable environment. The suggestion unit collects environmental data within the home using, for example, a temperature sensor, a humidity sensor, and an illuminance sensor, and the generation AI makes suggestions to maintain a comfortable environment. The suggestion unit collects temperature data within the home using, for example, a temperature sensor, and the generation AI makes suggestions to maintain a comfortable environment. The suggestion unit can also collect humidity data within the home using a humidity sensor, and the generation AI can make suggestions to maintain a comfortable environment. Furthermore, the suggestion unit can collect illuminance data within the home using an illuminance sensor, and the generation AI can make suggestions to maintain a comfortable environment.As a result, the monitoring AI robot system according to the embodiment can provide safety and security within the home and support the user's daily life.

[0030] The moving unit can detect and avoid obstacles using an infrared sensor or an ultrasonic sensor. The infrared sensor or ultrasonic sensor is disposed, for example, in front of the robot and detects obstacles. The moving unit can detect and avoid obstacles using, for example, an infrared sensor. The infrared sensor identifies the location of an obstacle by emitting infrared rays and detecting their reflection. The moving unit calculates an avoidance route based on the position information of the obstacle detected by the infrared sensor and moves safely. The moving unit can also detect and avoid obstacles using an ultrasonic sensor. The ultrasonic sensor identifies the location of an obstacle by emitting ultrasonic waves and detecting their reflection. The moving unit calculates an avoidance route based on the position information of the obstacle detected by the ultrasonic sensor and moves safely. Furthermore, the moving unit can combine an infrared sensor and an ultrasonic sensor to perform more accurate obstacle detection and avoidance. This allows the moving unit to avoid obstacles and move safely. Some or all of the above-described processing in the moving unit may be performed, for example, using AI or without AI. For example, the movement unit can input the location information of obstacles detected by infrared sensors or ultrasonic sensors into the generation AI and have the generation AI calculate an avoidance route.

[0031] The dialogue unit can recognize the user's voice and generate a response using a generation AI. The dialogue unit recognizes the user's voice using, for example, speech recognition technology. The speech recognition technology converts the user's voice into a digital signal and analyzes its content. The dialogue unit generates an appropriate response using a generation AI based on the content of the user's voice analyzed by the speech recognition technology. The generation AI generates a response using, for example, a text generation AI (e.g., LLM). The generation AI receives a prompt for generating an appropriate response based on the content of the user's voice and generates a response. For example, the generation AI receives a prompt such as "Please generate an appropriate response to this question" and generates an appropriate response to the user's question. The dialogue unit can also recognize the user's movements using a motion detection sensor and generate an appropriate response using the generation AI. The motion detection sensor detects the user's movements and analyzes their content. The dialogue unit generates an appropriate response using the generation AI based on the content of the user's movements analyzed by the motion detection sensor. This allows the dialogue unit to realize a natural dialogue with the user. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input the user's voice and actions analyzed by voice recognition technology and motion detection sensors into the generation AI, and have the generation AI generate a response.

[0032] The detection unit detects abnormalities using a temperature sensor or a motion sensor, and the generation AI can learn the user's behavioral patterns to detect abnormalities. The detection unit detects abnormalities using, for example, a temperature sensor. The temperature sensor measures the temperature in the home and collects the data. The detection unit detects abnormalities based on the temperature data collected by the temperature sensor. For example, if the temperature exceeds the normal range, the detection unit detects an abnormality. The detection unit can also detect abnormalities using a motion sensor. The motion sensor detects motion in the home and collects the data. The detection unit detects abnormalities based on the motion data collected by the motion sensor. For example, if there is no motion for a long period of time, the detection unit detects an abnormality. Furthermore, the detection unit can learn the user's behavioral patterns using the generation AI and detect abnormalities. The generation AI learns the user's behavioral patterns and detects abnormalities if they differ from the normal behavioral patterns. For example, the generation AI learns the user's normal behavioral patterns and detects an abnormality if there is no motion for a long period of time. This allows the detection unit to detect abnormalities early and take appropriate action. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit may input data collected by a temperature sensor or a motion sensor to the generation AI, and have the generation AI detect anomalies.

[0033] The suggestion unit collects environmental data on temperature, humidity, and illuminance within the home, and the generation AI can make suggestions to maintain the environment. The suggestion unit, for example, uses a temperature sensor to collect temperature data within the home. The temperature sensor measures the temperature within the home and collects the data. The suggestion unit makes suggestions to the generation AI to maintain a comfortable environment based on the temperature data collected by the temperature sensor. For example, if the temperature is too high, the suggestion unit suggests using an air conditioner. The suggestion unit can also collect humidity data within the home using a humidity sensor. The humidity sensor measures the humidity within the home and collects the data. The suggestion unit makes suggestions to the generation AI to maintain a comfortable environment based on the humidity data collected by the humidity sensor. For example, if the humidity is too low, the suggestion unit suggests using a humidifier. The suggestion unit can also collect illuminance data within the home using an illuminance sensor. The illuminance sensor measures the illuminance within the home and collects the data. The suggestion unit makes suggestions to the generation AI to maintain a comfortable environment based on the illuminance data collected by the illuminance sensor. For example, if the illuminance is too low, the suggestion unit suggests using lighting. This allows the suggestion unit to maintain a comfortable home environment. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit may input data collected by a temperature sensor, a humidity sensor, and an illuminance sensor into the generation AI, and cause the generation AI to make suggestions for maintaining the environment.

[0034] The dialogue unit can remind the user to take their medicine and specifically manage their daily schedule. For example, the dialogue unit reminds the user to take their medicine. The dialogue unit manages the user's medicine taking schedule and provides reminders at appropriate times. For example, the dialogue unit provides a voice reminder when it is nearly time for the user to take their medicine. The dialogue unit can also manage the user's daily schedule. The dialogue unit manages the user's schedule and provides reminders at appropriate times. For example, the dialogue unit provides a voice reminder when the user's appointment is approaching. This allows the dialogue unit to support the user's health management and schedule management. Some or all of the above-mentioned processing in the dialogue unit may be performed using AI, for example, or may be performed without using AI. For example, the dialogue unit can input the user's medicine taking schedule and daily schedule into the generation AI and have the generation AI execute the timing of the reminder.

[0035] The movement unit can learn the furniture layout in the home and automatically generate a movement route. The movement unit, for example, learns the furniture layout in the home. Learning the furniture layout is performed, for example, using image recognition technology or analysis of sensor information. The movement unit automatically generates a movement route based on the learned furniture layout data. For example, the movement unit scans the furniture layout and generates an optimal route that avoids obstacles. Also, if the furniture layout is changed, the movement unit can learn the new layout and update the route. Furthermore, the movement unit can preferentially select routes that the user frequently takes based on the furniture layout. In this way, the movement unit can provide an optimal movement route based on the furniture layout. Some or all of the above-mentioned processing in the movement unit may be performed using, for example, AI, or may be performed without using AI. For example, the movement unit can input learned furniture layout data into a generation AI and cause the generation AI to automatically generate a movement route.

[0036] The movement unit monitors the user's health condition during movement and can interrupt the movement as necessary. The movement unit, for example, monitors the user's health condition. The health condition monitoring is performed, for example, using a vital sign measurement method or an abnormality detection algorithm. The movement unit interrupts the movement as necessary based on the health condition monitoring data. For example, if the user's heart rate is abnormally high, the movement unit interrupts the movement and checks the user's condition. Also, if the user falls, the movement unit can interrupt the movement and issue an alert. Furthermore, if the user does not move for a long period of time, the movement unit can interrupt the movement and call out to the user. This allows the movement unit to perform movement according to the user's health condition. Some or all of the above-mentioned processing in the movement unit may be performed, for example, using AI or without AI. For example, the movement unit can input health condition monitoring data to a generation AI and have the generation AI interrupt the movement.

[0037] When traveling, the traveling unit can select a safe traveling route based on the movements of the pet within the home. The traveling unit, for example, considers the movements of the pet within the home. To consider the movements of the pet, for example, an animal movement detection method or an analysis of the pet's behavioral patterns is used. The traveling unit selects a safe traveling route based on the movements of the pet. For example, the traveling unit selects a route that avoids areas where the pet is present. The traveling unit can also monitor the movements of the pet in real time to avoid collisions. Furthermore, if the pet is in a specific location, it can select a route that avoids that location. This allows the traveling unit to perform safe traveling taking the movements of the pet into consideration. Some or all of the above-described processing in the traveling unit may be performed using, for example, AI, or may be performed without using AI. For example, the traveling unit can input data on the movements of the pet into a generating AI and cause the generating AI to select a safe traveling route.

[0038] The moving unit can provide a moving environment based on the temperature and humidity inside the home during movement. The moving unit, for example, considers the temperature and humidity inside the home. To consider the temperature and humidity, for example, a method of using a temperature sensor or a humidity sensor is used. The moving unit provides a comfortable moving environment based on the temperature and humidity data. For example, the moving unit selects a route that avoids high temperature locations. It can also select a route that avoids high humidity locations. Furthermore, it can also move by prioritizing locations with comfortable temperatures and humidity. In this way, the moving unit can provide a comfortable moving environment. Some or all of the above-mentioned processing in the moving unit may be performed using, for example, AI, or may be performed without using AI. For example, the moving unit can input temperature and humidity data into the generating AI and cause the generating AI to provide a comfortable moving environment.

[0039] During a dialogue, the dialogue unit can generate a response based on the user's past conversation history. The dialogue unit, for example, references the user's past conversation history. To reference the past conversation history, for example, a method for saving the conversation history or a method for analyzing the history data is used. The dialogue unit allows the generation AI to generate an optimal response based on the past conversation history. For example, the dialogue unit provides a related topic based on what the user has previously said. The dialogue unit can also select a preferred topic from the user's past conversation history and engage in a dialogue. Furthermore, the dialogue unit can provide appropriate advice based on what the user has previously said. This allows the dialogue unit to generate an optimal response based on the past conversation history. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input past conversation history data into the generation AI and have the generation AI generate a response.

[0040] The dialogue unit can monitor the user's health condition during dialogue and suggest contacting a medical institution if necessary. The dialogue unit, for example, monitors the user's health condition. The health condition monitoring is performed, for example, using a vital sign measurement method or an anomaly detection algorithm. The dialogue unit suggests contacting a medical institution if necessary based on the health condition monitoring data. For example, the dialogue unit estimates the user's health condition from the tone of the user's voice and suggests contacting a medical institution if an abnormality is detected. The dialogue unit can also suggest contacting a medical institution if the user complains of feeling unwell. Furthermore, the dialogue unit can monitor the user's health condition and suggest contacting a medical institution if an abnormality is detected. This allows the dialogue unit to take appropriate action according to the user's health condition. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input health condition monitoring data to a generation AI and cause the generation AI to suggest contacting a medical institution.

[0041] The dialogue unit can provide related topics based on the user's hobbies and interests during dialogue. The dialogue unit, for example, identifies the user's hobbies and interests. To identify the hobbies and interests, for example, survey results or past dialogue history are used. The dialogue unit allows the generation AI to provide related topics based on the hobbies and interests. For example, when a user talks about a favorite movie, the dialogue unit provides related movie topics. Also, when a user talks about a hobby, the dialogue unit can provide related information. Furthermore, when a user talks about news that interests the user, the dialogue unit can provide related news topics. This allows the dialogue unit to conduct dialogue based on the user's hobbies and interests. Some or all of the above-mentioned processing in the dialogue unit may be performed using AI, for example, or may be performed without using AI. For example, the dialogue unit can input data on hobbies and interests into the generation AI and cause the generation AI to provide related topics.

[0042] The dialogue unit can select the dialogue timing based on the user's schedule during dialogue. The dialogue unit, for example, references the user's schedule. To reference the schedule, for example, a method for acquiring schedule data or a schedule analysis algorithm is used. The dialogue unit allows the generation AI to select the optimal dialogue timing based on the schedule. For example, the dialogue unit references the user's schedule and conducts the dialogue during a time when the user is free. The dialogue can also be conducted to avoid times when the user is busy. Furthermore, the dialogue can be conducted at an appropriate time according to the user's schedule. This allows the dialogue unit to conduct a dialogue according to the user's schedule. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input schedule data to the generation AI and have the generation AI select the dialogue timing.

[0043] The detection unit can perform early detection of anomalies based on the user's past behavioral patterns during detection. The detection unit, for example, references the user's past behavioral patterns. To reference the past behavioral patterns, for example, a method for saving behavioral history or a method for analyzing history data is used. The detection unit allows the generation AI to perform early detection of anomalies based on the past behavioral patterns. For example, the detection unit can detect anomalies early based on the user's past behavioral patterns. The detection unit can also find signs of anomalies from the user's past behavioral patterns. Furthermore, the detection unit can learn the user's behavioral patterns and detect anomalies early. This allows the detection unit to perform early detection of anomalies based on the past behavioral patterns. Some or all of the above-described processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input past behavioral pattern data to the generation AI and cause the generation AI to perform early detection of anomalies.

[0044] Upon detection, the detection unit can identify the cause of the abnormality based on the environmental data within the home. The detection unit, for example, references the environmental data within the home. To reference the environmental data, for example, a data collection method or a data analysis algorithm is used. The detection unit causes the generation AI to identify the cause of the abnormality based on the environmental data. For example, the detection unit identifies the cause of the abnormality based on temperature and humidity data. The detection unit can also identify the cause of the abnormality based on illuminance and sound data. Furthermore, the detection unit can integrate the environmental data within the home to identify the cause of the abnormality. Some or all of the above-mentioned processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input the environmental data to the generation AI and cause the generation AI to identify the cause of the abnormality.

[0045] The detection unit can detect an abnormality based on the behavior of a pet in the home at the time of detection. The detection unit, for example, takes into account the behavior of the pet in the home. To take the pet's behavior into account, for example, an animal movement detection method or an analysis of the pet's behavior pattern is used. In the detection unit, the generation AI detects an abnormality based on the pet's behavior. For example, if the pet exhibits abnormal behavior, the detection unit detects the abnormality. The detection unit can also learn the pet's behavior pattern and detect an abnormality. Furthermore, the pet's movement can be monitored in real time and an abnormality can be detected. This allows the detection unit to detect an abnormality taking into account the behavior of the pet in the home. Some or all of the above-mentioned processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input pet behavior data to the generation AI and cause the generation AI to detect an abnormality.

[0046] During detection, the detection unit can detect an abnormality based on the power consumption data within the home. The detection unit, for example, references the power consumption data within the home. To reference the power consumption data, for example, a data collection method or a data analysis algorithm is used. The detection unit causes the generation AI to detect an abnormality based on the power consumption data. For example, if power consumption is abnormally high, the detection unit detects the abnormality. The detection unit can also learn power consumption patterns and detect abnormalities. Furthermore, the power consumption data can be monitored in real time to detect abnormalities. This allows the detection unit to detect abnormalities by referring to the power consumption data within the home. Some or all of the above-mentioned processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input the power consumption data to the generation AI and cause the generation AI to detect an abnormality.

[0047] When making a proposal, the suggestion unit can make a proposal based on the user's past environmental data. The suggestion unit, for example, references the user's past environmental data. To reference the past environmental data, for example, a data storage method or a data analysis algorithm is used. The suggestion unit allows the generation AI to make an optimal proposal based on the past environmental data. For example, the suggestion unit makes an optimal proposal based on environmental data that the user found comfortable in the past. The suggestion unit can also suggest a comfortable environment from the user's past environmental data. Furthermore, the suggestion unit can analyze the user's past environmental data and make an optimal proposal. This allows the suggestion unit to make an optimal proposal based on the past environmental data. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input past environmental data to the generation AI and cause the generation AI to generate a proposal.

[0048] When making a proposal, the suggestion unit can monitor the user's health condition and make health management suggestions as needed. The suggestion unit, for example, monitors the user's health condition. The health condition monitoring is performed, for example, using a vital sign measurement method or an anomaly detection algorithm. The suggestion unit makes health management suggestions as needed based on the health condition monitoring data. For example, the suggestion unit can monitor the user's health condition and suggest appropriate exercise. The suggestion unit can also monitor the user's health condition and suggest appropriate meals. Furthermore, the suggestion unit can monitor the user's health condition and suggest appropriate rest. This allows the suggestion unit to make health management suggestions according to the user's health condition. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input health condition monitoring data to a generation AI and cause the generation AI to execute health management suggestions.

[0049] When making a proposal, the suggestion unit can make a proposal based on the health condition of pets in the household. The suggestion unit, for example, considers the health condition of pets in the household. To consider the health condition of the pets, for example, a method for collecting animal health data and health condition evaluation criteria are used. The suggestion unit allows the generation AI to make an optimal proposal based on the pet's health condition. For example, the suggestion unit can monitor the pet's health condition and suggest appropriate exercise. The suggestion unit can also monitor the pet's health condition and suggest appropriate meals. Furthermore, the suggestion unit can monitor the pet's health condition and suggest appropriate rest. This allows the suggestion unit to make a proposal according to the health condition of pets in the household. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the pet's health data into the generation AI and cause the generation AI to generate a proposal.

[0050] When making a proposal, the proposal unit can make an energy-saving proposal by referring to energy consumption data within the home. The proposal unit, for example, references the energy consumption data within the home. To refer to the energy consumption data, for example, a data collection method or a data analysis algorithm is used. The proposal unit causes the generation AI to make an energy-saving proposal based on the energy consumption data. For example, the proposal unit proposes an optimal energy-saving method based on the energy consumption data. The energy consumption data can also be analyzed to make a proposal to reduce wasteful energy consumption. Furthermore, the energy consumption data can be referenced to make a proposal for efficient energy use. This allows the proposal unit to make an energy-saving proposal based on the energy consumption data within the home. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input the energy consumption data to the generation AI and cause the generation AI to generate an energy-saving proposal.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The movement unit can learn the furniture layout in the home and automatically generate a movement route. The movement unit, for example, learns the furniture layout in the home. Learning the furniture layout is performed, for example, using image recognition technology or analysis of sensor information. The movement unit automatically generates a movement route based on the learned furniture layout data. For example, the movement unit scans the furniture layout and generates an optimal route that avoids obstacles. Also, if the furniture layout is changed, the movement unit can learn the new layout and update the route. Furthermore, the movement unit can preferentially select routes that the user frequently takes based on the furniture layout. In this way, the movement unit can provide an optimal movement route based on the furniture layout. Some or all of the above-mentioned processing in the movement unit may be performed using, for example, AI, or may be performed without using AI. For example, the movement unit can input learned furniture layout data into a generation AI and cause the generation AI to automatically generate a movement route.

[0053] When traveling, the traveling unit can select a safe traveling route based on the movements of the pet within the home. The traveling unit, for example, considers the movements of the pet within the home. To consider the movements of the pet, for example, an animal movement detection method or an analysis of the pet's behavioral patterns is used. The traveling unit selects a safe traveling route based on the movements of the pet. For example, the traveling unit selects a route that avoids areas where the pet is present. The traveling unit can also monitor the movements of the pet in real time to avoid collisions. Furthermore, if the pet is in a specific location, it can select a route that avoids that location. This allows the traveling unit to perform safe traveling taking the movements of the pet into consideration. Some or all of the above-described processing in the traveling unit may be performed using, for example, AI, or may be performed without using AI. For example, the traveling unit can input data on the movements of the pet into a generating AI and cause the generating AI to select a safe traveling route.

[0054] During a dialogue, the dialogue unit can generate a response based on the user's past conversation history. The dialogue unit, for example, references the user's past conversation history. To reference the past conversation history, for example, a method for saving the conversation history or a method for analyzing the history data is used. The dialogue unit allows the generation AI to generate an optimal response based on the past conversation history. For example, the dialogue unit provides a related topic based on what the user has previously said. The dialogue unit can also select a preferred topic from the user's past conversation history and engage in a dialogue. Furthermore, the dialogue unit can provide appropriate advice based on what the user has previously said. This allows the dialogue unit to generate an optimal response based on the past conversation history. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input past conversation history data into the generation AI and have the generation AI generate a response.

[0055] When making a proposal, the proposal unit can make an energy-saving proposal by referring to energy consumption data within the home. The proposal unit, for example, references the energy consumption data within the home. To refer to the energy consumption data, for example, a data collection method or a data analysis algorithm is used. The proposal unit causes the generation AI to make an energy-saving proposal based on the energy consumption data. For example, the proposal unit proposes an optimal energy-saving method based on the energy consumption data. The energy consumption data can also be analyzed to make a proposal to reduce wasteful energy consumption. Furthermore, the energy consumption data can be referenced to make a proposal for efficient energy use. This allows the proposal unit to make an energy-saving proposal based on the energy consumption data within the home. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input the energy consumption data to the generation AI and cause the generation AI to generate an energy-saving proposal.

[0056] During detection, the detection unit can detect an abnormality based on the power consumption data within the home. The detection unit, for example, references the power consumption data within the home. To reference the power consumption data, for example, a data collection method or a data analysis algorithm is used. The detection unit causes the generation AI to detect an abnormality based on the power consumption data. For example, if power consumption is abnormally high, the detection unit detects the abnormality. The detection unit can also learn power consumption patterns and detect abnormalities. Furthermore, the power consumption data can be monitored in real time to detect abnormalities. This allows the detection unit to detect abnormalities by referring to the power consumption data within the home. Some or all of the above-mentioned processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input the power consumption data to the generation AI and cause the generation AI to detect an abnormality.

[0057] The dialogue unit can monitor the user's health condition during dialogue and suggest contacting a medical institution if necessary. The dialogue unit, for example, monitors the user's health condition. The health condition monitoring is performed, for example, using a vital sign measurement method or an anomaly detection algorithm. The dialogue unit suggests contacting a medical institution if necessary based on the health condition monitoring data. For example, the dialogue unit estimates the user's health condition from the tone of the user's voice and suggests contacting a medical institution if an abnormality is detected. The dialogue unit can also suggest contacting a medical institution if the user complains of feeling unwell. Furthermore, the dialogue unit can monitor the user's health condition and suggest contacting a medical institution if an abnormality is detected. This allows the dialogue unit to take appropriate action according to the user's health condition. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input health condition monitoring data to a generation AI and cause the generation AI to suggest contacting a medical institution.

[0058] When making a proposal, the suggestion unit can make a proposal based on the health condition of pets in the household. The suggestion unit, for example, considers the health condition of pets in the household. To consider the health condition of the pets, for example, a method for collecting animal health data and health condition evaluation criteria are used. The suggestion unit allows the generation AI to make an optimal proposal based on the pet's health condition. For example, the suggestion unit can monitor the pet's health condition and suggest appropriate exercise. The suggestion unit can also monitor the pet's health condition and suggest appropriate meals. Furthermore, the suggestion unit can monitor the pet's health condition and suggest appropriate rest. This allows the suggestion unit to make a proposal according to the health condition of pets in the household. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the pet's health data into the generation AI and cause the generation AI to generate a proposal.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The mobile unit moves autonomously around the home. It uses infrared and ultrasonic sensors to detect and avoid obstacles. This allows the mobile unit to move smoothly around the home while avoiding various obstacles. Step 2: The dialogue unit recognizes the user's voice and actions and engages in dialogue. The dialogue unit uses voice recognition technology to recognize the user's voice and uses generation AI to generate an appropriate response. It can also recognize the user's actions using a motion detection sensor and use generation AI to generate an appropriate response. This allows the dialogue unit to achieve natural dialogue with the user. Step 3: The detection unit detects an abnormality and issues an alert. The detection unit detects abnormalities using temperature and motion sensors, and the generation AI learns the user's behavior patterns to detect abnormalities. This allows the detection unit to quickly detect abnormalities in the home and notify the user. Step 4: The suggestion unit collects environmental data within the home and makes suggestions to maintain a comfortable environment. The suggestion unit collects environmental data within the home using temperature sensors, humidity sensors, and illuminance sensors, and the generation AI makes suggestions to maintain a comfortable environment. In this way, the suggestion unit optimizes the home environment and supports the user's comfortable life.

[0061] (Example 2) The AI-powered robot system according to an embodiment of the present invention autonomously navigates the home, interacts with users through communication, and issues alerts when abnormalities are detected. This AI-powered robot system is designed for target users such as elderly people, pet owners, and single-person households. Specifically, the robot autonomously navigates the home, recognizes the user's voice and movements, and engages in conversation. For example, when an elderly person speaks to the robot, the robot uses a generative AI to generate an appropriate response and continue the conversation. The robot also monitors pet movements and notifies the owner if an abnormality is detected. Furthermore, if the robot detects an abnormality, it issues an alert to family members or caregivers. To detect an abnormality, the generative AI learns the user's behavioral patterns and detects abnormal behavior. For example, an alert is issued if an elderly person remains motionless for an extended period of time or if a pet exhibits abnormal behavior. This robot is designed to provide safety and security within the home and supports the user's daily life. For example, the robot can remind the user to take their medication and manage their daily schedule. The robot also collects environmental data (such as temperature, humidity, and illuminance) within the home and makes suggestions to maintain a comfortable environment. In this way, the monitoring AI robot system equipped with generative AI aims to provide safety and security within the home through communication with the user and to support the user's daily life. In this way, the monitoring AI robot system can provide safety and security within the home and support the user's daily life.

[0062] The AI ​​monitoring robot system according to the embodiment includes a moving unit, a dialogue unit, a detection unit, and a suggestion unit. The moving unit autonomously moves within a home. The moving unit detects and avoids obstacles using, for example, an infrared sensor or an ultrasonic sensor. The moving unit detects and avoids obstacles using, for example, an infrared sensor. The moving unit can also detect and avoid obstacles using an ultrasonic sensor. Furthermore, the moving unit can combine an infrared sensor and an ultrasonic sensor to achieve more accurate obstacle detection and avoidance. The dialogue unit recognizes a user's voice and movements and engages in dialogue. The dialogue unit recognizes a user's voice using, for example, voice recognition technology, and generates an appropriate response using a generation AI. The dialogue unit recognizes a user's voice using, for example, voice recognition technology, and generates an appropriate response using a generation AI. The dialogue unit can also recognize a user's movements using a motion detection sensor and generate an appropriate response using a generation AI. Furthermore, the dialogue unit can combine voice recognition technology and a motion detection sensor to achieve more natural dialogue. The detection unit detects an abnormality and issues an alert. The detection unit detects abnormalities using, for example, a temperature sensor or a motion sensor, and the generation AI learns the user's behavioral patterns to detect the abnormalities. The detection unit detects abnormalities using, for example, a temperature sensor, and the generation AI learns the user's behavioral patterns to detect the abnormalities. The detection unit can also detect abnormalities using a motion sensor, and the generation AI can learn the user's behavioral patterns to detect the abnormalities. Furthermore, the detection unit can combine a temperature sensor and a motion sensor to perform more accurate abnormality detection. The suggestion unit collects environmental data within the home and makes suggestions to maintain a comfortable environment. The suggestion unit collects environmental data within the home using, for example, a temperature sensor, a humidity sensor, and an illuminance sensor, and the generation AI makes suggestions to maintain a comfortable environment. The suggestion unit collects temperature data within the home using, for example, a temperature sensor, and the generation AI makes suggestions to maintain a comfortable environment. The suggestion unit can also collect humidity data within the home using a humidity sensor, and the generation AI can make suggestions to maintain a comfortable environment. Furthermore, the suggestion unit can collect illuminance data within the home using an illuminance sensor, and the generation AI can make suggestions to maintain a comfortable environment.As a result, the monitoring AI robot system according to the embodiment can provide safety and security within the home and support the user's daily life.

[0063] The moving unit can detect and avoid obstacles using an infrared sensor or an ultrasonic sensor. The infrared sensor or ultrasonic sensor is disposed, for example, in front of the robot and detects obstacles. The moving unit can detect and avoid obstacles using, for example, an infrared sensor. The infrared sensor identifies the location of an obstacle by emitting infrared rays and detecting their reflection. The moving unit calculates an avoidance route based on the position information of the obstacle detected by the infrared sensor and moves safely. The moving unit can also detect and avoid obstacles using an ultrasonic sensor. The ultrasonic sensor identifies the location of an obstacle by emitting ultrasonic waves and detecting their reflection. The moving unit calculates an avoidance route based on the position information of the obstacle detected by the ultrasonic sensor and moves safely. Furthermore, the moving unit can combine an infrared sensor and an ultrasonic sensor to perform more accurate obstacle detection and avoidance. This allows the moving unit to avoid obstacles and move safely. Some or all of the above-described processing in the moving unit may be performed, for example, using AI or without AI. For example, the movement unit can input the location information of obstacles detected by infrared sensors or ultrasonic sensors into the generation AI and have the generation AI calculate an avoidance route.

[0064] The dialogue unit can recognize the user's voice and generate a response using a generation AI. The dialogue unit recognizes the user's voice using, for example, speech recognition technology. The speech recognition technology converts the user's voice into a digital signal and analyzes its content. The dialogue unit generates an appropriate response using a generation AI based on the content of the user's voice analyzed by the speech recognition technology. The generation AI generates a response using, for example, a text generation AI (e.g., LLM). The generation AI receives a prompt for generating an appropriate response based on the content of the user's voice and generates a response. For example, the generation AI receives a prompt such as "Please generate an appropriate response to this question" and generates an appropriate response to the user's question. The dialogue unit can also recognize the user's movements using a motion detection sensor and generate an appropriate response using the generation AI. The motion detection sensor detects the user's movements and analyzes their content. The dialogue unit generates an appropriate response using the generation AI based on the content of the user's movements analyzed by the motion detection sensor. This allows the dialogue unit to realize a natural dialogue with the user. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input the user's voice and actions analyzed by voice recognition technology and motion detection sensors into the generation AI, and have the generation AI generate a response.

[0065] The detection unit detects abnormalities using a temperature sensor or a motion sensor, and the generation AI can learn the user's behavioral patterns to detect abnormalities. The detection unit detects abnormalities using, for example, a temperature sensor. The temperature sensor measures the temperature in the home and collects the data. The detection unit detects abnormalities based on the temperature data collected by the temperature sensor. For example, if the temperature exceeds the normal range, the detection unit detects an abnormality. The detection unit can also detect abnormalities using a motion sensor. The motion sensor detects motion in the home and collects the data. The detection unit detects abnormalities based on the motion data collected by the motion sensor. For example, if there is no motion for a long period of time, the detection unit detects an abnormality. Furthermore, the detection unit can learn the user's behavioral patterns using the generation AI and detect abnormalities. The generation AI learns the user's behavioral patterns and detects abnormalities if they differ from the normal behavioral patterns. For example, the generation AI learns the user's normal behavioral patterns and detects an abnormality if there is no motion for a long period of time. This allows the detection unit to detect abnormalities early and take appropriate action. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit may input data collected by a temperature sensor or a motion sensor to the generation AI, and have the generation AI detect anomalies.

[0066] The suggestion unit collects environmental data on temperature, humidity, and illuminance within the home, and the generation AI can make suggestions to maintain the environment. The suggestion unit, for example, uses a temperature sensor to collect temperature data within the home. The temperature sensor measures the temperature within the home and collects the data. The suggestion unit makes suggestions to the generation AI to maintain a comfortable environment based on the temperature data collected by the temperature sensor. For example, if the temperature is too high, the suggestion unit suggests using an air conditioner. The suggestion unit can also collect humidity data within the home using a humidity sensor. The humidity sensor measures the humidity within the home and collects the data. The suggestion unit makes suggestions to the generation AI to maintain a comfortable environment based on the humidity data collected by the humidity sensor. For example, if the humidity is too low, the suggestion unit suggests using a humidifier. The suggestion unit can also collect illuminance data within the home using an illuminance sensor. The illuminance sensor measures the illuminance within the home and collects the data. The suggestion unit makes suggestions to the generation AI to maintain a comfortable environment based on the illuminance data collected by the illuminance sensor. For example, if the illuminance is too low, the suggestion unit suggests using lighting. This allows the suggestion unit to maintain a comfortable home environment. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit may input data collected by a temperature sensor, a humidity sensor, and an illuminance sensor into the generation AI, and cause the generation AI to make suggestions for maintaining the environment.

[0067] The dialogue unit can remind the user to take their medicine and specifically manage their daily schedule. For example, the dialogue unit reminds the user to take their medicine. The dialogue unit manages the user's medicine taking schedule and provides reminders at appropriate times. For example, the dialogue unit provides a voice reminder when it is nearly time for the user to take their medicine. The dialogue unit can also manage the user's daily schedule. The dialogue unit manages the user's schedule and provides reminders at appropriate times. For example, the dialogue unit provides a voice reminder when the user's appointment is approaching. This allows the dialogue unit to support the user's health management and schedule management. Some or all of the above-mentioned processing in the dialogue unit may be performed using AI, for example, or may be performed without using AI. For example, the dialogue unit can input the user's medicine taking schedule and daily schedule into the generation AI and have the generation AI execute the timing of the reminder.

[0068] The movement unit can estimate the user's emotions and adjust the movement speed and route based on the estimated user emotions. The movement unit, for example, estimates the user's emotions. Emotion estimation is performed using, for example, facial expression recognition technology or voice analysis technology. The movement unit adjusts the movement speed and route based on the user's emotions estimated by the emotion estimation technology. For example, if the user is relaxed, the movement unit slows down the movement speed and moves quietly. Also, if the user is in a hurry, the movement unit can increase the movement speed and select the shortest route. Furthermore, if the user is feeling anxious, the movement unit can adjust the movement speed and stay close to the user. This allows the movement unit to move according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the movement unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the movement unit can input the user's emotion data estimated by emotion estimation technology into the generation AI and have the generation AI adjust the movement speed and route.

[0069] The movement unit can learn the furniture layout in the home and automatically generate a movement route. The movement unit, for example, learns the furniture layout in the home. Learning the furniture layout is performed, for example, using image recognition technology or analysis of sensor information. The movement unit automatically generates a movement route based on the learned furniture layout data. For example, the movement unit scans the furniture layout and generates an optimal route that avoids obstacles. Also, if the furniture layout is changed, the movement unit can learn the new layout and update the route. Furthermore, the movement unit can preferentially select routes that the user frequently takes based on the furniture layout. In this way, the movement unit can provide an optimal movement route based on the furniture layout. Some or all of the above-mentioned processing in the movement unit may be performed using, for example, AI, or may be performed without using AI. For example, the movement unit can input learned furniture layout data into a generation AI and cause the generation AI to automatically generate a movement route.

[0070] The movement unit monitors the user's health condition during movement and can interrupt the movement as necessary. The movement unit, for example, monitors the user's health condition. The health condition monitoring is performed, for example, using a vital sign measurement method or an abnormality detection algorithm. The movement unit interrupts the movement as necessary based on the health condition monitoring data. For example, if the user's heart rate is abnormally high, the movement unit interrupts the movement and checks the user's condition. Also, if the user falls, the movement unit can interrupt the movement and issue an alert. Furthermore, if the user does not move for a long period of time, the movement unit can interrupt the movement and call out to the user. This allows the movement unit to perform movement according to the user's health condition. Some or all of the above-mentioned processing in the movement unit may be performed, for example, using AI or without AI. For example, the movement unit can input health condition monitoring data to a generation AI and have the generation AI interrupt the movement.

[0071] The movement unit can estimate the user's emotions and determine movement priorities based on the estimated user emotions. The movement unit, for example, estimates the user's emotions. Emotion estimation is performed using, for example, facial expression recognition technology or voice analysis technology. The movement unit determines movement priorities based on the user's emotions estimated by emotion estimation technology. For example, if the user is feeling anxious, the movement unit preferentially moves closer to the user. Also, if the user is relaxed, the movement unit can prioritize other tasks. Furthermore, if the user is in a hurry, the movement unit can select the shortest route to move. This allows the movement unit to determine movement priorities according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the movement unit may be performed using, for example, AI, or without AI. For example, the movement unit can input the user's emotion data estimated by the emotion estimation technology into the generation AI and have the generation AI determine the movement priority.

[0072] When traveling, the traveling unit can select a safe traveling route based on the movements of the pet within the home. The traveling unit, for example, considers the movements of the pet within the home. To consider the movements of the pet, for example, an animal movement detection method or an analysis of the pet's behavioral patterns is used. The traveling unit selects a safe traveling route based on the movements of the pet. For example, the traveling unit selects a route that avoids areas where the pet is present. The traveling unit can also monitor the movements of the pet in real time to avoid collisions. Furthermore, if the pet is in a specific location, it can select a route that avoids that location. This allows the traveling unit to perform safe traveling taking the movements of the pet into consideration. Some or all of the above-described processing in the traveling unit may be performed using, for example, AI, or may be performed without using AI. For example, the traveling unit can input data on the movements of the pet into a generating AI and cause the generating AI to select a safe traveling route.

[0073] The moving unit can provide a moving environment based on the temperature and humidity inside the home during movement. The moving unit, for example, considers the temperature and humidity inside the home. To consider the temperature and humidity, for example, a method of using a temperature sensor or a humidity sensor is used. The moving unit provides a comfortable moving environment based on the temperature and humidity data. For example, the moving unit selects a route that avoids high temperature locations. It can also select a route that avoids high humidity locations. Furthermore, it can also move by prioritizing locations with comfortable temperatures and humidity. In this way, the moving unit can provide a comfortable moving environment. Some or all of the above-mentioned processing in the moving unit may be performed using, for example, AI, or may be performed without using AI. For example, the moving unit can input temperature and humidity data into the generating AI and cause the generating AI to provide a comfortable moving environment.

[0074] The dialogue unit can estimate the user's emotions and adjust the tone and content of the dialogue based on the estimated user emotions. The dialogue unit, for example, estimates the user's emotions. Emotion estimation is performed using, for example, facial expression recognition technology or voice analysis technology. The dialogue unit adjusts the tone and content of the dialogue based on the user's emotions estimated by the emotion estimation technology. For example, if the user is sad, the dialogue unit can offer encouraging words in a gentle tone. If the user is happy, the dialogue unit can offer empathetic words in a bright tone. Furthermore, if the user is angry, the dialogue unit can also engage in the dialogue in a calm tone. This allows the dialogue unit to conduct a dialogue according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or without AI. For example, the dialogue unit can input the user's emotional data estimated by emotion estimation technology into the generation AI, and have the generation AI adjust the tone and content of the dialogue.

[0075] During a dialogue, the dialogue unit can generate a response based on the user's past conversation history. The dialogue unit, for example, references the user's past conversation history. To reference the past conversation history, for example, a method for saving the conversation history or a method for analyzing the history data is used. The dialogue unit allows the generation AI to generate an optimal response based on the past conversation history. For example, the dialogue unit provides a related topic based on what the user has previously said. The dialogue unit can also select a preferred topic from the user's past conversation history and engage in a dialogue. Furthermore, the dialogue unit can provide appropriate advice based on what the user has previously said. This allows the dialogue unit to generate an optimal response based on the past conversation history. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input past conversation history data into the generation AI and have the generation AI generate a response.

[0076] The dialogue unit can monitor the user's health condition during dialogue and suggest contacting a medical institution if necessary. The dialogue unit, for example, monitors the user's health condition. The health condition monitoring is performed, for example, using a vital sign measurement method or an anomaly detection algorithm. The dialogue unit suggests contacting a medical institution if necessary based on the health condition monitoring data. For example, the dialogue unit estimates the user's health condition from the tone of the user's voice and suggests contacting a medical institution if an abnormality is detected. The dialogue unit can also suggest contacting a medical institution if the user complains of feeling unwell. Furthermore, the dialogue unit can monitor the user's health condition and suggest contacting a medical institution if an abnormality is detected. This allows the dialogue unit to take appropriate action according to the user's health condition. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input health condition monitoring data to a generation AI and cause the generation AI to suggest contacting a medical institution.

[0077] The dialogue unit can estimate the user's emotions and adjust the frequency of dialogue based on the estimated user emotions. The dialogue unit, for example, estimates the user's emotions. Emotion estimation is performed using, for example, facial expression recognition technology or voice analysis technology. The dialogue unit adjusts the frequency of dialogue based on the user's emotions estimated by the emotion estimation technology. For example, if the user feels lonely, the dialogue unit may dialogue more frequently. Also, if the user is busy, the dialogue unit may dialogue less frequently. Furthermore, if the user is relaxed, the dialogue unit may dialogue at an appropriate frequency. This allows the dialogue unit to adjust the frequency of dialogue according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input the user's emotion data estimated by the emotion estimation technology into the generation AI and have the generation AI adjust the frequency of dialogue.

[0078] The dialogue unit can provide related topics based on the user's hobbies and interests during dialogue. The dialogue unit, for example, identifies the user's hobbies and interests. To identify the hobbies and interests, for example, survey results or past dialogue history are used. The dialogue unit allows the generation AI to provide related topics based on the hobbies and interests. For example, when a user talks about a favorite movie, the dialogue unit provides related movie topics. Also, when a user talks about a hobby, the dialogue unit can provide related information. Furthermore, when a user talks about news that interests the user, the dialogue unit can provide related news topics. This allows the dialogue unit to conduct dialogue based on the user's hobbies and interests. Some or all of the above-mentioned processing in the dialogue unit may be performed using AI, for example, or may be performed without using AI. For example, the dialogue unit can input data on hobbies and interests into the generation AI and cause the generation AI to provide related topics.

[0079] The dialogue unit can select the dialogue timing based on the user's schedule during dialogue. The dialogue unit, for example, references the user's schedule. To reference the schedule, for example, a method for acquiring schedule data or a schedule analysis algorithm is used. The dialogue unit allows the generation AI to select the optimal dialogue timing based on the schedule. For example, the dialogue unit references the user's schedule and conducts the dialogue during a time when the user is free. The dialogue can also be conducted to avoid times when the user is busy. Furthermore, the dialogue can be conducted at an appropriate time according to the user's schedule. This allows the dialogue unit to conduct a dialogue according to the user's schedule. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input schedule data to the generation AI and have the generation AI select the dialogue timing.

[0080] The detection unit can estimate the user's emotion and adjust the sensitivity of anomaly detection based on the estimated user's emotion. The detection unit, for example, estimates the user's emotion. Emotion estimation is performed using, for example, facial expression recognition technology or voice analysis technology. The detection unit adjusts the sensitivity of anomaly detection based on the user's emotion estimated by the emotion estimation technology. For example, if the user is feeling anxious, the detection unit increases the sensitivity of anomaly detection. Also, if the user is relaxed, the detection unit can return the sensitivity of anomaly detection to normal. Furthermore, if the user is excited, the detection unit can adjust the sensitivity of anomaly detection. In this way, the detection unit can adjust the sensitivity of anomaly detection according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or without AI. For example, the detection unit can input user emotion data estimated by emotion estimation technology into the generation AI and have the generation AI adjust the sensitivity of anomaly detection.

[0081] The detection unit can perform early detection of anomalies based on the user's past behavioral patterns during detection. The detection unit, for example, references the user's past behavioral patterns. To reference the past behavioral patterns, for example, a method for saving behavioral history or a method for analyzing history data is used. The detection unit allows the generation AI to perform early detection of anomalies based on the past behavioral patterns. For example, the detection unit can detect anomalies early based on the user's past behavioral patterns. The detection unit can also find signs of anomalies from the user's past behavioral patterns. Furthermore, the detection unit can learn the user's behavioral patterns and detect anomalies early. This allows the detection unit to perform early detection of anomalies based on the past behavioral patterns. Some or all of the above-described processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input past behavioral pattern data to the generation AI and cause the generation AI to perform early detection of anomalies.

[0082] Upon detection, the detection unit can identify the cause of the abnormality based on the environmental data within the home. The detection unit, for example, references the environmental data within the home. To reference the environmental data, for example, a data collection method or a data analysis algorithm is used. The detection unit causes the generation AI to identify the cause of the abnormality based on the environmental data. For example, the detection unit identifies the cause of the abnormality based on temperature and humidity data. The detection unit can also identify the cause of the abnormality based on illuminance and sound data. Furthermore, the detection unit can integrate the environmental data within the home to identify the cause of the abnormality. Some or all of the above-mentioned processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input the environmental data to the generation AI and cause the generation AI to identify the cause of the abnormality.

[0083] The detection unit can estimate the user's emotion and determine the priority of anomaly detection based on the estimated user's emotion. The detection unit, for example, estimates the user's emotion. Emotion estimation is performed using, for example, facial expression recognition technology or voice analysis technology. The detection unit determines the priority of anomaly detection based on the user's emotion estimated by emotion estimation technology. For example, if the user is feeling anxious, the detection unit increases the priority of anomaly detection. Also, if the user is relaxed, the detection unit can return the priority of anomaly detection to normal. Furthermore, if the user is excited, the detection unit can adjust the priority of anomaly detection. In this way, the detection unit can determine the priority of anomaly detection according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or without AI. For example, the detection unit can input user emotion data estimated by emotion estimation technology into the generation AI and have the generation AI determine the priority of anomaly detection.

[0084] The detection unit can detect an abnormality based on the behavior of a pet in the home at the time of detection. The detection unit, for example, takes into account the behavior of the pet in the home. To take the pet's behavior into account, for example, an animal movement detection method or an analysis of the pet's behavior pattern is used. In the detection unit, the generation AI detects an abnormality based on the pet's behavior. For example, if the pet exhibits abnormal behavior, the detection unit detects the abnormality. The detection unit can also learn the pet's behavior pattern and detect an abnormality. Furthermore, the pet's movement can be monitored in real time and an abnormality can be detected. This allows the detection unit to detect an abnormality taking into account the behavior of the pet in the home. Some or all of the above-mentioned processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input pet behavior data to the generation AI and cause the generation AI to detect an abnormality.

[0085] During detection, the detection unit can detect an abnormality based on the power consumption data within the home. The detection unit, for example, references the power consumption data within the home. To reference the power consumption data, for example, a data collection method or a data analysis algorithm is used. The detection unit causes the generation AI to detect an abnormality based on the power consumption data. For example, if power consumption is abnormally high, the detection unit detects the abnormality. The detection unit can also learn power consumption patterns and detect abnormalities. Furthermore, the power consumption data can be monitored in real time to detect abnormalities. This allows the detection unit to detect abnormalities by referring to the power consumption data within the home. Some or all of the above-mentioned processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input the power consumption data to the generation AI and cause the generation AI to detect an abnormality.

[0086] The suggestion unit can estimate the user's emotion and adjust the suggestion content based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion. Emotion estimation is performed using, for example, facial expression recognition technology or voice analysis technology. The suggestion unit adjusts the suggestion content based on the user's emotion estimated by the emotion estimation technology. For example, if the user is relaxed, the suggestion unit makes suggestions to help the user relax. Also, if the user is feeling anxious, the suggestion unit can make suggestions to alleviate the anxiety. Furthermore, if the user is excited, the suggestion unit can make suggestions to calm the user. This allows the suggestion unit to adjust the suggestion content according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI. For example, the suggestion unit may input the user's emotion data estimated by the emotion estimation technology into the generation AI and cause the generation AI to adjust the suggestion content.

[0087] When making a proposal, the suggestion unit can make a proposal based on the user's past environmental data. The suggestion unit, for example, references the user's past environmental data. To reference the past environmental data, for example, a data storage method or a data analysis algorithm is used. The suggestion unit allows the generation AI to make an optimal proposal based on the past environmental data. For example, the suggestion unit makes an optimal proposal based on environmental data that the user found comfortable in the past. The suggestion unit can also suggest a comfortable environment from the user's past environmental data. Furthermore, the suggestion unit can analyze the user's past environmental data and make an optimal proposal. This allows the suggestion unit to make an optimal proposal based on the past environmental data. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input past environmental data to the generation AI and cause the generation AI to generate a proposal.

[0088] When making a proposal, the suggestion unit can monitor the user's health condition and make health management suggestions as needed. The suggestion unit, for example, monitors the user's health condition. The health condition monitoring is performed, for example, using a vital sign measurement method or an anomaly detection algorithm. The suggestion unit makes health management suggestions as needed based on the health condition monitoring data. For example, the suggestion unit can monitor the user's health condition and suggest appropriate exercise. The suggestion unit can also monitor the user's health condition and suggest appropriate meals. Furthermore, the suggestion unit can monitor the user's health condition and suggest appropriate rest. This allows the suggestion unit to make health management suggestions according to the user's health condition. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input health condition monitoring data to a generation AI and cause the generation AI to execute health management suggestions.

[0089] The suggestion unit can estimate the user's emotion and determine the priority of suggestions based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion. Emotion estimation is performed using, for example, facial expression recognition technology or voice analysis technology. The suggestion unit determines the priority of suggestions based on the user's emotion estimated by the emotion estimation technology. For example, if the user is feeling anxious, the suggestion unit prioritizes suggestions that will alleviate the anxiety. Also, if the user is relaxed, the suggestion unit can prioritize suggestions that will help the user relax. Furthermore, if the user is excited, the suggestion unit can prioritize suggestions that will calm the user. In this way, the suggestion unit can determine the priority of suggestions according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or without AI. For example, the suggestion unit can input user emotion data estimated by emotion estimation technology to the generation AI and have the generation AI determine the priority of suggestions.

[0090] When making a proposal, the suggestion unit can make a proposal based on the health condition of pets in the household. The suggestion unit, for example, considers the health condition of pets in the household. To consider the health condition of the pets, for example, a method for collecting animal health data and health condition evaluation criteria are used. The suggestion unit allows the generation AI to make an optimal proposal based on the pet's health condition. For example, the suggestion unit can monitor the pet's health condition and suggest appropriate exercise. The suggestion unit can also monitor the pet's health condition and suggest appropriate meals. Furthermore, the suggestion unit can monitor the pet's health condition and suggest appropriate rest. This allows the suggestion unit to make a proposal according to the health condition of pets in the household. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the pet's health data into the generation AI and cause the generation AI to generate a proposal.

[0091] When making a proposal, the proposal unit can make an energy-saving proposal by referring to energy consumption data within the home. The proposal unit, for example, references the energy consumption data within the home. To refer to the energy consumption data, for example, a data collection method or a data analysis algorithm is used. The proposal unit causes the generation AI to make an energy-saving proposal based on the energy consumption data. For example, the proposal unit proposes an optimal energy-saving method based on the energy consumption data. The energy consumption data can also be analyzed to make a proposal to reduce wasteful energy consumption. Furthermore, the energy consumption data can be referenced to make a proposal for efficient energy use. This allows the proposal unit to make an energy-saving proposal based on the energy consumption data within the home. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input the energy consumption data to the generation AI and cause the generation AI to generate an energy-saving proposal. === Hard Collateral 1-1 === Each of the multiple elements, including the movement unit, dialogue unit, detection unit, and suggestion unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the movement unit is realized by the control unit 46A of the smart device 14 and detects and avoids obstacles using an infrared sensor or an ultrasonic sensor. The dialogue unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and recognizes the user's voice using voice recognition technology and generates an appropriate response using a generation AI. The detection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and detects abnormalities using a temperature sensor or a motion sensor, and the generation AI learns the user's behavioral patterns to detect the abnormalities. The suggestion unit is realized, for example, by the control unit 46A of the smart device 14 and collects environmental data within the home using a temperature sensor, a humidity sensor, and an illuminance sensor, and the generation AI makes suggestions to maintain a comfortable environment. === Hard Collateral 1-2 === Each of the multiple elements, including the movement unit, dialogue unit, detection unit, and suggestion unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the movement unit is realized by the control unit 46A of the smart glasses 214 and detects and avoids obstacles using an infrared sensor or an ultrasonic sensor. The dialogue unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and recognizes the user's voice using voice recognition technology and generates an appropriate response using a generation AI. The detection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and detects abnormalities using a temperature sensor or a motion sensor, and the generation AI learns the user's behavioral patterns to detect the abnormality. The suggestion unit is realized, for example, by the control unit 46A of the smart glasses 214 and collects environmental data within the home using a temperature sensor, a humidity sensor, and an illuminance sensor, and the generation AI makes suggestions to maintain a comfortable environment. === Hard Collateral 1-3 === Each of the multiple elements, including the movement unit, dialogue unit, detection unit, and suggestion unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the movement unit is realized by the control unit 46A of the headset-type terminal 314 and detects and avoids obstacles using an infrared sensor or an ultrasonic sensor. The dialogue unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and recognizes the user's voice using voice recognition technology and generates an appropriate response using a generation AI. The detection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and detects abnormalities using a temperature sensor or a motion sensor, and the generation AI learns the user's behavioral patterns to detect the abnormality. The suggestion unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and collects environmental data within the home using a temperature sensor, a humidity sensor, and an illuminance sensor, and the generation AI makes suggestions to maintain a comfortable environment. === Hard Collateral 1-4 === Each of the multiple elements, including the movement unit, dialogue unit, detection unit, and suggestion unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the movement unit is realized by the control unit 46A of the robot 414 and detects and avoids obstacles using infrared sensors and ultrasonic sensors. The dialogue unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and recognizes the user's voice using voice recognition technology and generates an appropriate response using a generation AI. The detection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and detects abnormalities using a temperature sensor and a motion sensor, and the generation AI learns the user's behavioral patterns to detect the abnormalities. The suggestion unit is realized, for example, by the control unit 46A of the robot 414 and collects environmental data within the home using a temperature sensor, a humidity sensor, and an illuminance sensor, and the generation AI makes suggestions to maintain a comfortable environment.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The movement unit can learn the furniture layout in the home and automatically generate a movement route. The movement unit, for example, learns the furniture layout in the home. Learning the furniture layout is performed, for example, using image recognition technology or analysis of sensor information. The movement unit automatically generates a movement route based on the learned furniture layout data. For example, the movement unit scans the furniture layout and generates an optimal route that avoids obstacles. Also, if the furniture layout is changed, the movement unit can learn the new layout and update the route. Furthermore, the movement unit can preferentially select routes that the user frequently takes based on the furniture layout. In this way, the movement unit can provide an optimal movement route based on the furniture layout. Some or all of the above-mentioned processing in the movement unit may be performed using, for example, AI, or may be performed without using AI. For example, the movement unit can input learned furniture layout data into a generation AI and cause the generation AI to automatically generate a movement route.

[0094] The dialogue unit can estimate the user's emotions and adjust the tone and content of the dialogue based on the estimated user emotions. The dialogue unit, for example, estimates the user's emotions. Emotion estimation is performed using, for example, facial expression recognition technology or voice analysis technology. The dialogue unit adjusts the tone and content of the dialogue based on the user's emotions estimated by the emotion estimation technology. For example, if the user is sad, the dialogue unit can offer encouraging words in a gentle tone. If the user is happy, the dialogue unit can offer empathetic words in a bright tone. Furthermore, if the user is angry, the dialogue unit can also engage in the dialogue in a calm tone. This allows the dialogue unit to conduct a dialogue according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or without AI. For example, the dialogue unit can input the user's emotional data estimated by emotion estimation technology into the generation AI, and have the generation AI adjust the tone and content of the dialogue.

[0095] The detection unit can estimate the user's emotion and adjust the sensitivity of anomaly detection based on the estimated user's emotion. The detection unit, for example, estimates the user's emotion. Emotion estimation is performed using, for example, facial expression recognition technology or voice analysis technology. The detection unit adjusts the sensitivity of anomaly detection based on the user's emotion estimated by the emotion estimation technology. For example, if the user is feeling anxious, the detection unit increases the sensitivity of anomaly detection. Also, if the user is relaxed, the detection unit can return the sensitivity of anomaly detection to normal. Furthermore, if the user is excited, the detection unit can adjust the sensitivity of anomaly detection. In this way, the detection unit can adjust the sensitivity of anomaly detection according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or without AI. For example, the detection unit can input user emotion data estimated by emotion estimation technology into the generation AI and have the generation AI adjust the sensitivity of anomaly detection.

[0096] The suggestion unit can estimate the user's emotion and adjust the suggestion content based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion. Emotion estimation is performed using, for example, facial expression recognition technology or voice analysis technology. The suggestion unit adjusts the suggestion content based on the user's emotion estimated by the emotion estimation technology. For example, if the user is relaxed, the suggestion unit makes suggestions to help the user relax. Also, if the user is feeling anxious, the suggestion unit can make suggestions to alleviate the anxiety. Furthermore, if the user is excited, the suggestion unit can make suggestions to calm the user. This allows the suggestion unit to adjust the suggestion content according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI. For example, the suggestion unit may input the user's emotion data estimated by the emotion estimation technology into the generation AI and cause the generation AI to adjust the suggestion content.

[0097] When traveling, the traveling unit can select a safe traveling route based on the movements of the pet within the home. The traveling unit, for example, considers the movements of the pet within the home. To consider the movements of the pet, for example, an animal movement detection method or an analysis of the pet's behavioral patterns is used. The traveling unit selects a safe traveling route based on the movements of the pet. For example, the traveling unit selects a route that avoids areas where the pet is present. The traveling unit can also monitor the movements of the pet in real time to avoid collisions. Furthermore, if the pet is in a specific location, it can select a route that avoids that location. This allows the traveling unit to perform safe traveling taking the movements of the pet into consideration. Some or all of the above-described processing in the traveling unit may be performed using, for example, AI, or may be performed without using AI. For example, the traveling unit can input data on the movements of the pet into a generating AI and cause the generating AI to select a safe traveling route.

[0098] During a dialogue, the dialogue unit can generate a response based on the user's past conversation history. The dialogue unit, for example, references the user's past conversation history. To reference the past conversation history, for example, a method for saving the conversation history or a method for analyzing the history data is used. The dialogue unit allows the generation AI to generate an optimal response based on the past conversation history. For example, the dialogue unit provides a related topic based on what the user has previously said. The dialogue unit can also select a preferred topic from the user's past conversation history and engage in a dialogue. Furthermore, the dialogue unit can provide appropriate advice based on what the user has previously said. This allows the dialogue unit to generate an optimal response based on the past conversation history. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input past conversation history data into the generation AI and have the generation AI generate a response.

[0099] When making a proposal, the proposal unit can make an energy-saving proposal by referring to energy consumption data within the home. The proposal unit, for example, references the energy consumption data within the home. To refer to the energy consumption data, for example, a data collection method or a data analysis algorithm is used. The proposal unit causes the generation AI to make an energy-saving proposal based on the energy consumption data. For example, the proposal unit proposes an optimal energy-saving method based on the energy consumption data. The energy consumption data can also be analyzed to make a proposal to reduce wasteful energy consumption. Furthermore, the energy consumption data can be referenced to make a proposal for efficient energy use. This allows the proposal unit to make an energy-saving proposal based on the energy consumption data within the home. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input the energy consumption data to the generation AI and cause the generation AI to generate an energy-saving proposal.

[0100] During detection, the detection unit can detect an abnormality based on the power consumption data within the home. The detection unit, for example, references the power consumption data within the home. To reference the power consumption data, for example, a data collection method or a data analysis algorithm is used. The detection unit causes the generation AI to detect an abnormality based on the power consumption data. For example, if power consumption is abnormally high, the detection unit detects the abnormality. The detection unit can also learn power consumption patterns and detect abnormalities. Furthermore, the power consumption data can be monitored in real time to detect abnormalities. This allows the detection unit to detect abnormalities by referring to the power consumption data within the home. Some or all of the above-mentioned processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input the power consumption data to the generation AI and cause the generation AI to detect an abnormality.

[0101] The dialogue unit can monitor the user's health condition during dialogue and suggest contacting a medical institution if necessary. The dialogue unit, for example, monitors the user's health condition. The health condition monitoring is performed, for example, using a vital sign measurement method or an anomaly detection algorithm. The dialogue unit suggests contacting a medical institution if necessary based on the health condition monitoring data. For example, the dialogue unit estimates the user's health condition from the tone of the user's voice and suggests contacting a medical institution if an abnormality is detected. The dialogue unit can also suggest contacting a medical institution if the user complains of feeling unwell. Furthermore, the dialogue unit can monitor the user's health condition and suggest contacting a medical institution if an abnormality is detected. This allows the dialogue unit to take appropriate action according to the user's health condition. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input health condition monitoring data to a generation AI and cause the generation AI to suggest contacting a medical institution.

[0102] When making a proposal, the suggestion unit can make a proposal based on the health condition of pets in the household. The suggestion unit, for example, considers the health condition of pets in the household. To consider the health condition of the pets, for example, a method for collecting animal health data and health condition evaluation criteria are used. The suggestion unit allows the generation AI to make an optimal proposal based on the pet's health condition. For example, the suggestion unit can monitor the pet's health condition and suggest appropriate exercise. The suggestion unit can also monitor the pet's health condition and suggest appropriate meals. Furthermore, the suggestion unit can monitor the pet's health condition and suggest appropriate rest. This allows the suggestion unit to make a proposal according to the health condition of pets in the household. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the pet's health data into the generation AI and cause the generation AI to generate a proposal.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The mobile unit moves autonomously around the home. It uses infrared and ultrasonic sensors to detect and avoid obstacles. This allows the mobile unit to move smoothly around the home while avoiding various obstacles. Step 2: The dialogue unit recognizes the user's voice and actions and engages in dialogue. The dialogue unit uses voice recognition technology to recognize the user's voice and uses generation AI to generate an appropriate response. It can also recognize the user's actions using a motion detection sensor and use generation AI to generate an appropriate response. This allows the dialogue unit to achieve natural dialogue with the user. Step 3: The detection unit detects an abnormality and issues an alert. The detection unit detects abnormalities using temperature and motion sensors, and the generation AI learns the user's behavior patterns to detect abnormalities. This allows the detection unit to quickly detect abnormalities in the home and notify the user. Step 4: The suggestion unit collects environmental data within the home and makes suggestions to maintain a comfortable environment. The suggestion unit collects environmental data within the home using temperature sensors, humidity sensors, and illuminance sensors, and the generation AI makes suggestions to maintain a comfortable environment. In this way, the suggestion unit optimizes the home environment and supports the user's comfortable life.

[0105] 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.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0107] 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.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 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.

[0111] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0112] The 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.

[0113] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0115] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0116] Fig. 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.

[0117] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0119] 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.

[0120] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0121] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0135] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0136] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0137] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0139] 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.

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

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 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.

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

[0144] The 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.

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

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

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

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0153] 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.

[0154] 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.

[0155] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] [Explanation of symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a mobile unit that moves autonomously within the home; a dialogue unit that recognizes the user's voice and actions and engages in dialogue; A detection unit that detects an abnormality and issues an alert; A proposal unit that collects environmental data in the home and makes specific proposals for maintaining the environment. A system characterized by:

2. The moving unit is Detect and avoid obstacles using infrared or ultrasonic sensors 2. The system of claim 1.

3. The dialogue unit Recognizes the user's voice and generates a response using generative AI 2. The system of claim 1.

4. The detection unit Temperature or motion sensors are used to detect abnormalities, and the generation AI learns the user's behavioral patterns to detect abnormalities.

2. The system of claim 1.

5. The proposal unit Collects environmental data on temperature, humidity, and light levels within the home, and the AI ​​makes suggestions to maintain the environment.

2. The system of claim 1.

6. The dialogue unit Reminding users to take their medication and specifically managing their daily schedule 2. The system of claim 1.

7. The moving unit is Estimate the user's emotions and adjust the speed and route based on the estimated user emotions.

2. The system of claim 1.

8. The moving unit is Learns furniture layout in the home and automatically generates movement routes 2. The system of claim 1.

Images