System

Abstract

To provide a system for solving issues such as shortage of assisting dogs and denial of entry to stores.SOLUTION: A system pertaining an embodiment of the present invention functions as an assisting dog robot, the system comprising a reception unit, an operation unit, a control unit, and a navigation unit. The reception unit receives a user's instruction. The operation unit performs operation on the basis of the instruction received by the reception unit. The control unit controls the operation performed by the operation unit. The navigation unit performs navigation on the basis of the operation controlled by the control unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has problems such as a shortage of assistance dogs and refusal of entry for assistance dogs into stores, and does not provide sufficient means to solve these problems.

[0005] The system according to the embodiment aims to solve problems such as a shortage of assistance dogs and refusal to enter stores. [Means for solving the problem]

[0006] The system according to the embodiment is a system that functions as a service dog robot, and includes a reception unit, an operation unit, a control unit, and a navigation unit. The reception unit receives instructions from a user. The operation unit performs an operation based on the instructions received by the reception unit. The control unit controls the operation performed by the operation unit. The navigation unit performs navigation based on the operation controlled by the control unit. Effect of the Invention

[0007] The system according to the embodiment can solve problems such as a shortage of assistance dogs and refusal to enter stores. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The assistance dog robot system according to the embodiment of the present invention is a system designed to fulfill the role of an assistance dog. This assistance dog robot system has various functions to support users such as visually impaired people, hearing impaired people, and physically disabled people. For example, the assistance dog robot system has a voice recognition function for receiving instructions from a user. When a user instructs the assistance dog robot system to "turn right," the assistance dog robot system understands the instruction and turns right. In addition, the assistance dog robot system has a sensor for detecting obstacles, and supports the user so that the user can move safely. For example, when the assistance dog robot system detects an obstacle ahead, it issues a warning to the user and performs an avoidance operation. In addition, the assistance dog robot system has a GPS function, and performs navigation for the user to reach the destination. For example, when a user instructs the assistance dog robot system to "guide me to the station," the assistance dog robot system uses the GPS to calculate the optimal route and guide the user. In addition, the assistance dog robot system is connected to the Internet and can obtain traffic and weather information in real time. This allows the user to move safely based on the latest information. This assistance dog robot system not only solves the problem of the shortage of assistance dogs, but also solves social problems such as the refusal of assistance dogs to enter stores. Since the assistance dog robot system is not an animal, it will not be refused entry to a store, and users can use it with peace of mind. In addition, the assistance dog robot system can operate stably for a long period of time by performing regular maintenance. In the future, this technology can be expanded to other fields such as rescue dogs. For example, the assistance dog robot system can save lives in search and rescue activities at disaster sites. In this way, the present invention is a technology that solves social problems such as the shortage of assistance dogs and the refusal of assistance dogs to enter stores, and can also be applied to other fields such as rescue dogs. As a result, the assistance dog robot system can play the role of an assistance dog and support the user.

[0029] The assistance dog robot system according to the embodiment includes a reception unit, an operation unit, a control unit, and a navigation unit. The reception unit receives an instruction from a user. The instruction from the user includes, for example, a voice instruction, a touch operation, a gesture, and the like, but is not limited to such examples. The reception unit receives, for example, a voice instruction from the user using a voice recognition technology. The reception unit can also receive a touch operation from the user using a touch panel. Furthermore, the reception unit can recognize a gesture from the user using a camera. For example, the reception unit recognizes a voice instruction from the user with high accuracy using a voice recognition technology. The touch panel allows the user to input an instruction by touching the screen. The camera tracks the movement of the user's hand and recognizes the gesture. The operation unit performs an operation based on the instruction received by the reception unit. The operation includes, for example, movement, operation, notification, and the like, but is not limited to such examples. The operation unit moves the assistance dog robot system using, for example, a motor. The operation unit can also lift and operate an object using an arm. Furthermore, the operation unit can notify the user using a speaker. For example, the operating unit uses a motor to move the assistance dog robot system forward. The arm can lift or operate an object. The speaker notifies the user by voice. The control unit controls the operation performed by the operating unit. The control includes, for example, starting and stopping the operation, adjusting the speed, and the like, but is not limited to such examples. The control unit adjusts, for example, the speed of the motor of the operating unit. The control unit can also control the movement of the arm of the operating unit. Furthermore, the control unit can adjust the volume of the speaker of the operating unit. For example, the control unit adjusts the speed of the motor to control the moving speed of the assistance dog robot system. The arm movement is controlled to lift or operate an object. The speaker volume is adjusted to notify the user. The navigation unit performs navigation based on the operation controlled by the control unit. The navigation includes, for example, route guidance, destination setting, and the like, but is not limited to such examples. The navigation unit identifies the current location of the user using, for example, GPS technology, and guides the user along a route to the destination.The navigation unit is also connected to the Internet and can obtain traffic and weather information in real time. Furthermore, the navigation unit can learn the user's behavioral patterns and propose an optimal route. For example, the navigation unit identifies the user's current location using GPS technology and calculates an optimal route. It connects to the Internet to obtain traffic and weather information in real time and reflects the information in the navigation. It learns the user's behavioral patterns and proposes an optimal route based on the user's past movement history. As a result, the assistance dog robot system according to the embodiment can receive instructions from the user, perform operations, control, and navigate.

[0030] The reception unit receives a user's instruction. The user's instruction includes, but is not limited to, a voice instruction, a touch operation, a gesture, and the like. The reception unit receives the user's voice instruction, for example, using a voice recognition technology. Specifically, the voice recognition technology includes an advanced voice analysis algorithm using natural language processing (NLP), and can accurately analyze the contents of the user's speech and understand the intention. For example, if the user instructs "move forward," the voice recognition technology analyzes the instruction and transmits an appropriate command to the operation unit. The reception unit can also receive the user's touch operation using a touch panel. The touch panel uses a technology such as a capacitive type or a resistive film type, and the user can input an instruction by touching the screen. Furthermore, the reception unit can recognize the user's gesture using a camera. The camera utilizes computer vision technology to track the user's hand movement and recognize the gesture. For example, if the user performs a waving motion, the camera captures the movement, and the gesture recognition algorithm analyzes the movement and transmits an appropriate instruction to the operation unit. This allows the reception unit to receive user instructions with high accuracy by any method, such as voice, touch, or gesture, thereby improving user convenience.

[0031] The operating unit performs an operation based on the instruction received by the receiving unit. The operation includes, for example, movement, operation, notification, etc., but is not limited to such examples. For example, the operating unit uses a motor to move the assistance dog robot system. Specifically, a DC motor or a stepping motor is used as the motor, and the assistance dog robot system can perform operations such as forward movement, backward movement, and rotation. The operating unit can also lift and operate an object using an arm. The arm has a multi-joint structure and is controlled by a servo motor or an actuator. This allows the arm to perform complex operations, for example, lift an object and move it to a specified location. Furthermore, the operating unit can also notify the user using a speaker. The speaker uses voice synthesis technology to provide information to the user by voice. For example, the operating unit uses a motor to move the assistance dog robot system forward. The arm can lift and operate an object. The speaker notifies the user by voice. This allows the operating unit to perform various operations based on the user's instructions and meet the user's needs.

[0032] The control unit controls the operation performed by the operating unit. The control includes, for example, starting and stopping the operation, adjusting the speed, and the like, but is not limited to such examples. The control unit adjusts, for example, the speed of the motor of the operating unit. Specifically, the control unit can use PWM (pulse width modulation) technology to control the rotation speed of the motor. This allows the moving speed of the assistance dog robot system to be finely adjusted. The control unit can also control the movement of the arm of the operating unit. The movement of the arm is controlled by a servo motor or an actuator, and the control unit can precisely control the movement of the arm by sending appropriate commands to these devices. Furthermore, the control unit can adjust the volume of the speaker of the operating unit. The volume of the speaker is adjusted using digital signal processing (DSP) technology, and a notification can be given to the user at an appropriate volume. For example, the control unit adjusts the speed of the motor to control the moving speed of the assistance dog robot system. The arm movement is controlled to lift or operate an object. The speaker volume is adjusted to notify the user. This allows the control unit to precisely control the operation of the operating unit and optimize the operation of the assistance dog robot system.

[0033] The navigation unit performs navigation based on the operation controlled by the control unit. The navigation includes, for example, route guidance, destination setting, and the like, but is not limited to such examples. For example, the navigation unit identifies the current location of the user using GPS technology and guides the user along a route to the destination. Specifically, the navigation unit identifies the current location of the user with high accuracy using a GPS receiver, and calculates an optimal route by combining the location with map data. The navigation unit is also connected to the Internet and can acquire traffic information and weather information in real time. This allows the navigation unit to dynamically adjust the route based on the latest information and provide optimal navigation to the user. Furthermore, the navigation unit can learn the user's behavioral patterns and propose an optimal route. For example, a machine learning algorithm can be used to analyze the user's past movement history and behavioral patterns, and propose a route based on the user's preferences and habits. This allows the navigation unit to identify the user's current location using GPS technology and calculate an optimal route. The navigation unit is connected to the Internet to acquire traffic information and weather information in real time, and reflects the information in the navigation. The navigation unit learns the user's behavioral patterns and proposes an optimal route based on the user's past movement history. This enables the navigation unit to provide the user with highly accurate and flexible navigation, thereby improving user convenience.

[0034] The reception unit may have a voice recognition function. The voice recognition function includes, for example, types of voice commands, recognition accuracy, and the like, but is not limited to such examples. The reception unit receives a user's voice instruction using, for example, a voice recognition technology. For example, the voice recognition technology can analyze the user's voice and recognize a specific command. For example, the voice recognition technology can use a voice recognition model using deep learning. The voice recognition model learns a large amount of voice data and realizes highly accurate voice recognition. For example, when the user instructs "turn right," the voice recognition technology analyzes the voice and generates a command for turning right. The voice recognition technology can analyze the user's voice in real time and instantly execute the instruction. As a result, the voice recognition function can receive the user's voice instruction.

[0035] The operating unit may include a sensor for detecting an obstacle. Examples of sensors for detecting an obstacle include, but are not limited to, an infrared sensor and an ultrasonic sensor. The operating unit may detect an obstacle ahead using, for example, an infrared sensor. The infrared sensor can identify the position of an obstacle by emitting infrared rays and detecting the reflection of the infrared rays. The operating unit may also detect an obstacle using an ultrasonic sensor. The ultrasonic sensor can measure the distance of an obstacle by emitting ultrasonic waves and detecting the reflection of the ultrasonic waves. The operating unit may also detect an obstacle using a camera. The camera can identify the shape and position of an obstacle using image analysis technology. For example, the operating unit may detect an obstacle ahead using an infrared sensor and perform an operation to avoid the obstacle. The ultrasonic sensor measures the distance of the obstacle and controls the operation to maintain a safe distance. The camera can identify the shape and position of the obstacle using image analysis technology and calculate the optimal route for avoiding the obstacle. This allows the operating unit to detect an obstacle and perform an operation to avoid the obstacle.

[0036] The navigation unit may have a GPS function. The GPS function includes, for example, a method of acquiring location information, accuracy, and the like, but is not limited to such examples. The navigation unit, for example, identifies the current location of the user using GPS technology. The GPS technology can acquire location information by receiving signals from multiple satellites and analyzing the signals. The navigation unit can calculate a route to the destination based on the acquired location information. For example, the navigation unit identifies the current location of the user using GPS technology and calculates an optimal route. The GPS technology can provide highly accurate location information and perform navigation for the user to reach the destination. As a result, the GPS function can guide the user to the destination.

[0037] The navigation unit is connected to the Internet and can acquire traffic information and weather information. The traffic information and weather information include, for example, traffic congestion information, rainfall forecast, and the like, but are not limited to such examples. The navigation unit, for example, connects to the Internet and acquires traffic information in real time. The traffic information includes, for example, traffic congestion status and road closure information. The navigation unit can calculate an optimal route based on the acquired traffic information. The navigation unit can also connect to the Internet and acquire weather information in real time. The weather information includes, for example, rainfall forecast and temperature change. The navigation unit can provide appropriate advice to the user based on the acquired weather information. For example, the navigation unit suggests a route that avoids traffic congestion based on traffic congestion information. If rain is predicted based on the weather information, the navigation unit advises the user to bring an umbrella. This makes it possible to acquire the latest traffic information and weather information in real time and reflect it in the navigation.

[0038] The operating unit may have a function of monitoring the user's health condition. The user's health condition may include, but is not limited to, for example, a heart rate, blood pressure, and body temperature. For example, the operating unit may monitor the user's heart rate using a heart rate sensor. The heart rate sensor may contact the user's skin to measure the heart rate. The operating unit may also monitor the user's blood pressure using a blood pressure sensor. The blood pressure sensor may be worn on the user's arm to measure the blood pressure. The operating unit may also monitor the user's body temperature using a body temperature sensor. The body temperature sensor may contact the user's skin to measure the body temperature. For example, the operating unit may monitor the user's heart rate in real time using the heart rate sensor, and issue a warning if an abnormality is detected. The blood pressure sensor may periodically measure the user's blood pressure, and notify a medical institution if an abnormality is detected. The body temperature sensor may continuously monitor the user's body temperature, and may encourage the user to rest if a fever is detected. This allows the user's health condition to be monitored and appropriate support to be provided.

[0039] The control unit can learn the user's behavioral patterns and provide support. The user's behavioral patterns include, but are not limited to, daily routines and past behavioral history. The control unit can learn, for example, the user's daily routine and provide appropriate support. The daily routine includes, for example, the daily wake-up time, meal time, and exercise time. The control unit can provide support appropriate for a specific time period based on the user's behavioral history. For example, the control unit learns that the user wakes up at 7 o'clock every morning and sounds an alarm at 7 o'clock to encourage the user to wake up. The control unit also learns that the user eats lunch at 12 o'clock every day and issues a notification at 12 o'clock to encourage the user to prepare lunch. Furthermore, the control unit learns that the user exercises at 9 o'clock every night and issues a notification at 9 o'clock to encourage the user to start exercising. This makes it possible to learn the user's behavioral patterns and provide more appropriate support.

[0040] The reception unit may have a function of recognizing sign language and gestures. The function of recognizing sign language and gestures includes, for example, a type of camera, a recognition algorithm, and the like, but is not limited to such examples. The reception unit, for example, recognizes the user's sign language and gestures using a camera. The camera can track the user's hand movements and recognize the sign language and gestures. The recognition algorithm can analyze the user's hand movements and recognize specific sign language and gestures. For example, the reception unit recognizes the user's sign language using a camera and accepts instructions. The recognition algorithm analyzes the user's hand movements and recognizes specific sign language. This makes it possible to recognize sign language and gestures and accept instructions from the user.

[0041] The control unit can have a function of automatically calling an ambulance in an emergency. The function of automatically calling an ambulance in an emergency includes, for example, the installation of an emergency button, detection of an abnormality by a sensor, and the like, but is not limited to such examples. For example, the control unit installs an emergency button, and the user can call an ambulance by pressing the button in an emergency. The control unit can also detect an abnormality using a sensor and automatically call an ambulance. The sensor can detect, for example, an abnormality in the user's heart rate or blood pressure. For example, the control unit installs an emergency button, and the user can press the button in an emergency to call an ambulance. The sensor detects an abnormality in the user's heart rate or blood pressure and automatically call an ambulance. This makes it possible to automatically call an ambulance in an emergency.

[0042] The reception unit can analyze the user's past instruction history and select an instruction reception method. For example, the reception unit preferentially suggests an instruction method (such as voice or gesture) that the user has used frequently in the past. The reception unit can also predict and suggest an instruction method to be used in a specific time period from the user's past instruction history. Furthermore, the reception unit can automatically display related instructions as candidates based on the contents of instructions given by the user in the past. For example, the reception unit preferentially suggests an instruction method that the user has used frequently in the past. The reception unit predicts and suggests an instruction method to be used in a specific time period from the user's past instruction history. Based on the contents of instructions given by the user in the past, related instructions are automatically displayed as candidates. In this way, the optimal instruction reception method can be selected by analyzing the past instruction history.

[0043] The reception unit can perform filtering based on the user's current situation and environment when receiving an instruction. For example, when the user is outdoors, the reception unit adjusts the sensitivity of the voice recognition taking into account the surrounding noise level. In addition, when the user is indoors, the reception unit can filter the environmental sound to accurately receive the instruction. Furthermore, when the user is moving, the reception unit can preferentially receive instructions related to the current position based on GPS information. For example, when the user is outdoors, the reception unit adjusts the sensitivity of the voice recognition taking into account the surrounding noise level. When the user is indoors, the reception unit filters the environmental sound to accurately receive the instruction. When the user is moving, the reception unit preferentially receives the instruction related to the current position based on GPS information. This makes it possible to receive more accurate instructions by filtering the instruction according to the user's situation and environment.

[0044] When receiving an instruction, the reception unit can preferentially receive the instruction in consideration of the geographical position information of the user. For example, when the user is in a specific location, the reception unit preferentially receives an instruction related to the location. Furthermore, when the user is moving, the reception unit can also receive optimal instructions based on the current location. Furthermore, when the user approaches the destination, the reception unit can also preferentially receive an instruction related to the destination. For example, when the user is in a specific location, the reception unit preferentially receives an instruction related to the location. When the user is moving, the reception unit accepts optimal instructions based on the current location. When the user approaches the destination, the reception unit preferentially accepts an instruction related to the destination. In this way, by considering the geographical position information, it is possible to preferentially receive highly relevant instructions.

[0045] The reception unit can analyze the user's social media activity and receive related instructions when receiving the instruction. The reception unit receives related instructions based on, for example, information shared by the user on social media. The reception unit can also preferentially receive instructions related to current interests from the user's social media activity. Furthermore, the reception unit can also receive instructions related to places and events that the user follows on social media. For example, the reception unit receives related instructions based on information shared by the user on social media. The reception unit preferentially receives instructions related to current interests from the user's social media activity. The reception unit receives instructions related to places and events that the user follows on social media. In this way, it is possible to receive related instructions by analyzing social media activity.

[0046] The operation unit can apply an avoidance algorithm according to the type of obstacle during operation. For example, the operation unit applies an algorithm for avoiding fixed obstacles (such as walls and pillars). The operation unit can also apply an algorithm for avoiding moving obstacles (such as people and cars). The operation unit can also apply an algorithm for avoiding small obstacles (such as stones and steps). For example, the operation unit applies an algorithm for avoiding fixed obstacles. It applies an algorithm for avoiding moving obstacles. It applies an algorithm for avoiding small obstacles. In this way, safer operation can be performed by applying an avoidance algorithm according to the type of obstacle.

[0047] The operation unit can monitor the user's health condition during operation and select an appropriate operation. For example, when the user is tired, the operation unit performs slow operation. Also, when the user is in a healthy state, the operation unit can perform normal operation. Furthermore, when the user is in poor health, the operation unit can minimize operation and encourage rest. For example, when the user is tired, the operation unit performs slow operation. When the user is in a healthy state, the operation unit performs normal operation. When the user is in poor health, the operation unit minimizes operation and encourages rest. In this way, by monitoring the user's health condition, an appropriate operation can be selected.

[0048] The operation unit can select an operation in consideration of the geographical position information of the user when operating. For example, when the user is in a specific location, the operation unit prioritizes an operation related to the location. Furthermore, when the user is moving, the operation unit can also perform an optimal operation based on the current location. Furthermore, when the user approaches a destination, the operation unit can also prioritize an operation related to the destination. For example, when the user is in a specific location, the operation unit prioritizes an operation related to the location. When the user is moving, the operation unit performs an optimal operation based on the current location. When the user approaches a destination, the operation unit prioritizes an operation related to the destination. In this way, the optimal operation can be selected by considering the geographical position information.

[0049] During operation, the operation unit can analyze the user's social media activity and perform a related operation. For example, the operation unit performs a related operation based on information shared by the user on social media. The operation unit can also prioritize an operation related to a current interest from the user's social media activity. Furthermore, the operation unit can also perform an operation related to a place or event that the user follows on social media. For example, the operation unit performs a related operation based on information shared by the user on social media. The operation unit prioritizes an operation related to a current interest from the user's social media activity. The operation unit performs an operation related to a place or event that the user follows on social media. In this way, the related operation can be performed by analyzing social media activity.

[0050] During control, the control unit can learn the user's behavioral patterns and select a control method. The control unit, for example, selects the optimal control method based on actions performed by the user in the past. The control unit can also select a control method suitable for a specific time period from the user's behavioral patterns. Furthermore, the control unit can analyze the user's behavioral patterns and select the most efficient control method. For example, the control unit selects the optimal control method based on actions performed by the user in the past. A control method suitable for a specific time period is selected from the user's behavioral patterns. The user's behavioral patterns are analyzed and the most efficient control method is selected. In this way, the user's behavioral patterns can be learned and the optimal control method can be selected.

[0051] The control unit can customize a response method in an emergency during control. The control unit, for example, has a function of automatically calling an ambulance in an emergency. The control unit can also have a function of automatically sending a notification to the user's emergency contact in an emergency. The control unit can also have a function of automatically contacting the nearest medical institution in an emergency. For example, the control unit has a function of automatically calling an ambulance in an emergency. The control unit has a function of automatically sending a notification to the user's emergency contact in an emergency. The control unit has a function of automatically contacting the nearest medical institution in an emergency. This allows for faster and more appropriate response by customizing the response method in an emergency.

[0052] The control unit can select a control method in consideration of the geographical location information of the user during control. For example, when the user is in a specific location, the control unit prioritizes control related to the location. Furthermore, when the user is moving, the control unit can also perform optimal control based on the current location. Furthermore, when the user approaches a destination, the control unit can also prioritize control related to the destination. For example, when the user is in a specific location, the control unit prioritizes control related to the location. When the user is moving, the control unit performs optimal control based on the current location. When the user approaches a destination, the control unit prioritizes control related to the destination. In this way, the optimal control method can be selected by considering the geographical location information.

[0053] During control, the control unit can analyze the user's social media activity and perform related control. The control unit performs related control based on, for example, information shared by the user on social media. The control unit can also prioritize control related to current interests from the user's social media activity. Furthermore, the control unit can also perform control related to places and events the user follows on social media. For example, the control unit performs related control based on information shared by the user on social media. The control unit prioritizes control related to current interests from the user's social media activity. Control related to places and events the user follows on social media is performed. In this way, related control can be performed by analyzing social media activity.

[0054] During navigation, the navigation unit can select a route by referring to past navigation data. The navigation unit, for example, suggests an optimal route based on routes used by the user in the past. The navigation unit can also suggest a route that avoids congestion based on the user's past navigation data. Furthermore, the navigation unit can analyze the user's past navigation data and suggest the most efficient route. For example, the navigation unit suggests an optimal route based on routes used by the user in the past. A route that avoids congestion is suggested based on the user's past navigation data. The navigation unit analyzes the user's past navigation data and suggests the most efficient route. In this way, the optimal route can be selected by referring to past navigation data.

[0055] The navigation unit can obtain traffic information and weather information in real time during navigation and provide an optimal route. The navigation unit proposes an optimal route based on, for example, real-time traffic congestion information. The navigation unit can also propose an optimal route taking real-time weather information into consideration. Furthermore, the navigation unit can also propose an optimal route based on the real-time operation status of public transportation. For example, the navigation unit proposes an optimal route based on real-time traffic congestion information. The navigation unit proposes an optimal route taking real-time weather information into consideration. The navigation unit proposes an optimal route based on the real-time operation status of public transportation. In this way, an optimal route can be provided by obtaining traffic information and weather information in real time.

[0056] The navigation unit can select a route taking into consideration the geographical location information of the user during navigation. For example, when the user is in a specific location, the navigation unit preferentially proposes a route related to the location. Furthermore, when the user is moving, the navigation unit can also propose an optimal route based on the current location. Furthermore, when the user approaches a destination, the navigation unit can also preferentially propose a route related to the destination. For example, when the user is in a specific location, the navigation unit preferentially proposes a route related to the location. When the user is moving, the navigation unit proposes an optimal route based on the current location. When the user is approaching a destination, the navigation unit preferentially proposes a route related to the destination. In this way, the optimal route can be selected by taking into consideration the geographical location information.

[0057] The navigation unit can analyze the user's social media activity during navigation and provide related navigation information. The navigation unit provides related navigation information based on, for example, information shared by the user on social media. The navigation unit can also provide preferentially navigation information related to current interests from the user's social media activity. The navigation unit can also provide navigation information related to places and events that the user follows on social media. For example, the navigation unit provides related navigation information based on information shared by the user on social media. The navigation unit provides preferentially navigation information related to current interests from the user's social media activity. The navigation unit provides navigation information related to places and events that the user follows on social media. In this way, it is possible to provide related navigation information by analyzing social media activity.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The assistance dog robot system may further include a health management unit that monitors the user's health condition. The health management unit may measure the user's heart rate, blood pressure, body temperature, etc. in real time, and may issue a warning if an abnormality is detected. For example, if the heart rate is abnormally high, a notification may be issued urging the user to rest. Also, if the blood pressure is high, a notification may be issued recommending contacting a medical institution. Furthermore, if the body temperature is high, a warning may be issued that there is a possibility of fever, and appropriate measures may be urged. This allows the user's health condition to be constantly monitored and appropriate support to be provided.

[0060] The assistance dog robot system may further include a behavior prediction unit that learns and predicts the user's behavioral patterns. The behavior prediction unit predicts the user's most likely next behavior based on the user's daily routine and past behavioral history. For example, the behavior prediction unit may learn that the user wakes up at 7:00 every morning, and sound an alarm at 7:00 to prompt the user to wake up. The assistance dog robot system may also learn that the user eats lunch at 12:00 every day, and send a notification at 12:00 to prompt the user to prepare lunch. The assistance dog robot system may also learn that the user exercises at 9:00 every night, and send a notification at 9:00 to prompt the user to start exercising. This allows the assistance dog robot system to learn the user's behavioral patterns and provide more appropriate support.

[0061] The assistance dog robot system may further include a geographic information unit that selects an action taking into consideration the geographic location information of the user. When the user is in a specific location, the geographic information unit prioritizes an action related to that location. Also, when the user is moving, the geographic information unit can perform an optimal action based on the current location. Furthermore, when the user approaches a destination, the geographic information unit can prioritize an action related to the destination. For example, when the user is in a specific location, the geographic information unit prioritizes an action related to that location. When the user is moving, the geographic information unit performs an optimal action based on the current location. When the user approaches a destination, the geographic information unit prioritizes an action related to the destination. In this way, the optimal action can be selected by taking the geographic location information into consideration.

[0062] The assistance dog robot system may further include a social media analysis unit that analyzes the user's social media activity and performs a related action. The social media analysis unit performs a related action based on information shared by the user on social media. Also, it is possible to prioritize an action related to a current interest from the user's social media activity. Furthermore, it is possible to perform an action related to a place or event that the user follows on social media. For example, a related action is performed based on information shared by the user on social media. From the user's social media activity, it is possible to prioritize an action related to a current interest. It is possible to perform an action related to a place or event that the user follows on social media. In this way, it is possible to perform a related action by analyzing social media activity.

[0063] The assistance dog robot system may further include a geographic control unit that selects a control method taking into consideration the geographical location information of the user. When the user is in a specific location, the geographic control unit prioritizes control related to that location. Also, when the user is moving, the geographic control unit can perform optimal control based on the current location. Furthermore, when the user approaches a destination, the geographic control unit can also prioritize control related to the destination. For example, when the user is in a specific location, control related to that location is prioritized. When the user is moving, optimal control is performed based on the current location. When the user approaches a destination, control related to the destination is prioritized. In this way, the optimal control method can be selected by taking into consideration the geographical location information.

[0064] The process flow of the first embodiment will be briefly described below.

[0065] Step 1: The reception unit receives user instructions. The user instructions include voice instructions, touch operations, gestures, etc. For example, the reception unit receives the user's voice instructions using voice recognition technology, receives the user's touch operations using a touch panel, and recognizes the user's gestures using a camera. Step 2: The operating unit performs an operation based on the instruction received by the receiving unit. The operation includes movement, operation, notification, etc. For example, the operating unit moves the assistance dog robot system using a motor, lifts and operates objects using an arm, and notifies the user using a speaker. Step 3: The control unit controls the action performed by the operating unit. Control includes starting and stopping the action, adjusting the speed, etc. For example, the control unit adjusts the speed of the motor of the operating unit, controls the movement of the arm, and adjusts the volume of the speaker. Step 4: The navigation unit performs navigation based on the operations controlled by the control unit. Navigation includes route guidance, destination setting, etc. For example, the navigation unit identifies the user's current location using GPS technology, provides route guidance to the destination, connects to the Internet to obtain real-time traffic and weather information, learns the user's behavioral patterns, and suggests the optimal route.

[0066] (Example 2) The assistance dog robot system according to the embodiment of the present invention is a system designed to fulfill the role of an assistance dog. This assistance dog robot system has various functions to support users such as visually impaired people, hearing impaired people, and physically disabled people. For example, the assistance dog robot system has a voice recognition function for receiving instructions from a user. When a user instructs the assistance dog robot system to "turn right," the assistance dog robot system understands the instruction and turns right. In addition, the assistance dog robot system has a sensor for detecting obstacles, and supports the user so that the user can move safely. For example, when the assistance dog robot system detects an obstacle ahead, it issues a warning to the user and performs an avoidance operation. In addition, the assistance dog robot system has a GPS function, and performs navigation for the user to reach the destination. For example, when a user instructs the assistance dog robot system to "guide me to the station," the assistance dog robot system uses the GPS to calculate the optimal route and guide the user. In addition, the assistance dog robot system is connected to the Internet and can obtain traffic and weather information in real time. This allows the user to move safely based on the latest information. This assistance dog robot system not only solves the problem of the shortage of assistance dogs, but also solves social problems such as the refusal of assistance dogs to enter stores. Since the assistance dog robot system is not an animal, it will not be refused entry to a store, and users can use it with peace of mind. In addition, the assistance dog robot system can operate stably for a long period of time by performing regular maintenance. In the future, this technology can be expanded to other fields such as rescue dogs. For example, the assistance dog robot system can save lives in search and rescue activities at disaster sites. In this way, the present invention is a technology that solves social problems such as the shortage of assistance dogs and the refusal of assistance dogs to enter stores, and can also be applied to other fields such as rescue dogs. As a result, the assistance dog robot system can play the role of an assistance dog and support the user.

[0067] The assistance dog robot system according to the embodiment includes a reception unit, an operation unit, a control unit, and a navigation unit. The reception unit receives an instruction from a user. The instruction from the user includes, for example, a voice instruction, a touch operation, a gesture, and the like, but is not limited to such examples. The reception unit receives, for example, a voice instruction from the user using a voice recognition technology. The reception unit can also receive a touch operation from the user using a touch panel. Furthermore, the reception unit can recognize a gesture from the user using a camera. For example, the reception unit recognizes a voice instruction from the user with high accuracy using a voice recognition technology. The touch panel allows the user to input an instruction by touching the screen. The camera tracks the movement of the user's hand and recognizes the gesture. The operation unit performs an operation based on the instruction received by the reception unit. The operation includes, for example, movement, operation, notification, and the like, but is not limited to such examples. The operation unit moves the assistance dog robot system using, for example, a motor. The operation unit can also lift and operate an object using an arm. Furthermore, the operation unit can notify the user using a speaker. For example, the operating unit uses a motor to move the assistance dog robot system forward. The arm can lift or operate an object. The speaker notifies the user by voice. The control unit controls the operation performed by the operating unit. The control includes, for example, starting and stopping the operation, adjusting the speed, and the like, but is not limited to such examples. The control unit adjusts, for example, the speed of the motor of the operating unit. The control unit can also control the movement of the arm of the operating unit. Furthermore, the control unit can adjust the volume of the speaker of the operating unit. For example, the control unit adjusts the speed of the motor to control the moving speed of the assistance dog robot system. The arm movement is controlled to lift or operate an object. The speaker volume is adjusted to notify the user. The navigation unit performs navigation based on the operation controlled by the control unit. The navigation includes, for example, route guidance, destination setting, and the like, but is not limited to such examples. The navigation unit identifies the current location of the user using, for example, GPS technology, and guides the user along a route to the destination.The navigation unit is also connected to the Internet and can obtain traffic and weather information in real time. Furthermore, the navigation unit can learn the user's behavioral patterns and propose an optimal route. For example, the navigation unit identifies the user's current location using GPS technology and calculates an optimal route. It connects to the Internet to obtain traffic and weather information in real time and reflects the information in the navigation. It learns the user's behavioral patterns and proposes an optimal route based on the user's past movement history. As a result, the assistance dog robot system according to the embodiment can receive instructions from the user, perform operations, control, and navigate.

[0068] The reception unit receives a user's instruction. The user's instruction includes, but is not limited to, a voice instruction, a touch operation, a gesture, and the like. The reception unit receives the user's voice instruction, for example, using a voice recognition technology. Specifically, the voice recognition technology includes an advanced voice analysis algorithm using natural language processing (NLP), and can accurately analyze the contents of the user's speech and understand the intention. For example, if the user instructs "move forward," the voice recognition technology analyzes the instruction and transmits an appropriate command to the operation unit. The reception unit can also receive the user's touch operation using a touch panel. The touch panel uses a technology such as a capacitive type or a resistive film type, and the user can input an instruction by touching the screen. Furthermore, the reception unit can recognize the user's gesture using a camera. The camera utilizes computer vision technology to track the user's hand movement and recognize the gesture. For example, if the user performs a waving motion, the camera captures the movement, and the gesture recognition algorithm analyzes the movement and transmits an appropriate instruction to the operation unit. This allows the reception unit to receive user instructions with high accuracy by any method, such as voice, touch, or gesture, thereby improving user convenience.

[0069] The operating unit performs an operation based on the instruction received by the receiving unit. The operation includes, for example, movement, operation, notification, etc., but is not limited to such examples. For example, the operating unit uses a motor to move the assistance dog robot system. Specifically, a DC motor or a stepping motor is used as the motor, and the assistance dog robot system can perform operations such as forward movement, backward movement, and rotation. The operating unit can also lift and operate an object using an arm. The arm has a multi-joint structure and is controlled by a servo motor or an actuator. This allows the arm to perform complex operations, for example, lift an object and move it to a specified location. Furthermore, the operating unit can also notify the user using a speaker. The speaker uses voice synthesis technology to provide information to the user by voice. For example, the operating unit uses a motor to move the assistance dog robot system forward. The arm can lift and operate an object. The speaker notifies the user by voice. This allows the operating unit to perform various operations based on the user's instructions and meet the user's needs.

[0070] The control unit controls the operation performed by the operating unit. The control includes, for example, starting and stopping the operation, adjusting the speed, and the like, but is not limited to such examples. The control unit adjusts, for example, the speed of the motor of the operating unit. Specifically, the control unit can use PWM (pulse width modulation) technology to control the rotation speed of the motor. This allows the moving speed of the assistance dog robot system to be finely adjusted. The control unit can also control the movement of the arm of the operating unit. The movement of the arm is controlled by a servo motor or an actuator, and the control unit can precisely control the movement of the arm by sending appropriate commands to these devices. Furthermore, the control unit can adjust the volume of the speaker of the operating unit. The volume of the speaker is adjusted using digital signal processing (DSP) technology, and a notification can be given to the user at an appropriate volume. For example, the control unit adjusts the speed of the motor to control the moving speed of the assistance dog robot system. The arm movement is controlled to lift or operate an object. The speaker volume is adjusted to notify the user. This allows the control unit to precisely control the operation of the operating unit and optimize the operation of the assistance dog robot system.

[0071] The navigation unit performs navigation based on the operation controlled by the control unit. The navigation includes, for example, route guidance, destination setting, and the like, but is not limited to such examples. For example, the navigation unit identifies the current location of the user using GPS technology and guides the user along a route to the destination. Specifically, the navigation unit identifies the current location of the user with high accuracy using a GPS receiver, and calculates an optimal route by combining the location with map data. The navigation unit is also connected to the Internet and can acquire traffic information and weather information in real time. This allows the navigation unit to dynamically adjust the route based on the latest information and provide optimal navigation to the user. Furthermore, the navigation unit can learn the user's behavioral patterns and propose an optimal route. For example, a machine learning algorithm can be used to analyze the user's past movement history and behavioral patterns, and propose a route based on the user's preferences and habits. This allows the navigation unit to identify the user's current location using GPS technology and calculate an optimal route. The navigation unit is connected to the Internet to acquire traffic information and weather information in real time, and reflects the information in the navigation. The navigation unit learns the user's behavioral patterns and proposes an optimal route based on the user's past movement history. This enables the navigation unit to provide the user with highly accurate and flexible navigation, thereby improving user convenience.

[0072] The reception unit may have a voice recognition function. The voice recognition function includes, for example, types of voice commands, recognition accuracy, and the like, but is not limited to such examples. The reception unit receives a user's voice instruction using, for example, a voice recognition technology. For example, the voice recognition technology can analyze the user's voice and recognize a specific command. For example, the voice recognition technology can use a voice recognition model using deep learning. The voice recognition model learns a large amount of voice data and realizes highly accurate voice recognition. For example, when the user instructs "turn right," the voice recognition technology analyzes the voice and generates a command for turning right. The voice recognition technology can analyze the user's voice in real time and instantly execute the instruction. As a result, the voice recognition function can receive the user's voice instruction.

[0073] The operating unit may include a sensor for detecting an obstacle. Examples of sensors for detecting an obstacle include, but are not limited to, an infrared sensor and an ultrasonic sensor. The operating unit may detect an obstacle ahead using, for example, an infrared sensor. The infrared sensor can identify the position of an obstacle by emitting infrared rays and detecting the reflection of the infrared rays. The operating unit may also detect an obstacle using an ultrasonic sensor. The ultrasonic sensor can measure the distance of an obstacle by emitting ultrasonic waves and detecting the reflection of the ultrasonic waves. The operating unit may also detect an obstacle using a camera. The camera can identify the shape and position of an obstacle using image analysis technology. For example, the operating unit may detect an obstacle ahead using an infrared sensor and perform an operation to avoid the obstacle. The ultrasonic sensor measures the distance of the obstacle and controls the operation to maintain a safe distance. The camera can identify the shape and position of the obstacle using image analysis technology and calculate the optimal route for avoiding the obstacle. This allows the operating unit to detect an obstacle and perform an operation to avoid the obstacle.

[0074] The navigation unit may have a GPS function. The GPS function includes, for example, a method of acquiring location information, accuracy, and the like, but is not limited to such examples. The navigation unit, for example, identifies the current location of the user using GPS technology. The GPS technology can acquire location information by receiving signals from multiple satellites and analyzing the signals. The navigation unit can calculate a route to the destination based on the acquired location information. For example, the navigation unit identifies the current location of the user using GPS technology and calculates an optimal route. The GPS technology can provide highly accurate location information and perform navigation for the user to reach the destination. As a result, the GPS function can guide the user to the destination.

[0075] The navigation unit is connected to the Internet and can acquire traffic information and weather information. The traffic information and weather information include, for example, traffic congestion information, rainfall forecast, and the like, but are not limited to such examples. The navigation unit, for example, connects to the Internet and acquires traffic information in real time. The traffic information includes, for example, traffic congestion status and road closure information. The navigation unit can calculate an optimal route based on the acquired traffic information. The navigation unit can also connect to the Internet and acquire weather information in real time. The weather information includes, for example, rainfall forecast and temperature change. The navigation unit can provide appropriate advice to the user based on the acquired weather information. For example, the navigation unit suggests a route that avoids traffic congestion based on traffic congestion information. If rain is predicted based on the weather information, the navigation unit advises the user to bring an umbrella. This makes it possible to acquire the latest traffic information and weather information in real time and reflect it in the navigation.

[0076] The operating unit may have a function of monitoring the user's health condition. The user's health condition may include, but is not limited to, for example, a heart rate, blood pressure, and body temperature. For example, the operating unit may monitor the user's heart rate using a heart rate sensor. The heart rate sensor may contact the user's skin to measure the heart rate. The operating unit may also monitor the user's blood pressure using a blood pressure sensor. The blood pressure sensor may be worn on the user's arm to measure the blood pressure. The operating unit may also monitor the user's body temperature using a body temperature sensor. The body temperature sensor may contact the user's skin to measure the body temperature. For example, the operating unit may monitor the user's heart rate in real time using the heart rate sensor, and issue a warning if an abnormality is detected. The blood pressure sensor may periodically measure the user's blood pressure, and notify a medical institution if an abnormality is detected. The body temperature sensor may continuously monitor the user's body temperature, and may encourage the user to rest if a fever is detected. This allows the user's health condition to be monitored and appropriate support to be provided.

[0077] The control unit can learn the user's behavioral patterns and provide support. The user's behavioral patterns include, but are not limited to, daily routines and past behavioral history. The control unit can learn, for example, the user's daily routine and provide appropriate support. The daily routine includes, for example, the daily wake-up time, meal time, and exercise time. The control unit can provide support appropriate for a specific time period based on the user's behavioral history. For example, the control unit learns that the user wakes up at 7 o'clock every morning and sounds an alarm at 7 o'clock to encourage the user to wake up. The control unit also learns that the user eats lunch at 12 o'clock every day and issues a notification at 12 o'clock to encourage the user to prepare lunch. Furthermore, the control unit learns that the user exercises at 9 o'clock every night and issues a notification at 9 o'clock to encourage the user to start exercising. This makes it possible to learn the user's behavioral patterns and provide more appropriate support.

[0078] The reception unit may have a function of recognizing sign language and gestures. The function of recognizing sign language and gestures includes, for example, a type of camera, a recognition algorithm, and the like, but is not limited to such examples. The reception unit, for example, recognizes the user's sign language and gestures using a camera. The camera can track the user's hand movements and recognize the sign language and gestures. The recognition algorithm can analyze the user's hand movements and recognize specific sign language and gestures. For example, the reception unit recognizes the user's sign language using a camera and accepts instructions. The recognition algorithm analyzes the user's hand movements and recognizes specific sign language. This makes it possible to recognize sign language and gestures and accept instructions from the user.

[0079] The control unit can have a function of automatically calling an ambulance in an emergency. The function of automatically calling an ambulance in an emergency includes, for example, the installation of an emergency button, detection of an abnormality by a sensor, and the like, but is not limited to such examples. For example, the control unit installs an emergency button, and the user can call an ambulance by pressing the button in an emergency. The control unit can also detect an abnormality using a sensor and automatically call an ambulance. The sensor can detect, for example, an abnormality in the user's heart rate or blood pressure. For example, the control unit installs an emergency button, and the user can press the button in an emergency to call an ambulance. The sensor detects an abnormality in the user's heart rate or blood pressure and automatically call an ambulance. This makes it possible to automatically call an ambulance in an emergency.

[0080] The reception unit can estimate the user's emotion and adjust the instruction reception method based on the estimated user's emotion. For example, when the user is stressed, the reception unit provides a simple interface and minimizes instruction steps. Also, when the user is relaxed, the reception unit can provide detailed instruction options and suggest a customizable instruction method. Furthermore, when the user is in a hurry, the reception unit can prioritize voice input and quickly accept instructions. For example, when the user is stressed, the reception unit provides a simple interface and minimizes instruction steps. When the user is relaxed, detailed instruction options are provided and a customizable instruction method is suggested. When the user is in a hurry, voice input is prioritized and instructions are quickly accepted. In this way, by adjusting the instruction acceptance method according to the user's emotion, more appropriate instructions can be accepted. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0081] The reception unit can analyze the user's past instruction history and select an instruction reception method. For example, the reception unit preferentially suggests an instruction method (such as voice or gesture) that the user has used frequently in the past. The reception unit can also predict and suggest an instruction method to be used in a specific time period from the user's past instruction history. Furthermore, the reception unit can automatically display related instructions as candidates based on the contents of instructions given by the user in the past. For example, the reception unit preferentially suggests an instruction method that the user has used frequently in the past. The reception unit predicts and suggests an instruction method to be used in a specific time period from the user's past instruction history. Based on the contents of instructions given by the user in the past, related instructions are automatically displayed as candidates. In this way, the optimal instruction reception method can be selected by analyzing the past instruction history.

[0082] The reception unit can perform filtering based on the user's current situation and environment when receiving an instruction. For example, when the user is outdoors, the reception unit adjusts the sensitivity of the voice recognition taking into account the surrounding noise level. In addition, when the user is indoors, the reception unit can filter the environmental sound to accurately receive the instruction. Furthermore, when the user is moving, the reception unit can preferentially receive instructions related to the current position based on GPS information. For example, when the user is outdoors, the reception unit adjusts the sensitivity of the voice recognition taking into account the surrounding noise level. When the user is indoors, the reception unit filters the environmental sound to accurately receive the instruction. When the user is moving, the reception unit preferentially receives the instruction related to the current position based on GPS information. This makes it possible to receive more accurate instructions by filtering the instruction according to the user's situation and environment.

[0083] The reception unit can estimate the user's emotion and determine the priority of instructions to be received based on the estimated user's emotion. For example, when the user is nervous, the reception unit preferentially receives instructions with high urgency. Also, when the user is relaxed, the reception unit can preferentially receive normal instructions. Furthermore, when the user is in a hurry, the reception unit can preferentially receive instructions that require a quick response. For example, when the user is nervous, the reception unit preferentially receives instructions with high urgency. When the user is relaxed, the reception unit preferentially receives normal instructions. When the user is in a hurry, the reception unit preferentially receives instructions that require a quick response. In this way, by determining the priority of instructions according to the user's emotion, more appropriate instructions can be received. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0084] When receiving an instruction, the reception unit can preferentially receive the instruction in consideration of the geographical position information of the user. For example, when the user is in a specific location, the reception unit preferentially receives an instruction related to the location. Furthermore, when the user is moving, the reception unit can also receive optimal instructions based on the current location. Furthermore, when the user approaches the destination, the reception unit can also preferentially receive an instruction related to the destination. For example, when the user is in a specific location, the reception unit preferentially receives an instruction related to the location. When the user is moving, the reception unit accepts optimal instructions based on the current location. When the user approaches the destination, the reception unit preferentially accepts an instruction related to the destination. In this way, by considering the geographical position information, it is possible to preferentially receive highly relevant instructions.

[0085] The reception unit can analyze the user's social media activity and receive related instructions when receiving the instruction. The reception unit receives related instructions based on, for example, information shared by the user on social media. The reception unit can also preferentially receive instructions related to current interests from the user's social media activity. Furthermore, the reception unit can also receive instructions related to places and events that the user follows on social media. For example, the reception unit receives related instructions based on information shared by the user on social media. The reception unit preferentially receives instructions related to current interests from the user's social media activity. The reception unit receives instructions related to places and events that the user follows on social media. In this way, it is possible to receive related instructions by analyzing social media activity.

[0086] The operation unit can estimate the user's emotion and adjust the operation method based on the estimated user's emotion. For example, when the user is nervous, the operation unit performs a slow operation to provide a sense of security. In addition, when the user is relaxed, the operation unit can also perform a normal operation. In addition, when the user is in a hurry, the operation unit can perform a quick operation to efficiently support the user. For example, when the user is nervous, the operation unit performs a slow operation to provide a sense of security. When the user is relaxed, the operation unit performs a normal operation. When the user is in a hurry, the operation unit performs a quick operation to efficiently support the user. In this way, by adjusting the operation method according to the user's emotion, a more appropriate operation can be performed. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0087] The operation unit can apply an avoidance algorithm according to the type of obstacle during operation. For example, the operation unit applies an algorithm for avoiding fixed obstacles (such as walls and pillars). The operation unit can also apply an algorithm for avoiding moving obstacles (such as people and cars). The operation unit can also apply an algorithm for avoiding small obstacles (such as stones and steps). For example, the operation unit applies an algorithm for avoiding fixed obstacles. It applies an algorithm for avoiding moving obstacles. It applies an algorithm for avoiding small obstacles. In this way, safer operation can be performed by applying an avoidance algorithm according to the type of obstacle.

[0088] The operation unit can monitor the user's health condition during operation and select an appropriate operation. For example, when the user is tired, the operation unit performs slow operation. Also, when the user is in a healthy state, the operation unit can perform normal operation. Furthermore, when the user is in poor health, the operation unit can minimize operation and encourage rest. For example, when the user is tired, the operation unit performs slow operation. When the user is in a healthy state, the operation unit performs normal operation. When the user is in poor health, the operation unit minimizes operation and encourages rest. In this way, by monitoring the user's health condition, an appropriate operation can be selected.

[0089] The operation unit can estimate the user's emotion and determine the priority of the operation based on the estimated emotion of the user. For example, when the user is nervous, the operation unit prioritizes an operation with high urgency. Also, when the user is relaxed, the operation unit can also prioritize a normal operation. Furthermore, when the user is in a hurry, the operation unit can also prioritize an operation that requires a quick response. For example, when the user is nervous, the operation unit prioritizes an operation with high urgency. When the user is relaxed, the operation unit prioritizes a normal operation. When the user is in a hurry, the operation unit prioritizes an operation that requires a quick response. In this way, by determining the priority of the operation according to the user's emotion, it is possible to perform a more appropriate operation. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0090] The operation unit can select an operation in consideration of the geographical position information of the user when operating. For example, when the user is in a specific location, the operation unit prioritizes an operation related to the location. Furthermore, when the user is moving, the operation unit can also perform an optimal operation based on the current location. Furthermore, when the user approaches a destination, the operation unit can also prioritize an operation related to the destination. For example, when the user is in a specific location, the operation unit prioritizes an operation related to the location. When the user is moving, the operation unit performs an optimal operation based on the current location. When the user approaches a destination, the operation unit prioritizes an operation related to the destination. In this way, the optimal operation can be selected by considering the geographical position information.

[0091] During operation, the operation unit can analyze the user's social media activity and perform a related operation. For example, the operation unit performs a related operation based on information shared by the user on social media. The operation unit can also prioritize an operation related to a current interest from the user's social media activity. Furthermore, the operation unit can also perform an operation related to a place or event that the user follows on social media. For example, the operation unit performs a related operation based on information shared by the user on social media. The operation unit prioritizes an operation related to a current interest from the user's social media activity. The operation unit performs an operation related to a place or event that the user follows on social media. In this way, the related operation can be performed by analyzing social media activity.

[0092] The control unit can estimate the user's emotion and adjust the operation control method based on the estimated user's emotion. For example, when the user is nervous, the control unit controls the user to perform a slow movement. Also, when the user is relaxed, the control unit can control the user to perform a normal movement. Furthermore, when the user is in a hurry, the control unit can control the user to perform a quick movement. For example, when the user is nervous, the control unit controls the user to perform a slow movement. When the user is relaxed, the control unit controls the user to perform a normal movement. When the user is in a hurry, the control unit controls the user to perform a quick movement. This allows more appropriate control to be performed by adjusting the operation control method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI.

[0093] During control, the control unit can learn the user's behavioral patterns and select a control method. The control unit, for example, selects the optimal control method based on actions performed by the user in the past. The control unit can also select a control method suitable for a specific time period from the user's behavioral patterns. Furthermore, the control unit can analyze the user's behavioral patterns and select the most efficient control method. For example, the control unit selects the optimal control method based on actions performed by the user in the past. A control method suitable for a specific time period is selected from the user's behavioral patterns. The user's behavioral patterns are analyzed and the most efficient control method is selected. In this way, the user's behavioral patterns can be learned and the optimal control method can be selected.

[0094] The control unit can customize a response method in an emergency during control. The control unit, for example, has a function of automatically calling an ambulance in an emergency. The control unit can also have a function of automatically sending a notification to the user's emergency contact in an emergency. The control unit can also have a function of automatically contacting the nearest medical institution in an emergency. For example, the control unit has a function of automatically calling an ambulance in an emergency. The control unit has a function of automatically sending a notification to the user's emergency contact in an emergency. The control unit has a function of automatically contacting the nearest medical institution in an emergency. This allows for faster and more appropriate response by customizing the response method in an emergency.

[0095] The control unit can estimate the user's emotion and determine the priority of control based on the estimated user's emotion. For example, when the user is nervous, the control unit prioritizes control with high urgency. Also, when the user is relaxed, the control unit can also prioritize normal control. Furthermore, when the user is in a hurry, the control unit can also prioritize control that requires a quick response. For example, when the user is nervous, the control unit prioritizes control with high urgency. When the user is relaxed, the control unit prioritizes normal control. When the user is in a hurry, the control unit prioritizes control that requires a quick response. In this way, by determining the priority of control according to the user's emotion, more appropriate control can be performed. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0096] The control unit can select a control method in consideration of the geographical location information of the user during control. For example, when the user is in a specific location, the control unit prioritizes control related to the location. Furthermore, when the user is moving, the control unit can also perform optimal control based on the current location. Furthermore, when the user approaches a destination, the control unit can also prioritize control related to the destination. For example, when the user is in a specific location, the control unit prioritizes control related to the location. When the user is moving, the control unit performs optimal control based on the current location. When the user approaches a destination, the control unit prioritizes control related to the destination. In this way, the optimal control method can be selected by considering the geographical location information.

[0097] During control, the control unit can analyze the user's social media activity and perform related control. The control unit performs related control based on, for example, information shared by the user on social media. The control unit can also prioritize control related to current interests from the user's social media activity. Furthermore, the control unit can also perform control related to places and events the user follows on social media. For example, the control unit performs related control based on information shared by the user on social media. The control unit prioritizes control related to current interests from the user's social media activity. Control related to places and events the user follows on social media is performed. In this way, related control can be performed by analyzing social media activity.

[0098] The navigation unit can estimate the user's emotion and adjust the navigation method based on the estimated user's emotion. For example, when the user is nervous, the navigation unit provides a navigation method that is simple and highly visible. Also, when the user is relaxed, the navigation unit can provide a navigation method that includes detailed information. Furthermore, when the user is in a hurry, the navigation unit can provide a navigation method that focuses on the main points. For example, when the user is nervous, the navigation unit provides a navigation method that is simple and highly visible. When the user is relaxed, the navigation unit provides a navigation method that includes detailed information. When the user is in a hurry, the navigation unit provides a navigation method that focuses on the main points. This makes it possible to provide more appropriate navigation by adjusting the navigation method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0099] During navigation, the navigation unit can select a route by referring to past navigation data. The navigation unit, for example, suggests an optimal route based on routes used by the user in the past. The navigation unit can also suggest a route that avoids congestion based on the user's past navigation data. Furthermore, the navigation unit can analyze the user's past navigation data and suggest the most efficient route. For example, the navigation unit suggests an optimal route based on routes used by the user in the past. A route that avoids congestion is suggested based on the user's past navigation data. The navigation unit analyzes the user's past navigation data and suggests the most efficient route. In this way, the optimal route can be selected by referring to past navigation data.

[0100] The navigation unit can obtain traffic information and weather information in real time during navigation and provide an optimal route. The navigation unit proposes an optimal route based on, for example, real-time traffic congestion information. The navigation unit can also propose an optimal route taking real-time weather information into consideration. Furthermore, the navigation unit can also propose an optimal route based on the real-time operation status of public transportation. For example, the navigation unit proposes an optimal route based on real-time traffic congestion information. The navigation unit proposes an optimal route taking real-time weather information into consideration. The navigation unit proposes an optimal route based on the real-time operation status of public transportation. In this way, an optimal route can be provided by obtaining traffic information and weather information in real time.

[0101] The navigation unit can estimate the user's emotion and determine the priority of navigation based on the estimated emotion of the user. For example, when the user is nervous, the navigation unit prioritizes navigation with high urgency. Also, when the user is relaxed, the navigation unit can also prioritize normal navigation. Furthermore, when the user is in a hurry, the navigation unit can also prioritize navigation that requires a quick response. For example, when the user is nervous, the navigation unit prioritizes navigation with high urgency. When the user is relaxed, the navigation unit prioritizes normal navigation. When the user is in a hurry, the navigation unit prioritizes navigation that requires a quick response. In this way, by determining the priority of navigation according to the emotion of the user, more appropriate navigation can be provided. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0102] The navigation unit can select a route taking into consideration the geographical location information of the user during navigation. For example, when the user is in a specific location, the navigation unit preferentially proposes a route related to the location. Furthermore, when the user is moving, the navigation unit can also propose an optimal route based on the current location. Furthermore, when the user approaches a destination, the navigation unit can also preferentially propose a route related to the destination. For example, when the user is in a specific location, the navigation unit preferentially proposes a route related to the location. When the user is moving, the navigation unit proposes an optimal route based on the current location. When the user is approaching a destination, the navigation unit preferentially proposes a route related to the destination. In this way, the optimal route can be selected by taking into consideration the geographical location information.

[0103] The navigation unit can analyze the user's social media activity during navigation and provide related navigation information. The navigation unit provides related navigation information based on, for example, information shared by the user on social media. The navigation unit can also provide preferentially navigation information related to current interests from the user's social media activity. The navigation unit can also provide navigation information related to places and events that the user follows on social media. For example, the navigation unit provides related navigation information based on information shared by the user on social media. The navigation unit provides preferentially navigation information related to current interests from the user's social media activity. The navigation unit provides navigation information related to places and events that the user follows on social media. In this way, it is possible to provide related navigation information by analyzing social media activity.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The assistance dog robot system may further include a health management unit that monitors the user's health condition. The health management unit may measure the user's heart rate, blood pressure, body temperature, etc. in real time, and may issue a warning if an abnormality is detected. For example, if the heart rate is abnormally high, a notification may be issued urging the user to rest. Also, if the blood pressure is high, a notification may be issued recommending contacting a medical institution. Furthermore, if the body temperature is high, a warning may be issued that there is a possibility of fever, and appropriate measures may be urged. This allows the user's health condition to be constantly monitored and appropriate support to be provided.

[0106] The assistance dog robot system may further include an audio feedback unit that estimates the user's emotions and adjusts the audio feedback based on the estimated emotions. For example, when the user is stressed, the audio feedback unit provides audio with a calm and soothing tone. When the user is relaxed, the audio feedback unit may provide audio with a bright and cheerful tone. When the user is in a hurry, the audio feedback unit may provide quick and concise audio feedback. This makes it possible to provide appropriate audio feedback according to the user's emotions.

[0107] The assistance dog robot system may further include a behavior prediction unit that learns and predicts the user's behavioral patterns. The behavior prediction unit predicts the user's most likely next behavior based on the user's daily routine and past behavioral history. For example, the behavior prediction unit may learn that the user wakes up at 7:00 every morning, and sound an alarm at 7:00 to prompt the user to wake up. The assistance dog robot system may also learn that the user eats lunch at 12:00 every day, and send a notification at 12:00 to prompt the user to prepare lunch. The assistance dog robot system may also learn that the user exercises at 9:00 every night, and send a notification at 9:00 to prompt the user to start exercising. This allows the assistance dog robot system to learn the user's behavioral patterns and provide more appropriate support.

[0108] The assistance dog robot system may further include a navigation adjustment unit that estimates the user's emotions and adjusts the navigation method based on the estimated emotions. For example, if the user is nervous, a simple and highly visible navigation method may be provided. If the user is relaxed, a navigation method including detailed information may be provided. Furthermore, if the user is in a hurry, a navigation method that focuses on the main points may be provided. In this way, more appropriate navigation can be provided by adjusting the navigation method according to the user's emotions.

[0109] The assistance dog robot system may further include a geographic information unit that selects an action taking into consideration the geographic location information of the user. When the user is in a specific location, the geographic information unit prioritizes an action related to that location. Also, when the user is moving, the geographic information unit can perform an optimal action based on the current location. Furthermore, when the user approaches a destination, the geographic information unit can prioritize an action related to the destination. For example, when the user is in a specific location, the geographic information unit prioritizes an action related to that location. When the user is moving, the geographic information unit performs an optimal action based on the current location. When the user approaches a destination, the geographic information unit prioritizes an action related to the destination. In this way, the optimal action can be selected by taking the geographic location information into consideration.

[0110] The assistance dog robot system may further include an action priority unit that estimates the user's emotions and determines the priority order of actions based on the estimated emotions. For example, if the user is nervous, actions with high urgency may be prioritized. Also, if the user is relaxed, normal actions may be prioritized. Furthermore, if the user is in a hurry, actions that require a quick response may be prioritized. In this way, more appropriate actions can be performed by determining the priority order of actions according to the user's emotions.

[0111] The assistance dog robot system may further include a social media analysis unit that analyzes the user's social media activity and performs a related action. The social media analysis unit performs a related action based on information shared by the user on social media. Also, it is possible to prioritize an action related to a current interest from the user's social media activity. Furthermore, it is possible to perform an action related to a place or event that the user follows on social media. For example, a related action is performed based on information shared by the user on social media. From the user's social media activity, it is possible to prioritize an action related to a current interest. It is possible to perform an action related to a place or event that the user follows on social media. In this way, it is possible to perform a related action by analyzing social media activity.

[0112] The assistance dog robot system may further include a control priority unit that estimates the user's emotions and determines the priority of control based on the estimated emotions. For example, if the user is nervous, priority is given to highly urgent control. Also, if the user is relaxed, priority can be given to normal control. Furthermore, if the user is in a hurry, priority can be given to control that requires a quick response. In this way, more appropriate control can be performed by determining the priority of control according to the user's emotions.

[0113] The assistance dog robot system may further include a geographic control unit that selects a control method taking into consideration the geographical location information of the user. When the user is in a specific location, the geographic control unit prioritizes control related to that location. Also, when the user is moving, the geographic control unit can perform optimal control based on the current location. Furthermore, when the user approaches a destination, the geographic control unit can also prioritize control related to the destination. For example, when the user is in a specific location, control related to that location is prioritized. When the user is moving, optimal control is performed based on the current location. When the user approaches a destination, control related to the destination is prioritized. In this way, the optimal control method can be selected by taking into consideration the geographical location information.

[0114] The assistance dog robot system may further include a navigation priority unit that estimates the user's emotions and determines the priority of navigation based on the estimated emotions. For example, if the user is nervous, navigation with high urgency may be given priority. Also, if the user is relaxed, normal navigation may be given priority. Furthermore, if the user is in a hurry, navigation requiring a quick response may be given priority. In this way, more appropriate navigation can be provided by determining the priority of navigation according to the user's emotions.

[0115] The process flow of the second embodiment will be briefly described below.

[0116] Step 1: The reception unit receives user instructions. The user instructions include voice instructions, touch operations, gestures, etc. For example, the reception unit receives the user's voice instructions using voice recognition technology, receives the user's touch operations using a touch panel, and recognizes the user's gestures using a camera. Step 2: The operating unit performs an operation based on the instruction received by the receiving unit. The operation includes movement, operation, notification, etc. For example, the operating unit moves the assistance dog robot system using a motor, lifts and operates objects using an arm, and notifies the user using a speaker. Step 3: The control unit controls the action performed by the operating unit. Control includes starting and stopping the action, adjusting the speed, etc. For example, the control unit adjusts the speed of the motor of the operating unit, controls the movement of the arm, and adjusts the volume of the speaker. Step 4: The navigation unit performs navigation based on the operations controlled by the control unit. Navigation includes route guidance, destination setting, etc. For example, the navigation unit identifies the user's current location using GPS technology, provides route guidance to the destination, connects to the Internet to obtain real-time traffic and weather information, learns the user's behavioral patterns, and suggests the optimal route.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0118] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0119] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0120] Each of the multiple elements including the above-mentioned reception unit, operation unit, control unit, and navigation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives user instructions using the voice recognition technology, touch panel, and camera of the smart device 14. The operation unit performs operations using the motor, arm, and speaker of the smart device 14. The control unit controls the operation of the operation unit by the control unit 46A of the smart device 14. The navigation unit performs navigation using the GPS technology and Internet connection function of the smart device 14. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 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.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] 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.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0128] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] 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.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0131] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0135] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements including the above-mentioned reception unit, operation unit, control unit, and navigation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives a user's instruction using the voice recognition technology, touch panel, and camera of the smart glasses 214. The operation unit performs operations using the motor, arm, and speaker of the smart glasses 214. The control unit controls the operation of the operation unit by the control unit 46A of the smart glasses 214. The navigation unit performs navigation using the GPS technology and Internet connection function of the smart glasses 214. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] 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.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0144] 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.

[0145] 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.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0147] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0151] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements including the above-mentioned reception unit, operation unit, control unit, and navigation unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit receives user instructions using the voice recognition technology, touch panel, and camera of the headset type terminal 314. The operation unit performs operations using the motors, arms, and speakers of the headset type terminal 314. The control unit controls the operation of the operation unit by the control unit 46A of the headset type terminal 314. The navigation unit performs navigation using the GPS technology and Internet connection function of the headset type terminal 314. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0155] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] 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.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0160] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0161] 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.

[0162] 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.

[0163] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0164] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0165] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0166] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0168] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0169] Each of the multiple elements including the reception unit, operation unit, control unit, and navigation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives user instructions using the voice recognition technology, touch panel, and camera of the robot 414. The operation unit performs operations using the motors, arms, and speakers of the robot 414. The control unit controls the operation of the operation unit by the control unit 46A of the robot 414. The navigation unit performs navigation using the GPS technology and Internet connection function of the robot 414. The correspondence between each unit and the device and control unit is not limited to the above example, and various changes are possible.

[0170] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0171] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0172] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0173] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0174] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0175] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0176] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0177] 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.

[0178] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0179] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0180] 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.

[0181] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0182] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0183] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0184] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0185] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0186] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0187] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0188] (Appendix 1) A system that functions as a guide dog robot, A reception unit that receives an instruction from a user; an operation unit that performs an operation based on the instruction received by the reception unit; A control unit that controls the operation performed by the operation unit; A navigation unit that performs navigation based on the operation controlled by the control unit. A system characterized by: (Appendix 2) The reception unit is Equipped with voice recognition function 2. The system of claim 1, (Appendix 3) The operation unit includes: Equipped with sensors to detect obstacles 2. The system of claim 1, (Appendix 4) The navigation unit includes: Equipped with GPS function 2. The system of claim 1, (Appendix 5) The navigation unit includes: Connected to the Internet to obtain traffic and weather information 2. The system of claim 1, (Appendix 6) The operation unit includes: Equipped with a function to monitor the user's health condition 2. The system of claim 1, (Appendix 7) The control unit is Learn user behavior patterns and provide support 2. The system of claim 1, (Appendix 8) The reception unit is Equipped with the ability to recognize sign language and gestures 2. The system of claim 1, (Appendix 9) The control unit is Automatically calls an ambulance in case of an emergency 2. The system of claim 1, (Appendix 10) The reception unit is To estimate a user's emotion and adjust a method of receiving an instruction based on the estimated user's emotion 2. The system of claim 1, (Appendix 11) The reception unit is Analyze the user's past instruction history and select the method of accepting instructions 2. The system of claim 1, (Appendix 12) The reception unit is When receiving instructions, filtering is performed based on the user's current situation and environment. 2. The system of claim 1, (Appendix 13) The reception unit is A user's emotion is estimated, and a priority order of instructions to be accepted is determined based on the estimated user's emotion. 2. The system of claim 1, (Appendix 14) The reception unit is When accepting instructions, the instructions are given priority in consideration of the user's geographical location information. 2. The system of claim 1, (Appendix 15) The reception unit is Upon receiving an instruction, analyze the user's social media activity and receive relevant instructions. 2. The system of claim 1, (Appendix 16) The operation unit includes: Inferring a user's emotion and adjusting a method of operation based on the estimated user's emotion 2. The system of claim 1, (Appendix 17) The operation unit includes: During operation, it applies avoidance algorithms depending on the type of obstacle. 2. The system of claim 1, (Appendix 18) The operation unit includes: During operation, the user's health condition is monitored and appropriate operations are selected. 2. The system of claim 1, (Appendix 19) The operation unit includes: Estimate the user's emotions and prioritize actions based on the estimated user's emotions. 2. The system of claim 1, (Appendix 20) The operation unit includes: When performing an action, the action is selected taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 21) The operation unit includes: Upon operation, analyze the user's social media activity and take relevant action 2. The system of claim 1, (Appendix 22) The control unit is To estimate a user's emotion and adjust a control method of an action based on the estimated user's emotion. 2. The system of claim 1, (Appendix 23) The control unit is When controlling, the system learns the user's behavioral patterns and selects the control method. 2. The system of claim 1, (Appendix 24) The control unit is When in control, customize how you respond to emergencies 2. The system of claim 1, (Appendix 25) The control unit is Estimate the user's emotions and determine control priorities based on the estimated user's emotions. 2. The system of claim 1, (Appendix 26) The control unit is When controlling, the control method is selected taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 27) The control unit is During control, analyze users' social media activities and take relevant controls 2. The system of claim 1, (Appendix 28) The navigation unit includes: Inferring a user's emotions and adjusting a navigation method based on the estimated user's emotions 2. The system of claim 1, (Appendix 29) The navigation unit includes: When navigating, route selection is performed by referring to past navigation data. 2. The system of claim 1, (Appendix 30) The navigation unit includes: When navigating, traffic and weather information is obtained in real time to provide the optimal route. 2. The system of claim 1, (Appendix 31) The navigation unit includes: Infer user emotions and determine navigation priorities based on the inferred user emotions 2. The system of claim 1, (Appendix 32) The navigation unit includes: When navigating, route selection takes into account the user's geographic location 2. The system of claim 1, (Appendix 33) The navigation unit includes: Analyze your social media activity during navigation to provide relevant navigation information 2. The system of claim 1, [Explanation of symbols]

[0189] 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 system that functions as a guide dog robot, A reception unit that receives an instruction from a user; an operation unit that performs an operation based on the instruction received by the reception unit; A control unit that controls the operation performed by the operation unit; A navigation unit that performs navigation based on the operation controlled by the control unit. A system characterized by:

2. The reception unit is Equipped with voice recognition function 2. The system of claim 1 .

3. The operation unit includes: Equipped with sensors to detect obstacles 2. The system of claim 1 .

4. The navigation unit includes: Equipped with GPS function 2. The system of claim 1 .

5. The navigation unit includes: Connected to the Internet to obtain traffic and weather information 2. The system of claim 1 .

6. The operation unit includes: Equipped with a function to monitor the user's health condition 2. The system of claim 1 .

7. The control unit is Learn user behavior patterns and provide support 2. The system of claim 1 .

8. The reception unit is Equipped with the ability to recognize sign language and gestures 2. The system of claim 1 .

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