System

Abstract

An object of a system according to an embodiment is to efficiently eliminate an operation error caused by an obstacle in an automatic cleaning robot.SOLUTION: A system according to an embodiment includes an obstacle detection unit, an operation error monitoring unit, a countermeasure generation unit, and an operation execution unit. The obstacle detection unit detects an obstacle. The operation error monitoring unit monitors an operation error based on the obstacle detected by the obstacle detection unit. The countermeasure generation unit generates a countermeasure for eliminating the operating error detected by the operating error monitoring unit. The operation execution unit executes an operation based on the countermeasure generated by the countermeasure generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In conventional technology, when an automatic cleaning robot experiences an operational error due to an obstacle, it is difficult to efficiently resolve the error.

[0005] The system according to the embodiment aims to efficiently eliminate operational errors caused by obstacles in an automatic cleaning robot. [Means for solving the problem]

[0006] A system according to an embodiment includes an obstacle detection unit, an operation error monitoring unit, a countermeasure generation unit, and an operation execution unit. The obstacle detection unit detects obstacles. The operation error monitoring unit monitors operation errors based on the obstacles detected by the obstacle detection unit. The countermeasure generation unit generates countermeasures for resolving the operation errors detected by the operation error monitoring unit. The operation execution unit executes operations based on the countermeasures generated by the countermeasure generation unit. [Effects of the Invention]

[0007] The system according to the embodiment enables the automatic cleaning robot to efficiently eliminate operation errors caused by obstacles. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The automatic cleaning robot according to the embodiment of the present invention is a system that utilizes generative AI to eliminate operational errors, particularly immobility due to obstacles, thereby enabling the automatic cleaning robot to efficiently eliminate operational errors and prevent immobility due to obstacles.

[0029] An automatic cleaning robot according to an embodiment includes an obstacle detection unit, an operation error monitoring unit, a countermeasure generation unit, and an operation execution unit. The obstacle detection unit detects obstacles. For example, the obstacle detection unit uses a sensor to monitor the surrounding environment and detect obstacles. The obstacle detection unit can also visually detect obstacles using a camera. For example, the obstacle detection unit can detect obstacles even in the dark using an infrared sensor. The operation error monitoring unit monitors operation errors based on the obstacles detected by the obstacle detection unit. For example, if the robot gets stuck under furniture, the operation error monitoring unit recognizes the situation. The operation error monitoring unit also detects an operation error when the robot is trapped by multiple obstacles. The countermeasure generation unit generates a countermeasure to resolve the operation error detected by the operation error monitoring unit. For example, if the robot gets stuck under furniture, the countermeasure generation unit instructs the robot to retreat. If the robot is trapped by multiple obstacles, the countermeasure generation unit calculates an optimal avoidance route and guides the robot along that route. The operation execution unit executes an operation based on the countermeasure generated by the countermeasure generation unit. For example, a robot that receives an instruction to move backward will move backward to escape from an obstacle. Also, a robot that receives an instruction to move along an avoidance route will avoid the obstacle by following that route. In this way, the automatic cleaning robot according to the embodiment can efficiently resolve operation errors and prevent immobilization due to obstacles.

[0030] The obstacle detection unit can identify the type of obstacle and propose the optimal avoidance method in real time. For example, the obstacle detection unit uses a generative AI to analyze sensor data and identify the type of obstacle. For example, it can distinguish between different obstacles such as furniture, cables, and pets, and propose the optimal avoidance method for each. After identifying the type of obstacle, the obstacle detection unit references past avoidance data for that obstacle and proposes the optimal avoidance method in real time. For example, it can back up if the obstacle gets under furniture, or rotate if it gets tangled in a cable. The obstacle detection unit can also identify the type of obstacle and propose an avoidance method in real time based on that information. For example, it can stop if a pet approaches and wait until the pet moves away. This allows it to propose the optimal avoidance method according to the type of obstacle.

[0031] The motion error monitoring unit analyzes the robot's motion history and learns how it dealt with specific obstacles in the past, allowing it to improve its response in the future. For example, the generation AI analyzes the robot's motion history and learns how it dealt with specific obstacles in the past. For example, it learns how to avoid getting under furniture, so that it can respond quickly the next time it encounters the same situation. The motion error monitoring unit also stores obstacles that the robot has encountered in the past and how it dealt with them in a database, and the generation AI uses that data to improve its response in the future. For example, it learns how to avoid getting tangled in cables, so that it can avoid them more efficiently the next time. The motion error monitoring unit also analyzes the robot's motion history and learns how it dealt with specific obstacles. For example, it learns how to stop when a pet approaches, so that it can stop more quickly the next time. This allows it to learn past response methods and improve its response in the future.

[0032] The obstacle detection unit can detect obstacles using information other than vision by adding an audio or vibration sensor. For example, the generation AI uses an audio sensor to detect the sound of an obstacle. For example, it detects the sound of a pet barking or the sound of furniture moving, and identifies obstacles based on that. The generation AI also uses a vibration sensor to detect the vibration of an obstacle. For example, it detects the vibrations when the robot comes into contact with furniture, and identifies obstacles based on that. The obstacle detection unit can also detect obstacles using information other than vision by combining audio and vibration sensors. For example, it detects the sound of a pet barking with an audio sensor and the contact of furniture with a vibration sensor, which allows for more accurate identification of obstacles. This makes it possible to detect obstacles using information other than vision.

[0033] The operation error monitoring unit can explain the situation to the user by voice when the robot detects an obstacle and ask for instructions from the user. For example, when the generation AI detects an obstacle, the operation error monitoring unit can explain the situation to the user by voice. For example, it can ask the user, "It's gone under the furniture. Do you want to back up?". Also, when the robot detects an obstacle, the operation error monitoring unit can explain the situation to the user by voice and ask for instructions from the user. For example, it can ask the user, "It's tangled in a cable. Do you want to turn around and get out?". Also, the operation error monitoring unit adds a function to explain the situation to the user by voice and ask for instructions from the user when the generation AI detects an obstacle. For example, it can ask the user, "A pet is approaching. Do you want to stop?". This makes it possible to explain the situation to the user and ask for instructions.

[0034] The countermeasure generation unit can 3D map the position and shape of obstacles and calculate the optimal escape route in three dimensions. For example, the generation AI in the countermeasure generation unit 3D maps the position and shape of obstacles based on sensor data and calculates the optimal escape route. For example, if the robot gets under furniture, it will grasp the shape of the furniture in 3D and calculate the optimal escape route. The countermeasure generation unit also 3D maps the position and shape of obstacles using the generation AI and calculates the optimal escape route in three dimensions based on that information. For example, if the robot is surrounded by multiple obstacles, it will calculate the optimal escape route based on the 3D map. The countermeasure generation unit also 3D maps the position and shape of obstacles using the generation AI based on sensor data and calculates the optimal escape route in three dimensions. For example, if the robot gets tangled in a cable, it will grasp the shape of the cable in 3D and calculate the optimal escape route. This allows the position and shape of obstacles to be 3D mapped and the optimal escape route to be calculated.

[0035] The countermeasure generation unit can simulate multiple countermeasures when the robot makes a motion error and select the most effective countermeasure. For example, when the generation AI makes a motion error, the countermeasure generation unit simulates multiple countermeasures and selects the most effective countermeasure. For example, it simulates countermeasures such as moving backward, rotating, and stopping, and selects the optimal countermeasure. Also, when the robot makes a motion error, the countermeasure generation unit simulates multiple countermeasures and selects the most effective countermeasure. For example, if the robot gets under furniture, it simulates countermeasures such as moving backward, rotating, and stopping, and selects the optimal countermeasure. Also, when the generation AI makes a motion error, the countermeasure generation unit simulates multiple countermeasures and selects the most effective countermeasure. For example, if the robot gets tangled in a cable, it simulates countermeasures such as moving backward, rotating, and stopping, and selects the optimal countermeasure. In this way, multiple countermeasures can be simulated and the most effective countermeasure can be selected.

[0036] When generating a countermeasure to resolve a movement error, the countermeasure generation unit can cooperate with other robots or devices to propose a method of working together to avoid an obstacle. For example, when the generation AI generates a countermeasure to resolve a movement error, the countermeasure generation unit proposes a method of working together with other robots or devices to avoid an obstacle. For example, multiple robots cooperate to avoid an obstacle. The countermeasure generation unit also cooperates with other robots or devices to generate a countermeasure to resolve a movement error. For example, it cooperates with a smart home device to propose a method of avoiding an obstacle. When the generation AI generates a countermeasure to resolve a movement error, the countermeasure generation unit also cooperates with other robots or devices to propose a method of working together to avoid an obstacle. For example, the other robot moves the obstacle. This makes it possible to propose a method of working together with other robots or devices to avoid an obstacle.

[0037] When generating a countermeasure, the countermeasure generation unit can refer to the user's past instruction history and propose a countermeasure that matches the user's preferences. For example, when the generation AI generates a countermeasure, the countermeasure generation unit refers to the user's past instruction history and proposes a countermeasure that matches the user's preferences. For example, if a backward movement was instructed in the past, the countermeasure generation unit proposes backward movement. The countermeasure generation unit also refers to the user's past instruction history, and the generation AI generates a countermeasure. For example, if a rotation was instructed in the past, the countermeasure generation unit proposes rotation. The countermeasure generation unit also refers to the user's past instruction history, and proposes a countermeasure that matches the user's preferences. For example, if a stop was instructed in the past, the countermeasure generation unit proposes stop movement. In this way, the user's past instruction history can be referred to and a countermeasure that matches the user's preferences.

[0038] The action execution unit can try out multiple action patterns to resolve a robot's action error and select the most effective action. For example, the action execution unit allows the generation AI to try out multiple action patterns to resolve a robot's action error and select the most effective action. For example, actions such as moving backward, turning, and stopping are tried and the optimal action is selected. Also, the action execution unit allows the generation AI to try out multiple action patterns to resolve a robot's action error and select the most effective action. For example, if the robot gets under furniture, actions such as moving backward, turning, and stopping are tried and the optimal action is selected. Also, the action execution unit allows the generation AI to try out multiple action patterns to resolve a robot's action error and select the most effective action. For example, if the robot gets tangled in a cable, actions such as moving backward, turning, and stopping are tried and the optimal action is selected. In this way, multiple action patterns can be tried and the most effective action can be selected.

[0039] The action execution unit allows the generation AI to reevaluate the surrounding environment and execute an action to prevent the recurrence of the action error when the robot resolves a movement error. For example, the action execution unit allows the generation AI to reevaluate the surrounding environment and execute an action to prevent the recurrence of the movement error when the robot resolves a movement error. For example, the action execution unit reevaluates the furniture arrangement and executes an optimal action. Furthermore, the action execution unit allows the generation AI to reevaluate the surrounding environment and execute an action to prevent the recurrence of the movement error when the robot resolves a movement error. For example, the action execution unit reevaluates the surrounding environment and executes an action to prevent the recurrence of the movement error when the robot resolves a movement error. For example, the action execution unit reevaluates the surrounding environment and executes an optimal action. In this way, the generation AI can reevaluate the surrounding environment and execute an action to prevent the recurrence of the movement error.

[0040] The operation execution unit can execute a method for the robot to cooperate with other robots or devices and cooperate to avoid obstacles when resolving an operation error. For example, the operation execution unit executes a method for the robot to cooperate with other robots or devices and cooperate to avoid obstacles when resolving an operation error, such as when the generation AI executes a method for the robot to cooperate with other robots or devices and cooperate to avoid obstacles. For example, multiple robots cooperate to avoid obstacles. The operation execution unit also cooperates with other robots or devices to resolve the operation error. For example, the operation execution unit executes a method for the robot to cooperate with other robots or devices and cooperate to avoid obstacles when the generation AI executes a method for the robot to cooperate with other robots or devices and cooperate to avoid obstacles. For example, the other robot moves the obstacle. This allows the operation execution unit to execute a method for the robot to cooperate with other robots or devices and avoid obstacles.

[0041] The action execution unit, when the generation AI executes an action to resolve a motion error, explains the situation to the user aloud, thereby increasing the user's sense of security. For example, when the generation AI causes the robot to resolve a motion error, the action execution unit explains the situation to the user aloud. For example, it may explain to the user, "It's gone under the furniture. Back up." In addition, when the robot resolves a motion error, the action execution unit may explain the situation to the user aloud, thereby increasing the user's sense of security. For example, it may explain to the user, "It's tangled in a cable. Rotate to escape." In addition, when the generation AI causes the robot to resolve a motion error, the action execution unit may explain the situation to the user aloud, thereby increasing the user's sense of security. For example, it may explain to the user, "A pet is approaching. Stop." In this way, it is possible to explain the situation to the user and increase the user's sense of security.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The automatic cleaning robot may further include a voice recognition unit. The voice recognition unit receives voice instructions from the user and controls the robot's operation based on the instructions. For example, if the user commands "back up," the robot will back up. If the user commands "stop," the robot will stop where it is. Furthermore, the voice recognition unit can analyze the user's voice instructions and execute a combination of multiple instructions. For example, if the user commands "back up and then turn," the robot will back up and then turn. This allows the user to directly control the robot's operation by voice.

[0044] The automatic cleaning robot may further include a temperature sensor unit. The temperature sensor unit detects the ambient temperature and adjusts the robot's operation if an abnormal temperature change occurs. For example, if the robot approaches a high-temperature location, the temperature sensor unit stops the robot based on that information. Similarly, the robot's operation can be adjusted if the robot approaches a low-temperature location. Furthermore, the temperature sensor unit can monitor the internal temperature of the robot and limit its operation to prevent overheating. This allows the robot to operate safely.

[0045] The automatic cleaning robot may further include a humidity sensor unit. The humidity sensor unit detects the ambient humidity and adjusts the robot's operation in high-humidity locations. For example, in high-humidity locations, the robot's speed may be slowed down to prevent accidents on slippery floors. In addition, in low-humidity locations, the robot's operation may be adjusted to prevent static electricity from occurring. Furthermore, the humidity sensor unit may monitor the internal humidity of the robot and stop operation if the internal humidity is abnormally high. This allows the robot to operate safely according to the humidity.

[0046] The automatic cleaning robot can further include a light sensor unit. The light sensor unit detects the brightness of the surroundings and adjusts the robot's operation in dark places. For example, the robot's speed can be slowed down in dark places to reduce the risk of colliding with obstacles. In addition, the robot's operation can be returned to normal in bright places. Furthermore, the light sensor unit can monitor the amount of light inside the robot and automatically turn on the light when it becomes dark inside. This allows the robot to operate safely according to the brightness of the surroundings.

[0047] The automatic cleaning robot can further include a vibration sensor unit. The vibration sensor unit detects vibrations generated while the robot is operating, and adjusts the operation of the robot if abnormal vibrations are detected. For example, if the robot climbs over a step, the vibration sensor unit stops the robot based on that information. Similarly, the operation of the robot can be adjusted if the robot is in an unstable location. Furthermore, the vibration sensor unit can also monitor vibrations inside the robot, and can stop operation if the internal vibrations are abnormally large. This allows the robot to operate safely in accordance with the vibrations.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The obstacle detection unit detects obstacles. For example, it uses sensors to monitor the surrounding environment and detect obstacles. It can also detect obstacles visually using a camera. Furthermore, it can use an infrared sensor to detect obstacles even in dark places. Step 2: The operation error monitoring unit monitors operation errors based on the obstacles detected by the obstacle detection unit. For example, an operation error is detected when the robot gets stuck under furniture or when it is surrounded by multiple obstacles and cannot move. Step 3: The countermeasure generation unit generates countermeasures to resolve the operation errors detected by the operation error monitoring unit. For example, if the robot gets stuck under furniture, it will issue an instruction to retreat, and if it gets stuck surrounded by multiple obstacles, it will calculate the optimal avoidance route and guide the robot along that route. Step 4: The action execution unit executes an action based on the countermeasure generated by the countermeasure generation unit. For example, a robot that has received an instruction to retreat will retreat and escape from the obstacle, and a robot that has received an instruction to move along an avoidance route will avoid the obstacle by following that route.

[0050] (Example 2) The automatic cleaning robot according to the embodiment of the present invention is a system that utilizes generative AI to eliminate operational errors, particularly immobility due to obstacles, thereby enabling the automatic cleaning robot to efficiently eliminate operational errors and prevent immobility due to obstacles.

[0051] An automatic cleaning robot according to an embodiment includes an obstacle detection unit, an operation error monitoring unit, a countermeasure generation unit, and an operation execution unit. The obstacle detection unit detects obstacles. For example, the obstacle detection unit uses a sensor to monitor the surrounding environment and detect obstacles. The obstacle detection unit can also visually detect obstacles using a camera. For example, the obstacle detection unit can detect obstacles even in the dark using an infrared sensor. The operation error monitoring unit monitors operation errors based on the obstacles detected by the obstacle detection unit. For example, if the robot gets stuck under furniture, the operation error monitoring unit recognizes the situation. The operation error monitoring unit also detects an operation error when the robot is trapped by multiple obstacles. The countermeasure generation unit generates a countermeasure to resolve the operation error detected by the operation error monitoring unit. For example, if the robot gets stuck under furniture, the countermeasure generation unit instructs the robot to retreat. If the robot is trapped by multiple obstacles, the countermeasure generation unit calculates an optimal avoidance route and guides the robot along that route. The operation execution unit executes an operation based on the countermeasure generated by the countermeasure generation unit. For example, a robot that receives an instruction to move backward will move backward to escape from an obstacle. Also, a robot that receives an instruction to move along an avoidance route will avoid the obstacle by following that route. In this way, the automatic cleaning robot according to the embodiment can efficiently resolve operation errors and prevent immobilization due to obstacles.

[0052] The obstacle detection unit can identify the type of obstacle and propose the optimal avoidance method in real time. For example, the obstacle detection unit uses a generative AI to analyze sensor data and identify the type of obstacle. For example, it can distinguish between different obstacles such as furniture, cables, and pets, and propose the optimal avoidance method for each. After identifying the type of obstacle, the obstacle detection unit references past avoidance data for that obstacle and proposes the optimal avoidance method in real time. For example, it can back up if the obstacle gets under furniture, or rotate if it gets tangled in a cable. The obstacle detection unit can also identify the type of obstacle and propose an avoidance method in real time based on that information. For example, it can stop if a pet approaches and wait until the pet moves away. This allows it to propose the optimal avoidance method according to the type of obstacle.

[0053] The motion error monitoring unit analyzes the robot's motion history and learns how it dealt with specific obstacles in the past, allowing it to improve its response in the future. For example, the generation AI analyzes the robot's motion history and learns how it dealt with specific obstacles in the past. For example, it learns how to avoid getting under furniture, so that it can respond quickly the next time it encounters the same situation. The motion error monitoring unit also stores obstacles that the robot has encountered in the past and how it dealt with them in a database, and the generation AI uses that data to improve its response in the future. For example, it learns how to avoid getting tangled in cables, so that it can avoid them more efficiently the next time. The motion error monitoring unit also analyzes the robot's motion history and learns how it dealt with specific obstacles. For example, it learns how to stop when a pet approaches, so that it can stop more quickly the next time. This allows it to learn past response methods and improve its response in the future.

[0054] The motion error monitoring unit uses the emotion estimation function to analyze the emotions the user feels toward the robot's motion and can generate motion patterns that do not cause stress to the user. For example, the generation AI of the motion error monitoring unit uses the emotion estimation function to analyze the emotions the user feels toward the robot's motion in real time. For example, if the user feels stressed, the robot's motion is adjusted to reduce the stress. The motion error monitoring unit also uses the emotion estimation function to analyze the emotions the user feels toward the robot's motion and generates motion patterns that do not cause stress to the user. For example, if the user feels anxious when the robot approaches an obstacle, the robot's motion is slowed down. The motion error monitoring unit also uses the emotion estimation function to analyze the emotions the user feels toward the robot's motion and generates motion patterns that do not cause stress to the user. For example, if the user is surprised when the robot comes into contact with an obstacle, the robot will operate more carefully next time. This makes it possible to generate motion patterns that do not cause stress to the user.

[0055] The obstacle detection unit can detect obstacles using information other than vision by adding an audio or vibration sensor. For example, the generation AI uses an audio sensor to detect the sound of an obstacle. For example, it detects the sound of a pet barking or the sound of furniture moving, and identifies obstacles based on that. The generation AI also uses a vibration sensor to detect the vibration of an obstacle. For example, it detects the vibrations when the robot comes into contact with furniture, and identifies obstacles based on that. The obstacle detection unit can also detect obstacles using information other than vision by combining audio and vibration sensors. For example, it detects the sound of a pet barking with an audio sensor and the contact of furniture with a vibration sensor, which allows for more accurate identification of obstacles. This makes it possible to detect obstacles using information other than vision.

[0056] The operation error monitoring unit can explain the situation to the user by voice when the robot detects an obstacle and ask for instructions from the user. For example, when the generation AI detects an obstacle, the operation error monitoring unit can explain the situation to the user by voice. For example, it can ask the user, "It's gone under the furniture. Do you want to back up?". Also, when the robot detects an obstacle, the operation error monitoring unit can explain the situation to the user by voice and ask for instructions from the user. For example, it can ask the user, "It's tangled in a cable. Do you want to turn around and get out?". Also, the operation error monitoring unit adds a function to explain the situation to the user by voice and ask for instructions from the user when the generation AI detects an obstacle. For example, it can ask the user, "A pet is approaching. Do you want to stop?". This makes it possible to explain the situation to the user and ask for instructions.

[0057] The operation error monitoring unit uses the emotion estimation function to monitor the user's emotion in real time when the robot approaches an obstacle, and can adjust the robot's operation if the user feels anxious. For example, the generation AI in the operation error monitoring unit uses the emotion estimation function to monitor the user's emotion in real time when the robot approaches an obstacle. For example, if the user feels anxious, the operation error monitoring unit slows down the robot's operation. The operation error monitoring unit also uses the emotion estimation function to monitor the user's emotion in real time when the robot approaches an obstacle, and can adjust the robot's operation if the user feels anxious. For example, the robot stops. The operation error monitoring unit also uses the emotion estimation function to monitor the user's emotion in real time when the robot approaches an obstacle, and can adjust the robot's operation if the user feels anxious. For example, the robot retreats. This allows the robot's operation to be adjusted if the user feels anxious.

[0058] The countermeasure generation unit can 3D map the position and shape of obstacles and calculate the optimal escape route in three dimensions. For example, the generation AI in the countermeasure generation unit 3D maps the position and shape of obstacles based on sensor data and calculates the optimal escape route. For example, if the robot gets under furniture, it will grasp the shape of the furniture in 3D and calculate the optimal escape route. The countermeasure generation unit also 3D maps the position and shape of obstacles using the generation AI and calculates the optimal escape route in three dimensions based on that information. For example, if the robot is surrounded by multiple obstacles, it will calculate the optimal escape route based on the 3D map. The countermeasure generation unit also 3D maps the position and shape of obstacles using the generation AI based on sensor data and calculates the optimal escape route in three dimensions. For example, if the robot gets tangled in a cable, it will grasp the shape of the cable in 3D and calculate the optimal escape route. This allows the position and shape of obstacles to be 3D mapped and the optimal escape route to be calculated.

[0059] The countermeasure generation unit can simulate multiple countermeasures when the robot makes a motion error and select the most effective countermeasure. For example, when the generation AI makes a motion error, the countermeasure generation unit simulates multiple countermeasures and selects the most effective countermeasure. For example, it simulates countermeasures such as moving backward, rotating, and stopping, and selects the optimal countermeasure. Also, when the robot makes a motion error, the countermeasure generation unit simulates multiple countermeasures and selects the most effective countermeasure. For example, if the robot gets under furniture, it simulates countermeasures such as moving backward, rotating, and stopping, and selects the optimal countermeasure. Also, when the generation AI makes a motion error, the countermeasure generation unit simulates multiple countermeasures and selects the most effective countermeasure. For example, if the robot gets tangled in a cable, it simulates countermeasures such as moving backward, rotating, and stopping, and selects the optimal countermeasure. In this way, multiple countermeasures can be simulated and the most effective countermeasure can be selected.

[0060] The countermeasure generation unit uses the emotion estimation function to analyze the emotions the user feels in response to the robot's operation error, and can prioritize generating countermeasures that will reassure the user. In the countermeasure generation unit, for example, the generation AI uses the emotion estimation function to analyze the emotions the user feels in response to the robot's operation error in real time. For example, if the user feels anxious, the countermeasure generation unit quickly generates a countermeasure. In addition, the countermeasure generation unit uses the emotion estimation function to analyze the emotions the user feels in response to the robot's operation error, and prioritize generating countermeasures that will reassure the user. For example, if the user feels stressed, the countermeasure generation unit quickly generates a countermeasure. In addition, the generation AI uses the emotion estimation function to analyze the emotions the user feels in response to the robot's operation error, and prioritize generating countermeasures that will reassure the user. For example, if the user is surprised, the countermeasure is quickly generated. This makes it possible to prioritize generating countermeasures that will reassure the user.

[0061] When generating a countermeasure to resolve a movement error, the countermeasure generation unit can cooperate with other robots or devices to propose a method of working together to avoid an obstacle. For example, when the generation AI generates a countermeasure to resolve a movement error, the countermeasure generation unit proposes a method of working together with other robots or devices to avoid an obstacle. For example, multiple robots cooperate to avoid an obstacle. The countermeasure generation unit also cooperates with other robots or devices to generate a countermeasure to resolve a movement error. For example, it cooperates with a smart home device to propose a method of avoiding an obstacle. When the generation AI generates a countermeasure to resolve a movement error, the countermeasure generation unit also cooperates with other robots or devices to propose a method of working together to avoid an obstacle. For example, the other robot moves the obstacle. This makes it possible to propose a method of working together with other robots or devices to avoid an obstacle.

[0062] When generating a countermeasure, the countermeasure generation unit can refer to the user's past instruction history and propose a countermeasure that matches the user's preferences. For example, when the generation AI generates a countermeasure, the countermeasure generation unit refers to the user's past instruction history and proposes a countermeasure that matches the user's preferences. For example, if a backward movement was instructed in the past, the countermeasure generation unit proposes backward movement. The countermeasure generation unit also refers to the user's past instruction history, and the generation AI generates a countermeasure. For example, if a rotation was instructed in the past, the countermeasure generation unit proposes rotation. The countermeasure generation unit also refers to the user's past instruction history, and proposes a countermeasure that matches the user's preferences. For example, if a stop was instructed in the past, the countermeasure generation unit proposes stop movement. In this way, the user's past instruction history can be referred to and a countermeasure that matches the user's preferences.

[0063] The countermeasure generation unit uses the emotion estimation function to monitor in real time the emotions the user feels in response to the robot's operation error, and can quickly generate countermeasures if the user feels anxious. In the countermeasure generation unit, for example, the generation AI uses the emotion estimation function to monitor in real time the emotions the user feels in response to the robot's operation error. For example, if the user feels anxious, the countermeasure generation unit quickly generates countermeasures. In addition, the countermeasure generation unit uses the emotion estimation function to monitor in real time the emotions the user feels in response to the robot's operation error, and quickly generates countermeasures if the user feels anxious. For example, if the user feels stressed, the countermeasure generation unit uses the emotion estimation function to monitor in real time the emotions the user feels in response to the robot's operation error, and quickly generates countermeasures if the user feels anxious. For example, if the user is surprised, the countermeasure is quickly generated. In this way, it is possible to quickly generate countermeasures if the user feels anxious.

[0064] The action execution unit can try out multiple action patterns to resolve a robot's action error and select the most effective action. For example, the action execution unit allows the generation AI to try out multiple action patterns to resolve a robot's action error and select the most effective action. For example, actions such as moving backward, turning, and stopping are tried and the optimal action is selected. Also, the action execution unit allows the generation AI to try out multiple action patterns to resolve a robot's action error and select the most effective action. For example, if the robot gets under furniture, actions such as moving backward, turning, and stopping are tried and the optimal action is selected. Also, the action execution unit allows the generation AI to try out multiple action patterns to resolve a robot's action error and select the most effective action. For example, if the robot gets tangled in a cable, actions such as moving backward, turning, and stopping are tried and the optimal action is selected. In this way, multiple action patterns can be tried and the most effective action can be selected.

[0065] The action execution unit allows the generation AI to reevaluate the surrounding environment and execute an action to prevent the recurrence of the action error when the robot resolves a movement error. For example, the action execution unit allows the generation AI to reevaluate the surrounding environment and execute an action to prevent the recurrence of the movement error when the robot resolves a movement error. For example, the action execution unit reevaluates the furniture arrangement and executes an optimal action. Furthermore, the action execution unit allows the generation AI to reevaluate the surrounding environment and execute an action to prevent the recurrence of the movement error when the robot resolves a movement error. For example, the action execution unit reevaluates the surrounding environment and executes an action to prevent the recurrence of the movement error when the robot resolves a movement error. For example, the action execution unit reevaluates the surrounding environment and executes an optimal action. In this way, the generation AI can reevaluate the surrounding environment and execute an action to prevent the recurrence of the movement error.

[0066] The action execution unit uses the emotion estimation function to analyze the emotions the user feels about the robot's operation error being resolved, and can prioritize executing actions that will make the user feel at ease. In the action execution unit, for example, the generation AI uses the emotion estimation function to analyze the emotions the user feels about the robot's operation error being resolved in real time. For example, if the user feels anxious, the action execution unit quickly executes an action. In addition, the action execution unit uses the emotion estimation function to analyze the emotions the user feels about the robot's operation error being resolved, and prioritizes executing actions that will make the user feel at ease. For example, if the user feels stressed, the action execution unit quickly executes an action. In addition, the generation AI uses the emotion estimation function to analyze the emotions the user feels about the robot's operation error being resolved, and prioritizes executing actions that will make the user feel at ease. For example, if the user is surprised, the action is quickly executed. This allows the action that will make the user feel at ease to be prioritized.

[0067] The operation execution unit can execute a method for the robot to cooperate with other robots or devices and cooperate to avoid obstacles when resolving an operation error. For example, the operation execution unit executes a method for the robot to cooperate with other robots or devices and cooperate to avoid obstacles when resolving an operation error, such as when the generation AI executes a method for the robot to cooperate with other robots or devices and cooperate to avoid obstacles. For example, multiple robots cooperate to avoid obstacles. The operation execution unit also cooperates with other robots or devices to resolve the operation error. For example, the operation execution unit executes a method for the robot to cooperate with other robots or devices and cooperate to avoid obstacles when the generation AI executes a method for the robot to cooperate with other robots or devices and cooperate to avoid obstacles. For example, the other robot moves the obstacle. This allows the operation execution unit to execute a method for the robot to cooperate with other robots or devices and avoid obstacles.

[0068] The action execution unit, when the generation AI executes an action to resolve a motion error, explains the situation to the user aloud, thereby increasing the user's sense of security. For example, when the generation AI causes the robot to resolve a motion error, the action execution unit explains the situation to the user aloud. For example, it may explain to the user, "It's gone under the furniture. Back up." In addition, when the robot resolves a motion error, the action execution unit may explain the situation to the user aloud, thereby increasing the user's sense of security. For example, it may explain to the user, "It's tangled in a cable. Rotate to escape." In addition, when the generation AI causes the robot to resolve a motion error, the action execution unit may explain the situation to the user aloud, thereby increasing the user's sense of security. For example, it may explain to the user, "A pet is approaching. Stop." In this way, it is possible to explain the situation to the user and increase the user's sense of security.

[0069] The action execution unit uses the emotion estimation function to monitor in real time the emotions the user feels about the robot's operation error being resolved, and can adjust the action if the user feels anxious. For example, the action execution unit uses the emotion estimation function to monitor in real time the emotions the user feels about the robot's operation error being resolved. For example, if the user feels anxious, it slows down the robot's operation. The action execution unit also uses the emotion estimation function to monitor in real time the emotions the user feels about the robot's operation error being resolved, and adjusts the action if the user feels anxious. For example, the robot stops. The action execution unit also uses the emotion estimation function to monitor in real time the emotions the user feels about the robot's operation error being resolved, and adjusts the action if the user feels anxious. For example, the robot moves backward. This makes it possible to adjust the action if the user feels anxious.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The automatic cleaning robot may further include a voice recognition unit. The voice recognition unit receives voice instructions from the user and controls the robot's operation based on the instructions. For example, if the user commands "back up," the robot will back up. If the user commands "stop," the robot will stop where it is. Furthermore, the voice recognition unit can analyze the user's voice instructions and execute a combination of multiple instructions. For example, if the user commands "back up and then turn," the robot will back up and then turn. This allows the user to directly control the robot's operation by voice.

[0072] The automatic cleaning robot may further include a temperature sensor unit. The temperature sensor unit detects the ambient temperature and adjusts the robot's operation if an abnormal temperature change occurs. For example, if the robot approaches a high-temperature location, the temperature sensor unit stops the robot based on that information. Similarly, the robot's operation can be adjusted if the robot approaches a low-temperature location. Furthermore, the temperature sensor unit can monitor the internal temperature of the robot and limit its operation to prevent overheating. This allows the robot to operate safely.

[0073] The automatic cleaning robot may further include a humidity sensor unit. The humidity sensor unit detects the ambient humidity and adjusts the robot's operation in high-humidity locations. For example, in high-humidity locations, the robot's speed may be slowed down to prevent accidents on slippery floors. In addition, in low-humidity locations, the robot's operation may be adjusted to prevent static electricity from occurring. Furthermore, the humidity sensor unit may monitor the internal humidity of the robot and stop operation if the internal humidity is abnormally high. This allows the robot to operate safely according to the humidity.

[0074] The automatic cleaning robot can further include a light sensor unit. The light sensor unit detects the brightness of the surroundings and adjusts the robot's operation in dark places. For example, the robot's speed can be slowed down in dark places to reduce the risk of colliding with obstacles. In addition, the robot's operation can be returned to normal in bright places. Furthermore, the light sensor unit can monitor the amount of light inside the robot and automatically turn on the light when it becomes dark inside. This allows the robot to operate safely according to the brightness of the surroundings.

[0075] The automatic cleaning robot can further include a vibration sensor unit. The vibration sensor unit detects vibrations generated while the robot is operating, and adjusts the operation of the robot if abnormal vibrations are detected. For example, if the robot climbs over a step, the vibration sensor unit stops the robot based on that information. Similarly, the operation of the robot can be adjusted if the robot is in an unstable location. Furthermore, the vibration sensor unit can also monitor vibrations inside the robot, and can stop operation if the internal vibrations are abnormally large. This allows the robot to operate safely in accordance with the vibrations.

[0076] The automatic cleaning robot can further use the emotion estimation function to analyze the emotion the user feels toward the robot's actions and generate a motion pattern that brings joy to the user. For example, if the user feels joy toward the robot's actions, the robot can repeat those actions. Also, if the user feels excited toward the robot's actions, the robot can emphasize those actions. Furthermore, the emotion estimation function can be used to analyze the emotion the user feels toward the robot's actions and generate a motion pattern that brings joy to the user. For example, if the user feels joy when the robot avoids an obstacle, the robot can repeat that avoidance method. In this way, a motion pattern that brings joy to the user can be generated.

[0077] The automatic cleaning robot can further use the emotion estimation function to analyze the emotion the user feels toward the robot's actions and generate an action pattern that makes the user feel at ease. For example, if the user feels at ease with the robot's actions, the robot will repeat that action. On the other hand, if the user feels uneasy with the robot's actions, the robot can avoid that action. Furthermore, the emotion estimation function can analyze the emotion the user feels toward the robot's actions and generate an action pattern that makes the user feel at ease. For example, if the user feels at ease when the robot avoids an obstacle, the robot will repeat that avoidance method. In this way, an action pattern that makes the user feel at ease can be generated.

[0078] The automatic cleaning robot can further use the emotion estimation function to analyze the emotion the user feels toward the robot's actions and generate a motion pattern that will surprise the user. For example, if the user feels surprised by the robot's actions, the robot can repeat that motion. Also, if the user feels excited by the robot's actions, the robot can emphasize that motion. Furthermore, the emotion estimation function can be used to analyze the emotion the user feels toward the robot's actions and generate a motion pattern that will surprise the user. For example, if the user feels surprised when the robot avoids an obstacle, the robot can repeat that avoidance method. In this way, a motion pattern that will surprise the user can be generated.

[0079] The automatic cleaning robot can further use the emotion estimation function to analyze the emotion the user feels toward the robot's actions and generate a motion pattern that excites the user. For example, if the user feels excited about the robot's actions, the robot can repeat those actions. Also, if the user feels joy toward the robot's actions, the robot can emphasize those actions. Furthermore, the emotion estimation function can be used to analyze the emotion the user feels toward the robot's actions and generate a motion pattern that excites the user. For example, if the user feels excited when the robot avoids an obstacle, the robot can repeat that avoidance method. In this way, a motion pattern that excites the user can be generated.

[0080] The automatic cleaning robot can further use the emotion estimation function to analyze the emotion the user feels toward the robot's actions and generate an action pattern that satisfies the user. For example, if the user feels satisfied with the robot's actions, the robot can repeat those actions. Also, if the user feels joy toward the robot's actions, the robot can emphasize those actions. Furthermore, the emotion estimation function can analyze the emotion the user feels toward the robot's actions and generate an action pattern that satisfies the user. For example, if the user feels satisfied when the robot avoids an obstacle, the robot can repeat that avoidance method. In this way, an action pattern that satisfies the user can be generated.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The obstacle detection unit detects obstacles. For example, it uses sensors to monitor the surrounding environment and detect obstacles. It can also detect obstacles visually using a camera. Furthermore, it can use an infrared sensor to detect obstacles even in dark places. Step 2: The operation error monitoring unit monitors operation errors based on the obstacles detected by the obstacle detection unit. For example, an operation error is detected when the robot gets stuck under furniture or when it is surrounded by multiple obstacles and cannot move. Step 3: The countermeasure generation unit generates countermeasures to resolve the operation errors detected by the operation error monitoring unit. For example, if the robot gets stuck under furniture, it will issue an instruction to retreat, and if it gets stuck surrounded by multiple obstacles, it will calculate the optimal avoidance route and guide the robot along that route. Step 4: The action execution unit executes an action based on the countermeasure generated by the countermeasure generation unit. For example, a robot that has received an instruction to retreat will retreat and escape from the obstacle, and a robot that has received an instruction to move along an avoidance route will avoid the obstacle by following that route.

[0083] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0089] 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.

[0090] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0091] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0092] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0093] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0094] 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.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0097] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0099] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0104] 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.

[0105] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0107] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0108] 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.

[0109] 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.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0111] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0112] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0113] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] 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.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0124] 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.

[0125] 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.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0133] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0134] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0135] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0136] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0137] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0138] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0139] 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.

[0140] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0141] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0142] 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.

[0143] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0144] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0145] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0146] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0147] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0148] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0149] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0150] 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. In an automatic cleaning robot equipped with generative AI, an obstacle detection unit that detects an obstacle; an operation error monitoring unit that monitors an operation error based on the obstacle detected by the obstacle detection unit; a countermeasure generation unit that generates a countermeasure for eliminating the operation error detected by the operation error monitoring unit; an action execution unit that executes an action based on the countermeasure generated by the countermeasure generation unit; A system characterized by:

2. The obstacle detection unit Identify the type of obstacle and propose the optimal avoidance method in real time.

2. The system of claim 1.

3. The operation error monitoring unit Analyze the robot's motion history and learn how it dealt with specific obstacles in the past to improve its response in the future.

2. The system of claim 1.

4. The operation error monitoring unit Analyze the user's feelings toward the robot's movements and generate movement patterns that do not cause stress to the user.

2. The system of claim 1.

5. The obstacle detection unit Adding sound or vibration sensors to detect obstacles using information other than vision 2. The system of claim 1.

6. The operation error monitoring unit When the robot detects the obstacle, it explains the situation to the user by voice and asks for instructions from the user.

2. The system of claim 1.

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