system

Abstract

The system according to this embodiment aims to flexibly control the movement of a moving object in response to the environment and user instructions. [Solution] The system according to the embodiment comprises a collection unit, a reception unit, a calculation unit, and an execution unit. The collection unit collects sensor information detected by sensors attached to the moving object. The reception unit receives instructions from the user regarding the moving object. The calculation unit calculates the movement of the moving object based on the information collected by the collection unit and the instructions received by the reception unit. The execution unit performs control of the moving object based on the movement calculated by the calculation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, it is difficult to flexibly control the movement of a moving object according to the environment or user instructions, and there is room for improvement.

[0005] The system according to the embodiment aims to flexibly control the movement of a moving object according to the environment or user instructions.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, a reception unit, a calculation unit, and an execution unit. The data collection unit collects sensor information detected by sensors attached to the moving object. The reception unit receives instructions from the user regarding the moving object. The calculation unit calculates the movement of the moving object based on the information collected by the data collection unit and the instructions received by the reception unit. The execution unit performs control of the moving object based on the movement calculated by the calculation unit. [Effects of the Invention]

[0007] The system according to this embodiment can flexibly control the movement of a moving object in response to the environment and user instructions. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An AI control system according to an embodiment of the present invention is a system that automatically adjusts the movement and actions of a robot according to the environment and task. This AI control system collects sensor information detected by sensors installed on a moving object, receives instructions from the user regarding the moving object, calculates the movement of the moving object based on the collected information and the received instructions, and executes control of the moving object based on the calculated movement. For example, the AI ​​control system can collect environmental information using cameras, distance sensors, infrared sensors, etc. This allows the robot to understand its surroundings and identify the location and distance of obstacles. Next, the AI ​​control system can understand the user's instructions using speech recognition technology and analyze the content of the instructions using text analysis technology. This allows the user to give instructions to the robot by voice, and the robot can act according to those instructions. Based on the collected information and the received instructions, the AI ​​control system calculates the movement of the moving object. For example, it can calculate obstacle avoidance and appropriate movement based on the collected environmental information. The AI ​​control system can also estimate the user's emotions and calculate the movement of the moving object based on the estimated emotions. This allows the robot to act in accordance with the user's emotions. Based on the calculated movement, the AI ​​control system executes control of the moving object. For example, it can move objects to designated locations or lift specific objects. This allows robots to perform tasks according to user instructions. Furthermore, the AI ​​control system can automatically adjust sensor sensitivity according to specific time periods and conditions when collecting ambient environmental information. It can also filter the collected environmental information in real time, extracting only the important information. This allows robots to efficiently collect environmental information and perform appropriate actions. As a result, the AI ​​control system can automatically adjust the robot's movements and actions according to the environment and task.

[0029] The AI ​​control system according to this embodiment comprises a data collection unit, a reception unit, a calculation unit, and an execution unit. The data collection unit collects sensor information detected by sensors attached to the moving object. The data collection unit can collect environmental information using, for example, a camera, a distance sensor, and an infrared sensor. The data collection unit can acquire images of the surroundings using a camera, measure the distance to an object using a distance sensor, and acquire temperature information using an infrared sensor. The reception unit receives instructions from the user regarding the moving object. The reception unit can understand the user's instructions using, for example, speech recognition technology and analyze the content of the instructions using text analysis technology. The reception unit can convert the user's voice instructions into text data using speech recognition technology and analyze the content of the instructions using text analysis technology. The calculation unit calculates the movement of the moving object based on the information collected by the data collection unit and the instructions received by the reception unit. The calculation unit can calculate obstacle avoidance and appropriate movement based on the collected environmental information. Based on the collected environmental information, the calculation unit can calculate the optimal path and determine movement to avoid obstacles. The execution unit performs control of the moving object based on the movement calculated by the calculation unit. The execution unit can, for example, move a moving object to a specified location or lift a specific object. Based on the calculated movement, the execution unit can control the motors and actuators of the moving object to perform the specified action. As a result, the AI ​​control system according to the embodiment can automatically adjust the robot's movements and actions according to the environment and task.

[0030] The data collection unit collects sensor information detected by sensors attached to moving objects. For example, the unit can collect environmental information using cameras, distance sensors, and infrared sensors. Specifically, cameras acquire high-resolution images, allowing for a detailed understanding of the surrounding environment. Distance sensors use lasers or ultrasound to accurately measure the distance to objects and identify the location and size of obstacles. Infrared sensors acquire temperature information, enabling detection of ambient temperature changes and the location of heat sources. This sensor information is collected in real time and transmitted to a central database. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server, making it accessible to the analysis and calculation units. Furthermore, by adjusting the data collection frequency and accuracy, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance. In addition, the data collection unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the data collection unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0031] The reception unit receives user instructions for moving objects. For example, the reception unit can understand user instructions using speech recognition technology and analyze the instructions using text analysis technology. Specifically, speech recognition technology converts the user's voice into text data with high accuracy and analyzes the instructions based on that text data. Deep learning models are used in the speech recognition technology, enabling high recognition accuracy even in noisy environments. Text analysis technology uses natural language processing (NLP) to understand the user's instructions and identify appropriate actions. For example, if the user instructs "move forward," the text analysis technology identifies the action "move forward" and generates an instruction for the moving object to move forward. By combining these technologies, the reception unit can respond to a variety of user instructions and enable intuitive operation. Furthermore, the reception unit can collect user feedback and continuously improve the accuracy of speech recognition and text analysis. For example, after a user gives an instruction, feedback on the result is collected and used as training data for the model. This allows the reception unit to accurately understand the user's intent and quickly execute appropriate actions.

[0032] The calculation unit calculates the movement of a moving object based on the information collected by the collection unit and the instructions received by the reception unit. For example, the calculation unit can calculate obstacle avoidance and appropriate movement based on collected environmental information. Specifically, the calculation unit analyzes collected camera images and distance sensor data to identify the surrounding terrain and the location of obstacles. Next, it calculates the optimal path, taking into account the user's instructions. For example, if the user instructs "move to the destination," the calculation unit calculates the shortest path from the current location to the destination and determines the movement to avoid obstacles along that path. The calculation unit uses an AI algorithm to calculate the optimal path in real time and control the movement of the moving object. Furthermore, the calculation unit can also utilize historical data and statistical information to perform long-term risk assessment and trend analysis. For example, based on past movement data, it can predict fluctuations in risk in specific areas and time periods and formulate future countermeasures. In addition, the calculation unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the computing unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0033] The execution unit controls the moving object based on the motion calculated by the calculation unit. For example, the execution unit can move the moving object to a specified location or lift a specific object. Specifically, the execution unit controls the motors and actuators of the moving object based on the calculated motion to perform the specified action. For example, it adjusts the rotation speed and direction of the motors so that the moving object can reach its destination while avoiding obstacles. Also, when lifting a specific object, it controls the actuators to safely lift the object and move it to the specified location. The execution unit can perform these actions in real time and accurately control the movement of the moving object. Furthermore, the execution unit can receive sensor information as feedback and continuously adjust the accuracy of its actions. For example, if a new obstacle is detected during movement, the execution unit can immediately correct its actions and select a safe path. The execution unit can also collect user feedback and continuously improve the accuracy and efficiency of its actions. As a result, the execution unit can efficiently and safely control the movement of the moving object and reliably perform its tasks.

[0034] The data collection unit can collect environmental information detected by a camera or distance sensor and infrared sensor as sensor information. For example, the data collection unit can acquire images of the surroundings using a camera, measure the distance to objects using a distance sensor, and acquire temperature information using an infrared sensor. This allows for the collection of environmental information using a variety of sensors. Some or all of the above-described processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input image data acquired by a camera into a generating AI and have the generating AI extract environmental information from the image data.

[0035] The reception unit can understand user instructions using speech recognition technology and analyze the content of those instructions using text analysis technology. For example, the reception unit can convert the user's voice instructions into text data using speech recognition technology and analyze the content of those instructions using text analysis technology. This allows for accurate understanding of user instructions using speech recognition and text analysis. Some or all of the above-described processes in the reception unit may be performed using AI or not. For example, the reception unit can input voice data into a generating AI and have the generating AI perform the conversion from voice data to text data.

[0036] The calculation unit can calculate obstacle avoidance or optimal movement based on collected environmental information. For example, the calculation unit calculates the optimal path and determines movement to avoid obstacles based on the collected environmental information. This allows for obstacle avoidance and calculation of appropriate movement based on environmental information. Some or all of the above processing in the calculation unit may be performed using AI or not. For example, the calculation unit can input collected environmental information into a generating AI and have the generating AI perform the process of calculating the optimal movement.

[0037] The execution unit can move a moving object to a specified location and have the moving object lift a specific object. For example, the execution unit can move the moving object to a specified location and perform the action of lifting a specific object. This makes it possible to move the moving object to a specified location and lift a specific object. Some or all of the above processing in the execution unit may be performed using AI or not. For example, the execution unit can input the calculated movement to a generating AI and have the generating AI perform the control of the moving object.

[0038] The data collection unit can automatically adjust the sensor sensitivity according to specific time periods and conditions when collecting ambient environmental information. For example, the data collection unit can adjust the sensor sensitivity for daytime and nighttime, and change the sensitivity according to the intensity of ambient light. This allows for automatic adjustment of the sensor sensitivity according to specific time periods and conditions. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input sensor data into a generating AI and have the generating AI perform the sensitivity adjustment.

[0039] The collection unit can filter the collected environmental information in real time and extract important information. For example, the collection unit can extract only the information that meets specific conditions from the collected environmental information and treat it as important information. This allows for efficient collection of environmental information by extracting only the important information. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input environmental information into a generating AI and have the generating AI perform the extraction of important information.

[0040] The data collection unit can change the types of environmental information it collects in real time based on the user's past behavior history. For example, the data collection unit can prioritize collecting information about places the user has frequently visited in the past. The data collection unit can also choose not to collect information about places the user has avoided in the past. The data collection unit can also analyze the user's past behavior patterns and dynamically change the necessary environmental information. This allows the types of environmental information to be dynamically changed based on the user's past behavior history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's behavior history data into a generating AI and have the generating AI perform the process of changing the types of environmental information.

[0041] The data collection unit can set the accuracy of the environmental information it collects according to the robot's current task. For example, when the robot is performing a precise task, the data collection unit collects high-accuracy environmental information. When the robot is performing rough movements, the data collection unit can also collect low-accuracy environmental information. The data collection unit can also dynamically adjust the required accuracy of the environmental information when the robot is performing a specific task. This allows the accuracy of the environmental information to be adjusted according to the robot's current task. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input task information into a generating AI and have the generating AI perform the process of adjusting the accuracy of the environmental information.

[0042] The data collection unit can optimize the collection of environmental information based on the robot's location. For example, if the robot is in a specific area, the data collection unit prioritizes collecting environmental information related to that area. If the robot is moving, the data collection unit can also collect environmental information of the destination in advance. When the robot is performing a specific task, the data collection unit can also prioritize collecting environmental information related to that task. This allows for the optimization of environmental information based on the robot's location. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the robot's location information into a generating AI and have the generating AI perform the optimization of environmental information.

[0043] The data collection unit can set the environmental information to be collected according to the robot's battery level. For example, if the robot's battery is low, the data collection unit will collect only essential environmental information. If the robot's battery is sufficient, the data collection unit can also collect detailed environmental information. The data collection unit can also dynamically adjust the amount of environmental information collected according to the robot's battery level. This allows the environmental information to be adjusted according to the robot's battery level. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input battery level data into a generating AI and have the generating AI set the environmental information.

[0044] The reception unit can analyze the user's past instruction history and select an efficient method for receiving instructions. For example, the reception unit can prioritize suggesting instruction methods that the user has frequently used in the past. The reception unit can also predict and suggest instruction methods to be used during specific time periods based on the user's past instruction history. The reception unit can also analyze the user's past instruction history and select the most efficient method for receiving instructions. This allows the reception unit to select the optimal method for receiving instructions based on the user's past instruction history. Some or all of the above processes in the reception unit may be performed using AI or not. For example, the reception unit can input the user's instruction history data into a generating AI and have the generating AI perform the process of selecting an instruction receiving method.

[0045] The reception unit can prioritize instructions based on the user's current situation when receiving them. For example, if the user is in a hurry, the reception unit will prioritize important instructions. If the user is relaxed, the reception unit can also accept detailed instructions. The reception unit can also dynamically adjust the priority of instructions based on the user's current situation. This allows instructions to be prioritized based on the user's current situation. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input user situation data into a generating AI and have the generating AI perform the process of determining the priority of instructions.

[0046] The reception unit can optimize instructions by considering the user's geographical location when receiving instructions. For example, if the user is in a specific area, the reception unit will prioritize receiving instructions related to that area. If the user is on the move, the reception unit can also receive instructions for the user's destination in advance. The reception unit can also dynamically optimize instructions based on the user's geographical location. This allows for the optimization of instructions based on the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's geographical location into a generating AI and have the generating AI perform the optimization of the instructions.

[0047] The reception unit can select an efficient reception method when receiving instructions, taking into account the user's device information. For example, if the user is using a smartphone, the reception unit can provide a reception method adapted to the screen size. If the user is using a tablet, the reception unit can also provide a reception method optimized for a larger screen. If the user is using a smartwatch, the reception unit can also provide a concise and highly visible reception method. This allows the reception unit to select the optimal reception method based on the user's device information. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's device information into a generating AI and have the generating AI perform the process of selecting a reception method.

[0048] The calculation unit can change the obstacle avoidance method in real time based on the collected environmental information. For example, if there are many obstacles, the calculation unit will carefully calculate the avoidance method. If there are few obstacles, the calculation unit can also calculate the avoidance method quickly. The calculation unit can also dynamically change the optimal avoidance method based on the collected environmental information. This allows the obstacle avoidance method to be dynamically changed based on the collected environmental information. Some or all of the above processing in the calculation unit may be performed using AI or not. For example, the calculation unit can input environmental information into a generating AI and have the generating AI execute the process of changing the avoidance method.

[0049] The calculation unit can select an efficient motion pattern according to the robot's current task during calculation. For example, if the robot is performing a precise task, the calculation unit will select a careful motion pattern. If the robot is performing rough movements, the calculation unit can also select a rapid motion pattern. The calculation unit can also dynamically select the optimal motion pattern according to the robot's current task. This allows the robot to select the optimal motion pattern according to its current task. Some or all of the above processing in the calculation unit may be performed using AI or not. For example, the calculation unit can input task information into a generating AI and have the generating AI perform the process of selecting a motion pattern.

[0050] The calculation unit can select an efficient operation pattern during calculations, taking into account the robot's battery level. For example, if the robot's battery is low, the calculation unit can select an energy-efficient operation pattern. If the robot's battery is sufficient, the calculation unit can also select a rapid operation pattern. The calculation unit can also dynamically select the optimal operation pattern according to the robot's battery level. This allows for the selection of the optimal operation pattern according to the robot's battery level. Some or all of the above processing in the calculation unit may be performed using AI, or without AI. For example, the calculation unit can input battery level data into a generating AI and have the generating AI execute the process of selecting an operation pattern.

[0051] The calculation unit can select an efficient motion pattern during calculations, taking into account the robot's position information. For example, if the robot is in a specific area, the calculation unit can select the optimal motion pattern for that area. If the robot is moving, the calculation unit can also pre-calculate the motion pattern for the destination. The calculation unit can also dynamically select the optimal motion pattern based on the robot's position information. This allows for the selection of the optimal motion pattern based on the robot's position information. Some or all of the above-described processes in the calculation unit may be performed using AI or not. For example, the calculation unit can input position information into a generating AI and have the generating AI execute the process of selecting a motion pattern.

[0052] The execution unit can change the robot's motion pattern in real time during execution according to the robot's current task. For example, if the robot is performing a precise task, the execution unit will execute a careful motion pattern. If the robot is performing rough movements, the execution unit can also execute a rapid motion pattern. The execution unit can also dynamically change the motion pattern according to the robot's current task. This allows the motion pattern to be dynamically changed according to the robot's current task. Some or all of the above processing in the execution unit may be performed using AI or not. For example, the execution unit can input task information into a generating AI and have the generating AI execute the process of changing the motion pattern.

[0053] The execution unit can set an operation pattern during execution, taking into account the robot's battery level. For example, if the robot's battery is low, the execution unit will execute an energy-efficient operation pattern. If the robot's battery is sufficient, the execution unit can also execute a rapid operation pattern. The execution unit can also dynamically adjust the operation pattern according to the robot's battery level. This allows the operation pattern to be adjusted according to the robot's battery level. Some or all of the above processing in the execution unit may be performed using AI or not. For example, the execution unit can input battery level data to a generating AI and have the generating AI execute the process of setting the operation pattern.

[0054] The execution unit can select an efficient motion pattern during execution, taking into account the robot's position information. For example, if the robot is in a specific area, the execution unit will execute the motion pattern best suited to that area. If the robot is moving, the execution unit can also pre-calculate and execute the motion pattern for the destination. The execution unit can also dynamically select and execute the optimal motion pattern based on the robot's position information. This allows for the selection of the optimal motion pattern based on the robot's position information. Some or all of the above-described processes in the execution unit may be performed using AI or without AI. For example, the execution unit can input position information into a generating AI and have the generating AI execute the process of selecting a motion pattern.

[0055] The execution unit can set an action pattern during execution, taking into account the environment information surrounding the robot. For example, if the robot is in a narrow space, the execution unit will execute a cautious action pattern. If the robot is in a wide space, the execution unit can also execute a rapid action pattern. The execution unit can also dynamically adjust the action pattern based on the environment information surrounding the robot. This allows the action pattern to be adjusted based on the environment information surrounding the robot. Some or all of the above-described processes in the execution unit may be performed using AI or not. For example, the execution unit can input environmental information into a generating AI and have the generating AI execute the process of setting the action pattern.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The AI ​​control system can also be equipped with a learning unit. This unit can accumulate past motion data and environmental information, and optimize the robot's motion patterns based on this data. For example, the learning unit can analyze data from past obstacle avoidances and learn more efficient avoidance methods. It can also analyze user instruction history and customize actions based on user preferences and habits. Furthermore, the learning unit can dynamically adjust motion patterns in response to environmental changes. As a result, the AI ​​control system can become smarter and more efficient over time.

[0058] The AI ​​control system can also be equipped with a predictive unit. Based on collected environmental information and user instruction history, the predictive unit can forecast future situations and plan appropriate actions in advance. For example, it can use weather forecast data to move the robot indoors before it rains. It can also use user schedule information to prepare the robot to coincide with the user's return home. Furthermore, the predictive unit can analyze traffic information and calculate the optimal route to avoid congestion. This allows the AI ​​control system to respond flexibly to future situations.

[0059] The AI ​​control system can also be equipped with a communication unit. This unit can share information with other robots and devices and operate collaboratively. For example, the communication unit can enable multiple robots to cooperate, divide tasks, and work efficiently. Furthermore, the communication unit can connect with smart home devices to control home appliances. Additionally, the communication unit can connect with cloud services via the internet to acquire the latest information and incorporate it into its operations. This allows the AI ​​control system to operate in conjunction with a wider network.

[0060] The AI ​​control system can also be equipped with an energy management unit. This unit can monitor the robot's battery level and develop strategies for efficient energy use. For example, if the battery is low, it can switch to a low-energy-consumption operating mode. Furthermore, the energy management unit can automatically charge the robot's battery using solar panels or wireless charging stations. Additionally, it can analyze the energy consumption history and suggest improvements to enhance energy efficiency. This allows the AI ​​control system to operate stably over extended periods.

[0061] The AI ​​control system can also be equipped with a safety management unit. This unit can detect potential hazards during robot operation and take appropriate measures. For example, it can issue a warning to prevent the robot from coming into contact with a hot object. It can also perform actions to prevent the robot from falling when it approaches stairs or steps. Furthermore, the safety management unit can temporarily suspend the robot's operation if there are people or pets within its range of motion. This allows the AI ​​control system to operate with safety in mind.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The collection unit collects sensor information detected by sensors attached to the moving object. The collection unit can collect environmental information using, for example, a camera, distance sensor, and infrared sensor. It can acquire images of the surroundings using a camera, measure the distance to an object using a distance sensor, and acquire temperature information using an infrared sensor. Step 2: The reception unit receives the user's instructions regarding the moving object. The reception unit understands the user's instructions using, for example, speech recognition technology and analyzes the content of the instructions using text analysis technology. The user's voice instructions are converted into text data using speech recognition technology, and the content of the instructions is analyzed using text analysis technology. Step 3: The calculation unit calculates the movement of the moving object based on the information collected by the collection unit and the instructions received by the reception unit. For example, the calculation unit calculates obstacle avoidance and appropriate movement based on the collected environmental information. Based on the collected environmental information, it calculates the optimal path and determines the movement to avoid obstacles. Step 4: The execution unit controls the moving object based on the motion calculated by the calculation unit. For example, the execution unit moves the moving object to a specified location or lifts a specific object. Based on the calculated motion, it controls the motors and actuators of the moving object to perform the specified action.

[0064] (Example of form 2) An AI control system according to an embodiment of the present invention is a system that automatically adjusts the movement and actions of a robot according to the environment and task. This AI control system collects sensor information detected by sensors installed on a moving object, receives instructions from the user regarding the moving object, calculates the movement of the moving object based on the collected information and the received instructions, and executes control of the moving object based on the calculated movement. For example, the AI ​​control system can collect environmental information using cameras, distance sensors, infrared sensors, etc. This allows the robot to understand its surroundings and identify the location and distance of obstacles. Next, the AI ​​control system can understand the user's instructions using speech recognition technology and analyze the content of the instructions using text analysis technology. This allows the user to give instructions to the robot by voice, and the robot can act according to those instructions. Based on the collected information and the received instructions, the AI ​​control system calculates the movement of the moving object. For example, it can calculate obstacle avoidance and appropriate movement based on the collected environmental information. The AI ​​control system can also estimate the user's emotions and calculate the movement of the moving object based on the estimated emotions. This allows the robot to act in accordance with the user's emotions. Based on the calculated movement, the AI ​​control system executes control of the moving object. For example, it can move objects to designated locations or lift specific objects. This allows robots to perform tasks according to user instructions. Furthermore, the AI ​​control system can automatically adjust sensor sensitivity according to specific time periods and conditions when collecting ambient environmental information. It can also filter the collected environmental information in real time, extracting only the important information. This allows robots to efficiently collect environmental information and perform appropriate actions. As a result, the AI ​​control system can automatically adjust the robot's movements and actions according to the environment and task.

[0065] The AI ​​control system according to this embodiment comprises a data collection unit, a reception unit, a calculation unit, and an execution unit. The data collection unit collects sensor information detected by sensors attached to the moving object. The data collection unit can collect environmental information using, for example, a camera, a distance sensor, and an infrared sensor. The data collection unit can acquire images of the surroundings using a camera, measure the distance to an object using a distance sensor, and acquire temperature information using an infrared sensor. The reception unit receives instructions from the user regarding the moving object. The reception unit can understand the user's instructions using, for example, speech recognition technology and analyze the content of the instructions using text analysis technology. The reception unit can convert the user's voice instructions into text data using speech recognition technology and analyze the content of the instructions using text analysis technology. The calculation unit calculates the movement of the moving object based on the information collected by the data collection unit and the instructions received by the reception unit. The calculation unit can calculate obstacle avoidance and appropriate movement based on the collected environmental information. Based on the collected environmental information, the calculation unit can calculate the optimal path and determine movement to avoid obstacles. The execution unit performs control of the moving object based on the movement calculated by the calculation unit. The execution unit can, for example, move a moving object to a specified location or lift a specific object. Based on the calculated movement, the execution unit can control the motors and actuators of the moving object to perform the specified action. As a result, the AI ​​control system according to the embodiment can automatically adjust the robot's movements and actions according to the environment and task.

[0066] The data collection unit collects sensor information detected by sensors attached to moving objects. For example, the unit can collect environmental information using cameras, distance sensors, and infrared sensors. Specifically, cameras acquire high-resolution images, allowing for a detailed understanding of the surrounding environment. Distance sensors use lasers or ultrasound to accurately measure the distance to objects and identify the location and size of obstacles. Infrared sensors acquire temperature information, enabling detection of ambient temperature changes and the location of heat sources. This sensor information is collected in real time and transmitted to a central database. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server, making it accessible to the analysis and calculation units. Furthermore, by adjusting the data collection frequency and accuracy, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance. In addition, the data collection unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the data collection unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0067] The reception unit receives user instructions for moving objects. For example, the reception unit can understand user instructions using speech recognition technology and analyze the instructions using text analysis technology. Specifically, speech recognition technology converts the user's voice into text data with high accuracy and analyzes the instructions based on that text data. Deep learning models are used in the speech recognition technology, enabling high recognition accuracy even in noisy environments. Text analysis technology uses natural language processing (NLP) to understand the user's instructions and identify appropriate actions. For example, if the user instructs "move forward," the text analysis technology identifies the action "move forward" and generates an instruction for the moving object to move forward. By combining these technologies, the reception unit can respond to a variety of user instructions and enable intuitive operation. Furthermore, the reception unit can collect user feedback and continuously improve the accuracy of speech recognition and text analysis. For example, after a user gives an instruction, feedback on the result is collected and used as training data for the model. This allows the reception unit to accurately understand the user's intent and quickly execute appropriate actions.

[0068] The calculation unit calculates the movement of a moving object based on the information collected by the collection unit and the instructions received by the reception unit. For example, the calculation unit can calculate obstacle avoidance and appropriate movement based on collected environmental information. Specifically, the calculation unit analyzes collected camera images and distance sensor data to identify the surrounding terrain and the location of obstacles. Next, it calculates the optimal path, taking into account the user's instructions. For example, if the user instructs "move to the destination," the calculation unit calculates the shortest path from the current location to the destination and determines the movement to avoid obstacles along that path. The calculation unit uses an AI algorithm to calculate the optimal path in real time and control the movement of the moving object. Furthermore, the calculation unit can also utilize historical data and statistical information to perform long-term risk assessment and trend analysis. For example, based on past movement data, it can predict fluctuations in risk in specific areas and time periods and formulate future countermeasures. In addition, the calculation unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the computing unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0069] The execution unit controls the moving object based on the motion calculated by the calculation unit. For example, the execution unit can move the moving object to a specified location or lift a specific object. Specifically, the execution unit controls the motors and actuators of the moving object based on the calculated motion to perform the specified action. For example, it adjusts the rotation speed and direction of the motors so that the moving object can reach its destination while avoiding obstacles. Also, when lifting a specific object, it controls the actuators to safely lift the object and move it to the specified location. The execution unit can perform these actions in real time and accurately control the movement of the moving object. Furthermore, the execution unit can receive sensor information as feedback and continuously adjust the accuracy of its actions. For example, if a new obstacle is detected during movement, the execution unit can immediately correct its actions and select a safe path. The execution unit can also collect user feedback and continuously improve the accuracy and efficiency of its actions. As a result, the execution unit can efficiently and safely control the movement of the moving object and reliably perform its tasks.

[0070] The data collection unit can collect environmental information detected by a camera or distance sensor and infrared sensor as sensor information. For example, the data collection unit can acquire images of the surroundings using a camera, measure the distance to objects using a distance sensor, and acquire temperature information using an infrared sensor. This allows for the collection of environmental information using a variety of sensors. Some or all of the above-described processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input image data acquired by a camera into a generating AI and have the generating AI extract environmental information from the image data.

[0071] The reception unit can understand user instructions using speech recognition technology and analyze the content of those instructions using text analysis technology. For example, the reception unit can convert the user's voice instructions into text data using speech recognition technology and analyze the content of those instructions using text analysis technology. This allows for accurate understanding of user instructions using speech recognition and text analysis. Some or all of the above-described processes in the reception unit may be performed using AI or not. For example, the reception unit can input voice data into a generating AI and have the generating AI perform the conversion from voice data to text data.

[0072] The calculation unit can calculate obstacle avoidance or optimal movement based on collected environmental information. For example, the calculation unit calculates the optimal path and determines movement to avoid obstacles based on the collected environmental information. This allows for obstacle avoidance and calculation of appropriate movement based on environmental information. Some or all of the above processing in the calculation unit may be performed using AI or not. For example, the calculation unit can input collected environmental information into a generating AI and have the generating AI perform the process of calculating the optimal movement.

[0073] The execution unit can move a moving object to a specified location and have the moving object lift a specific object. For example, the execution unit can move the moving object to a specified location and perform the action of lifting a specific object. This makes it possible to move the moving object to a specified location and lift a specific object. Some or all of the above processing in the execution unit may be performed using AI or not. For example, the execution unit can input the calculated movement to a generating AI and have the generating AI perform the control of the moving object.

[0074] The calculation unit can estimate the user's emotions and calculate the movement of moving objects based on the estimated user emotions. For example, the calculation unit analyzes the user's facial expressions and voice data to estimate emotions. Based on the estimated emotions, it calculates the movement of moving objects. This allows the system to perform actions that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the calculation unit may be performed using AI or not. For example, the calculation unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0075] The data collection unit can automatically adjust the sensor sensitivity according to specific time periods and conditions when collecting ambient environmental information. For example, the data collection unit can adjust the sensor sensitivity for daytime and nighttime, and change the sensitivity according to the intensity of ambient light. This allows for automatic adjustment of the sensor sensitivity according to specific time periods and conditions. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input sensor data into a generating AI and have the generating AI perform the sensitivity adjustment.

[0076] The collection unit can filter the collected environmental information in real time and extract important information. For example, the collection unit can extract only the information that meets specific conditions from the collected environmental information and treat it as important information. This allows for efficient collection of environmental information by extracting only the important information. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input environmental information into a generating AI and have the generating AI perform the extraction of important information.

[0077] The data collection unit can estimate the user's emotions and determine the priority of environmental information to collect based on the estimated user emotions. For example, if the user is stressed, the data collection unit may prioritize collecting relaxing environmental information. If the user is excited, the data collection unit may also prioritize collecting safety-conscious environmental information. If the user is tired, the data collection unit may also prioritize collecting restful environmental information. This allows the priority of environmental information to be determined based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform the process of determining the priority of environmental information.

[0078] The data collection unit can change the types of environmental information it collects in real time based on the user's past behavior history. For example, the data collection unit can prioritize collecting information about places the user has frequently visited in the past. The data collection unit can also choose not to collect information about places the user has avoided in the past. The data collection unit can also analyze the user's past behavior patterns and dynamically change the necessary environmental information. This allows the types of environmental information to be dynamically changed based on the user's past behavior history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's behavior history data into a generating AI and have the generating AI perform the process of changing the types of environmental information.

[0079] The data collection unit can set the accuracy of the environmental information it collects according to the robot's current task. For example, when the robot is performing a precise task, the data collection unit collects high-accuracy environmental information. When the robot is performing rough movements, the data collection unit can also collect low-accuracy environmental information. The data collection unit can also dynamically adjust the required accuracy of the environmental information when the robot is performing a specific task. This allows the accuracy of the environmental information to be adjusted according to the robot's current task. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input task information into a generating AI and have the generating AI perform the process of adjusting the accuracy of the environmental information.

[0080] The data collection unit can estimate the user's emotions and change the filtering criteria for the environmental information it collects based on the estimated user emotions. For example, if the user is relaxed, the data collection unit may prioritize filtering for relaxing environmental information. If the user is stressed, the data collection unit may also prioritize filtering for safety-conscious environmental information. If the user is having fun, the data collection unit may also prioritize filtering for entertaining environmental information. This allows the filtering criteria for environmental information to be changed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit may input user emotion data into a generative AI and have the generative AI perform the process of changing the filtering criteria.

[0081] The data collection unit can optimize the collection of environmental information based on the robot's location. For example, if the robot is in a specific area, the data collection unit prioritizes collecting environmental information related to that area. If the robot is moving, the data collection unit can also collect environmental information of the destination in advance. When the robot is performing a specific task, the data collection unit can also prioritize collecting environmental information related to that task. This allows for the optimization of environmental information based on the robot's location. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the robot's location information into a generating AI and have the generating AI perform the optimization of environmental information.

[0082] The data collection unit can set the environmental information to be collected according to the robot's battery level. For example, if the robot's battery is low, the data collection unit will collect only essential environmental information. If the robot's battery is sufficient, the data collection unit can also collect detailed environmental information. The data collection unit can also dynamically adjust the amount of environmental information collected according to the robot's battery level. This allows the environmental information to be adjusted according to the robot's battery level. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input battery level data into a generating AI and have the generating AI set the environmental information.

[0083] The reception desk can estimate the user's emotions and adjust how instructions are received based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. If the user is in a hurry, the reception desk can prioritize voice input and receive instructions quickly. This allows the reception desk to adjust how instructions are received based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform the process of adjusting how instructions are received.

[0084] The reception unit can analyze the user's past instruction history and select an efficient method for receiving instructions. For example, the reception unit can prioritize suggesting instruction methods that the user has frequently used in the past. The reception unit can also predict and suggest instruction methods to be used during specific time periods based on the user's past instruction history. The reception unit can also analyze the user's past instruction history and select the most efficient method for receiving instructions. This allows the reception unit to select the optimal method for receiving instructions based on the user's past instruction history. Some or all of the above processes in the reception unit may be performed using AI or not. For example, the reception unit can input the user's instruction history data into a generating AI and have the generating AI perform the process of selecting an instruction receiving method.

[0085] The reception unit can prioritize instructions based on the user's current situation when receiving them. For example, if the user is in a hurry, the reception unit will prioritize important instructions. If the user is relaxed, the reception unit can also accept detailed instructions. The reception unit can also dynamically adjust the priority of instructions based on the user's current situation. This allows instructions to be prioritized based on the user's current situation. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input user situation data into a generating AI and have the generating AI perform the process of determining the priority of instructions.

[0086] The reception unit can estimate the user's emotions and modify the method of analyzing instructions based on the estimated emotions. For example, if the user is nervous, the reception unit can provide a simple and easy-to-understand analysis method. If the user is relaxed, the reception unit can also provide a detailed analysis method. If the user is in a hurry, the reception unit can also provide a quick and concise analysis method. This allows the method of analyzing instructions to be modified based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input user emotion data into a generative AI and have the generative AI perform the process of modifying the method of analyzing instructions.

[0087] The reception unit can optimize instructions by considering the user's geographical location when receiving instructions. For example, if the user is in a specific area, the reception unit will prioritize receiving instructions related to that area. If the user is on the move, the reception unit can also receive instructions for the user's destination in advance. The reception unit can also dynamically optimize instructions based on the user's geographical location. This allows for the optimization of instructions based on the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's geographical location into a generating AI and have the generating AI perform the optimization of the instructions.

[0088] The reception unit can select an efficient reception method when receiving instructions, taking into account the user's device information. For example, if the user is using a smartphone, the reception unit can provide a reception method adapted to the screen size. If the user is using a tablet, the reception unit can also provide a reception method optimized for a larger screen. If the user is using a smartwatch, the reception unit can also provide a concise and highly visible reception method. This allows the reception unit to select the optimal reception method based on the user's device information. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's device information into a generating AI and have the generating AI perform the process of selecting a reception method.

[0089] The calculation unit can estimate the user's emotions and adjust the movement speed of moving objects based on the estimated user emotions. For example, if the user is relaxed, the calculation unit calculates a relaxed movement speed. If the user is in a hurry, the calculation unit can also calculate a fast movement speed. If the user is excited, the calculation unit can also calculate a visually stimulating movement speed. This allows the movement speed of moving objects to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the calculation unit may be performed using AI or not. For example, the calculation unit can input user emotion data into a generative AI and have the generative AI perform the process of adjusting the movement speed.

[0090] The calculation unit can change the obstacle avoidance method in real time based on the collected environmental information. For example, if there are many obstacles, the calculation unit will carefully calculate the avoidance method. If there are few obstacles, the calculation unit can also calculate the avoidance method quickly. The calculation unit can also dynamically change the optimal avoidance method based on the collected environmental information. This allows the obstacle avoidance method to be dynamically changed based on the collected environmental information. Some or all of the above processing in the calculation unit may be performed using AI or not. For example, the calculation unit can input environmental information into a generating AI and have the generating AI execute the process of changing the avoidance method.

[0091] The calculation unit can select an efficient motion pattern according to the robot's current task during calculation. For example, if the robot is performing a precise task, the calculation unit will select a careful motion pattern. If the robot is performing rough movements, the calculation unit can also select a rapid motion pattern. The calculation unit can also dynamically select the optimal motion pattern according to the robot's current task. This allows the robot to select the optimal motion pattern according to its current task. Some or all of the above processing in the calculation unit may be performed using AI or not. For example, the calculation unit can input task information into a generating AI and have the generating AI perform the process of selecting a motion pattern.

[0092] The calculation unit can select an efficient operation pattern during calculations, taking into account the robot's battery level. For example, if the robot's battery is low, the calculation unit can select an energy-efficient operation pattern. If the robot's battery is sufficient, the calculation unit can also select a rapid operation pattern. The calculation unit can also dynamically select the optimal operation pattern according to the robot's battery level. This allows for the selection of the optimal operation pattern according to the robot's battery level. Some or all of the above processing in the calculation unit may be performed using AI, or without AI. For example, the calculation unit can input battery level data into a generating AI and have the generating AI execute the process of selecting an operation pattern.

[0093] The calculation unit can select an efficient motion pattern during calculations, taking into account the robot's position information. For example, if the robot is in a specific area, the calculation unit can select the optimal motion pattern for that area. If the robot is moving, the calculation unit can also pre-calculate the motion pattern for the destination. The calculation unit can also dynamically select the optimal motion pattern based on the robot's position information. This allows for the selection of the optimal motion pattern based on the robot's position information. Some or all of the above-described processes in the calculation unit may be performed using AI or not. For example, the calculation unit can input position information into a generating AI and have the generating AI execute the process of selecting a motion pattern.

[0094] The execution unit can estimate the user's emotions and adjust the movement of moving objects based on the estimated user emotions. For example, if the user is relaxed, the execution unit will perform slow movements. If the user is in a hurry, the execution unit can also perform rapid movements. If the user is excited, the execution unit can also perform visually stimulating movements. This allows the movement of moving objects to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the execution unit may be performed using AI or not. For example, the execution unit can input user emotion data into a generative AI and have the generative AI perform the process of adjusting the movement.

[0095] The execution unit can change the robot's motion pattern in real time during execution according to the robot's current task. For example, if the robot is performing a precise task, the execution unit will execute a careful motion pattern. If the robot is performing rough movements, the execution unit can also execute a rapid motion pattern. The execution unit can also dynamically change the motion pattern according to the robot's current task. This allows the motion pattern to be dynamically changed according to the robot's current task. Some or all of the above processing in the execution unit may be performed using AI or not. For example, the execution unit can input task information into a generating AI and have the generating AI execute the process of changing the motion pattern.

[0096] The execution unit can set an operation pattern during execution, taking into account the robot's battery level. For example, if the robot's battery is low, the execution unit will execute an energy-efficient operation pattern. If the robot's battery is sufficient, the execution unit can also execute a rapid operation pattern. The execution unit can also dynamically adjust the operation pattern according to the robot's battery level. This allows the operation pattern to be adjusted according to the robot's battery level. Some or all of the above processing in the execution unit may be performed using AI or not. For example, the execution unit can input battery level data to a generating AI and have the generating AI execute the process of setting the operation pattern.

[0097] The execution unit can estimate the user's emotions and adjust the movement speed of moving objects based on the estimated user emotions. For example, if the user is relaxed, the execution unit will perform a slow movement speed. If the user is in a hurry, the execution unit can also perform a fast movement speed. If the user is excited, the execution unit can also perform a visually stimulating movement speed. This allows the movement speed of moving objects to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the execution unit may be performed using AI or not. For example, the execution unit can input user emotion data into a generative AI and have the generative AI perform the process of adjusting the movement speed.

[0098] The execution unit can select an efficient motion pattern during execution, taking into account the robot's position information. For example, if the robot is in a specific area, the execution unit will execute the motion pattern best suited to that area. If the robot is moving, the execution unit can also pre-calculate and execute the motion pattern for the destination. The execution unit can also dynamically select and execute the optimal motion pattern based on the robot's position information. This allows for the selection of the optimal motion pattern based on the robot's position information. Some or all of the above-described processes in the execution unit may be performed using AI or without AI. For example, the execution unit can input position information into a generating AI and have the generating AI execute the process of selecting a motion pattern.

[0099] The execution unit can set an action pattern during execution, taking into account the environment information surrounding the robot. For example, if the robot is in a narrow space, the execution unit will execute a cautious action pattern. If the robot is in a wide space, the execution unit can also execute a rapid action pattern. The execution unit can also dynamically adjust the action pattern based on the environment information surrounding the robot. This allows the action pattern to be adjusted based on the environment information surrounding the robot. Some or all of the above-described processes in the execution unit may be performed using AI or not. For example, the execution unit can input environmental information into a generating AI and have the generating AI execute the process of setting the action pattern.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The AI ​​control system can also be equipped with a learning unit. This unit can accumulate past motion data and environmental information, and optimize the robot's motion patterns based on this data. For example, the learning unit can analyze data from past obstacle avoidances and learn more efficient avoidance methods. It can also analyze user instruction history and customize actions based on user preferences and habits. Furthermore, the learning unit can dynamically adjust motion patterns in response to environmental changes. As a result, the AI ​​control system can become smarter and more efficient over time.

[0102] The AI ​​control system can also be equipped with a predictive unit. Based on collected environmental information and user instruction history, the predictive unit can forecast future situations and plan appropriate actions in advance. For example, it can use weather forecast data to move the robot indoors before it rains. It can also use user schedule information to prepare the robot to coincide with the user's return home. Furthermore, the predictive unit can analyze traffic information and calculate the optimal route to avoid congestion. This allows the AI ​​control system to respond flexibly to future situations.

[0103] The AI ​​control system can also be equipped with a communication unit. This unit can share information with other robots and devices and operate collaboratively. For example, the communication unit can enable multiple robots to cooperate, divide tasks, and work efficiently. Furthermore, the communication unit can connect with smart home devices to control home appliances. Additionally, the communication unit can connect with cloud services via the internet to acquire the latest information and incorporate it into its operations. This allows the AI ​​control system to operate in conjunction with a wider network.

[0104] The AI ​​control system can also be equipped with an energy management unit. This unit can monitor the robot's battery level and develop strategies for efficient energy use. For example, if the battery is low, it can switch to a low-energy-consumption operating mode. Furthermore, the energy management unit can automatically charge the robot's battery using solar panels or wireless charging stations. Additionally, it can analyze the energy consumption history and suggest improvements to enhance energy efficiency. This allows the AI ​​control system to operate stably over extended periods.

[0105] The AI ​​control system can also be equipped with a safety management unit. This unit can detect potential hazards during robot operation and take appropriate measures. For example, it can issue a warning to prevent the robot from coming into contact with a hot object. It can also perform actions to prevent the robot from falling when it approaches stairs or steps. Furthermore, the safety management unit can temporarily suspend the robot's operation if there are people or pets within its range of motion. This allows the AI ​​control system to operate with safety in mind.

[0106] The AI ​​control system can also be equipped with an emotional feedback unit. This unit can monitor the user's emotions in real time and reflect them in the robot's actions. For example, if the user is stressed, the emotional feedback unit can make the robot's actions gentler. Conversely, if the user is happy, the emotional feedback unit can make the robot's actions more energetic. Furthermore, if the user is tired, the emotional feedback unit can provide a relaxing environment for the robot. This allows the AI ​​control system to respond flexibly to the user's emotions.

[0107] The AI ​​control system can also be equipped with an emotion learning unit. This unit can accumulate user emotion data and learn long-term emotion patterns. For example, it can learn what emotions a user experiences in specific situations and plan the optimal action accordingly. It can also predict changes in the user's emotions and prepare countermeasures in advance. Furthermore, it can analyze emotion data from multiple users and identify common emotion patterns. This enables the AI ​​control system to provide more personalized emotional responses.

[0108] The AI ​​control system can also be equipped with an emotional dialogue unit. This unit can understand emotions through interaction with the user and provide appropriate responses. For example, if the user is sad, the emotional dialogue unit can offer words of comfort. It can also offer words of empathy if the user is agitated. Furthermore, if the user is feeling anxious, the emotional dialogue unit can provide reassuring information. This allows the AI ​​control system to deepen its emotional connection with the user.

[0109] The AI ​​control system can also be equipped with an emotion prediction unit. This unit can predict future emotions based on the user's past emotional data and behavioral patterns. For example, it can predict how a user will feel about a specific event and prepare countermeasures in advance. It can also predict stressful times based on the user's schedule and provide a relaxing environment. Furthermore, the emotion prediction unit can monitor the user's health and predict changes in their emotions. This allows the AI ​​control system to proactively respond to the user's emotions.

[0110] The AI ​​control system can also be equipped with an emotion sharing unit. This unit can share emotional information among multiple users and operate collaboratively. For example, it can collect emotional data from all family members to understand the overall emotional state of the family. It can also share emotional information among colleagues at work to monitor the emotional state of the entire team. Furthermore, it can share emotional information through social networks to strengthen emotional connections with friends and acquaintances. This enables the AI ​​control system to operate collaboratively based on emotional information.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The collection unit collects sensor information detected by sensors attached to the moving object. The collection unit can collect environmental information using, for example, a camera, distance sensor, and infrared sensor. It can acquire images of the surroundings using a camera, measure the distance to an object using a distance sensor, and acquire temperature information using an infrared sensor. Step 2: The reception unit receives the user's instructions regarding the moving object. The reception unit understands the user's instructions using, for example, speech recognition technology and analyzes the content of the instructions using text analysis technology. The user's voice instructions are converted into text data using speech recognition technology, and the content of the instructions is analyzed using text analysis technology. Step 3: The calculation unit calculates the movement of the moving object based on the information collected by the collection unit and the instructions received by the reception unit. For example, the calculation unit calculates obstacle avoidance and appropriate movement based on the collected environmental information. Based on the collected environmental information, it calculates the optimal path and determines the movement to avoid obstacles. Step 4: The execution unit controls the moving object based on the motion calculated by the calculation unit. For example, the execution unit moves the moving object to a specified location or lifts a specific object. Based on the calculated motion, it controls the motors and actuators of the moving object to perform the specified action.

[0113] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0114] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0115] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0116] For example, the collection unit can collect environmental information using the camera 42 and distance sensor of the smart device 14. For example, the reception unit can receive voice instructions from the user using the microphone 38B of the smart device 14, and the control unit 46A can analyze the content of the instructions. For example, the calculation unit can calculate the movement of a moving object based on the information collected and the instructions by the specific processing unit 290 of the data processing device 12. For example, the execution unit can execute control of the moving object based on the movement calculated by the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0124] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0127] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] For example, the data collection unit can collect environmental information using the camera 42 and distance sensor of the smart glasses 214. For example, the reception unit can receive voice instructions from the user using the microphone 238 of the smart glasses 214, and the control unit 46A can analyze the content of the instructions. For example, the calculation unit can calculate the movement of a moving object based on the information collected and the instructions by the specific processing unit 290 of the data processing device 12. For example, the execution unit can execute control of the moving object based on the movement calculated by the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] For example, the collection unit can collect environmental information using the camera 42 and distance sensor of the headset terminal 314. For example, the reception unit can receive voice instructions from the user using the microphone 238 of the headset terminal 314, and the control unit 46A can analyze the content of the instructions. For example, the calculation unit can calculate the movement of a moving object based on the information collected and the instructions by the specific processing unit 290 of the data processing device 12. For example, the execution unit can execute control of the moving object based on the movement calculated by the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] As shown in Figure 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.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0157] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0158] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0160] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0161] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0162] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0163] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0164] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0165] For example, the collection unit can collect environmental information using the camera 42 and distance sensor of the robot 414. For example, the reception unit can receive voice instructions from the user using the microphone 238 of the robot 414, and the control unit 46A can analyze the content of the instructions. For example, the calculation unit can calculate the movement of the moving object based on the information collected and the instructions by the specific processing unit 290 of the data processing device 12. For example, the execution unit can execute control of the moving object based on the movement calculated by the control unit 46A of the robot 414. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0166] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0167] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0168] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0169] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0170] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0171] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0174] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0175] 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.

[0176] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0177] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0178] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0179] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0180] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0181] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0182] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0183] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0184] (Note 1) A collection unit that collects sensor information detected by sensors attached to a moving object, A reception unit that receives instructions from the user regarding the moving object, A calculation unit calculates the movement of the moving object based on the information collected by the collection unit and the instructions received by the reception unit. The system comprises an execution unit that controls the moving object based on the motion calculated by the calculation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Environmental information detected by a camera or distance sensor and an infrared sensor is collected as sensor information. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is It uses speech recognition technology to understand user instructions and text analysis technology to analyze the content of those instructions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The calculation unit, Based on the collected environmental information, the system calculates obstacle avoidance or optimal movement. The system described in Appendix 1, characterized by the features described herein. (Note 5) The execution unit is, Move the aforementioned moving object to a designated location, and cause the moving object to lift a specific object. The system described in Appendix 1, characterized by the features described herein. (Note 6) The calculation unit, The system estimates the user's emotions and calculates the movement of the moving object based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting ambient environmental information, the sensor sensitivity is automatically adjusted according to specific time periods and conditions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is The collected environmental information is filtered in real time, and important information is extracted. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and determines the priority of environmental information to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The types of environmental information collected are changed in real time based on the user's past behavior history. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is The accuracy of the environmental information collected is set according to the robot's current task. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is We will estimate the user's emotions and change the filtering criteria for the environmental information we collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is The process of collecting environmental information is made more efficient based on the robot's location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is The environmental information to be collected is set according to the robot's battery level. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned reception unit is It estimates the user's emotions and adjusts how instructions are received based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned reception unit is Analyze the user's past instruction history and select the most efficient method for receiving instructions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned reception unit is When receiving instructions, the instructions are prioritized based on the user's current status. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned reception unit is The system estimates the user's emotions and modifies the instruction analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned reception unit is When receiving instructions, the system optimizes the instructions by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned reception unit is When receiving instructions, the system selects an efficient method of receiving them, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The calculation unit, It estimates the user's emotions and adjusts the movement speed of moving objects based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The calculation unit, Based on collected environmental information, the method of avoiding obstacles is changed in real time. The system described in Appendix 1, characterized by the features described herein. (Note 23) The calculation unit, During calculations, an efficient motion pattern is selected according to the robot's current task. The system described in Appendix 1, characterized by the features described herein. (Note 24) The calculation unit, During calculations, the robot's battery level is taken into consideration to select an efficient operating pattern. The system described in Appendix 1, characterized by the features described herein. (Note 25) The calculation unit, During calculations, the robot's position information is taken into consideration to select an efficient motion pattern. The system described in Appendix 1, characterized by the features described herein. (Note 26) The execution unit is, It estimates the user's emotions and adjusts the movement of moving objects based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The execution unit is, During execution, the robot's behavior pattern is changed in real time according to its current task. The system described in Appendix 1, characterized by the features described herein. (Note 28) The execution unit is, During execution, the robot's operating pattern is set while taking into account its remaining battery level. The system described in Appendix 1, characterized by the features described herein. (Note 29) The execution unit is, It estimates the user's emotions and adjusts the movement speed of moving objects based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The execution unit is, During execution, an efficient motion pattern is selected, taking into account the robot's position information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The execution unit is, During execution, the robot's movement pattern is set considering the surrounding environment information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects sensor information detected by sensors attached to a moving object, A reception unit that receives instructions from the user regarding the moving object, A calculation unit calculates the movement of the moving object based on the information collected by the collection unit and the instructions received by the reception unit. The system comprises an execution unit that controls the moving object based on the motion calculated by the calculation unit. A system characterized by the following features.

2. The aforementioned collection unit is Environmental information detected by a camera or distance sensor and an infrared sensor is collected as sensor information. The system according to feature 1.

3. The aforementioned reception unit is It uses speech recognition technology to understand user instructions and text analysis technology to analyze the content of those instructions. The system according to feature 1.

4. The calculation unit, Based on the collected environmental information, the system calculates obstacle avoidance or optimal movement. The system according to feature 1.

5. The execution unit is, Move the aforementioned moving object to a designated location, and cause the moving object to lift a specific object. The system according to feature 1.

6. The calculation unit, The system estimates the user's emotions and calculates the movement of the moving object based on the estimated user emotions. The system according to feature 1.

7. The aforementioned collection unit is When collecting ambient environmental information, the sensor sensitivity is automatically adjusted according to specific time periods and conditions. The system according to feature 1.

8. The aforementioned collection unit is The collected environmental information is filtered in real time, and important information is extracted. The system according to feature 1.

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