system

Abstract

The system according to the embodiment aims to enable a robot to learn by itself and adjust its movements flexibly. [Solution] A system according to an embodiment includes an instruction receiving unit, a learning unit, and an optimization unit. The instruction receiving unit receives instructions via programming. The learning unit allows the robot to learn on its own based on the instructions received by the instruction receiving unit. The optimization unit adjusts the robot's behavior in real time based on the results of learning by the learning unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has traditionally involved controlling robot movements through programming, which makes it difficult to respond flexibly.

[0005] The system according to the embodiment aims to enable a robot to learn by itself and adjust its movements flexibly. [Means for solving the problem]

[0006] The system according to the embodiment includes an instruction receiving unit, a learning unit, and an optimization unit. The instruction receiving unit receives instructions via programming. The learning unit allows the robot to learn on its own based on the instructions received by the instruction receiving unit. The optimization unit adjusts the robot's behavior in real time based on the results of learning by the learning unit. [Effects of the Invention]

[0007] The system according to the embodiment allows the robot to learn by itself and adjust its movements flexibly. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A robot system according to an embodiment of the present invention utilizes generative AI to infinitely expand its range of activities. While conventional robots are programmed to operate, this robot system utilizes generative AI to significantly expand the robot's range of activities. Specifically, the overall robot performance is improved by combining programmed instructions with generative AI learning. This can improve Japan's competitiveness in preparation for labor shortages and an aging population with a declining birthrate. For example, conventional robots were programmed to perform specific tasks. Their primary role was to repeat specific movements, such as assembly work in a factory or picking in a warehouse. However, these robots were limited to pre-programmed movements and had difficulty adapting to environmental changes or new tasks. In this invention, generative AI allows the robot to learn on its own and flexibly adapt to environmental changes and new tasks. For example, generative AI can analyze the robot's motion data and generate optimal motion patterns. This allows the robot to quickly adapt to new tasks. Furthermore, generative AI can optimize the robot's motion in real time. For example, to maximize work efficiency in a factory, generative AI can optimize the robot's motion and eliminate unnecessary movements. This improves work efficiency and productivity. Furthermore, the robot of the present invention is an important tool for preparing for labor shortages and an aging population with a declining birthrate. For example, it is expected to be used in fields where there is a labor shortage, such as elderly care and medical support. By using generative AI, the robot can learn on its own and provide services tailored to individual needs. Thus, the present invention relates to a robot that utilizes generative AI to infinitely expand its activities. By combining program instructions with learning responses provided by generative AI, the overall performance of the robot can be improved. This will help prepare for labor shortages and an aging population with a declining birthrate and improve Japan's competitiveness. This allows the robot system to optimize the robot's behavior using generative AI and flexibly respond to environmental changes and new tasks.

[0029] A robot system according to an embodiment includes an instruction receiving unit, a learning unit, and an optimization unit. The instruction receiving unit receives instructions via programming. The instruction receiving unit can receive instructions based on, for example, the type of code or the content of the instruction. The learning unit uses a generative AI to allow the robot to learn on its own based on the instructions received by the instruction receiving unit. The learning unit can perform learning using methods such as reinforcement learning and supervised learning. The learning unit can also analyze the robot's motion data and generate optimal motion patterns. For example, the learning unit generates motion patterns based on the robot's motion data, aiming to minimize energy consumption and shorten work time. The optimization unit optimizes the robot's motion in real time based on the results learned by the learning unit. The optimization unit can adjust the motion in real time based on, for example, response time and adjustment frequency. The optimization unit can also optimize the robot's motion to maximize work efficiency in a factory. For example, the optimization unit can eliminate unnecessary movements according to the work environment in the factory, improving work efficiency. As a result, the robot system according to an embodiment can optimize the robot's motion using a generative AI and flexibly respond to environmental changes and new tasks. Some or all of the above-described processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit can optimize the robot's movement using an AI model that inputs the movement patterns generated by the learning unit and outputs optimal movement patterns. Furthermore, the optimization unit can also build a system for optimizing the robot's movement in real time. For example, the optimization unit analyzes the robot's movement data in real time and generates optimal movement patterns. This allows the robot's movement to be efficiently optimized and productivity to be improved.

[0030] The learning unit can analyze the robot's motion data and generate efficient motion patterns. Efficient motion patterns include, but are not limited to, minimizing energy consumption and shortening work time. The learning unit, for example, collects and analyzes the robot's motion data. For example, the learning unit generates an optimal motion pattern based on the robot's motion data. The learning unit can also analyze the motion data and generate an efficient motion pattern using a generation AI. For example, the learning unit inputs the motion data to the generation AI and outputs an optimal motion pattern. This allows the robot's motion data to be analyzed to generate an optimal motion pattern, enabling efficient motion. Some or all of the above-described processing in the learning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the learning unit can input the motion data to the generation AI and cause the generation AI to generate an efficient motion pattern.

[0031] The optimization unit can optimize the robot's operation to improve work efficiency in a factory. Examples of improving work efficiency include, but are not limited to, shortening work time and reducing errors. The optimization unit, for example, analyzes the work environment in a factory and generates an optimal operation pattern. For example, the optimization unit collects and analyzes work data in the factory. The optimization unit can also analyze the work data and generate an optimal operation pattern using a generation AI. For example, the optimization unit inputs the work data into the generation AI and outputs an optimal operation pattern. This optimizes the robot's operation to maximize work efficiency in the factory and improves productivity. Some or all of the above-described processing in the optimization unit may be performed using AI, for example, or may be performed without using AI. For example, the optimization unit inputs work data into the generation AI and causes the generation AI to generate an optimal operation pattern.

[0032] The learning unit can learn to adapt to environmental changes and new tasks. Examples of environmental changes include, but are not limited to, temperature changes and the appearance of obstacles. Examples of new tasks include, but are not limited to, new work procedures and new work targets. The learning unit, for example, collects and analyzes data to adapt to environmental changes and new tasks. For example, the learning unit generates optimal movement patterns based on environmental data and task data. The learning unit can also use a generation AI to analyze the environmental data and task data and generate optimal movement patterns. For example, the learning unit inputs the environmental data and task data into the generation AI and outputs optimal movement patterns. This improves the adaptability of the robot by learning to flexibly adapt to environmental changes and new tasks. Some or all of the above-described processing in the learning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the learning unit inputs environmental data and task data into the generation AI and causes the generation AI to generate optimal movement patterns.

[0033] The optimization unit can eliminate unnecessary movements and improve work efficiency. Examples of unnecessary movements include, but are not limited to, unnecessary travel and redundant work. For example, the optimization unit analyzes the robot's motion data and identifies unnecessary movements. For example, the optimization unit generates a motion pattern for eliminating unnecessary movements based on the motion data. The optimization unit can also analyze the motion data and generate a motion pattern for eliminating unnecessary movements using a generation AI. For example, the optimization unit inputs the motion data to the generation AI and outputs a motion pattern for eliminating unnecessary movements. This eliminates unnecessary movements, thereby improving work efficiency and productivity. Some or all of the above-described processing in the optimization unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the optimization unit inputs the motion data to the generation AI and causes the generation AI to generate a motion pattern for eliminating unnecessary movements.

[0034] The learning unit can perform learning to adapt to elderly care and medical support. Elderly care includes, but is not limited to, mobility assistance and meal assistance. Medical support includes, but is not limited to, patient monitoring and medication distribution. The learning unit collects and analyzes data for elderly care and medical support. For example, the learning unit generates optimal movement patterns based on nursing data and medical data. The learning unit can also analyze the nursing data and medical data using a generation AI to generate optimal movement patterns. For example, the learning unit inputs nursing data and medical data into the generation AI and outputs optimal movement patterns. This allows the robot to learn to adapt to elderly care and medical support, thereby enabling it to be effectively used in these fields. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without AI. For example, the learning unit inputs nursing data and medical data into the generation AI and causes the generation AI to generate optimal movement patterns.

[0035] The instruction accepting unit can analyze the user's past instruction history and select the optimal instruction accepting method. For example, the instruction accepting unit can automatically display instructions that the user frequently input in the past as candidates. The instruction accepting unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the instruction accepting unit can predict and suggest instructions to be used in a specific time period based on the user's past instruction history. This makes it possible to select the optimal instruction accepting method by analyzing the user's past instruction history, thereby improving user convenience. Some or all of the above-described processing in the instruction accepting unit can be performed using, or without, AI. For example, the instruction accepting unit can input past instruction history data into a generation AI and cause the generation AI to select the optimal instruction accepting method.

[0036] The instruction receiving unit can perform filtering based on the user's current situation and environment when receiving instructions. For example, when the user is in a noisy environment, the instruction receiving unit suppresses voice input and prioritizes text input. Furthermore, when the user is moving, the instruction receiving unit can prioritize simple instruction input and avoid complex operations. Furthermore, when the user is in a specific location, the instruction receiving unit can prioritize receiving instructions related to that location. This allows appropriate instructions to be received by filtering based on the user's current situation and environment. Some or all of the above-described processing in the instruction receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction receiving unit can input current situation and environmental data to the generation AI and have the generation AI perform filtering.

[0037] When receiving an instruction, the instruction receiving unit can prioritize receiving highly relevant instructions by taking into account the user's geographical location information. For example, when the user is in a specific location, the instruction receiving unit can prioritize receiving instructions related to that location. Furthermore, when the user is traveling, the instruction receiving unit can also prioritize receiving instructions related to traveling. Furthermore, when the user is at home, the instruction receiving unit can also prioritize receiving instructions related to the home. In this way, by taking the user's geographical location information into account, highly relevant instructions can be prioritized. Some or all of the above-described processing in the instruction receiving unit may be performed using AI, for example, or may be performed without using AI. For example, the instruction receiving unit can input geographical location information data to the generation AI and cause the generation AI to select highly relevant instructions.

[0038] When receiving an instruction, the instruction receiving unit can analyze the user's social media activity and receive related instructions. For example, if the user posts about a specific topic on social media, the instruction receiving unit can prioritize receiving instructions related to that topic. Furthermore, if the user is participating in a specific event on social media, the instruction receiving unit can prioritize receiving instructions related to the event. Furthermore, if the user has checked in to a specific location on social media, the instruction receiving unit can prioritize receiving instructions related to the location. This allows related instructions to be received by analyzing the user's social media activity. Some or all of the above-described processing in the instruction receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction receiving unit can input social media activity data to a generation AI and cause the generation AI to select related instructions.

[0039] During learning, the learning unit can apply an optimal learning algorithm by referring to the robot's past operation data. For example, the learning unit applies an optimal learning algorithm based on the robot's past successful operation patterns. The learning unit can also apply a learning algorithm to avoid the robot's past unsuccessful operation patterns. Furthermore, the learning unit can analyze the robot's past operation data and apply the most efficient learning algorithm. By referring to the robot's past operation data, the optimal learning algorithm can be applied and the accuracy of learning can be improved. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past operation data into the generation AI and cause the generation AI to apply the optimal learning algorithm.

[0040] During learning, the learning unit can apply different learning methods depending on the operating environment of the robot. For example, the learning unit applies a learning method depending on the working environment in a factory. The learning unit can also apply a learning method depending on the working environment in a medical setting. Furthermore, the learning unit can also apply a learning method depending on the working environment in a nursing care setting. This allows the effect of learning to be maximized by applying a learning method depending on the operating environment of the robot. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input operating environment data into the generation AI and cause the generation AI to apply the optimal learning method.

[0041] During learning, the learning unit can weight the learning data based on the robot's operation history. For example, the learning unit weights the operation patterns that the robot has been successful in the past while learning. The learning unit can also weight the operation patterns that the robot has been unsuccessful in the past while learning. Furthermore, the learning unit can analyze the robot's operation history and weight the most efficient learning data while learning. Thus, weighting the learning data based on the robot's operation history can improve the accuracy of learning. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input operation history data to a generation AI and cause the generation AI to weight the learning data.

[0042] During learning, the learning unit can improve the accuracy of learning by referring to external data related to the robot's movements. The learning unit, for example, improves the accuracy of learning by referring to external data related to the robot's movements. The learning unit can also analyze the external data related to the robot's movements and apply an optimal learning method. Furthermore, the learning unit can improve the accuracy of learning based on the external data related to the robot's movements. In this way, the accuracy of learning can be improved by referring to the external data related to the robot's movements. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input external data to the generation AI and cause the generation AI to improve the accuracy of learning.

[0043] During optimization, the optimization unit can analyze the robot's motion data in real time and generate an optimal motion pattern. For example, the optimization unit can analyze the robot's motion data in real time and generate an optimal motion pattern. The optimization unit can also analyze the robot's motion data in real time and generate the most efficient motion pattern. Furthermore, the optimization unit can analyze the robot's motion data in real time and generate a motion pattern that eliminates unnecessary movements. As a result, by analyzing the robot's motion data in real time, an optimal motion pattern can be generated, enabling efficient motion. Some or all of the above-mentioned processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit can input motion data to a generation AI and cause the generation AI to generate an optimal motion pattern.

[0044] During optimization, the optimization unit can apply different optimization methods depending on the operating environment of the robot. For example, the optimization unit applies an optimization method depending on the working environment in a factory. The optimization unit can also apply an optimization method depending on the working environment in a medical setting. Furthermore, the optimization unit can also apply an optimization method depending on the working environment in a nursing care setting. In this way, by applying an optimization method depending on the operating environment of the robot, the effect of optimization can be maximized. Some or all of the above-mentioned processing in the optimization unit may be performed using AI, for example, or may be performed without using AI. For example, the optimization unit can input operating environment data to the generation AI and cause the generation AI to apply the optimal optimization method.

[0045] During optimization, the optimization unit can weight the optimization based on the robot's operation history. For example, the optimization unit optimizes by weighting operation patterns that the robot has been successful in the past. The optimization unit can also optimize by weighting operation patterns that the robot has been unsuccessful in the past. Furthermore, the optimization unit can analyze the robot's operation history and optimize by weighting the most efficient operation pattern. In this way, by weighting the optimization based on the robot's operation history, the accuracy of the optimization can be improved. Some or all of the above-described processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit can input operation history data to a generation AI and cause the generation AI to perform optimization weighting.

[0046] During optimization, the optimization unit can improve the accuracy of optimization by referring to external data related to the robot's motion. For example, the optimization unit can improve the accuracy of optimization by referring to external data related to the robot's motion. The optimization unit can also analyze the external data related to the robot's motion and apply an optimal optimization method. Furthermore, the optimization unit can improve the accuracy of optimization based on the external data related to the robot's motion. In this way, the accuracy of optimization can be improved by referring to the external data related to the robot's motion. Some or all of the above-described processing in the optimization unit may be performed using AI, for example, or may be performed without using AI. For example, the optimization unit can input external data to the generation AI and cause the generation AI to improve the accuracy of optimization.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The robot system may further include a sensor data analysis unit. The sensor data analysis unit collects and analyzes data from various sensors installed in the robot. For example, the sensor data analysis unit can grasp the robot's operating environment in real time based on data from temperature sensors, humidity sensors, light sensors, etc. This allows the robot to quickly respond to changes in the environment and perform optimal operations. The sensor data analysis unit also has an anomaly detection function and can stop the robot's operation if it detects abnormal data. Furthermore, the sensor data analysis unit can provide the collected data to a learning unit, which can use the data to generate more accurate operation patterns.

[0049] When analyzing a robot's operational data, the learning unit can also refer to data from other robots. For example, by collecting and analyzing operational data from other robots operating in the same factory, it is possible to generate more efficient operational patterns. Also, by referring to data from robots operating in different environments, it is possible to learn optimal operational patterns for different environments. Furthermore, the learning unit can find solutions to common problems based on the operational data of other robots, improving overall efficiency.

[0050] The optimization unit can aim to minimize energy consumption when optimizing the robot's behavior. For example, it can analyze the robot's behavior patterns and prioritize behavior that is more energy-efficient. The optimization unit can also monitor the robot's remaining battery level and select behavior patterns that reduce energy consumption when the battery is low. Furthermore, the optimization unit can collect energy consumption data and provide it to the learning unit, which can use the data to generate more energy-efficient behavior patterns in the future.

[0051] The learning unit can incorporate user feedback when analyzing the robot's motion data. For example, a user can evaluate the robot's motion and provide the evaluation data to the learning unit. Based on the user's evaluation data, the learning unit can generate motion patterns that better suit the user's needs. Also, by reflecting user feedback in real time, the robot's motion can be quickly improved. Furthermore, the learning unit can accumulate user feedback data and use it for long-term improvements.

[0052] The optimization unit can take task priorities into account when optimizing the robot's operations. For example, when there are multiple tasks, it can determine the priority of the tasks based on their importance and urgency and execute them in the optimal order. The optimization unit can also monitor the progress of tasks in real time and readjust priorities as necessary. Furthermore, the optimization unit can provide task priority data to the learning unit, which can be used as data to achieve more efficient task management in the future.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The instruction receiving unit receives a programming instruction. The instruction receiving unit can receive the instruction based on, for example, the type of code or the content of the instruction. Step 2: The learning unit uses the generation AI to make the robot learn on its own based on the instructions received by the instruction receiving unit. The learning unit can perform learning using methods such as reinforcement learning and supervised learning. The learning unit can also analyze the robot's motion data and generate optimal motion patterns. For example, the learning unit generates motion patterns based on the robot's motion data that aim to minimize energy consumption and shorten work time. Step 3: The optimization unit optimizes the robot's behavior in real time based on the results learned by the learning unit. The optimization unit can adjust the behavior in real time based on, for example, response time or adjustment frequency. The optimization unit can also optimize the robot's behavior to maximize work efficiency in the factory. For example, the optimization unit eliminates unnecessary movements depending on the work environment in the factory, thereby improving work efficiency. Some or all of the above-described processing in the optimization unit may be performed using, for example, AI. For example, the optimization unit can optimize the behavior using an AI model that inputs the behavior patterns generated by the learning unit and outputs optimal behavior patterns. Furthermore, the optimization unit can also build a system for optimizing the robot's behavior in real time. For example, the optimization unit analyzes the robot's behavior data in real time and generates optimal behavior patterns. This allows the robot's behavior to be efficiently optimized and productivity to be improved.

[0055] (Example 2) A robot system according to an embodiment of the present invention utilizes generative AI to infinitely expand its range of activities. While conventional robots are programmed to operate, this robot system utilizes generative AI to significantly expand the robot's range of activities. Specifically, the overall robot performance is improved by combining programmed instructions with generative AI learning. This can improve Japan's competitiveness in preparation for labor shortages and an aging population with a declining birthrate. For example, conventional robots were programmed to perform specific tasks. Their primary role was to repeat specific movements, such as assembly work in a factory or picking in a warehouse. However, these robots were limited to pre-programmed movements and had difficulty adapting to environmental changes or new tasks. In this invention, generative AI allows the robot to learn on its own and flexibly adapt to environmental changes and new tasks. For example, generative AI can analyze the robot's motion data and generate optimal motion patterns. This allows the robot to quickly adapt to new tasks. Furthermore, generative AI can optimize the robot's motion in real time. For example, to maximize work efficiency in a factory, generative AI can optimize the robot's motion and eliminate unnecessary movements. This improves work efficiency and productivity. Furthermore, the robot of the present invention is an important tool for preparing for labor shortages and an aging population with a declining birthrate. For example, it is expected to be used in fields where there is a labor shortage, such as elderly care and medical support. By using generative AI, the robot can learn on its own and provide services tailored to individual needs. Thus, the present invention relates to a robot that utilizes generative AI to infinitely expand its activities. By combining program instructions with learning responses provided by generative AI, the overall performance of the robot can be improved. This will help prepare for labor shortages and an aging population with a declining birthrate and improve Japan's competitiveness. This allows the robot system to optimize the robot's behavior using generative AI and flexibly respond to environmental changes and new tasks.

[0056] A robot system according to an embodiment includes an instruction receiving unit, a learning unit, and an optimization unit. The instruction receiving unit receives instructions via programming. The instruction receiving unit can receive instructions based on, for example, the type of code or the content of the instruction. The learning unit uses a generative AI to allow the robot to learn on its own based on the instructions received by the instruction receiving unit. The learning unit can perform learning using methods such as reinforcement learning and supervised learning. The learning unit can also analyze the robot's motion data and generate optimal motion patterns. For example, the learning unit generates motion patterns based on the robot's motion data, aiming to minimize energy consumption and shorten work time. The optimization unit optimizes the robot's motion in real time based on the results learned by the learning unit. The optimization unit can adjust the motion in real time based on, for example, response time and adjustment frequency. The optimization unit can also optimize the robot's motion to maximize work efficiency in a factory. For example, the optimization unit can eliminate unnecessary movements according to the work environment in the factory, improving work efficiency. As a result, the robot system according to an embodiment can optimize the robot's motion using a generative AI and flexibly respond to environmental changes and new tasks. Some or all of the above-described processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit can optimize the robot's movement using an AI model that inputs the movement patterns generated by the learning unit and outputs optimal movement patterns. Furthermore, the optimization unit can also build a system for optimizing the robot's movement in real time. For example, the optimization unit analyzes the robot's movement data in real time and generates optimal movement patterns. This allows the robot's movement to be efficiently optimized and productivity to be improved.

[0057] The learning unit can analyze the robot's motion data and generate efficient motion patterns. Efficient motion patterns include, but are not limited to, minimizing energy consumption and shortening work time. The learning unit, for example, collects and analyzes the robot's motion data. For example, the learning unit generates an optimal motion pattern based on the robot's motion data. The learning unit can also analyze the motion data and generate an efficient motion pattern using a generation AI. For example, the learning unit inputs the motion data to the generation AI and outputs an optimal motion pattern. This allows the robot's motion data to be analyzed to generate an optimal motion pattern, enabling efficient motion. Some or all of the above-described processing in the learning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the learning unit can input the motion data to the generation AI and cause the generation AI to generate an efficient motion pattern.

[0058] The optimization unit can optimize the robot's operation to improve work efficiency in a factory. Examples of improving work efficiency include, but are not limited to, shortening work time and reducing errors. The optimization unit, for example, analyzes the work environment in a factory and generates an optimal operation pattern. For example, the optimization unit collects and analyzes work data in the factory. The optimization unit can also analyze the work data and generate an optimal operation pattern using a generation AI. For example, the optimization unit inputs the work data into the generation AI and outputs an optimal operation pattern. This optimizes the robot's operation to maximize work efficiency in the factory and improves productivity. Some or all of the above-described processing in the optimization unit may be performed using AI, for example, or may be performed without using AI. For example, the optimization unit inputs work data into the generation AI and causes the generation AI to generate an optimal operation pattern.

[0059] The learning unit can learn to adapt to environmental changes and new tasks. Examples of environmental changes include, but are not limited to, temperature changes and the appearance of obstacles. Examples of new tasks include, but are not limited to, new work procedures and new work targets. The learning unit, for example, collects and analyzes data to adapt to environmental changes and new tasks. For example, the learning unit generates optimal movement patterns based on environmental data and task data. The learning unit can also use a generation AI to analyze the environmental data and task data and generate optimal movement patterns. For example, the learning unit inputs the environmental data and task data into the generation AI and outputs optimal movement patterns. This improves the adaptability of the robot by learning to flexibly adapt to environmental changes and new tasks. Some or all of the above-described processing in the learning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the learning unit inputs environmental data and task data into the generation AI and causes the generation AI to generate optimal movement patterns.

[0060] The optimization unit can eliminate unnecessary movements and improve work efficiency. Examples of unnecessary movements include, but are not limited to, unnecessary travel and redundant work. For example, the optimization unit analyzes the robot's motion data and identifies unnecessary movements. For example, the optimization unit generates a motion pattern for eliminating unnecessary movements based on the motion data. The optimization unit can also analyze the motion data and generate a motion pattern for eliminating unnecessary movements using a generation AI. For example, the optimization unit inputs the motion data to the generation AI and outputs a motion pattern for eliminating unnecessary movements. This eliminates unnecessary movements, thereby improving work efficiency and productivity. Some or all of the above-described processing in the optimization unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the optimization unit inputs the motion data to the generation AI and causes the generation AI to generate a motion pattern for eliminating unnecessary movements.

[0061] The learning unit can perform learning to adapt to elderly care and medical support. Elderly care includes, but is not limited to, mobility assistance and meal assistance. Medical support includes, but is not limited to, patient monitoring and medication distribution. The learning unit collects and analyzes data for elderly care and medical support. For example, the learning unit generates optimal movement patterns based on nursing data and medical data. The learning unit can also analyze the nursing data and medical data using a generation AI to generate optimal movement patterns. For example, the learning unit inputs nursing data and medical data into the generation AI and outputs optimal movement patterns. This allows the robot to learn to adapt to elderly care and medical support, thereby enabling it to be effectively used in these fields. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without AI. For example, the learning unit inputs nursing data and medical data into the generation AI and causes the generation AI to generate optimal movement patterns.

[0062] The instruction receiving unit can estimate the user's emotions and adjust the instruction receiving method based on the estimated user emotions. For example, if the user is feeling stressed, the instruction receiving unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the instruction receiving unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the instruction receiving unit can prioritize voice input and enable quick input of instructions. This allows for an easy-to-use interface to be provided by adjusting the instruction receiving method based on the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the instruction receiving unit can be performed using, for example, an AI, or without an AI. For example, the instruction receiving unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the interface based on the emotion.

[0063] The instruction accepting unit can analyze the user's past instruction history and select the optimal instruction accepting method. For example, the instruction accepting unit can automatically display instructions that the user frequently input in the past as candidates. The instruction accepting unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the instruction accepting unit can predict and suggest instructions to be used in a specific time period based on the user's past instruction history. This makes it possible to select the optimal instruction accepting method by analyzing the user's past instruction history, thereby improving user convenience. Some or all of the above-described processing in the instruction accepting unit can be performed using, or without, AI. For example, the instruction accepting unit can input past instruction history data into a generation AI and cause the generation AI to select the optimal instruction accepting method.

[0064] The instruction receiving unit can perform filtering based on the user's current situation and environment when receiving instructions. For example, when the user is in a noisy environment, the instruction receiving unit suppresses voice input and prioritizes text input. Furthermore, when the user is moving, the instruction receiving unit can prioritize simple instruction input and avoid complex operations. Furthermore, when the user is in a specific location, the instruction receiving unit can prioritize receiving instructions related to that location. This allows appropriate instructions to be received by filtering based on the user's current situation and environment. Some or all of the above-described processing in the instruction receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction receiving unit can input current situation and environmental data to the generation AI and have the generation AI perform filtering.

[0065] The instruction receiving unit can estimate the user's emotions and determine the priority of instructions to be received based on the estimated user's emotions. For example, when the user is nervous, the instruction receiving unit can prioritize receiving important instructions. Furthermore, when the user is relaxed, the instruction receiving unit can prioritize receiving normal instructions. Furthermore, when the user is in a hurry, the instruction receiving unit can prioritize receiving urgent instructions. This allows important instructions to be prioritized by determining the priority of instructions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the instruction receiving unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the instruction receiving unit can input the user's emotion data into the generation AI and cause the generation AI to determine the priority of instructions based on the emotion.

[0066] When receiving an instruction, the instruction receiving unit can prioritize receiving highly relevant instructions by taking into account the user's geographical location information. For example, when the user is in a specific location, the instruction receiving unit can prioritize receiving instructions related to that location. Furthermore, when the user is traveling, the instruction receiving unit can also prioritize receiving instructions related to traveling. Furthermore, when the user is at home, the instruction receiving unit can also prioritize receiving instructions related to the home. In this way, by taking the user's geographical location information into account, highly relevant instructions can be prioritized. Some or all of the above-described processing in the instruction receiving unit may be performed using AI, for example, or may be performed without using AI. For example, the instruction receiving unit can input geographical location information data to the generation AI and cause the generation AI to select highly relevant instructions.

[0067] When receiving an instruction, the instruction receiving unit can analyze the user's social media activity and receive related instructions. For example, if the user posts about a specific topic on social media, the instruction receiving unit can prioritize receiving instructions related to that topic. Furthermore, if the user is participating in a specific event on social media, the instruction receiving unit can prioritize receiving instructions related to the event. Furthermore, if the user has checked in to a specific location on social media, the instruction receiving unit can prioritize receiving instructions related to the location. This allows related instructions to be received by analyzing the user's social media activity. Some or all of the above-described processing in the instruction receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction receiving unit can input social media activity data to a generation AI and cause the generation AI to select related instructions.

[0068] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. For example, if the user is relaxed, the learning unit can select detailed training data. Furthermore, if the user is in a hurry, the learning unit can select concise training data. Furthermore, if the user is excited, the learning unit can select visually stimulating training data. This enables more effective learning by selecting training data based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the learning unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the learning unit can input the user's emotion data into the generation AI and cause the generation AI to select training data based on the emotion.

[0069] During learning, the learning unit can apply an optimal learning algorithm by referring to the robot's past operation data. For example, the learning unit applies an optimal learning algorithm based on the robot's past successful operation patterns. The learning unit can also apply a learning algorithm to avoid the robot's past unsuccessful operation patterns. Furthermore, the learning unit can analyze the robot's past operation data and apply the most efficient learning algorithm. By referring to the robot's past operation data, the optimal learning algorithm can be applied and the accuracy of learning can be improved. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past operation data into the generation AI and cause the generation AI to apply the optimal learning algorithm.

[0070] During learning, the learning unit can apply different learning methods depending on the operating environment of the robot. For example, the learning unit applies a learning method depending on the working environment in a factory. The learning unit can also apply a learning method depending on the working environment in a medical setting. Furthermore, the learning unit can also apply a learning method depending on the working environment in a nursing care setting. This allows the effect of learning to be maximized by applying a learning method depending on the operating environment of the robot. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input operating environment data into the generation AI and cause the generation AI to apply the optimal learning method.

[0071] The learning unit can estimate the user's emotions and adjust the frequency of learning based on the estimated user emotions. For example, the learning unit can increase the frequency of learning when the user is relaxed. The learning unit can also decrease the frequency of learning when the user is in a hurry. Furthermore, the learning unit can adjust the frequency of learning when the user is excited. This enables effective learning by adjusting the frequency of learning based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the learning unit can be performed using an AI, for example, or without an AI. For example, the learning unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the learning frequency based on the emotion.

[0072] During learning, the learning unit can weight the learning data based on the robot's operation history. For example, the learning unit weights the operation patterns that the robot has been successful in the past while learning. The learning unit can also weight the operation patterns that the robot has been unsuccessful in the past while learning. Furthermore, the learning unit can analyze the robot's operation history and weight the most efficient learning data while learning. Thus, weighting the learning data based on the robot's operation history can improve the accuracy of learning. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input operation history data to a generation AI and cause the generation AI to weight the learning data.

[0073] During learning, the learning unit can improve the accuracy of learning by referring to external data related to the robot's movements. The learning unit, for example, improves the accuracy of learning by referring to external data related to the robot's movements. The learning unit can also analyze the external data related to the robot's movements and apply an optimal learning method. Furthermore, the learning unit can improve the accuracy of learning based on the external data related to the robot's movements. In this way, the accuracy of learning can be improved by referring to the external data related to the robot's movements. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input external data to the generation AI and cause the generation AI to improve the accuracy of learning.

[0074] The optimization unit can estimate the user's emotions and adjust the optimization criteria based on the estimated user emotions. For example, the optimization unit can apply detailed optimization criteria when the user is relaxed. The optimization unit can also apply simple optimization criteria when the user is in a hurry. Furthermore, the optimization unit can apply visually stimulating optimization criteria when the user is excited. This enables more appropriate optimization by adjusting the optimization criteria based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the optimization unit can be performed using, for example, an AI, or without an AI. For example, the optimization unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the optimization criteria based on the emotion.

[0075] During optimization, the optimization unit can analyze the robot's motion data in real time and generate an optimal motion pattern. For example, the optimization unit can analyze the robot's motion data in real time and generate an optimal motion pattern. The optimization unit can also analyze the robot's motion data in real time and generate the most efficient motion pattern. Furthermore, the optimization unit can analyze the robot's motion data in real time and generate a motion pattern that eliminates unnecessary movements. As a result, by analyzing the robot's motion data in real time, an optimal motion pattern can be generated, enabling efficient motion. Some or all of the above-mentioned processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit can input motion data to a generation AI and cause the generation AI to generate an optimal motion pattern.

[0076] During optimization, the optimization unit can apply different optimization methods depending on the operating environment of the robot. For example, the optimization unit applies an optimization method depending on the working environment in a factory. The optimization unit can also apply an optimization method depending on the working environment in a medical setting. Furthermore, the optimization unit can also apply an optimization method depending on the working environment in a nursing care setting. In this way, by applying an optimization method depending on the operating environment of the robot, the effect of optimization can be maximized. Some or all of the above-mentioned processing in the optimization unit may be performed using AI, for example, or may be performed without using AI. For example, the optimization unit can input operating environment data to the generation AI and cause the generation AI to apply the optimal optimization method.

[0077] The optimization unit can estimate the user's emotions and determine the priority of optimization based on the estimated user's emotions. For example, if the user is relaxed, the optimization unit can prioritize normal optimization. If the user is in a hurry, the optimization unit can also prioritize urgent optimization. Furthermore, if the user is excited, the optimization unit can also prioritize visually stimulating optimization. Thus, by determining the priority of optimization based on the user's emotions, important optimization can be prioritized. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the optimization unit can be performed using, for example, an AI, or without an AI. For example, the optimization unit can input the user's emotion data into the generation AI and cause the generation AI to determine the priority of optimization based on emotions.

[0078] During optimization, the optimization unit can weight the optimization based on the robot's operation history. For example, the optimization unit optimizes by weighting operation patterns that the robot has been successful in the past. The optimization unit can also optimize by weighting operation patterns that the robot has been unsuccessful in the past. Furthermore, the optimization unit can analyze the robot's operation history and optimize by weighting the most efficient operation pattern. In this way, by weighting the optimization based on the robot's operation history, the accuracy of the optimization can be improved. Some or all of the above-described processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit can input operation history data to a generation AI and cause the generation AI to perform optimization weighting.

[0079] During optimization, the optimization unit can improve the accuracy of optimization by referring to external data related to the robot's motion. For example, the optimization unit can improve the accuracy of optimization by referring to external data related to the robot's motion. The optimization unit can also analyze the external data related to the robot's motion and apply an optimal optimization method. Furthermore, the optimization unit can improve the accuracy of optimization based on the external data related to the robot's motion. In this way, the accuracy of optimization can be improved by referring to the external data related to the robot's motion. Some or all of the above-described processing in the optimization unit may be performed using AI, for example, or may be performed without using AI. For example, the optimization unit can input external data to the generation AI and cause the generation AI to improve the accuracy of optimization. === Hard Collateral 1-1 === Each of the multiple elements including the instruction receiving unit, learning unit, and optimization unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the instruction receiving unit is realized by the control unit 46A of the smart device 14 and receives instructions via programming. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and the robot learns by itself using a generative AI. The optimization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and optimizes the robot's behavior in real time based on the learned results. === Hard Collateral 1-2 === Each of the multiple elements including the instruction receiving unit, learning unit, and optimization unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the instruction receiving unit is realized by the control unit 46A of the smart glasses 214 and receives instructions via programming. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and the robot learns by itself using a generative AI. The optimization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and optimizes the robot's behavior in real time based on the learned results. === Hard Collateral 1-3 === Each of the multiple elements including the instruction receiving unit, learning unit, and optimization unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the instruction receiving unit is realized by the control unit 46A of the headset-type terminal 314 and receives instructions via programming. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and the robot learns by itself using a generative AI. The optimization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and optimizes the robot's behavior in real time based on the learned results. === Hard Collateral 1-4 === Each of the multiple elements including the instruction receiving unit, learning unit, and optimization unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the instruction receiving unit is realized by the control unit 46A of the robot 414 and receives instructions by programming. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and the robot learns by itself using a generative AI. The optimization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and optimizes the robot's behavior in real time based on the learned results.

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

[0081] The robot system may further include a sensor data analysis unit. The sensor data analysis unit collects and analyzes data from various sensors installed in the robot. For example, the sensor data analysis unit can grasp the robot's operating environment in real time based on data from temperature sensors, humidity sensors, light sensors, etc. This allows the robot to quickly respond to changes in the environment and perform optimal operations. The sensor data analysis unit also has an anomaly detection function and can stop the robot's operation if it detects abnormal data. Furthermore, the sensor data analysis unit can provide the collected data to a learning unit, which can use the data to generate more accurate operation patterns.

[0082] When analyzing a robot's operational data, the learning unit can also refer to data from other robots. For example, by collecting and analyzing operational data from other robots operating in the same factory, it is possible to generate more efficient operational patterns. Also, by referring to data from robots operating in different environments, it is possible to learn optimal operational patterns for different environments. Furthermore, the learning unit can find solutions to common problems based on the operational data of other robots, improving overall efficiency.

[0083] The optimization unit can aim to minimize energy consumption when optimizing the robot's behavior. For example, it can analyze the robot's behavior patterns and prioritize behavior that is more energy-efficient. The optimization unit can also monitor the robot's remaining battery level and select behavior patterns that reduce energy consumption when the battery is low. Furthermore, the optimization unit can collect energy consumption data and provide it to the learning unit, which can use the data to generate more energy-efficient behavior patterns in the future.

[0084] The learning unit can incorporate user feedback when analyzing the robot's motion data. For example, a user can evaluate the robot's motion and provide the evaluation data to the learning unit. Based on the user's evaluation data, the learning unit can generate motion patterns that better suit the user's needs. Also, by reflecting user feedback in real time, the robot's motion can be quickly improved. Furthermore, the learning unit can accumulate user feedback data and use it for long-term improvements.

[0085] The optimization unit can take task priorities into account when optimizing the robot's operations. For example, when there are multiple tasks, it can determine the priority of the tasks based on their importance and urgency and execute them in the optimal order. The optimization unit can also monitor the progress of tasks in real time and readjust priorities as necessary. Furthermore, the optimization unit can provide task priority data to the learning unit, which can be used as data to achieve more efficient task management in the future.

[0086] The learning unit can estimate the user's emotions and adjust the learning content based on the estimated user's emotions. For example, if the user is feeling stressed, the learning unit can prioritize learning easy tasks or relaxing content. Also, if the user is relaxed, the learning unit can also have the user learn more complex tasks or challenging content. Furthermore, if the user is excited, the learning unit can also have the user learn visually stimulating content. In this way, adjusting the learning content based on the user's emotions enables effective learning.

[0087] The instruction receiving unit can estimate the user's emotions and adjust the instruction receiving method based on the estimated user's emotions. For example, if the user is feeling stressed, a simple interface can be provided to minimize input steps. Alternatively, if the user is relaxed, the instruction receiving unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the instruction receiving unit can prioritize voice input to enable quick input of instructions. In this way, by adjusting the instruction receiving method based on the user's emotions, an easy-to-use interface can be provided for the user.

[0088] The learning unit can estimate the user's emotions and select learning data based on the estimated user emotions. For example, if the user is relaxed, detailed learning data can be selected. If the user is in a hurry, concise learning data can be selected. Furthermore, if the user is excited, visually stimulating learning data can be selected. Thus, by selecting learning data based on the user's emotions, more effective learning is possible.

[0089] The optimization unit can estimate the user's emotions and adjust the optimization criteria based on the estimated user's emotions. For example, if the user is relaxed, a detailed optimization criterion can be applied. If the user is in a hurry, a simple optimization criterion can be applied. Furthermore, if the user is excited, a visually stimulating optimization criterion can be applied. This allows for more appropriate optimization by adjusting the optimization criteria based on the user's emotions.

[0090] The optimization unit can estimate the user's emotions and determine the priority of optimization based on the estimated user's emotions. For example, if the user is relaxed, normal optimization can be prioritized. If the user is in a hurry, urgent optimization can be prioritized. Furthermore, if the user is excited, visually stimulating optimization can be prioritized. In this way, by determining the priority of optimization based on the user's emotions, important optimization can be prioritized.

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

[0092] Step 1: The instruction receiving unit receives a programming instruction. The instruction receiving unit can receive the instruction based on, for example, the type of code or the content of the instruction. Step 2: The learning unit uses the generation AI to make the robot learn on its own based on the instructions received by the instruction receiving unit. The learning unit can perform learning using methods such as reinforcement learning and supervised learning. The learning unit can also analyze the robot's motion data and generate optimal motion patterns. For example, the learning unit generates motion patterns based on the robot's motion data that aim to minimize energy consumption and shorten work time. Step 3: The optimization unit optimizes the robot's behavior in real time based on the results learned by the learning unit. The optimization unit can adjust the behavior in real time based on, for example, response time or adjustment frequency. The optimization unit can also optimize the robot's behavior to maximize work efficiency in the factory. For example, the optimization unit eliminates unnecessary movements depending on the work environment in the factory, thereby improving work efficiency. Some or all of the above-described processing in the optimization unit may be performed using, for example, AI. For example, the optimization unit can optimize the behavior using an AI model that inputs the behavior patterns generated by the learning unit and outputs optimal behavior patterns. Furthermore, the optimization unit can also build a system for optimizing the robot's behavior in real time. For example, the optimization unit analyzes the robot's behavior data in real time and generates optimal behavior patterns. This allows the robot's behavior to be efficiently optimized and productivity to be improved.

[0093] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0095] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0096] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0100] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0101] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0102] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0103] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0104] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0105] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0107] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0108] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0111] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0120] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0121] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0124] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0125] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0127] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0132] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0133] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0136] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0137] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0138] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0139] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0140] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0141] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0142] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0143] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0144] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0147] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0148] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0149] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0150] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0151] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0152] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0153] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0154] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0155] 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.

[0156] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0157] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0158] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0159] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0160] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0161] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0162] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0163] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0164] [Explanation of symbols]

[0165] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an instruction receiving unit that receives instructions by programming; a learning unit that allows the robot to learn by itself based on the instructions received by the instruction receiving unit; an optimization unit that adjusts the robot's operation in real time based on the results learned by the learning unit. A system characterized by:

2. The learning unit Analyzing robot movement data and generating efficient movement patterns 2. The system of claim 1.

3. The optimization unit Optimizing robot movements to improve work efficiency in factories 2. The system of claim 1.

4. The learning unit Learning to adapt to changes in the environment and new tasks 2. The system of claim 1.

5. The optimization unit Eliminate unnecessary movements and improve work efficiency 2. The system of claim 1.

6. The learning unit Learning to adapt to elderly care and medical support 2. The system of claim 1.

7. The instruction receiving unit Estimate the user's emotions and adjust the way instructions are received based on the estimated user emotions.

2. The system of claim 1.

8. The instruction receiving unit Analyze the user's past instruction history and select the optimal instruction reception method 2. The system of claim 1.

Images