system

Abstract

The system according to this embodiment aims to easily integrate a company's existing systems, services, and data sources with robots. [Solution] The system according to the embodiment comprises an infrastructure unit, a communication unit, a robot unit, a generation unit, and an integration unit. The infrastructure unit has functions such as data storage and processing, high-speed computing capability, and API provision. The communication unit uses the functions such as data storage and processing, high-speed computing capability, and API provision provided by the infrastructure unit to perform real-time communication, remote control, and immediate updates between the robot and the cloud. The robot unit uses the functions of real-time communication, remote control, and immediate updates provided by the communication unit to perform movement, operation, interaction, and environment recognition. The generation unit uses the functions of movement, operation, interaction, and environment recognition provided by the robot unit to perform natural language processing and automatic code generation. The integration unit uses the functions of natural language processing and automatic code generation provided by the generation unit to integrate the company's existing systems, services, and data sources with the robot.

Description

Technical Field

[0004]

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to integrate an enterprise's existing systems, services, and data sources with a robot, and efficient operation is difficult.

[0005] The system according to the embodiment aims to easily integrate an enterprise's existing systems, services, and data sources with a robot.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an infrastructure unit, a communication unit, a robot unit, a generation unit, and an integration unit. The infrastructure unit has functions such as data storage and processing, high-speed computing capability, and API provision. The communication unit uses the functions such as data storage and processing, high-speed computing capability, and API provision provided by the infrastructure unit to perform real-time communication, remote control, and immediate updates between the robot and the cloud. The robot unit uses the functions of real-time communication, remote control, and immediate updates provided by the communication unit to perform movement, operation, interaction, and environmental recognition. The generation unit uses the functions of movement, operation, interaction, and environmental recognition provided by the robot unit to perform natural language processing and automatic code generation. The integration unit uses the functions of natural language processing and automatic code generation provided by the generation unit to integrate the company's existing systems, services, and data sources with the robot. [Effects of the Invention]

[0007] The system according to this embodiment can easily integrate with a company's existing systems, services, and data sources with robots. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the tagged communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. 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), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. 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 data indicating the user input.

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

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

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

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

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

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

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

[0028] (Example of form 1) The next-generation robotics integration platform according to an embodiment of the present invention is a system that combines generative AI and iPaaS to provide an environment in which companies and developers can easily integrate their applications, services, and data into robots. This system includes a scalable and secure infrastructure, a 5G network, robot hardware, a generative AI model, and iPaaS functionality. For example, the infrastructure has functions such as data storage and processing, high-speed computing capabilities, and API provision, while the 5G network enables real-time communication between robots and the cloud, remote control, and instant updates. The robot hardware includes humanoid robots and cleaning robots, and has functions such as movement, operation, interaction, and environmental recognition. The generative AI model is used for natural language processing and automatic code generation to improve the intelligence of the robots. The iPaaS functionality plays a role in easily integrating existing systems, services, and data sources of companies with robots, providing functions such as drag-and-drop system integration, API management, and data transformation. Seamless system integration is possible, and existing systems and robots can be integrated without the need for specialized knowledge. In addition, automation by generative AI analyzes the needs of companies and automatically generates optimal robot functions. Furthermore, it features real-time adaptive capabilities that allow the robot to autonomously learn and adapt in response to changes in operations. A key feature is the development of a developer ecosystem, providing open APIs and SDKs to create an environment where developers can freely develop apps and services. A marketplace is also available, allowing developers to publish and sell their created apps and services. Potential use cases include automation of assembly lines and quality inspection in manufacturing, and inventory management and customer service in retail. This is expected to lead to improved operational efficiency, cost reduction, creation of new business opportunities, accelerated innovation, enhanced competitiveness and market dominance, and contributions to solving social issues. Specifically, generative AI automatically generates robot operation programs from company requirements, significantly reducing development time. It also utilizes natural language processing models to enable natural communication with users and analyzes data in real time to optimize robot performance and services.This will enable companies and developers to easily build an ecosystem that allows them to utilize and develop robots, accelerating operational efficiency improvements and the creation of new services. As a result, the next-generation robotics integration platform will provide an environment where companies and developers can easily integrate their applications, services, and data into robots.

[0029] The next-generation robotics integrated platform according to this embodiment comprises an infrastructure unit, a communication unit, a robot unit, a generation unit, and an integration unit. The infrastructure unit has functions such as data storage and processing, high-speed computing capability, and API provision. For example, the infrastructure unit efficiently stores and processes large amounts of data using a database. The infrastructure unit can also perform complex calculations quickly using hardware with high-speed computing capabilities. Furthermore, the infrastructure unit facilitates collaboration with external systems using RESTful APIs and GraphQL. The communication unit utilizes the functions of data storage and processing, high-speed computing capability, and API provision provided by the infrastructure unit to perform real-time communication, remote control, and instant updates between the robot and the cloud. For example, the communication unit uses a 5G network to achieve high-speed, high-capacity, and low-latency communication. Furthermore, the communication unit can ensure data security using secure communication protocols. Furthermore, the communication unit can perform real-time data synchronization and instantly update the robot's movements. The robot unit uses the functions of real-time communication, remote control, and instant updates provided by the communication unit to perform movement, operation, interaction, and environmental recognition. The robotics unit performs various tasks using, for example, humanoid robots and cleaning robots. The robotics unit can also recognize its surroundings using sensors and avoid obstacles. Furthermore, it can interact with users using voice recognition technology. The generation unit utilizes the movement, operation, interaction, and environmental recognition functions provided by the robotics unit to perform natural language processing and automatic code generation. For example, the generation unit uses generative AI to understand user instructions in natural language and generate appropriate actions. It can also use generative AI to automatically generate robot action programs based on corporate requirements. Furthermore, the generation unit can use generative AI to achieve natural communication with users. The integration unit utilizes the natural language processing and automatic code generation functions provided by the generation unit to integrate existing corporate systems, services, and data sources with the robots. For example, the integration unit can easily integrate systems through drag-and-drop system integration.Furthermore, the integration unit can efficiently connect with external systems using its API management function. In addition, the integration unit can unify different data formats using its data conversion function, enabling smooth data exchange between systems. As a result, the next-generation robotics integration platform according to this embodiment can provide an environment where companies and developers can easily integrate their own applications, services, and data into robots.

[0030] The infrastructure unit possesses functions such as data storage and processing, high-speed computing capabilities, and API provision. For example, the infrastructure unit efficiently stores and processes large amounts of data using databases. Specifically, it utilizes relational databases and NoSQL databases, selecting the optimal storage method according to the data type and application. Furthermore, the infrastructure unit can rapidly perform complex calculations using hardware with high-speed computing capabilities. For instance, it uses servers equipped with GPUs and FPGAs to rapidly train machine learning models and perform real-time data analysis. In addition, the infrastructure unit facilitates integration with external systems using RESTful APIs and GraphQL. This allows developers to easily utilize system functions through standardized interfaces. The infrastructure unit is designed with scalability in mind, allowing resources to be dynamically expanded and contracted according to demand. For example, by utilizing cloud-based infrastructure, it can handle peak loads and optimize cost efficiency. The infrastructure unit also includes data backup and recovery functions, ensuring system reliability and data security. As a result, the infrastructure unit can support stable operation as the foundation for the next-generation robotics integrated platform.

[0031] The Communications Department utilizes functions provided by the Infrastructure Department, such as data storage and processing, high-speed computing capabilities, and API provision, to enable real-time communication, remote control, and instant updates between robots and the cloud. For example, the Communications Department uses 5G networks to achieve high-speed, high-capacity, and low-latency communication. This allows robots to seamlessly interact with the cloud and send and receive data in real time. Furthermore, the Communications Department ensures data security using secure communication protocols. For example, it uses TLS or VPN to encrypt communication paths and prevent unauthorized access and data leaks. In addition, the Communications Department performs real-time data synchronization, instantly updating robot operations. This allows robots to operate based on the latest information and respond quickly to environmental changes. The Communications Department enables coordinated operation among multiple robots, facilitating efficient task sharing and cooperation. For example, multiple cleaning robots can work together to efficiently clean a large area. The Communications Department also provides remote control functionality, allowing operators to control robots remotely. This enables safe and efficient work even in hazardous environments or hard-to-access locations. The communications unit incorporates redundancy and failover capabilities to enhance system availability, enabling rapid recovery even in the event of a communication failure. This allows the communications unit to improve the reliability and performance of the next-generation robotics integration platform.

[0032] The robotics unit utilizes real-time communication, remote control, and instant update functions provided by the communications unit to perform movement, operation, interaction, and environmental recognition. For example, the robotics unit uses humanoid robots and cleaning robots to perform various tasks. Humanoid robots can perform complex movements and interactions, while cleaning robots can efficiently clean floors and carpets. The robotics unit can also recognize its surroundings using sensors and avoid obstacles. For example, it can use LIDAR and cameras to detect surrounding objects and calculate the optimal path. Furthermore, the robotics unit can interact with users using speech recognition technology. This allows users to give instructions to the robot in natural language, and the robot will act accordingly. The robotics unit can learn and self-improve by using machine learning algorithms to determine the optimal actions for the environment and task. For example, a cleaning robot can learn efficient cleaning patterns based on past cleaning data and apply them to future cleaning. The robotics unit also enables cooperative operation between multiple robots, achieving efficient task sharing and cooperation. For example, multiple robots can cooperate to transport large loads. The robot unit is equipped with a battery management system, enabling efficient energy management. This allows the robot to operate for extended periods, minimizing work interruptions. As the core of the next-generation robotics integrated platform, the robot unit can perform a variety of tasks with high precision and efficiency.

[0033] The generation unit utilizes the movement, operation, dialogue, and environmental recognition functions provided by the robot unit to perform natural language processing and automatic code generation. For example, the generation unit uses generative AI to understand user instructions in natural language and generate appropriate actions. Specifically, the generative AI analyzes voice and text instructions from the user and generates a robot action sequence based on that content. For example, if the instruction is "clean the room," the generative AI will instruct the cleaning robot on the specific cleaning area and cleaning method. Furthermore, the generation unit can use the generative AI to automatically generate robot action programs based on the company's requirements. This eliminates the need for developers to manually create programs and allows for efficient customization of robot actions. In addition, the generation unit can use the generative AI to achieve natural communication with the user. For example, the generative AI generates appropriate answers to user questions and responds via voice or text through the robot. The generation unit can use machine learning models to understand user intent and context, enabling more sophisticated dialogue. For example, if a user asks "What time is the next meeting?", the generative AI will refer to calendar information and provide the exact time. The generation unit has built a feedback loop to continuously improve the quality of the robot's movements and interactions, and can update the model based on user feedback. This allows the generation unit, as part of a next-generation robotics integrated platform, to provide advanced functionality tailored to user needs.

[0034] The Integration Unit integrates existing corporate systems, services, and data sources with robots, utilizing natural language processing and automatic code generation capabilities provided by the Generation Unit. For example, the Integration Unit enables easy system integration through drag-and-drop functionality. Specifically, users can configure data flows between different systems and customize robot behavior using an intuitive interface. Furthermore, the Integration Unit efficiently integrates with external systems using API management capabilities. This allows companies to rapidly deploy new robotics functions while leveraging their existing IT infrastructure. Additionally, the Integration Unit uses data transformation capabilities to unify different data formats and facilitate smooth data exchange between systems. For example, it can convert CSV data to JSON format, providing data in a format understandable by robots. The Integration Unit supports real-time data exchange, maintaining data consistency across systems. This allows robots to operate based on the latest information, optimizing corporate business processes. The Integration Unit incorporates security features to ensure data confidentiality and integrity. For example, it implements data encryption and access control to prevent unauthorized access and data breaches. This allows the integration unit to securely and efficiently integrate robots with the company's existing systems and services, improving the company's operational efficiency as part of a next-generation robotics integration platform.

[0035] The generation unit can automatically generate robot motion programs from a company's requirements using a generation AI. For example, the generation unit can analyze a company's business processes and generate an optimal robot motion program. Furthermore, the generation unit can customize the robot motion program based on the company's technical requirements. In addition, the generation unit can generate motion programs in real time according to the company's requirements using the generation AI. This significantly reduces development time by automatically generating robot motion programs based on the company's requirements. Some or all of the above-described processes in the generation unit may be performed using the generation AI, or they may not. For example, the generation unit can generate motion programs using a generation AI model that takes company requirements as input and outputs robot motion programs.

[0036] The generation unit can enable natural communication with the user by utilizing a natural language processing model with generative AI. For example, the generation unit can use generative AI to understand the user's statements in natural language and generate appropriate responses. The generation unit can also use generative AI to analyze the user's intent and construct an optimal dialogue flow. Furthermore, the generation unit can use generative AI to engage in real-time dialogue with the user. This enables natural communication with the user, thereby improving the robot's operability. Some or all of the above-described processes in the generation unit may be performed using generative AI or not. For example, the generation unit can achieve natural communication using a generative AI model that takes user statements as input and outputs responses.

[0037] The integration unit can perform drag-and-drop system integration, API management, and data conversion. For example, the integration unit provides an interface that allows users to integrate systems using drag-and-drop. The integration unit can also manage API versions and access control. Furthermore, the integration unit can convert different data formats and facilitate smooth data integration between systems. This makes system integration, API management, and data conversion easy. Some or all of the above-described processes in the integration unit may be performed using AI or not. For example, the integration unit can perform system integration using an AI model that takes user operations as input and outputs system integration settings.

[0038] The robotics unit can automate assembly lines and perform quality inspections in manufacturing. For example, the robotics unit can automate the attachment and assembly of parts on an assembly line. It can also perform quality inspections of products and detect defective items. Furthermore, the robotics unit can collect and analyze data in real time to improve the efficiency of the manufacturing process. This enables efficient automation and quality inspection of assembly lines in manufacturing. Some or all of the above processes in the robotics unit may be performed using AI, or they may not. For example, the robotics unit can automate an assembly line using an AI model that takes assembly line data as input and outputs the optimal assembly procedure.

[0039] The robotics unit can perform inventory management and customer service in the retail industry. For example, the robotics unit can automatically check inventory in stores and update inventory status in real time. It can also provide appropriate answers to customer questions. Furthermore, the robotics unit can analyze customer purchase history and provide personalized product recommendations. This enables efficient inventory management and customer service in the retail industry. Some or all of the above processes in the robotics unit may be performed using AI, or not. For example, the robotics unit can perform inventory management using an AI model that takes inventory data as input and outputs inventory status.

[0040] The infrastructure unit can apply different storage algorithms depending on the type of data when saving data. For example, the infrastructure unit can apply a compression algorithm to image data to save storage space. It can also index text data and save it in a searchable format. Furthermore, it can save video data in a streaming format to reduce the load during playback. In this way, by applying a storage algorithm according to the type of data, storage space can be saved and data can be saved in a searchable format. Some or all of the above processing in the infrastructure unit may be performed using AI or not. For example, the infrastructure unit can apply a storage algorithm using an AI model that takes the type of data as input and outputs the optimal storage algorithm.

[0041] The infrastructure department can optimize data processing by utilizing high-speed computing capabilities. For example, the infrastructure department can process large amounts of data in parallel to reduce processing time. It can also automate data preprocessing to improve the accuracy of analysis. Furthermore, the infrastructure department can analyze data in real time and provide immediate feedback. This allows for reduced data processing time and improved analysis accuracy by utilizing high-speed computing capabilities. Some or all of the above-mentioned processes in the infrastructure department may be performed using AI, or not. For example, the infrastructure department can optimize data processing using an AI model that takes large amounts of data as input and outputs the optimal processing method.

[0042] The infrastructure department can provide the most suitable API based on the user's geographical location when providing APIs. For example, if the user is in a specific region, the infrastructure department can provide an API tailored to that region. Furthermore, if the user is on the move, the infrastructure department can update the location information in real time and provide the most suitable API. Additionally, if the user is overseas, the infrastructure department can provide APIs that support the local language and currency. This allows for the provision of region-specific services by providing the most suitable API based on the user's geographical location. Some or all of the above processing in the infrastructure department may be performed using AI, or not. For example, the infrastructure department can provide APIs using an AI model that takes the user's geographical location as input and outputs the most suitable API.

[0043] The infrastructure unit can analyze users' social media activity when saving data and prioritize saving relevant data. For example, the infrastructure unit can prioritize saving data related to content that users frequently post. It can also prioritize saving data related to posts that have a large number of followers. Furthermore, if a user participates in a specific event, the infrastructure unit can prioritize saving data related to that event. This allows for efficient management of important data by prioritizing the saving of data related to users' social media activity. Some or all of the above processing in the infrastructure unit may be performed using AI or not. For example, the infrastructure unit can perform data saving using an AI model that takes user social media activity data as input and outputs relevant data.

[0044] The communication unit can adjust the level of detail in communication based on the importance of the data during communication. For example, the communication unit can apply a high-level communication protocol to important data to ensure data integrity. It can also apply a standard communication protocol to general data to optimize communication speed. Furthermore, it can apply a low-level communication protocol to non-important data to conserve communication resources. This allows for efficient use of communication resources by adjusting the level of detail in communication based on the importance of the data. Some or all of the above processing in the communication unit may be performed using AI or not. For example, the communication unit can adjust the level of detail in communication using an AI model that takes data importance as input and outputs the optimal communication protocol.

[0045] The communication unit can apply different communication protocols depending on the data category during communication. For example, the communication unit can apply a streaming protocol to video data to enable real-time playback. It can also apply a lightweight protocol to text data to improve communication speed. Furthermore, the communication unit can apply a dedicated protocol to sensor data to ensure data accuracy. In this way, communication speed and data accuracy can be optimized by applying a communication protocol according to the data category. Some or all of the above processing in the communication unit may be performed using AI or not. For example, the communication unit can apply a communication protocol using an AI model that takes the data category as input and outputs the optimal communication protocol.

[0046] The communications department can prioritize communications based on the data submission timing. For example, it can prioritize data with approaching deadlines to ensure timely submission. Alternatively, it can postpone data with later submission dates and prioritize other important communications. Furthermore, it can process data with unknown submission dates using standard communication procedures. This ensures that important data is submitted on time by prioritizing communications based on data submission timing. Some or all of the above processes in the communications department may be performed using AI or not. For example, the communications department can determine communication priorities using an AI model that takes data submission dates as input and outputs communication priorities.

[0047] The communication unit can adjust the order of communications based on the relevance of the data during communication. For example, the communication unit can prioritize the transmission of highly relevant data to achieve efficient data processing. It can also postpone less relevant data and prioritize important communications. Furthermore, the communication unit can analyze the relevance of data in real time and determine the optimal communication order. This enables efficient data processing by adjusting the order of communications based on the relevance of the data. Some or all of the above processing in the communication unit may be performed using AI or not. For example, the communication unit can adjust the order of communications using an AI model that takes the relevance of data as input and outputs the optimal communication order.

[0048] The robot unit can optimize its movements in response to environmental changes during robot movement. For example, if there is an obstacle, the robot unit can automatically select an alternative route. The robot unit can also adjust the robot's sensor sensitivity in response to changes in lighting. Furthermore, the robot unit can adjust the robot's operating speed in response to changes in temperature and humidity. This optimizes the robot's movements in response to environmental changes, enabling efficient movement. Some or all of the above processes in the robot unit may be performed using AI, or they may not. For example, the robot unit can optimize its movements using an AI model that takes environmental data as input and outputs the optimal action.

[0049] The robot unit can apply different operating methods depending on the type of object being operated. For example, when handling a light object, the robot unit can adjust the robot's gripping force. Furthermore, when handling a fragile object, the robot unit can adjust the robot's movements to be more careful. Additionally, when handling a large object, the robot unit can expand the robot's range of motion. This allows for appropriate operation by applying operating methods tailored to the type of object. Some or all of the above-described processes in the robot unit may be performed using AI, or they may not. For example, the robot unit can apply operating methods using an AI model that takes object data as input and outputs the optimal operating method.

[0050] The robot unit can select the optimal recognition method based on the user's geographical location information when recognizing the robot's environment. For example, if the user is in a specific region, the robot unit will apply an environment recognition method specific to that region. Furthermore, if the user is moving, the robot unit can update the location information in real time and select the optimal recognition method. Additionally, if the user is overseas, the robot unit can apply a recognition method adapted to the local environment. This enables appropriate environment recognition by selecting the optimal recognition method based on the user's geographical location information. Some or all of the above processing in the robot unit may be performed using AI, or without AI. For example, the robot unit can perform environment recognition using an AI model that takes the user's geographical location information as input and outputs the optimal recognition method.

[0051] The robot unit can analyze the user's social media activity when operating the robot and prioritize relevant operations. For example, the robot unit can prioritize operations related to content that the user frequently posts. It can also prioritize operations related to posts that have a large number of followers. Furthermore, if the user is participating in a specific event, the robot unit can prioritize operations related to that event. This allows for efficient operation by prioritizing operations related to the user's social media activity. Some or all of the above processing in the robot unit may be performed using AI or not. For example, the robot unit can perform operations using an AI model that takes the user's social media activity data as input and outputs relevant operations.

[0052] The generation unit can select the optimal dialogue method by referring to the user's past dialogue history during natural language processing. For example, the generation unit can select the optimal dialogue method based on the vocabulary and expressions the user has preferred to use in the past. The generation unit can also conduct dialogue by utilizing knowledge about specific topics derived from the user's past dialogue history. Furthermore, the generation unit can predictively advance the dialogue based on the content of questions the user has frequently asked in the past. In this way, the optimal dialogue method can be selected by referring to the user's past dialogue history. Some or all of the above processing in the generation unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the generation unit can select a dialogue method using a generative AI model that takes the user's dialogue history as input and outputs the optimal dialogue method.

[0053] The generation unit can apply different generation algorithms depending on the type of program during automatic code generation. For example, for web application code generation, the generation unit can apply an algorithm that uses a specific framework. Furthermore, for mobile application code generation, the generation unit can apply a platform-optimized algorithm. Additionally, for data analysis program code generation, the generation unit can apply an algorithm that uses a specific library. This allows for efficient code generation by applying a generation algorithm appropriate to the program type. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can perform code generation using a generation AI model that takes the program type as input and outputs the optimal generation algorithm.

[0054] The generation unit can prioritize conversations based on the user's submission timing during natural language processing. For example, the generation unit can prioritize conversations with approaching deadlines to ensure timely responses. It can also postpone conversations with distant submission dates and prioritize other important conversations. Furthermore, the generation unit can process conversations with unknown submission dates using the normal conversation procedure. This allows for timely processing of important conversations by prioritizing them based on the user's submission timing. Some or all of the above processing in the generation unit may be performed using or without a generative AI. For example, the generation unit can determine conversation priorities using a generative AI model that takes the user's submission timing as input and outputs the priority of conversations.

[0055] The generation unit can adjust the generation order based on the relationships between programs during automatic code generation. For example, the generation unit can prioritize the generation of highly relevant programs, thereby achieving efficient development. It can also postpone less relevant programs and prioritize important ones. Furthermore, the generation unit can analyze the relationships between programs in real time and determine the optimal generation order. This allows for efficient development by adjusting the generation order based on the relationships between programs. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can adjust the generation order using a generation AI model that takes the relationships between programs as input and outputs the optimal generation order.

[0056] The integration unit can apply different integration methods depending on the type of data when integrating systems. For example, the integration unit can integrate image data in a specific format to ensure data integrity. It can also index text data and integrate it in a searchable format. Furthermore, it can integrate video data in a streaming format to reduce the load during playback. By applying integration methods according to the type of data, data integrity is ensured and efficient integration becomes possible. Some or all of the above processing in the integration unit may be performed using AI or not. For example, the integration unit can apply integration methods using an AI model that takes the type of data as input and outputs the optimal integration method.

[0057] The integration unit can provide the most suitable APIs by referring to the user's past usage history when managing APIs. For example, the integration unit can prioritize providing APIs that the user has frequently used in the past. Furthermore, the integration unit can predict and provide APIs that the user will use during specific time periods based on their past usage history. In addition, the integration unit can analyze the user's past usage history and provide the most efficient APIs. This allows the integration unit to provide the most suitable APIs by referring to the user's past usage history. Some or all of the above processing in the integration unit may be performed using AI, or not. For example, the integration unit can provide APIs using an AI model that takes user usage history data as input and outputs the most suitable APIs.

[0058] The integration unit can select the optimal conversion method based on the user's geographical location information during data conversion. For example, if the user is in a specific region, the integration unit can apply a data conversion method specific to that region. Furthermore, if the user is on the move, the integration unit can update the location information in real time and select the optimal conversion method. Additionally, if the user is overseas, the integration unit can apply a data conversion method that corresponds to the local language and currency. This enables appropriate data conversion by selecting the optimal conversion method based on the user's geographical location information. Some or all of the above processing in the integration unit may be performed using AI, or not. For example, the integration unit can perform data conversion using an AI model that takes the user's geographical location information as input and outputs the optimal conversion method.

[0059] The integration unit can analyze a user's social media activity during system integration and prioritize the integration of relevant systems. For example, the integration unit can prioritize the integration of systems related to content that the user frequently posts. It can also prioritize the integration of systems related to posts that have a large number of followers. Furthermore, if the user participates in a specific event, the integration unit can prioritize the integration of systems related to that event. This enables efficient system integration by prioritizing the integration of systems related to the user's social media activity. Some or all of the above processing in the integration unit may be performed using AI or not. For example, the integration unit can perform system integration using an AI model that takes user social media activity data as input and outputs relevant systems.

[0060] The integration unit can make suggestions based on the user's calendar information when integrating systems. For example, the integration unit can refer to the appointments registered in the user's calendar and automatically integrate the relevant systems. The integration unit can also suggest systems related to specific events based on the user's calendar information. Furthermore, the integration unit can suggest the most suitable system integration based on the user's calendar information. This enables efficient system integration by suggesting the most suitable system integration based on the user's calendar information. Some or all of the above processing in the integration unit may be performed using AI or not. For example, the integration unit can perform system integration using an AI model that takes the user's calendar information as input and outputs the optimal system integration.

[0061] The integration unit can propose the optimal system integration based on the user's health status during system integration. For example, if the user is tired, the integration unit will prioritize integrating only the most important systems. If the user is healthy, the integration unit can also integrate all systems equally. Furthermore, if the user is unwell, the integration unit can propose system integration that includes rest points. This enables efficient system integration by proposing the optimal system integration based on the user's health status. Some or all of the above processing in the integration unit may be performed using AI or not. For example, the integration unit can perform system integration using an AI model that takes user health data as input and outputs the optimal system integration.

[0062] The integration unit can select the optimal integration method based on the user's device information when integrating systems. For example, if the user is using a smartphone, the integration unit can provide an integration method that matches the screen size. Furthermore, if the user is using a tablet, the integration unit can provide an integration method optimized for a larger screen. Additionally, if the user is using a smartwatch, the integration unit can provide a concise and highly visible integration method. This enables efficient system integration by selecting the optimal integration method based on the user's device information. Some or all of the above processing in the integration unit may be performed using AI, or not. For example, the integration unit can perform system integration using an AI model that takes user device information as input and outputs the optimal integration method.

[0063] The integration unit can provide multilingual integration during system integration, depending on the user's language settings. For example, the integration unit can automatically set the integration language based on the language settings of the user's device. The integration unit can also provide a language switching function if the user uses multiple languages. Furthermore, if the user selects a specific language, the integration unit can provide integration in that language. This enables system integration in the appropriate language by providing multilingual integration according to the user's language settings. Some or all of the above processing in the integration unit may be performed using AI or not. For example, the integration unit can perform system integration using an AI model that takes the user's language settings as input and outputs the optimal language settings.

[0064] The integration unit can provide the optimal integration method by referring to the user's past usage history when integrating systems. For example, the integration unit can prioritize providing integration methods that the user has frequently used in the past. Furthermore, the integration unit can predict and provide integration methods to be used during specific time periods based on the user's past usage history. In addition, the integration unit can analyze the user's past usage history and provide the most efficient integration method. This allows the integration unit to provide the optimal integration method by referring to the user's past usage history. Some or all of the above processing in the integration unit may be performed using AI, or not. For example, the integration unit can provide integration methods using an AI model that takes user usage history data as input and outputs the optimal integration method.

[0065] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0066] The next-generation robotics integrated platform can also include an energy management unit. This unit optimizes the robot's energy consumption, enabling efficient operation. For example, it analyzes the robot's movement patterns and selects routes that consume less energy. It can also monitor the robot's battery level in real time and, if necessary, instruct it to move to a charging station. Furthermore, the energy management unit can share energy among multiple robots, improving overall energy efficiency. This reduces robot operating costs and enables sustainable operation.

[0067] The next-generation robotics integrated platform can also include a security management unit. This unit ensures the security of robot operation and communication, preventing unauthorized access and data leaks. For example, it can monitor robot operation logs and issue alerts if abnormal behavior is detected. It can also encrypt communication data to prevent eavesdropping by third parties. Furthermore, it can implement access control, ensuring that only authorized users can operate the robots. This enables the secure operation of the robots.

[0068] The next-generation robotics integrated platform can also be equipped with an environmental monitoring unit. This unit monitors the robot's surrounding environment in real time to support optimal operation. For example, it collects environmental data such as temperature, humidity, and illuminance, and adjusts the robot's movements accordingly. It can also issue alerts if it detects abnormal environmental changes. Furthermore, it can analyze environmental data and optimize the robot's movement patterns, enabling efficient robot operation.

[0069] The next-generation robotics integrated platform can also include a user training unit. This unit supports users in effectively operating the robot. For example, it can provide interactive training programs, allowing users to learn how to operate the robot. It can also analyze the user's operation history and provide individually optimized training content. Furthermore, it can monitor training progress in real time and provide feedback as needed. This enables users to operate the robot effectively.

[0070] The next-generation robotics integrated platform can also include a maintenance management unit. This unit supports the regular maintenance of robots, enabling long-term operation. For example, it monitors robot operating hours and usage, and notifies users of maintenance timing. It can also analyze the deterioration status of robot components and identify parts that need replacing. Furthermore, it can record the history of maintenance work and use this information for future maintenance planning. This, in turn, enables the long-term operation of robots.

[0071] The next-generation robotics integrated platform can also include a user feedback unit. This unit collects user feedback and uses it to improve the system. For example, it records problems and areas for improvement that users encounter while operating the robot. It can also analyze user feedback and incorporate it into system updates. Furthermore, it can notify users of the feedback results and share the improvements. This enables system improvements that reflect user opinions.

[0072] The following briefly describes the processing flow for example form 1.

[0073] Step 1: The infrastructure unit has functions such as data storage and processing, high-speed computing capabilities, and API provision. For example, it efficiently stores and processes large amounts of data using a database and performs complex calculations quickly using hardware with high-speed computing capabilities. It also facilitates integration with external systems using RESTful APIs and GraphQL. Step 2: The communications department utilizes functions provided by the infrastructure department, such as data storage and processing, high-speed computing capabilities, and API provision, to perform real-time communication between the robot and the cloud, remote control, and instant updates. For example, it uses a 5G network to achieve high-speed, high-capacity, and low-latency communication, and secure communication protocols to ensure data security. It also performs real-time data synchronization to instantly update the robot's movements. Step 3: The robot unit utilizes real-time communication, remote control, and instant update functions provided by the communications unit to perform movement, operation, interaction, and environmental recognition. For example, it performs various tasks using humanoid robots and cleaning robots, recognizes the surrounding environment using sensors, and avoids obstacles. It also interacts with users using voice recognition technology. Step 4: The generation unit utilizes the movement, operation, dialogue, and environment recognition functions provided by the robot unit to perform natural language processing and automatic code generation. For example, it uses generation AI to understand user instructions in natural language and generate appropriate actions. It also automatically generates robot action programs based on company requirements, enabling natural communication with the user. Step 5: The integration unit uses the natural language processing and automatic code generation functions provided by the generation unit to integrate the company's existing systems, services, and data sources with the robot. For example, it can easily integrate systems by performing drag-and-drop system integration. It can also efficiently integrate with external systems using API management functions and unify different data formats using data conversion functions to ensure smooth data exchange between systems.

[0074] (Example of form 2) The next-generation robotics integration platform according to an embodiment of the present invention is a system that combines generative AI and iPaaS to provide an environment in which companies and developers can easily integrate their applications, services, and data into robots. This system includes a scalable and secure infrastructure, a 5G network, robot hardware, a generative AI model, and iPaaS functionality. For example, the infrastructure has functions such as data storage and processing, high-speed computing capabilities, and API provision, while the 5G network enables real-time communication between robots and the cloud, remote control, and instant updates. The robot hardware includes humanoid robots and cleaning robots, and has functions such as movement, operation, interaction, and environmental recognition. The generative AI model is used for natural language processing and automatic code generation to improve the intelligence of the robots. The iPaaS functionality plays a role in easily integrating existing systems, services, and data sources of companies with robots, providing functions such as drag-and-drop system integration, API management, and data transformation. Seamless system integration is possible, and existing systems and robots can be integrated without the need for specialized knowledge. In addition, automation by generative AI analyzes the needs of companies and automatically generates optimal robot functions. Furthermore, it features real-time adaptive capabilities that allow the robot to autonomously learn and adapt in response to changes in operations. A key feature is the development of a developer ecosystem, providing open APIs and SDKs to create an environment where developers can freely develop apps and services. A marketplace is also available, allowing developers to publish and sell their created apps and services. Potential use cases include automation of assembly lines and quality inspection in manufacturing, and inventory management and customer service in retail. This is expected to lead to improved operational efficiency, cost reduction, creation of new business opportunities, accelerated innovation, enhanced competitiveness and market dominance, and contributions to solving social issues. Specifically, generative AI automatically generates robot operation programs from company requirements, significantly reducing development time. It also utilizes natural language processing models to enable natural communication with users and analyzes data in real time to optimize robot performance and services.This will enable companies and developers to easily build an ecosystem that allows them to utilize and develop robots, accelerating operational efficiency improvements and the creation of new services. As a result, the next-generation robotics integration platform will provide an environment where companies and developers can easily integrate their applications, services, and data into robots.

[0075] The next-generation robotics integrated platform according to this embodiment comprises an infrastructure unit, a communication unit, a robot unit, a generation unit, and an integration unit. The infrastructure unit has functions such as data storage and processing, high-speed computing capability, and API provision. For example, the infrastructure unit efficiently stores and processes large amounts of data using a database. The infrastructure unit can also perform complex calculations quickly using hardware with high-speed computing capabilities. Furthermore, the infrastructure unit facilitates collaboration with external systems using RESTful APIs and GraphQL. The communication unit utilizes the functions of data storage and processing, high-speed computing capability, and API provision provided by the infrastructure unit to perform real-time communication, remote control, and instant updates between the robot and the cloud. For example, the communication unit uses a 5G network to achieve high-speed, high-capacity, and low-latency communication. Furthermore, the communication unit can ensure data security using secure communication protocols. Furthermore, the communication unit can perform real-time data synchronization and instantly update the robot's movements. The robot unit uses the functions of real-time communication, remote control, and instant updates provided by the communication unit to perform movement, operation, interaction, and environmental recognition. The robotics unit performs various tasks using, for example, humanoid robots and cleaning robots. The robotics unit can also recognize its surroundings using sensors and avoid obstacles. Furthermore, it can interact with users using voice recognition technology. The generation unit utilizes the movement, operation, interaction, and environmental recognition functions provided by the robotics unit to perform natural language processing and automatic code generation. For example, the generation unit uses generative AI to understand user instructions in natural language and generate appropriate actions. It can also use generative AI to automatically generate robot action programs based on corporate requirements. Furthermore, the generation unit can use generative AI to achieve natural communication with users. The integration unit utilizes the natural language processing and automatic code generation functions provided by the generation unit to integrate existing corporate systems, services, and data sources with the robots. For example, the integration unit can easily integrate systems through drag-and-drop system integration.Furthermore, the integration unit can efficiently connect with external systems using its API management function. In addition, the integration unit can unify different data formats using its data conversion function, enabling smooth data exchange between systems. As a result, the next-generation robotics integration platform according to this embodiment can provide an environment where companies and developers can easily integrate their own applications, services, and data into robots.

[0076] The infrastructure unit possesses functions such as data storage and processing, high-speed computing capabilities, and API provision. For example, the infrastructure unit efficiently stores and processes large amounts of data using databases. Specifically, it utilizes relational databases and NoSQL databases, selecting the optimal storage method according to the data type and application. Furthermore, the infrastructure unit can rapidly perform complex calculations using hardware with high-speed computing capabilities. For instance, it uses servers equipped with GPUs and FPGAs to rapidly train machine learning models and perform real-time data analysis. In addition, the infrastructure unit facilitates integration with external systems using RESTful APIs and GraphQL. This allows developers to easily utilize system functions through standardized interfaces. The infrastructure unit is designed with scalability in mind, allowing resources to be dynamically expanded and contracted according to demand. For example, by utilizing cloud-based infrastructure, it can handle peak loads and optimize cost efficiency. The infrastructure unit also includes data backup and recovery functions, ensuring system reliability and data security. As a result, the infrastructure unit can support stable operation as the foundation for the next-generation robotics integrated platform.

[0077] The Communications Department utilizes functions provided by the Infrastructure Department, such as data storage and processing, high-speed computing capabilities, and API provision, to enable real-time communication, remote control, and instant updates between robots and the cloud. For example, the Communications Department uses 5G networks to achieve high-speed, high-capacity, and low-latency communication. This allows robots to seamlessly interact with the cloud and send and receive data in real time. Furthermore, the Communications Department ensures data security using secure communication protocols. For example, it uses TLS or VPN to encrypt communication paths and prevent unauthorized access and data leaks. In addition, the Communications Department performs real-time data synchronization, instantly updating robot operations. This allows robots to operate based on the latest information and respond quickly to environmental changes. The Communications Department enables coordinated operation among multiple robots, facilitating efficient task sharing and cooperation. For example, multiple cleaning robots can work together to efficiently clean a large area. The Communications Department also provides remote control functionality, allowing operators to control robots remotely. This enables safe and efficient work even in hazardous environments or hard-to-access locations. The communications unit incorporates redundancy and failover capabilities to enhance system availability, enabling rapid recovery even in the event of a communication failure. This allows the communications unit to improve the reliability and performance of the next-generation robotics integration platform.

[0078] The robotics unit utilizes real-time communication, remote control, and instant update functions provided by the communications unit to perform movement, operation, interaction, and environmental recognition. For example, the robotics unit uses humanoid robots and cleaning robots to perform various tasks. Humanoid robots can perform complex movements and interactions, while cleaning robots can efficiently clean floors and carpets. The robotics unit can also recognize its surroundings using sensors and avoid obstacles. For example, it can use LIDAR and cameras to detect surrounding objects and calculate the optimal path. Furthermore, the robotics unit can interact with users using speech recognition technology. This allows users to give instructions to the robot in natural language, and the robot will act accordingly. The robotics unit can learn and self-improve by using machine learning algorithms to determine the optimal actions for the environment and task. For example, a cleaning robot can learn efficient cleaning patterns based on past cleaning data and apply them to future cleaning. The robotics unit also enables cooperative operation between multiple robots, achieving efficient task sharing and cooperation. For example, multiple robots can cooperate to transport large loads. The robot unit is equipped with a battery management system, enabling efficient energy management. This allows the robot to operate for extended periods, minimizing work interruptions. As the core of the next-generation robotics integrated platform, the robot unit can perform a variety of tasks with high precision and efficiency.

[0079] The generation unit utilizes the movement, operation, dialogue, and environmental recognition functions provided by the robot unit to perform natural language processing and automatic code generation. For example, the generation unit uses generative AI to understand user instructions in natural language and generate appropriate actions. Specifically, the generative AI analyzes voice and text instructions from the user and generates a robot action sequence based on that content. For example, if the instruction is "clean the room," the generative AI will instruct the cleaning robot on the specific cleaning area and cleaning method. Furthermore, the generation unit can use the generative AI to automatically generate robot action programs based on the company's requirements. This eliminates the need for developers to manually create programs and allows for efficient customization of robot actions. In addition, the generation unit can use the generative AI to achieve natural communication with the user. For example, the generative AI generates appropriate answers to user questions and responds via voice or text through the robot. The generation unit can use machine learning models to understand user intent and context, enabling more sophisticated dialogue. For example, if a user asks "What time is the next meeting?", the generative AI will refer to calendar information and provide the exact time. The generation unit has built a feedback loop to continuously improve the quality of the robot's movements and interactions, and can update the model based on user feedback. This allows the generation unit, as part of a next-generation robotics integrated platform, to provide advanced functionality tailored to user needs.

[0080] The Integration Unit integrates existing corporate systems, services, and data sources with robots, utilizing natural language processing and automatic code generation capabilities provided by the Generation Unit. For example, the Integration Unit enables easy system integration through drag-and-drop functionality. Specifically, users can configure data flows between different systems and customize robot behavior using an intuitive interface. Furthermore, the Integration Unit efficiently integrates with external systems using API management capabilities. This allows companies to rapidly deploy new robotics functions while leveraging their existing IT infrastructure. Additionally, the Integration Unit uses data transformation capabilities to unify different data formats and facilitate smooth data exchange between systems. For example, it can convert CSV data to JSON format, providing data in a format understandable by robots. The Integration Unit supports real-time data exchange, maintaining data consistency across systems. This allows robots to operate based on the latest information, optimizing corporate business processes. The Integration Unit incorporates security features to ensure data confidentiality and integrity. For example, it implements data encryption and access control to prevent unauthorized access and data breaches. This allows the integration unit to securely and efficiently integrate robots with the company's existing systems and services, improving the company's operational efficiency as part of a next-generation robotics integration platform.

[0081] The generation unit can automatically generate robot motion programs from a company's requirements using a generation AI. For example, the generation unit can analyze a company's business processes and generate an optimal robot motion program. Furthermore, the generation unit can customize the robot motion program based on the company's technical requirements. In addition, the generation unit can generate motion programs in real time according to the company's requirements using the generation AI. This significantly reduces development time by automatically generating robot motion programs based on the company's requirements. Some or all of the above-described processes in the generation unit may be performed using the generation AI, or they may not. For example, the generation unit can generate motion programs using a generation AI model that takes company requirements as input and outputs robot motion programs.

[0082] The generation unit can enable natural communication with the user by utilizing a natural language processing model with generative AI. For example, the generation unit can use generative AI to understand the user's statements in natural language and generate appropriate responses. The generation unit can also use generative AI to analyze the user's intent and construct an optimal dialogue flow. Furthermore, the generation unit can use generative AI to engage in real-time dialogue with the user. This enables natural communication with the user, thereby improving the robot's operability. Some or all of the above-described processes in the generation unit may be performed using generative AI or not. For example, the generation unit can achieve natural communication using a generative AI model that takes user statements as input and outputs responses.

[0083] The integration unit can perform drag-and-drop system integration, API management, and data conversion. For example, the integration unit provides an interface that allows users to integrate systems using drag-and-drop. The integration unit can also manage API versions and access control. Furthermore, the integration unit can convert different data formats and facilitate smooth data integration between systems. This makes system integration, API management, and data conversion easy. Some or all of the above-described processes in the integration unit may be performed using AI or not. For example, the integration unit can perform system integration using an AI model that takes user operations as input and outputs system integration settings.

[0084] The robotics unit can automate assembly lines and perform quality inspections in manufacturing. For example, the robotics unit can automate the attachment and assembly of parts on an assembly line. It can also perform quality inspections of products and detect defective items. Furthermore, the robotics unit can collect and analyze data in real time to improve the efficiency of the manufacturing process. This enables efficient automation and quality inspection of assembly lines in manufacturing. Some or all of the above processes in the robotics unit may be performed using AI, or they may not. For example, the robotics unit can automate an assembly line using an AI model that takes assembly line data as input and outputs the optimal assembly procedure.

[0085] The robotics unit can perform inventory management and customer service in the retail industry. For example, the robotics unit can automatically check inventory in stores and update inventory status in real time. It can also provide appropriate answers to customer questions. Furthermore, the robotics unit can analyze customer purchase history and provide personalized product recommendations. This enables efficient inventory management and customer service in the retail industry. Some or all of the above processes in the robotics unit may be performed using AI, or not. For example, the robotics unit can perform inventory management using an AI model that takes inventory data as input and outputs inventory status.

[0086] The infrastructure unit can estimate the user's emotions and adjust data storage priorities based on those emotions. For example, if the user is stressed, the infrastructure unit can prioritize saving important data for later review. If the user is relaxed, the infrastructure unit can apply normal data storage procedures and save all data evenly. Furthermore, if the user is in a hurry, the infrastructure unit can quickly save only the most important data and save other data later. This allows for the priority of saving important data by adjusting data storage priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the infrastructure unit may be performed using AI or not. For example, the infrastructure unit can adjust data storage priorities using an AI model that takes user emotion data as input and outputs data storage priorities.

[0087] The infrastructure unit can apply different storage algorithms depending on the type of data when saving data. For example, the infrastructure unit can apply a compression algorithm to image data to save storage space. It can also index text data and save it in a searchable format. Furthermore, it can save video data in a streaming format to reduce the load during playback. In this way, by applying a storage algorithm according to the type of data, storage space can be saved and data can be saved in a searchable format. Some or all of the above processing in the infrastructure unit may be performed using AI or not. For example, the infrastructure unit can apply a storage algorithm using an AI model that takes the type of data as input and outputs the optimal storage algorithm.

[0088] The infrastructure department can optimize data processing by utilizing high-speed computing capabilities. For example, the infrastructure department can process large amounts of data in parallel to reduce processing time. It can also automate data preprocessing to improve the accuracy of analysis. Furthermore, the infrastructure department can analyze data in real time and provide immediate feedback. This allows for reduced data processing time and improved analysis accuracy by utilizing high-speed computing capabilities. Some or all of the above-mentioned processes in the infrastructure department may be performed using AI, or not. For example, the infrastructure department can optimize data processing using an AI model that takes large amounts of data as input and outputs the optimal processing method.

[0089] The infrastructure unit can estimate the user's emotions and adjust the timing of data processing based on the estimated emotions. For example, if the user is concentrating, the infrastructure unit can perform data processing in the background to avoid interrupting their work. It can also prioritize data processing when the user is on a break, providing results when they resume work. Furthermore, if the user is in a hurry, the infrastructure unit can prioritize the most important data processing, postponing other processes. This allows for data processing without interrupting work by adjusting the timing of data processing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the infrastructure unit may be performed using AI or not. For example, the infrastructure unit can adjust the timing of data processing using an AI model that takes user emotion data as input and outputs the timing of data processing.

[0090] The infrastructure department can provide the most suitable API based on the user's geographical location when providing APIs. For example, if the user is in a specific region, the infrastructure department can provide an API tailored to that region. Furthermore, if the user is on the move, the infrastructure department can update the location information in real time and provide the most suitable API. Additionally, if the user is overseas, the infrastructure department can provide APIs that support the local language and currency. This allows for the provision of region-specific services by providing the most suitable API based on the user's geographical location. Some or all of the above processing in the infrastructure department may be performed using AI, or not. For example, the infrastructure department can provide APIs using an AI model that takes the user's geographical location as input and outputs the most suitable API.

[0091] The infrastructure unit can analyze users' social media activity when saving data and prioritize saving relevant data. For example, the infrastructure unit can prioritize saving data related to content that users frequently post. It can also prioritize saving data related to posts that have a large number of followers. Furthermore, if a user participates in a specific event, the infrastructure unit can prioritize saving data related to that event. This allows for efficient management of important data by prioritizing the saving of data related to users' social media activity. Some or all of the above processing in the infrastructure unit may be performed using AI or not. For example, the infrastructure unit can perform data saving using an AI model that takes user social media activity data as input and outputs relevant data.

[0092] The communication unit can estimate the user's emotions and determine communication priorities based on those emotions. For example, if the user is stressed, the communication unit can prioritize important communications to provide reassurance. If the user is relaxed, the communication unit can apply normal communication procedures and process all communications equally. Furthermore, if the user is in a hurry, the communication unit can quickly deliver only the most important communications and process others later. This allows for prioritizing important communications by determining communication priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the communication unit may be performed using AI or not. For example, the communication unit can determine communication priorities using an AI model that takes user emotion data as input and outputs communication priorities.

[0093] The communication unit can adjust the level of detail in communication based on the importance of the data during communication. For example, the communication unit can apply a high-level communication protocol to important data to ensure data integrity. It can also apply a standard communication protocol to general data to optimize communication speed. Furthermore, it can apply a low-level communication protocol to non-important data to conserve communication resources. This allows for efficient use of communication resources by adjusting the level of detail in communication based on the importance of the data. Some or all of the above processing in the communication unit may be performed using AI or not. For example, the communication unit can adjust the level of detail in communication using an AI model that takes data importance as input and outputs the optimal communication protocol.

[0094] The communication unit can apply different communication protocols depending on the data category during communication. For example, the communication unit can apply a streaming protocol to video data to enable real-time playback. It can also apply a lightweight protocol to text data to improve communication speed. Furthermore, the communication unit can apply a dedicated protocol to sensor data to ensure data accuracy. In this way, communication speed and data accuracy can be optimized by applying a communication protocol according to the data category. Some or all of the above processing in the communication unit may be performed using AI or not. For example, the communication unit can apply a communication protocol using an AI model that takes the data category as input and outputs the optimal communication protocol.

[0095] The communication unit can estimate the user's emotions and adjust the length of the communication based on the estimated emotions. For example, if the user is tense, the communication unit can send a short, to-the-point message. If the user is relaxed, the communication unit can send a longer message containing more detailed information. Furthermore, if the user is in a hurry, the communication unit can send a quick and concise message. By adjusting the length of the communication according to the user's emotions, an appropriate amount of information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the communication unit may be performed using AI or not. For example, the communication unit can adjust the length of the communication using an AI model that takes user emotion data as input and outputs the length of the communication.

[0096] The communications department can prioritize communications based on the data submission timing. For example, it can prioritize data with approaching deadlines to ensure timely submission. Alternatively, it can postpone data with later submission dates and prioritize other important communications. Furthermore, it can process data with unknown submission dates using standard communication procedures. This ensures that important data is submitted on time by prioritizing communications based on data submission timing. Some or all of the above processes in the communications department may be performed using AI or not. For example, the communications department can determine communication priorities using an AI model that takes data submission dates as input and outputs communication priorities.

[0097] The communication unit can adjust the order of communications based on the relevance of the data during communication. For example, the communication unit can prioritize the transmission of highly relevant data to achieve efficient data processing. It can also postpone less relevant data and prioritize important communications. Furthermore, the communication unit can analyze the relevance of data in real time and determine the optimal communication order. This enables efficient data processing by adjusting the order of communications based on the relevance of the data. Some or all of the above processing in the communication unit may be performed using AI or not. For example, the communication unit can adjust the order of communications using an AI model that takes the relevance of data as input and outputs the optimal communication order.

[0098] The robot unit can estimate the user's emotions and adjust its movements based on those emotions. For example, if the user is nervous, the robot unit can slow down its movements to provide a sense of security. If the user is relaxed, the robot unit can perform tasks at a normal speed. Furthermore, if the user is in a hurry, the robot unit can speed up its movements to improve efficiency. This allows the robot to provide a sense of security to the user by adjusting its movements according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the robot unit may be performed using AI or not. For example, the robot unit can adjust its movements using an AI model that takes user emotion data as input and outputs robot movements.

[0099] The robot unit can optimize its movements in response to environmental changes during robot movement. For example, if there is an obstacle, the robot unit can automatically select an alternative route. The robot unit can also adjust the robot's sensor sensitivity in response to changes in lighting. Furthermore, the robot unit can adjust the robot's operating speed in response to changes in temperature and humidity. This optimizes the robot's movements in response to environmental changes, enabling efficient movement. Some or all of the above processes in the robot unit may be performed using AI, or they may not. For example, the robot unit can optimize its movements using an AI model that takes environmental data as input and outputs the optimal action.

[0100] The robot unit can apply different operating methods depending on the type of object being operated. For example, when handling a light object, the robot unit can adjust the robot's gripping force. Furthermore, when handling a fragile object, the robot unit can adjust the robot's movements to be more careful. Additionally, when handling a large object, the robot unit can expand the robot's range of motion. This allows for appropriate operation by applying operating methods tailored to the type of object. Some or all of the above-described processes in the robot unit may be performed using AI, or they may not. For example, the robot unit can apply operating methods using an AI model that takes object data as input and outputs the optimal operating method.

[0101] The robot unit can estimate the user's emotions and adjust its dialogue method based on the estimated emotions. For example, if the user is nervous, the robot can engage in dialogue in a calm voice. If the user is relaxed, the robot can engage in dialogue in a cheerful voice. Furthermore, if the user is in a hurry, the robot can engage in quick and concise dialogue. By adjusting the robot's dialogue method according to the user's emotions, appropriate dialogue becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the robot unit may be performed using AI or not. For example, the robot unit can adjust its dialogue method using an AI model that takes user emotion data as input and outputs a dialogue method.

[0102] The robot unit can select the optimal recognition method based on the user's geographical location information when recognizing the robot's environment. For example, if the user is in a specific region, the robot unit will apply an environment recognition method specific to that region. Furthermore, if the user is moving, the robot unit can update the location information in real time and select the optimal recognition method. Additionally, if the user is overseas, the robot unit can apply a recognition method adapted to the local environment. This enables appropriate environment recognition by selecting the optimal recognition method based on the user's geographical location information. Some or all of the above processing in the robot unit may be performed using AI, or without AI. For example, the robot unit can perform environment recognition using an AI model that takes the user's geographical location information as input and outputs the optimal recognition method.

[0103] The robot unit can analyze the user's social media activity when operating the robot and prioritize relevant operations. For example, the robot unit can prioritize operations related to content that the user frequently posts. It can also prioritize operations related to posts that have a large number of followers. Furthermore, if the user is participating in a specific event, the robot unit can prioritize operations related to that event. This allows for efficient operation by prioritizing operations related to the user's social media activity. Some or all of the above processing in the robot unit may be performed using AI or not. For example, the robot unit can perform operations using an AI model that takes the user's social media activity data as input and outputs relevant operations.

[0104] The generation unit can estimate the user's emotions and determine the priority of the programs to generate based on the estimated emotions. For example, if the user is stressed, the generation unit will prioritize generating important programs. If the user is relaxed, the generation unit can also apply the normal program generation procedure. Furthermore, if the user is in a hurry, the generation unit can quickly generate only the most important programs. In this way, by prioritizing programs according to the user's emotions, important programs can be generated preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is 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 processing in the generation unit may be performed using a generative AI or not. For example, the generation unit can determine the program priority using a generative AI model that takes user emotion data as input and outputs the program priority.

[0105] The generation unit can select the optimal dialogue method by referring to the user's past dialogue history during natural language processing. For example, the generation unit can select the optimal dialogue method based on the vocabulary and expressions the user has preferred to use in the past. The generation unit can also conduct dialogue by utilizing knowledge about specific topics derived from the user's past dialogue history. Furthermore, the generation unit can predictively advance the dialogue based on the content of questions the user has frequently asked in the past. In this way, the optimal dialogue method can be selected by referring to the user's past dialogue history. Some or all of the above processing in the generation unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the generation unit can select a dialogue method using a generative AI model that takes the user's dialogue history as input and outputs the optimal dialogue method.

[0106] The generation unit can apply different generation algorithms depending on the type of program during automatic code generation. For example, for web application code generation, the generation unit can apply an algorithm that uses a specific framework. Furthermore, for mobile application code generation, the generation unit can apply a platform-optimized algorithm. Additionally, for data analysis program code generation, the generation unit can apply an algorithm that uses a specific library. This allows for efficient code generation by applying a generation algorithm appropriate to the program type. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can perform code generation using a generation AI model that takes the program type as input and outputs the optimal generation algorithm.

[0107] The generation unit can estimate the user's emotions and adjust the length of the generated program based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise program. If the user is relaxed, the generation unit can also generate a longer program with detailed explanations. Furthermore, if the user is excited, the generation unit can generate a program with visually stimulating effects. This allows for the generation of an appropriate program by adjusting the program length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generative AI or not. For example, the generation unit can adjust the program length using a generative AI model that takes user emotion data as input and outputs the program length.

[0108] The generation unit can prioritize conversations based on the user's submission timing during natural language processing. For example, the generation unit can prioritize conversations with approaching deadlines to ensure timely responses. It can also postpone conversations with distant submission dates and prioritize other important conversations. Furthermore, the generation unit can process conversations with unknown submission dates using the normal conversation procedure. This allows for timely processing of important conversations by prioritizing them based on the user's submission timing. Some or all of the above processing in the generation unit may be performed using or without a generative AI. For example, the generation unit can determine conversation priorities using a generative AI model that takes the user's submission timing as input and outputs the priority of conversations.

[0109] The generation unit can adjust the generation order based on the relationships between programs during automatic code generation. For example, the generation unit can prioritize the generation of highly relevant programs, thereby achieving efficient development. It can also postpone less relevant programs and prioritize important ones. Furthermore, the generation unit can analyze the relationships between programs in real time and determine the optimal generation order. This allows for efficient development by adjusting the generation order based on the relationships between programs. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can adjust the generation order using a generation AI model that takes the relationships between programs as input and outputs the optimal generation order.

[0110] The integration unit can estimate the user's emotions and determine the priority of systems to integrate based on the estimated user emotions. For example, if the user is stressed, the integration unit will prioritize integrating important systems. If the user is relaxed, the integration unit can also apply the normal integration procedure. Furthermore, if the user is in a hurry, the integration unit can quickly integrate only the most important systems. This allows for the priority of integrating important systems by determining the priority of systems to integrate according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the integration unit may be performed using AI or not. For example, the integration unit can determine system priorities using an AI model that takes user emotion data as input and outputs the priority of systems to integrate.

[0111] The integration unit can apply different integration methods depending on the type of data when integrating systems. For example, the integration unit can integrate image data in a specific format to ensure data integrity. It can also index text data and integrate it in a searchable format. Furthermore, it can integrate video data in a streaming format to reduce the load during playback. By applying integration methods according to the type of data, data integrity is ensured and efficient integration becomes possible. Some or all of the above processing in the integration unit may be performed using AI or not. For example, the integration unit can apply integration methods using an AI model that takes the type of data as input and outputs the optimal integration method.

[0112] The integration unit can provide the most suitable APIs by referring to the user's past usage history when managing APIs. For example, the integration unit can prioritize providing APIs that the user has frequently used in the past. Furthermore, the integration unit can predict and provide APIs that the user will use during specific time periods based on their past usage history. In addition, the integration unit can analyze the user's past usage history and provide the most efficient APIs. This allows the integration unit to provide the most suitable APIs by referring to the user's past usage history. Some or all of the above processing in the integration unit may be performed using AI, or not. For example, the integration unit can provide APIs using an AI model that takes user usage history data as input and outputs the most suitable APIs.

[0113] The integration unit can estimate the user's emotions and adjust the display method of the integrated system based on the estimated user emotions. For example, if the user is tense, the integration unit can provide a simple and highly visible display method. It can also provide a display method that includes detailed information if the user is relaxed. Furthermore, if the user is in a hurry, the integration unit can provide a concise display method. This allows for highly visible displays by adjusting the system's display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the integration unit may be performed using AI or not. For example, the integration unit can adjust the display method using an AI model that takes user emotion data as input and outputs a display method.

[0114] The integration unit can select the optimal conversion method based on the user's geographical location information during data conversion. For example, if the user is in a specific region, the integration unit can apply a data conversion method specific to that region. Furthermore, if the user is on the move, the integration unit can update the location information in real time and select the optimal conversion method. Additionally, if the user is overseas, the integration unit can apply a data conversion method that corresponds to the local language and currency. This enables appropriate data conversion by selecting the optimal conversion method based on the user's geographical location information. Some or all of the above processing in the integration unit may be performed using AI, or not. For example, the integration unit can perform data conversion using an AI model that takes the user's geographical location information as input and outputs the optimal conversion method.

[0115] The integration unit can analyze a user's social media activity during system integration and prioritize the integration of relevant systems. For example, the integration unit can prioritize the integration of systems related to content that the user frequently posts. It can also prioritize the integration of systems related to posts that have a large number of followers. Furthermore, if the user participates in a specific event, the integration unit can prioritize the integration of systems related to that event. This enables efficient system integration by prioritizing the integration of systems related to the user's social media activity. Some or all of the above processing in the integration unit may be performed using AI or not. For example, the integration unit can perform system integration using an AI model that takes user social media activity data as input and outputs relevant systems.

[0116] The integration unit can make suggestions based on the user's calendar information when integrating systems. For example, the integration unit can refer to the appointments registered in the user's calendar and automatically integrate the relevant systems. The integration unit can also suggest systems related to specific events based on the user's calendar information. Furthermore, the integration unit can suggest the most suitable system integration based on the user's calendar information. This enables efficient system integration by suggesting the most suitable system integration based on the user's calendar information. Some or all of the above processing in the integration unit may be performed using AI or not. For example, the integration unit can perform system integration using an AI model that takes the user's calendar information as input and outputs the optimal system integration.

[0117] The integration unit can propose the optimal system integration based on the user's health status during system integration. For example, if the user is tired, the integration unit will prioritize integrating only the most important systems. If the user is healthy, the integration unit can also integrate all systems equally. Furthermore, if the user is unwell, the integration unit can propose system integration that includes rest points. This enables efficient system integration by proposing the optimal system integration based on the user's health status. Some or all of the above processing in the integration unit may be performed using AI or not. For example, the integration unit can perform system integration using an AI model that takes user health data as input and outputs the optimal system integration.

[0118] The integration unit can select the optimal integration method based on the user's device information when integrating systems. For example, if the user is using a smartphone, the integration unit can provide an integration method that matches the screen size. Furthermore, if the user is using a tablet, the integration unit can provide an integration method optimized for a larger screen. Additionally, if the user is using a smartwatch, the integration unit can provide a concise and highly visible integration method. This enables efficient system integration by selecting the optimal integration method based on the user's device information. Some or all of the above processing in the integration unit may be performed using AI, or not. For example, the integration unit can perform system integration using an AI model that takes user device information as input and outputs the optimal integration method.

[0119] The integration unit can provide multilingual integration during system integration, depending on the user's language settings. For example, the integration unit can automatically set the integration language based on the language settings of the user's device. The integration unit can also provide a language switching function if the user uses multiple languages. Furthermore, if the user selects a specific language, the integration unit can provide integration in that language. This enables system integration in the appropriate language by providing multilingual integration according to the user's language settings. Some or all of the above processing in the integration unit may be performed using AI or not. For example, the integration unit can perform system integration using an AI model that takes the user's language settings as input and outputs the optimal language settings.

[0120] The integration unit can provide the optimal integration method by referring to the user's past usage history when integrating systems. For example, the integration unit can prioritize providing integration methods that the user has frequently used in the past. Furthermore, the integration unit can predict and provide integration methods to be used during specific time periods based on the user's past usage history. In addition, the integration unit can analyze the user's past usage history and provide the most efficient integration method. This allows the integration unit to provide the optimal integration method by referring to the user's past usage history. Some or all of the above processing in the integration unit may be performed using AI, or not. For example, the integration unit can provide integration methods using an AI model that takes user usage history data as input and outputs the optimal integration method.

[0121] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0122] The next-generation robotics integrated platform can also include an energy management unit. This unit optimizes the robot's energy consumption, enabling efficient operation. For example, it analyzes the robot's movement patterns and selects routes that consume less energy. It can also monitor the robot's battery level in real time and, if necessary, instruct it to move to a charging station. Furthermore, the energy management unit can share energy among multiple robots, improving overall energy efficiency. This reduces robot operating costs and enables sustainable operation.

[0123] The generation unit can estimate the user's emotions and adjust the difficulty of the program it generates based on those emotions. For example, if the user is stressed, the generation unit can generate a simple program, and if the user is relaxed, it can generate a more complex program. It can also generate a program that can be completed quickly if the user is in a hurry, and a more detailed program if the user has ample time. By adjusting the program difficulty according to the user's emotions, this reduces the user's burden and enables efficient program generation.

[0124] The communications unit can estimate the user's emotions and adjust the content of the communication based on those emotions. For example, if the user is stressed, the communications unit can provide concise and clear information; if the user is relaxed, it can provide detailed information. Furthermore, if the user is in a hurry, it can prioritize providing only essential information; if the user has ample time, it can provide all the information. This allows for the provision of appropriate information by adjusting the content of the communication according to the user's emotions.

[0125] The robot unit can estimate the user's emotions and adjust the robot's operating speed based on those emotions. For example, if the user is nervous, the robot's movements will be slowed down to provide a sense of security. If the user is relaxed, the robot can perform tasks at a normal operating speed. Furthermore, if the user is in a hurry, the robot's movements can be sped up to improve work efficiency. In this way, by adjusting the robot's operating speed according to the user's emotions, a sense of security can be provided to the user.

[0126] The integration unit can estimate the user's emotions and determine the priority of systems to integrate based on those emotions. For example, if the user is stressed, it will prioritize integrating important systems. If the user is relaxed, the normal integration procedure can be applied. Furthermore, if the user is in a hurry, only the most important systems can be quickly integrated. This allows for the priority of integrating important systems by determining the priority of systems to integrate according to the user's emotions.

[0127] The next-generation robotics integrated platform can also include a security management unit. This unit ensures the security of robot operation and communication, preventing unauthorized access and data leaks. For example, it can monitor robot operation logs and issue alerts if abnormal behavior is detected. It can also encrypt communication data to prevent eavesdropping by third parties. Furthermore, it can implement access control, ensuring that only authorized users can operate the robots. This enables the secure operation of the robots.

[0128] The next-generation robotics integrated platform can also be equipped with an environmental monitoring unit. This unit monitors the robot's surrounding environment in real time to support optimal operation. For example, it collects environmental data such as temperature, humidity, and illuminance, and adjusts the robot's movements accordingly. It can also issue alerts if it detects abnormal environmental changes. Furthermore, it can analyze environmental data and optimize the robot's movement patterns, enabling efficient robot operation.

[0129] The next-generation robotics integrated platform can also include a user training unit. This unit supports users in effectively operating the robot. For example, it can provide interactive training programs, allowing users to learn how to operate the robot. It can also analyze the user's operation history and provide individually optimized training content. Furthermore, it can monitor training progress in real time and provide feedback as needed. This enables users to operate the robot effectively.

[0130] The next-generation robotics integrated platform can also include a maintenance management unit. This unit supports the regular maintenance of robots, enabling long-term operation. For example, it monitors robot operating hours and usage, and notifies users of maintenance timing. It can also analyze the deterioration status of robot components and identify parts that need replacing. Furthermore, it can record the history of maintenance work and use this information for future maintenance planning. This, in turn, enables the long-term operation of robots.

[0131] The next-generation robotics integrated platform can also include a user feedback unit. This unit collects user feedback and uses it to improve the system. For example, it records problems and areas for improvement that users encounter while operating the robot. It can also analyze user feedback and incorporate it into system updates. Furthermore, it can notify users of the feedback results and share the improvements. This enables system improvements that reflect user opinions.

[0132] The following briefly describes the processing flow for example form 2.

[0133] Step 1: The infrastructure unit has functions such as data storage and processing, high-speed computing capabilities, and API provision. For example, it efficiently stores and processes large amounts of data using a database and performs complex calculations quickly using hardware with high-speed computing capabilities. It also facilitates integration with external systems using RESTful APIs and GraphQL. Step 2: The communications department utilizes functions provided by the infrastructure department, such as data storage and processing, high-speed computing capabilities, and API provision, to perform real-time communication between the robot and the cloud, remote control, and instant updates. For example, it uses a 5G network to achieve high-speed, high-capacity, and low-latency communication, and secure communication protocols to ensure data security. It also performs real-time data synchronization to instantly update the robot's movements. Step 3: The robot unit utilizes real-time communication, remote control, and instant update functions provided by the communications unit to perform movement, operation, interaction, and environmental recognition. For example, it performs various tasks using humanoid robots and cleaning robots, recognizes the surrounding environment using sensors, and avoids obstacles. It also interacts with users using voice recognition technology. Step 4: The generation unit utilizes the movement, operation, dialogue, and environment recognition functions provided by the robot unit to perform natural language processing and automatic code generation. For example, it uses generation AI to understand user instructions in natural language and generate appropriate actions. It also automatically generates robot action programs based on company requirements, enabling natural communication with the user. Step 5: The integration unit uses the natural language processing and automatic code generation functions provided by the generation unit to integrate the company's existing systems, services, and data sources with the robot. For example, it can easily integrate systems by performing drag-and-drop system integration. It can also efficiently integrate with external systems using API management functions and unify different data formats using data conversion functions to ensure smooth data exchange between systems.

[0134] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0136] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0137] Each of the multiple elements described above, including the infrastructure unit, communication unit, robot unit, generation unit, and integration unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the infrastructure unit is implemented by the database 24 and processor 28 of the data processing unit 12. The communication unit is implemented by the communication I / F 44 of the smart device 14 and the communication I / F 26 of the data processing unit 12. The robot unit is implemented by the control unit 46A and camera 42 of the smart device 14. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12. The integration unit is implemented by the control unit 46A of the smart device 14 and the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0138] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0139] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0140] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0141] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0142] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0144] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0145] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0146] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0147] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0148] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0149] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0150] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0152] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0153] Each of the multiple elements described above, including the infrastructure unit, communication unit, robot unit, generation unit, and integration unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the infrastructure unit is implemented by the database 24 and processor 28 of the data processing unit 12. The communication unit is implemented by the communication I / F 44 of the smart glasses 214 and the communication I / F 26 of the data processing unit 12. The robot unit is implemented by the control unit 46A and camera 42 of the smart glasses 214. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12. The integration unit is implemented by the control unit 46A of the smart glasses 214 and the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0154] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0155] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0156] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0157] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0158] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0160] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0161] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0162] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0163] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0164] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0165] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0166] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0167] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0168] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0169] Each of the multiple elements described above, including the infrastructure unit, communication unit, robot unit, generation unit, and integration unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the infrastructure unit is implemented by the database 24 and processor 28 of the data processing unit 12. The communication unit is implemented by the communication I / F 44 of the headset terminal 314 and the communication I / F 26 of the data processing unit 12. The robot unit is implemented by the control unit 46A and camera 42 of the headset terminal 314. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12. The integration unit is implemented by the control unit 46A of the headset terminal 314 and the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0170] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0171] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0172] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0173] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0174] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0175] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0176] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0177] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0178] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0179] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0180] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0181] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0182] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0183] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0184] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0185] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0186] Each of the multiple elements described above, including the infrastructure unit, communication unit, robot unit, generation unit, and integration unit, is realized by, for example, at least one of the robot 414 and the data processing unit 12. For example, the infrastructure unit is realized by the database 24 and processor 28 of the data processing unit 12. The communication unit is realized by the communication I / F 44 of the robot 414 and the communication I / F 26 of the data processing unit 12. The robot unit is realized by the control unit 46A and camera 42 of the robot 414. The generation unit is realized by the specific processing unit 290 of the data processing unit 12. The integration unit is realized by the control unit 46A of the robot 414 and the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0187] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0188] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0189] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0190] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0191] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0192] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0193] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0194] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0195] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0196] 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.

[0197] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0198] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0199] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0200] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0201] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0202] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0203] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0204] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0205] (Note 1) The infrastructure department has functions such as data storage and processing, high-speed computing capabilities, and API provision. A communication unit, utilizing the data storage and processing, high-speed computing capabilities, and API provision functions provided by the aforementioned infrastructure unit, performs real-time communication, remote control, and immediate updates between the robot and the cloud. A robot unit that performs movement, operation, interaction, and environmental recognition using the real-time communication, remote control, and instant update functions provided by the aforementioned communication unit, A generation unit that performs natural language processing and automatic code generation using the movement, operation, dialogue, and environment recognition functions provided by the robot unit, The system includes an integration unit that integrates existing corporate systems, services, and data sources with a robot, utilizing the natural language processing and automatic code generation functions provided by the generation unit. A system characterized by the following features. (Note 2) The generating unit is The AI ​​generates robot operation programs automatically based on company requirements. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Generative AI leverages natural language processing models to enable natural communication with users. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned integration unit is Perform system integration, API management, and data transformation using drag-and-drop functionality. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned robot section is Automating assembly lines and conducting quality inspections in the manufacturing industry. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned robot section is In the retail industry, this involves inventory management and customer service. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned infrastructure section is It estimates user sentiment and adjusts data storage priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned infrastructure section is When saving data, different saving algorithms are applied depending on the type of data. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned infrastructure section is Optimize data processing by utilizing high-speed computing capabilities. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned infrastructure section is It estimates the user's emotions and adjusts the timing of data processing based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned infrastructure section is When providing APIs, we will provide the most suitable API based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned infrastructure section is When saving data, the system analyzes the user's social media activity and prioritizes saving relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned communications unit is It estimates the user's emotions and determines communication priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned communications unit is During communication, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned communications unit is When communicating, different communication protocols are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned communications unit is It estimates the user's emotions and adjusts the length of the communication based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned communications unit is During communication, the priority of the communication is determined based on the timing of data submission. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned communications unit is During communication, the order of communication is adjusted based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned robot section is It estimates the user's emotions and adjusts the robot's actions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned robot section is The robot optimizes its movements in response to changes in the environment during its movement. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned robot section is When operating a robot, different operating methods are applied depending on the type of object being worked on. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned robot section is It estimates the user's emotions and adjusts the robot's interaction method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned robot section is When the robot recognizes its environment, it selects the optimal recognition method based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned robot section is When operating the robot, it analyzes the user's social media activity and prioritizes relevant actions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is It estimates the user's emotions and determines the priority of programs to generate based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is During natural language processing, the system selects the optimal dialogue method by referring to the user's past conversation history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The generating unit is When generating code automatically, different generation algorithms are applied depending on the type of program. The system described in Appendix 1, characterized by the features described herein. (Note 28) The generating unit is It estimates the user's emotions and adjusts the length of the program generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The generating unit is When processing natural language, prioritize conversations based on when the user submits their input. The system described in Appendix 1, characterized by the features described herein. (Note 30) The generating unit is During automatic code generation, the generation order is adjusted based on the relevance of the program. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned integration unit is The system estimates user emotions and prioritizes integration based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned integration unit is When integrating systems, different integration methods are applied depending on the type of data. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned integration unit is When managing APIs, refer to the user's past usage history to provide the most suitable API. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned integration unit is The system estimates user sentiment and adjusts how it displays information based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned integration unit is During data conversion, the optimal conversion method is selected based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned integration unit is During system integration, the system analyzes users' social media activity and prioritizes the integration of relevant systems. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned integration unit is When integrating with other systems, the system will refer to the user's calendar information to provide schedule-based suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned integration unit is When integrating systems, we propose the optimal system integration based on the user's health status. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned integration unit is During system integration, the optimal integration method is selected based on the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned integration unit is When integrating systems, we provide multilingual support based on the user's language settings. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned integration unit is When integrating systems, the system provides the optimal integration method by referring to the user's past usage history. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0206] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The infrastructure unit has functions such as data storage and processing, high-speed computing capabilities, and API provision. A communications unit utilizes the data storage and processing, high-speed computing capabilities, and API provision functions provided by the aforementioned infrastructure unit to perform real-time communication, remote control, and immediate updates between the robot and the cloud. A robot unit that performs movement, operation, interaction, and environmental recognition using the real-time communication, remote control, and instant update functions provided by the aforementioned communication unit, A generation unit that performs natural language processing and automatic code generation using the movement, operation, dialogue, and environment recognition functions provided by the robot unit, The system includes an integration unit that integrates existing corporate systems, services, and data sources with a robot, utilizing the natural language processing and automatic code generation functions provided by the generation unit. A system characterized by the following features.

2. The generating unit is The AI ​​generates robot operation programs automatically based on company requirements. The system according to feature 1.

3. The generating unit is Generative AI enables natural communication with users by utilizing natural language processing models. The system according to feature 1.

4. The aforementioned integration unit is Perform system integration, API management, and data transformation using drag-and-drop functionality. The system according to feature 1.

5. The aforementioned robot section is Automating assembly lines and conducting quality inspections in the manufacturing industry. The system according to feature 1.

6. The aforementioned robot section is In the retail industry, this involves inventory management and customer service. The system according to feature 1.

7. The aforementioned infrastructure section is It estimates user sentiment and adjusts data storage priorities based on the estimated user sentiment. The system according to feature 1.

8. The aforementioned infrastructure section is When saving data, different saving algorithms are applied depending on the type of data. The system according to feature 1.

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