system

Abstract

The system according to this embodiment aims to consistently manage the process from training and deployment of AI models to monitoring. [Solution] The system according to the embodiment comprises a collection unit, a management unit, a training unit, a deployment unit, and a monitoring unit. The collection unit collects data. The management unit manages the data collected by the collection unit. The training unit trains an AI model based on the data managed by the management unit. The deployment unit deploys the AI ​​model trained by the training unit. The monitoring unit monitors the AI ​​model deployed by the deployment unit.

Description

Technical Field

[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, and includes 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 the processes from training to deployment and monitoring of an AI model are not consistently managed, making it difficult to efficiently utilize AI.

[0005] The system according to the embodiment aims to consistently manage the processes from training to deployment and monitoring of an AI model.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, a management unit, a training unit, a deployment unit, and a monitoring unit. The data collection unit collects data. The management unit manages the data collected by the data collection unit. The training unit trains an AI model based on the data managed by the management unit. The deployment unit deploys the AI ​​model trained by the training unit. The monitoring unit monitors the AI ​​model deployed by the deployment unit. [Effects of the Invention]

[0007] The system according to this embodiment can consistently manage the process from training and deployment of AI models to monitoring. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​agent utilization platform according to an embodiment of the present invention is a platform for efficiently utilizing AI agents. This platform addresses the current state and challenges of AI agent utilization, including complex data processing and management, increasing operational costs, and integration issues with existing systems. The platform achieves centralized management of data and AI models, improved scalability and flexibility, cost reduction, and operational efficiency. The main components of the platform are the data layer, AI model layer, application layer, integration, and API. The data layer provides data storage and security, while the AI ​​model layer handles training, deployment, and monitoring. The application layer provides services and UI / UX design, and integration and API enable integration with existing systems and API utilization. The platform design principles emphasize scalability, modularity, usability, and security. Scalability provides expandability in line with service growth, modularity enables independent component design, usability provides an intuitive design for end users, and security adopts a zero-trust architecture. The implementation procedure involves first designing the platform, and then implementing each component. Performance indicators include user satisfaction, platform uptime, processing speed, and response time. Key factors for success include clear goal setting and effective project management. For example, an AI agent utilization platform enables efficient AI utilization by collecting data, training AI models, deploying them, and monitoring them. The data layer securely stores data using, for example, cloud storage or on-premises storage. The AI ​​model layer trains AI models using, for example, algorithms such as neural networks or decision trees. The application layer performs UI / UX design based on, for example, usability testing and design guidelines. Integration and APIs enable integration with existing systems through, for example, data exchange between systems and the use of APIs. As a result, the AI ​​agent utilization platform can efficiently collect, manage, train, deploy, and monitor data.This allows AI agent utilization platforms to efficiently collect, manage, train, deploy, and monitor data.

[0029] The AI ​​agent utilization platform according to this embodiment comprises a collection unit, a management unit, a training unit, a deployment unit, and a monitoring unit. The collection unit collects data. The collection unit can collect various types of data, such as sensor data, text data, and image data. For example, the collection unit can collect sensor data in real time and store it in a database. The collection unit can also collect text data using web scraping technology. Furthermore, the collection unit can collect image data using a camera or smartphone. For example, the collection unit can collect sensor data in real time and store it in a database. The collection unit can also collect text data using web scraping technology. The collection unit can also collect image data using a camera or smartphone. The management unit manages the data collected by the collection unit. For example, the management unit can manage data storage methods and access control. For example, the management unit can centrally manage data using a database. Furthermore, the management unit can periodically back up data. Furthermore, the management unit can set data access permissions to ensure security. For example, the management unit can centrally manage data using a database. The management department can also perform regular data backups. The management department can also set data access permissions and ensure security. The training department trains AI models based on the data managed by the management department. The training department can train AI models using algorithms such as neural networks and decision trees. For example, the training department can train image recognition models using neural networks. The training department can also train classification models using decision trees. Furthermore, the training department can also train reinforcement learning models using reinforcement learning algorithms. For example, the training department can train image recognition models using neural networks. The training department can also train classification models using decision trees. The training department can also train reinforcement learning models using reinforcement learning algorithms.The Deployment Unit deploys AI models trained by the Training Unit. The Deployment Unit can deploy AI models to, for example, cloud environments or on-premises environments. For example, the Deployment Unit can deploy AI models to cloud environments to ensure scalability. It can also deploy AI models to on-premises environments to ensure security. Furthermore, the Deployment Unit can deploy AI models using container technology. For example, the Deployment Unit can deploy AI models to cloud environments to ensure scalability. It can also deploy AI models to on-premises environments to ensure security. The Deployment Unit can also deploy AI models using container technology. The Monitoring Unit monitors the AI ​​models deployed by the Deployment Unit. For example, the Monitoring Unit can monitor the performance and errors of the AI ​​models. For example, the Monitoring Unit can monitor the performance of the AI ​​models in real time and detect anomalies. It can also collect error logs from the AI ​​models and identify the cause of errors. Furthermore, the Monitoring Unit can manage AI model versions and always monitor for the latest model. For example, the Monitoring Unit can monitor the performance of the AI ​​models in real time and detect anomalies. The monitoring unit can also collect error logs from the AI ​​model and identify the cause of the errors. The monitoring unit can also manage the version control of the AI ​​model and constantly monitor for the latest model. This allows the AI ​​agent utilization platform according to the embodiment to efficiently collect, manage, train, deploy, and monitor data.

[0030] The data collection unit collects data. The data collection unit can collect various types of data, such as sensor data, text data, and image data. Specifically, sensor data is collected using various sensors to gather environmental data such as temperature, humidity, pressure, vibration, and light in real time. This sensor data is collected through IoT devices and sent to a cloud-based database. Text data is collected from publicly available information on the internet, social media posts, and news articles using web scraping technology. Web scraping is a technology that automatically extracts necessary information from specific websites and is often implemented using programming languages ​​such as Python. Image data is collected using cameras and smartphones. For example, surveillance cameras capture video of a specific area in real time and save it as image data. Smartphones upload photos and videos taken by users to the cloud, and the data collection unit acquires this data. The data collection unit centrally manages this diverse data and stores it in a database. The frequency and method of data collection are adjusted according to the system requirements and purpose. For example, if real-time data collection is required, sensors and cameras operate continuously, transmitting data continuously. On the other hand, if periodic data collection is appropriate, the collection unit will collect data according to a schedule. This allows the collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The management department manages the data collected by the collection department. For example, the management department can manage data storage methods and access control. Specifically, it centrally manages data using a database to ensure data integrity and availability. The appropriate database type is selected depending on the application, such as a relational database or a NoSQL database. The management department regularly backs up data to prepare for data loss or corruption. Backups are performed both on-site and off-site, enabling data recovery in the event of a disaster. The management department also sets data access permissions to ensure security. Access control sets different permissions for each user to prevent unauthorized access to data. For example, administrators can access all data, while general users can only access specific data. Furthermore, the management department encrypts data to protect its confidentiality. Data encryption is performed both at storage and in transit to prevent eavesdropping and tampering by third parties. This allows the management department to manage collected data safely and efficiently, improving the reliability and security of the entire system.

[0032] The training unit trains AI models based on data managed by the management unit. The training unit can train AI models using algorithms such as neural networks and decision trees. Specifically, when training an image recognition model using a neural network, the collected image data is preprocessed and labeled. Preprocessing includes image resizing, normalization, and data augmentation. Labeling is the process of assigning the correct category or class to images and is crucial for improving the quality of the training data. The training unit then trains the neural network using the preprocessed data and optimizes the model's parameters. The training process spans multiple epochs and is repeated until the model's performance improves. When training a classification model using a decision tree, collected text data and sensor data are extracted as features to create a training dataset. Feature extraction includes text vectorization and statistical calculation of sensor data. The training unit then applies the decision tree algorithm using the features to construct the classification model. Furthermore, the training unit can also train reinforcement learning models using reinforcement learning algorithms. In reinforcement learning, an agent interacts with the environment and learns actions to maximize rewards. The training unit builds a simulation environment and supports the process by which agents learn optimal behavioral policies through trial and error. This allows the training unit to efficiently train diverse AI models and improve the overall system performance.

[0033] The deployment department deploys AI models trained by the training department. The deployment department can deploy AI models to cloud environments or on-premises environments, for example. Specifically, when deploying AI models to a cloud environment, the infrastructure of the cloud service provider is used to ensure the scalability and availability of the model. In a cloud environment, automatic scaling and load balancing of the model can be easily achieved, maintaining high performance for a large number of requests. When deploying AI models to an on-premises environment, the model is placed on servers or data centers within the company to meet security and privacy requirements. In an on-premises environment, local management and customization of data are possible, allowing for flexible operation according to specific business requirements. Furthermore, the deployment department can also deploy AI models using container technology. Container technology centrally manages model dependencies and environment settings, increasing portability across different environments. For example, models can be packaged using Docker containers and deployed to any environment, whether cloud or on-premises. This allows the deployment department to achieve rapid deployment and operation of AI models, improving the overall flexibility and efficiency of the system.

[0034] The monitoring department monitors the AI ​​models deployed by the deployment department. For example, the monitoring department can monitor the performance and errors of the AI ​​models. Specifically, it monitors the AI ​​model's performance in real time, measuring metrics such as response time and throughput. This allows for constant monitoring of the model's operational status and rapid response in the event of anomalies. The monitoring department collects error logs from the AI ​​models to identify the cause of errors. Error logs record detailed information about exceptions and errors that occurred during the model's operation, aiding in troubleshooting. Furthermore, the monitoring department manages AI model versions, ensuring that the latest model is always available. Version control tracks the model's update history and allows for rapid rollback if a problem occurs with a particular version. The monitoring department visualizes this monitoring data, making the situation visible in real time through a dashboard. The dashboard is designed to provide a clear overview of key metrics and alerts, enabling operations personnel to respond quickly. This allows the monitoring department to ensure stable operation of the AI ​​models and improve the overall reliability and performance of the system.

[0035] The collection unit can provide data storage and security. The collection unit can store data using, for example, cloud storage. The collection unit can also store data using, for example, on-premises storage. Furthermore, the collection unit can ensure security using data encryption technology. This enables the secure collection and storage of data. Some or all of the above-described processes in the collection unit may be performed using, for example, AI, or not using AI. For example, the collection unit can input data stored in cloud storage into a generating AI and have the generating AI perform data encryption.

[0036] The management department can centrally manage data. For example, the management department can centrally manage data using a database. The management department can also regularly back up data. For example, the management department can regularly back up data. Furthermore, the management department can set access permissions for data to ensure security. For example, the management department can set access permissions for data to ensure security. This makes centralized data management possible. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input data stored in the database into a generating AI and have the generating AI perform data backups.

[0037] The training unit can train AI models. For example, the training unit can train AI models using algorithms such as neural networks and decision trees. For example, the training unit can train image recognition models using neural networks. The training unit can also train classification models using decision trees. Furthermore, the training unit can train reinforcement learning models using reinforcement learning algorithms. For example, the training unit can train image recognition models using neural networks. The training unit can also train classification models using decision trees. The training unit can also train reinforcement learning models using reinforcement learning algorithms. This enables the training of AI models. Some or all of the above-described processes in the training unit are performed using generative AI. For example, the training unit can input neural network training data into the generative AI and have the generative AI perform the training.

[0038] The deployment unit can deploy AI models. For example, the deployment unit can deploy AI models to cloud environments or on-premises environments. For example, the deployment unit can deploy AI models to cloud environments to ensure scalability. It can also deploy AI models to on-premises environments to ensure security. Furthermore, the deployment unit can deploy AI models using container technology. For example, the deployment unit can deploy AI models to cloud environments to ensure scalability. It can also deploy AI models to on-premises environments to ensure security. The deployment unit can also deploy AI models using container technology. This enables the deployment of AI models. Some or all of the above-described processes in the deployment unit may be performed using AI, or not using AI. For example, the deployment unit can input an AI model deployed to a cloud environment into a generating AI and have the generating AI perform deployment optimization.

[0039] The monitoring unit can monitor the AI ​​model. For example, the monitoring unit can monitor the AI ​​model's performance and errors. For example, the monitoring unit can monitor the AI ​​model's performance in real time and detect anomalies. The monitoring unit can also collect error logs of the AI ​​model and identify the cause of errors. Furthermore, the monitoring unit can manage the AI ​​model's version and always monitor the latest model. For example, the monitoring unit can monitor the AI ​​model's performance in real time and detect anomalies. The monitoring unit can also collect error logs of the AI ​​model and identify the cause of errors. The monitoring unit can also manage the AI ​​model's version and always monitor the latest model. This makes it possible to monitor the AI ​​model. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input AI model performance data into a generating AI and have the generating AI perform anomaly detection.

[0040] The management unit can provide data scalability and flexibility. For example, the management unit can ensure the scalability of the database. The management unit can also modularize the data to provide flexibility. For example, the management unit can modularize the data to provide flexibility. This improves the scalability and flexibility of the data. Some or all of the above processes in the management unit may be performed using AI, for example, or not using AI. For example, the management unit can input the database scalability into a generating AI and have the generating AI perform scalability optimization.

[0041] The deployment unit can provide services and UI / UX design. For example, the deployment unit can provide API services. The deployment unit can also provide cloud services. For example, the deployment unit can provide cloud services. Furthermore, the deployment unit can conduct usability testing and provide UI / UX design. For example, the deployment unit conducts usability testing and provides UI / UX design. This provides services and UI / UX design. Some or all of the above processes in the deployment unit may be performed using AI, for example, or not using AI. For example, the deployment unit can input the API service design into a generative AI and have the generative AI perform design optimization.

[0042] The monitoring unit can enable integration with existing systems and API utilization. The monitoring unit can, for example, perform data exchange between systems. The monitoring unit can, for example, perform data exchange between systems. The monitoring unit can also perform integration with existing systems using APIs. For example, the monitoring unit can perform integration with existing systems using APIs. This enables integration with existing systems and API utilization. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input data exchange between systems into a generating AI and have the generating AI perform optimization of the exchange.

[0043] The data collection unit can evaluate the reliability of the data during data collection and prioritize the collection of reliable data. For example, the data collection unit can verify the source of the data and prioritize the collection of data from reliable sources. The data collection unit can also check the consistency of the data and prioritize the collection of consistent data. Furthermore, the data collection unit can evaluate the timeliness of the data and prioritize the collection of the most recent data. This ensures that reliable data is collected preferentially. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the reliability of the data into a generating AI and have the generating AI perform the reliability evaluation.

[0044] The data collection unit can apply different collection methods depending on the type of data. For example, in the case of text data, the data collection unit can use web scraping to collect it. The data collection unit can also use image recognition technology to collect image data. Furthermore, the data collection unit can use speech recognition technology to collect audio data. This allows for the application of collection methods appropriate to the type of data. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input text data into a generating AI and have the generating AI optimize the collection method.

[0045] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. The data collection unit can also collect data related to the user's destination if the user is on the move. For example, if the data collection unit is on the move, the data collection unit will collect data related to the user's destination. Furthermore, if the user is staying in a specific location for an extended period, the data collection unit can collect detailed data related to that location. For example, if the user is staying in a specific location for an extended period, the data collection unit will collect detailed data related to that location. This allows for the collection of highly relevant data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0046] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on information shared by the user on social media. The data collection unit can also analyze the activity of accounts followed by the user and collect relevant data. For example, the data collection unit can analyze the activity of accounts followed by the user and collect relevant data. Furthermore, the data collection unit can collect data related to groups and events in which the user participates. For example, the data collection unit can collect data related to groups and events in which the user participates. This allows for the collection of relevant data based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect the relevant data.

[0047] The management department can adjust the level of detail in data management based on the importance of the data. For example, the management department can manage highly important data in detail and back it up frequently. The management department can also manage less important data in a simplified manner and reduce the frequency of backups. Furthermore, the management department can set data access permissions according to importance. This allows the level of detail in management to be adjusted based on the importance of the data. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail in management.

[0048] The management unit can apply different management algorithms depending on the data category when managing data. For example, the management unit can apply a full-text search algorithm to text data. The management unit can also apply an image recognition algorithm to image data. Furthermore, the management unit can apply a speech recognition algorithm to audio data. This allows the management unit to apply management algorithms according to the data category. Some or all of the above processing in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input text data into a generating AI and have the generating AI perform the application of the management algorithm.

[0049] The management department can determine management priorities based on the data submission date when managing data. For example, the management department can prioritize and process newly submitted data quickly. The management department can also prioritize and process older data later. Furthermore, the management department can adjust the data management method according to the submission date. This allows the management department to determine management priorities based on the data submission date. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input the data submission dates into a generating AI and have the generating AI determine the management priorities.

[0050] The management unit can adjust the order of management based on the relevance of the data. For example, the management unit can prioritize and process highly relevant data quickly. The management unit can also prioritize and process less relevant data later. Furthermore, the management unit can adjust the level of detail in management according to the relevance of the data. This allows the order of management to be adjusted based on the relevance of the data. Some or all of the above processes in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the order of management.

[0051] The training unit can evaluate the reliability of the data during training and prioritize the use of reliable data. For example, the training unit can verify the source of the data and prioritize the use of data from reliable sources. The training unit can also check the consistency of the data and prioritize the use of consistent data. Furthermore, the training unit can evaluate the recency of the data and prioritize the use of the most recent data. This improves the accuracy of training by prioritizing the use of reliable data. Some or all of the above processes in the training unit are performed using generative AI. For example, the training unit can input the reliability of the data into the generative AI and have the generative AI perform the reliability evaluation.

[0052] The training unit can apply different training algorithms depending on the type of AI model during training. For example, in the case of an image recognition model, the training unit uses a convolutional neural network (CNN) for training. The training unit can also use a recurrent neural network (RNN) for training a natural language processing model. Furthermore, the training unit can use the Q-learning algorithm for training a reinforcement learning model. This improves training efficiency by applying training algorithms appropriate to the type of AI model. Some or all of the above processing in the training unit is performed using a generative AI. For example, the training unit can input the type of AI model into the generative AI and have the generative AI execute the application of the training algorithm.

[0053] The training unit can determine training priorities based on data submission timing during training. For example, the training unit can prioritize the use of newly submitted data to perform training quickly. The training unit can also postpone training on older data. Furthermore, the training unit can adjust the training method according to the submission timing. This allows the training unit to determine training priorities based on data submission timing. Some or all of the above processes in the training unit are performed using generative AI. For example, the training unit can input the data submission timing into the generative AI and have the generative AI determine the training priorities.

[0054] The training unit can adjust the training order based on the relevance of the data during training. For example, the training unit can prioritize highly relevant data and perform training quickly. The training unit can also postpone training on less relevant data. For example, the training unit can postpone training on less relevant data. Furthermore, the training unit can adjust the level of detail of the training according to the relevance of the data. For example, the training unit can adjust the level of detail of the training according to the relevance of the data. This allows the training order to be adjusted based on the relevance of the data. Some or all of the above processing in the training unit is performed using generative AI. For example, the training unit can input the relevance of the data into the generative AI and have the generative AI perform the adjustment of the training order.

[0055] The deployment unit can evaluate the reliability of AI models during deployment and prioritize the deployment of highly reliable models. For example, the deployment unit can check the test results of AI models and prioritize the deployment of highly reliable models. The deployment unit can also evaluate the past performance of AI models and prioritize the deployment of highly reliable models. Furthermore, the deployment unit can manage the versions of AI models and prioritize the deployment of highly reliable versions. For example, the deployment unit can manage the versions of AI models and prioritize the deployment of highly reliable versions. This improves the reliability of the system by prioritizing the deployment of highly reliable models. Some or all of the above processes in the deployment unit are performed using generative AI. For example, the deployment unit can input the reliability of the AI ​​models into the generative AI and have the generative AI perform the reliability evaluation.

[0056] The deployment unit can apply different deployment algorithms depending on the type of AI model during deployment. For example, in the case of an image recognition model, the deployment unit will use a specific deployment algorithm. The deployment unit can also use a different deployment algorithm for natural language processing models. Furthermore, the deployment unit can use yet another different deployment algorithm for reinforcement learning models. This improves deployment efficiency by applying a deployment algorithm appropriate to the type of AI model. Some or all of the above processing in the deployment unit is performed using a generative AI. For example, the deployment unit can input the type of AI model into the generative AI and have the generative AI execute the application of the deployment algorithm.

[0057] The deployment unit can determine deployment priorities based on the submission timing of AI models during deployment. For example, the deployment unit can prioritize the deployment of newly submitted AI models for quick processing. The deployment unit can also postpone the deployment of older AI models. Furthermore, the deployment unit can adjust the deployment method according to the submission timing. This allows the deployment priority to be determined based on the submission timing of AI models. Some or all of the above processing in the deployment unit is performed using a generative AI. For example, the deployment unit can input the submission timing of AI models into the generative AI and have the generative AI determine the deployment priority.

[0058] The deployment unit can adjust the deployment order based on the relevance of the AI ​​models during deployment. For example, the deployment unit can prioritize the deployment of highly relevant AI models for faster processing. The deployment unit can also postpone the deployment of less relevant AI models. Furthermore, the deployment unit can adjust the level of detail of the deployment according to the relevance of the AI ​​models. This allows the deployment order to be adjusted based on the relevance of the AI ​​models. Some or all of the above processing in the deployment unit is performed using a generative AI. For example, the deployment unit can input the relevance of the AI ​​models into the generative AI and have the generative AI perform the adjustment of the deployment order.

[0059] The monitoring unit can evaluate the performance of AI models during monitoring and prioritize monitoring high-performing models. For example, the monitoring unit can check the test results of AI models and prioritize monitoring high-performing models. The monitoring unit can also evaluate the past performance of AI models and prioritize monitoring high-performing models. Furthermore, the monitoring unit can manage the versions of AI models and prioritize monitoring high-performing versions. For example, the monitoring unit can manage the versions of AI models and prioritize monitoring high-performing versions. This improves system reliability by prioritizing the monitoring of high-performing models. Some or all of the above processes in the monitoring unit are performed using generative AI. For example, the monitoring unit can input the performance of AI models into the generative AI and have the generative AI perform the performance evaluation.

[0060] The monitoring unit can apply different monitoring algorithms depending on the type of AI model during monitoring. For example, in the case of an image recognition model, the monitoring unit will use a specific monitoring algorithm. The monitoring unit can also use a different monitoring algorithm for natural language processing models. Furthermore, the monitoring unit can use yet another different monitoring algorithm for reinforcement learning models. This improves the efficiency of monitoring by applying a monitoring algorithm appropriate to the type of AI model. Some or all of the above processing in the monitoring unit is performed using a generative AI. For example, the monitoring unit can input the type of AI model into the generative AI and have the generative AI execute the application of the monitoring algorithm.

[0061] The monitoring unit can determine monitoring priorities based on the submission timing of AI models during monitoring. For example, the monitoring unit can prioritize monitoring and quickly process newly submitted AI models. The monitoring unit can also postpone monitoring older AI models. For example, the monitoring unit can postpone monitoring older AI models. Furthermore, the monitoring unit can adjust its monitoring method according to the submission timing. For example, the monitoring unit can adjust its monitoring method according to the submission timing. This allows the monitoring unit to determine monitoring priorities based on the submission timing of AI models. Some or all of the above processing in the monitoring unit is performed using a generative AI. For example, the monitoring unit can input the submission timing of AI models into the generative AI and have the generative AI determine the monitoring priorities.

[0062] The monitoring unit can adjust the monitoring order based on the relevance of the AI ​​models during monitoring. For example, the monitoring unit can prioritize monitoring highly relevant AI models and process them quickly. The monitoring unit can also postpone monitoring less relevant AI models. For example, the monitoring unit can postpone monitoring less relevant AI models. Furthermore, the monitoring unit can adjust the level of detail of monitoring according to the relevance of the AI ​​models. For example, the monitoring unit can adjust the monitoring order based on the relevance of the AI ​​models. Some or all of the above processing in the monitoring unit is performed using a generating AI. For example, the monitoring unit can input the relevance of the AI ​​models into the generating AI and have the generating AI perform the adjustment of the monitoring order.

[0063] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0064] The data collection unit can analyze user behavior patterns during data collection and determine the optimal timing for collection. For example, the unit can learn the user's daily activities and collect data while avoiding busy times. It can also reduce the user's burden by collecting data while they are performing specific activities. Furthermore, the unit can adjust the frequency of data collection based on the user's behavior patterns. This enables efficient data collection tailored to user behavior.

[0065] The management department can prioritize data management based on its importance. For example, it can prioritize and process high-priority data quickly. It can also postpone the management of less important data. Furthermore, the management department can adjust the level of detail in management according to the importance of the data. This enables efficient data management based on data importance.

[0066] The training unit can select training data while considering data diversity during AI model training. For example, the training unit can use data from different sources in a balanced manner to improve the versatility of the AI ​​model. The training unit can also ensure data diversity to eliminate specific biases. Furthermore, the training unit can adjust the training algorithm based on data diversity. This enables the training of a well-balanced AI model.

[0067] The deployment unit can adjust the deployment method when deploying AI models, taking into account the user's network environment. For example, if the user is in a slow network environment, the deployment unit can reduce the amount of data deployed for efficient deployment. Conversely, if the user is in a high-speed network environment, the deployment unit can increase the amount of data deployed for more detailed deployment. Furthermore, the deployment unit can adjust the timing of deployment according to the user's network environment. This enables efficient deployment tailored to the user's network environment.

[0068] The monitoring unit can collect user feedback while monitoring AI models and improve its monitoring methods. For example, it can adjust the frequency and level of detail of monitoring based on user feedback. It can also analyze user feedback and improve its monitoring algorithms. Furthermore, it can determine monitoring priorities based on user feedback. This enables efficient monitoring that reflects user feedback.

[0069] The following briefly describes the processing flow for example form 1.

[0070] Step 1: The data collection unit collects data. The data collection unit can collect various types of data, such as sensor data, text data, and image data. The data collection unit collects sensor data in real time and stores it in a database. The data collection unit can also collect text data using web scraping technology. Furthermore, the data collection unit can collect image data using a camera or smartphone. Step 2: The management department manages the data collected by the collection department. For example, the management department can manage how the data is stored and how access is controlled. The management department can centrally manage the data using a database and perform regular data backups. The management department can also set access permissions for the data to ensure security. Step 3: The training unit trains the AI ​​model based on the data managed by the management unit. The training unit can train the AI ​​model using algorithms such as neural networks and decision trees. The training unit can also train an image recognition model using a neural network and a classification model using a decision tree. Furthermore, the training unit can also train a reinforcement learning model using a reinforcement learning algorithm. Step 4: The deployment unit deploys the AI ​​model trained by the training unit. The deployment unit can deploy the AI ​​model to a cloud environment or an on-premises environment, for example. The deployment unit can deploy the AI ​​model to a cloud environment to ensure scalability. Alternatively, the deployment unit can deploy the AI ​​model to an on-premises environment to ensure security. Furthermore, the deployment unit can also deploy the AI ​​model using container technology. Step 5: The monitoring unit monitors the AI ​​model deployed by the deployment unit. The monitoring unit can, for example, monitor the performance and errors of the AI ​​model. The monitoring unit monitors the AI ​​model's performance in real time and detects anomalies. The monitoring unit can also collect error logs of the AI ​​model and identify the cause of errors. Furthermore, the monitoring unit can manage the versioning of the AI ​​model and always monitor for the latest model.

[0071] (Example of form 2) The AI ​​agent utilization platform according to an embodiment of the present invention is a platform for efficiently utilizing AI agents. This platform addresses the current state and challenges of AI agent utilization, including complex data processing and management, increasing operational costs, and integration issues with existing systems. The platform achieves centralized management of data and AI models, improved scalability and flexibility, cost reduction, and operational efficiency. The main components of the platform are the data layer, AI model layer, application layer, integration, and API. The data layer provides data storage and security, while the AI ​​model layer handles training, deployment, and monitoring. The application layer provides services and UI / UX design, and integration and API enable integration with existing systems and API utilization. The platform design principles emphasize scalability, modularity, usability, and security. Scalability provides expandability in line with service growth, modularity enables independent component design, usability provides an intuitive design for end users, and security adopts a zero-trust architecture. The implementation procedure involves first designing the platform, and then implementing each component. Performance indicators include user satisfaction, platform uptime, processing speed, and response time. Key factors for success include clear goal setting and effective project management. For example, an AI agent utilization platform enables efficient AI utilization by collecting data, training AI models, deploying them, and monitoring them. The data layer securely stores data using, for example, cloud storage or on-premises storage. The AI ​​model layer trains AI models using, for example, algorithms such as neural networks or decision trees. The application layer performs UI / UX design based on, for example, usability testing and design guidelines. Integration and APIs enable integration with existing systems through, for example, data exchange between systems and the use of APIs. As a result, the AI ​​agent utilization platform can efficiently collect, manage, train, deploy, and monitor data.This allows AI agent utilization platforms to efficiently collect, manage, train, deploy, and monitor data.

[0072] The AI ​​agent utilization platform according to this embodiment comprises a collection unit, a management unit, a training unit, a deployment unit, and a monitoring unit. The collection unit collects data. The collection unit can collect various types of data, such as sensor data, text data, and image data. For example, the collection unit can collect sensor data in real time and store it in a database. The collection unit can also collect text data using web scraping technology. Furthermore, the collection unit can collect image data using a camera or smartphone. For example, the collection unit can collect sensor data in real time and store it in a database. The collection unit can also collect text data using web scraping technology. The collection unit can also collect image data using a camera or smartphone. The management unit manages the data collected by the collection unit. For example, the management unit can manage data storage methods and access control. For example, the management unit can centrally manage data using a database. Furthermore, the management unit can periodically back up data. Furthermore, the management unit can set data access permissions to ensure security. For example, the management unit can centrally manage data using a database. The management department can also perform regular data backups. The management department can also set data access permissions and ensure security. The training department trains AI models based on the data managed by the management department. The training department can train AI models using algorithms such as neural networks and decision trees. For example, the training department can train image recognition models using neural networks. The training department can also train classification models using decision trees. Furthermore, the training department can also train reinforcement learning models using reinforcement learning algorithms. For example, the training department can train image recognition models using neural networks. The training department can also train classification models using decision trees. The training department can also train reinforcement learning models using reinforcement learning algorithms.The Deployment Unit deploys AI models trained by the Training Unit. The Deployment Unit can deploy AI models to, for example, cloud environments or on-premises environments. For example, the Deployment Unit can deploy AI models to cloud environments to ensure scalability. It can also deploy AI models to on-premises environments to ensure security. Furthermore, the Deployment Unit can deploy AI models using container technology. For example, the Deployment Unit can deploy AI models to cloud environments to ensure scalability. It can also deploy AI models to on-premises environments to ensure security. The Deployment Unit can also deploy AI models using container technology. The Monitoring Unit monitors the AI ​​models deployed by the Deployment Unit. For example, the Monitoring Unit can monitor the performance and errors of the AI ​​models. For example, the Monitoring Unit can monitor the performance of the AI ​​models in real time and detect anomalies. It can also collect error logs from the AI ​​models and identify the cause of errors. Furthermore, the Monitoring Unit can manage AI model versions and always monitor for the latest model. For example, the Monitoring Unit can monitor the performance of the AI ​​models in real time and detect anomalies. The monitoring unit can also collect error logs from the AI ​​model and identify the cause of the errors. The monitoring unit can also manage the version control of the AI ​​model and constantly monitor for the latest model. This allows the AI ​​agent utilization platform according to the embodiment to efficiently collect, manage, train, deploy, and monitor data.

[0073] The data collection unit collects data. The data collection unit can collect various types of data, such as sensor data, text data, and image data. Specifically, sensor data is collected using various sensors to gather environmental data such as temperature, humidity, pressure, vibration, and light in real time. This sensor data is collected through IoT devices and sent to a cloud-based database. Text data is collected from publicly available information on the internet, social media posts, and news articles using web scraping technology. Web scraping is a technology that automatically extracts necessary information from specific websites and is often implemented using programming languages ​​such as Python. Image data is collected using cameras and smartphones. For example, surveillance cameras capture video of a specific area in real time and save it as image data. Smartphones upload photos and videos taken by users to the cloud, and the data collection unit acquires this data. The data collection unit centrally manages this diverse data and stores it in a database. The frequency and method of data collection are adjusted according to the system requirements and purpose. For example, if real-time data collection is required, sensors and cameras operate continuously, transmitting data continuously. On the other hand, if periodic data collection is appropriate, the collection unit will collect data according to a schedule. This allows the collection unit to collect data efficiently and effectively, improving the overall system performance.

[0074] The management department manages the data collected by the collection department. For example, the management department can manage data storage methods and access control. Specifically, it centrally manages data using a database to ensure data integrity and availability. The appropriate database type is selected depending on the application, such as a relational database or a NoSQL database. The management department regularly backs up data to prepare for data loss or corruption. Backups are performed both on-site and off-site, enabling data recovery in the event of a disaster. The management department also sets data access permissions to ensure security. Access control sets different permissions for each user to prevent unauthorized access to data. For example, administrators can access all data, while general users can only access specific data. Furthermore, the management department encrypts data to protect its confidentiality. Data encryption is performed both at storage and in transit to prevent eavesdropping and tampering by third parties. This allows the management department to manage collected data safely and efficiently, improving the reliability and security of the entire system.

[0075] The training unit trains AI models based on data managed by the management unit. The training unit can train AI models using algorithms such as neural networks and decision trees. Specifically, when training an image recognition model using a neural network, the collected image data is preprocessed and labeled. Preprocessing includes image resizing, normalization, and data augmentation. Labeling is the process of assigning the correct category or class to images and is crucial for improving the quality of the training data. The training unit then trains the neural network using the preprocessed data and optimizes the model's parameters. The training process spans multiple epochs and is repeated until the model's performance improves. When training a classification model using a decision tree, collected text data and sensor data are extracted as features to create a training dataset. Feature extraction includes text vectorization and statistical calculation of sensor data. The training unit then applies the decision tree algorithm using the features to construct the classification model. Furthermore, the training unit can also train reinforcement learning models using reinforcement learning algorithms. In reinforcement learning, an agent interacts with the environment and learns actions to maximize rewards. The training unit builds a simulation environment and supports the process by which agents learn optimal behavioral policies through trial and error. This allows the training unit to efficiently train diverse AI models and improve the overall system performance.

[0076] The deployment department deploys AI models trained by the training department. The deployment department can deploy AI models to cloud environments or on-premises environments, for example. Specifically, when deploying AI models to a cloud environment, the infrastructure of the cloud service provider is used to ensure the scalability and availability of the model. In a cloud environment, automatic scaling and load balancing of the model can be easily achieved, maintaining high performance for a large number of requests. When deploying AI models to an on-premises environment, the model is placed on servers or data centers within the company to meet security and privacy requirements. In an on-premises environment, local management and customization of data are possible, allowing for flexible operation according to specific business requirements. Furthermore, the deployment department can also deploy AI models using container technology. Container technology centrally manages model dependencies and environment settings, increasing portability across different environments. For example, models can be packaged using Docker containers and deployed to any environment, whether cloud or on-premises. This allows the deployment department to achieve rapid deployment and operation of AI models, improving the overall flexibility and efficiency of the system.

[0077] The monitoring department monitors the AI ​​models deployed by the deployment department. For example, the monitoring department can monitor the performance and errors of the AI ​​models. Specifically, it monitors the AI ​​model's performance in real time, measuring metrics such as response time and throughput. This allows for constant monitoring of the model's operational status and rapid response in the event of anomalies. The monitoring department collects error logs from the AI ​​models to identify the cause of errors. Error logs record detailed information about exceptions and errors that occurred during the model's operation, aiding in troubleshooting. Furthermore, the monitoring department manages AI model versions, ensuring that the latest model is always available. Version control tracks the model's update history and allows for rapid rollback if a problem occurs with a particular version. The monitoring department visualizes this monitoring data, making the situation visible in real time through a dashboard. The dashboard is designed to provide a clear overview of key metrics and alerts, enabling operations personnel to respond quickly. This allows the monitoring department to ensure stable operation of the AI ​​models and improve the overall reliability and performance of the system.

[0078] The collection unit can provide data storage and security. The collection unit can store data using, for example, cloud storage. The collection unit can also store data using, for example, on-premises storage. Furthermore, the collection unit can ensure security using data encryption technology. This enables the secure collection and storage of data. Some or all of the above-described processes in the collection unit may be performed using, for example, AI, or not using AI. For example, the collection unit can input data stored in cloud storage into a generating AI and have the generating AI perform data encryption.

[0079] The management department can centrally manage data. For example, the management department can centrally manage data using a database. The management department can also regularly back up data. For example, the management department can regularly back up data. Furthermore, the management department can set access permissions for data to ensure security. For example, the management department can set access permissions for data to ensure security. This makes centralized data management possible. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input data stored in the database into a generating AI and have the generating AI perform data backups.

[0080] The training unit can train AI models. For example, the training unit can train AI models using algorithms such as neural networks and decision trees. For example, the training unit can train image recognition models using neural networks. The training unit can also train classification models using decision trees. Furthermore, the training unit can train reinforcement learning models using reinforcement learning algorithms. For example, the training unit can train image recognition models using neural networks. The training unit can also train classification models using decision trees. The training unit can also train reinforcement learning models using reinforcement learning algorithms. This enables the training of AI models. Some or all of the above-described processes in the training unit are performed using generative AI. For example, the training unit can input neural network training data into the generative AI and have the generative AI perform the training.

[0081] The deployment unit can deploy AI models. For example, the deployment unit can deploy AI models to cloud environments or on-premises environments. For example, the deployment unit can deploy AI models to cloud environments to ensure scalability. It can also deploy AI models to on-premises environments to ensure security. Furthermore, the deployment unit can deploy AI models using container technology. For example, the deployment unit can deploy AI models to cloud environments to ensure scalability. It can also deploy AI models to on-premises environments to ensure security. The deployment unit can also deploy AI models using container technology. This enables the deployment of AI models. Some or all of the above-described processes in the deployment unit may be performed using AI, or not using AI. For example, the deployment unit can input an AI model deployed to a cloud environment into a generating AI and have the generating AI perform deployment optimization.

[0082] The monitoring unit can monitor the AI ​​model. For example, the monitoring unit can monitor the AI ​​model's performance and errors. For example, the monitoring unit can monitor the AI ​​model's performance in real time and detect anomalies. The monitoring unit can also collect error logs of the AI ​​model and identify the cause of errors. Furthermore, the monitoring unit can manage the AI ​​model's version and always monitor the latest model. For example, the monitoring unit can monitor the AI ​​model's performance in real time and detect anomalies. The monitoring unit can also collect error logs of the AI ​​model and identify the cause of errors. The monitoring unit can also manage the AI ​​model's version and always monitor the latest model. This makes it possible to monitor the AI ​​model. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input AI model performance data into a generating AI and have the generating AI perform anomaly detection.

[0083] The management unit can provide data scalability and flexibility. For example, the management unit can ensure the scalability of the database. The management unit can also modularize the data to provide flexibility. For example, the management unit can modularize the data to provide flexibility. This improves the scalability and flexibility of the data. Some or all of the above processes in the management unit may be performed using AI, for example, or not using AI. For example, the management unit can input the database scalability into a generating AI and have the generating AI perform scalability optimization.

[0084] The deployment unit can provide services and UI / UX design. For example, the deployment unit can provide API services. The deployment unit can also provide cloud services. For example, the deployment unit can provide cloud services. Furthermore, the deployment unit can conduct usability testing and provide UI / UX design. For example, the deployment unit conducts usability testing and provides UI / UX design. This provides services and UI / UX design. Some or all of the above processes in the deployment unit may be performed using AI, for example, or not using AI. For example, the deployment unit can input the API service design into a generative AI and have the generative AI perform design optimization.

[0085] The monitoring unit can enable integration with existing systems and API utilization. The monitoring unit can, for example, perform data exchange between systems. The monitoring unit can, for example, perform data exchange between systems. The monitoring unit can also perform integration with existing systems using APIs. For example, the monitoring unit can perform integration with existing systems using APIs. This enables integration with existing systems and API utilization. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input data exchange between systems into a generating AI and have the generating AI perform optimization of the exchange.

[0086] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the user's burden. The data collection unit can also increase the frequency of data collection and collect more detailed data if the user is relaxed. For example, if the user is relaxed, the data collection unit can increase the frequency of data collection and collect more detailed data. Furthermore, if the user is in a hurry, the data collection unit can temporarily stop data collection and prioritize the user's work. For example, if the user is in a hurry, the data collection unit can temporarily stop data collection and prioritize the user's work. This allows the timing of data collection to be adjusted according to the user's emotions. 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 processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0087] The data collection unit can evaluate the reliability of the data during data collection and prioritize the collection of reliable data. For example, the data collection unit can verify the source of the data and prioritize the collection of data from reliable sources. The data collection unit can also check the consistency of the data and prioritize the collection of consistent data. Furthermore, the data collection unit can evaluate the timeliness of the data and prioritize the collection of the most recent data. This ensures that reliable data is collected preferentially. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the reliability of the data into a generating AI and have the generating AI perform the reliability evaluation.

[0088] The data collection unit can apply different collection methods depending on the type of data. For example, in the case of text data, the data collection unit can use web scraping to collect it. The data collection unit can also use image recognition technology to collect image data. Furthermore, the data collection unit can use speech recognition technology to collect audio data. This allows for the application of collection methods appropriate to the type of data. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input text data into a generating AI and have the generating AI optimize the collection method.

[0089] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will postpone the collection of less important data. The data collection unit can also prioritize the collection of highly important data if the user is relaxed. For example, if the user is relaxed, the data collection unit will prioritize the collection of highly important data. Furthermore, if the user is in a hurry, the data collection unit can reduce the amount of data to collect and complete the collection quickly. For example, if the user is in a hurry, the data collection unit will reduce the amount of data to collect and complete the collection quickly. This allows the data collection unit to prioritize data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI, allowing the generating AI to perform emotion estimation.

[0090] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. The data collection unit can also collect data related to the user's destination if the user is on the move. For example, if the data collection unit is on the move, the data collection unit will collect data related to the user's destination. Furthermore, if the user is staying in a specific location for an extended period, the data collection unit can collect detailed data related to that location. For example, if the user is staying in a specific location for an extended period, the data collection unit will collect detailed data related to that location. This allows for the collection of highly relevant data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0091] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on information shared by the user on social media. The data collection unit can also analyze the activity of accounts followed by the user and collect relevant data. For example, the data collection unit can analyze the activity of accounts followed by the user and collect relevant data. Furthermore, the data collection unit can collect data related to groups and events in which the user participates. For example, the data collection unit can collect data related to groups and events in which the user participates. This allows for the collection of relevant data based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect the relevant data.

[0092] The management department can estimate the user's emotions and adjust the data management method based on the estimated emotions. For example, if the user is stressed, the management department can reduce the frequency of data management to alleviate the user's burden. The management department can also increase the frequency of data management and manage more detailed data if the user is relaxed. Furthermore, if the user is in a hurry, the management department can temporarily suspend data management and prioritize the user's work. This allows the data management method to be adjusted according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0093] The management department can adjust the level of detail in data management based on the importance of the data. For example, the management department can manage highly important data in detail and back it up frequently. The management department can also manage less important data in a simplified manner and reduce the frequency of backups. Furthermore, the management department can set data access permissions according to importance. This allows the level of detail in management to be adjusted based on the importance of the data. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail in management.

[0094] The management unit can apply different management algorithms depending on the data category when managing data. For example, the management unit can apply a full-text search algorithm to text data. The management unit can also apply an image recognition algorithm to image data. Furthermore, the management unit can apply a speech recognition algorithm to audio data. This allows the management unit to apply management algorithms according to the data category. Some or all of the above processing in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input text data into a generating AI and have the generating AI perform the application of the management algorithm.

[0095] The management unit can estimate the user's emotions and determine the priority of data to manage based on the estimated emotions. For example, if the user is stressed, the management unit will postpone managing less important data. The management unit can also prioritize managing high-importance data if the user is relaxed. Furthermore, if the user is in a hurry, the management unit can reduce the amount of data to manage and complete the management quickly. This allows the management unit to determine the priority of data to manage according to the user's emotions. 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 management unit may be performed using AI, for example, or without AI. For example, the management department can input user emotion data into a generative AI and have the AI ​​perform emotion estimation.

[0096] The management department can determine management priorities based on the data submission date when managing data. For example, the management department can prioritize and process newly submitted data quickly. The management department can also prioritize and process older data later. Furthermore, the management department can adjust the data management method according to the submission date. This allows the management department to determine management priorities based on the data submission date. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input the data submission dates into a generating AI and have the generating AI determine the management priorities.

[0097] The management unit can adjust the order of management based on the relevance of the data. For example, the management unit can prioritize and process highly relevant data quickly. The management unit can also prioritize and process less relevant data later. Furthermore, the management unit can adjust the level of detail in management according to the relevance of the data. This allows the order of management to be adjusted based on the relevance of the data. Some or all of the above processes in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the order of management.

[0098] The training unit can estimate the user's emotions and adjust the training method based on the estimated emotions. For example, if the user is relaxed, the training unit can conduct training at a relaxed pace. The training unit can also conduct effective training in a short amount of time if the user is in a hurry. Furthermore, if the user is excited, the training unit can conduct visually stimulating training. This allows the training method to be adjusted according to the user's emotions. 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-described processes in the training unit are performed using generative AI. For example, the training unit can input user emotion data into the generative AI and have the generative AI perform emotion estimation.

[0099] The training unit can evaluate the reliability of the data during training and prioritize the use of reliable data. For example, the training unit can verify the source of the data and prioritize the use of data from reliable sources. The training unit can also check the consistency of the data and prioritize the use of consistent data. Furthermore, the training unit can evaluate the recency of the data and prioritize the use of the most recent data. This improves the accuracy of training by prioritizing the use of reliable data. Some or all of the above processes in the training unit are performed using generative AI. For example, the training unit can input the reliability of the data into the generative AI and have the generative AI perform the reliability evaluation.

[0100] The training unit can apply different training algorithms depending on the type of AI model during training. For example, in the case of an image recognition model, the training unit uses a convolutional neural network (CNN) for training. The training unit can also use a recurrent neural network (RNN) for training a natural language processing model. Furthermore, the training unit can use the Q-learning algorithm for training a reinforcement learning model. This improves training efficiency by applying training algorithms appropriate to the type of AI model. Some or all of the above processing in the training unit is performed using a generative AI. For example, the training unit can input the type of AI model into the generative AI and have the generative AI execute the application of the training algorithm.

[0101] The training unit can estimate the user's emotions and determine training priorities based on those emotions. For example, if the user is stressed, the training unit will postpone less important training. The training unit can also prioritize more important training if the user is relaxed. Furthermore, if the user is in a hurry, the training unit can reduce the amount of training and complete it quickly. This allows the training priorities to be determined 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 training unit is performed using generative AI. For example, the training unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0102] The training unit can determine training priorities based on data submission timing during training. For example, the training unit can prioritize the use of newly submitted data to perform training quickly. The training unit can also postpone training on older data. Furthermore, the training unit can adjust the training method according to the submission timing. This allows the training unit to determine training priorities based on data submission timing. Some or all of the above processes in the training unit are performed using generative AI. For example, the training unit can input the data submission timing into the generative AI and have the generative AI determine the training priorities.

[0103] The training unit can adjust the training order based on the relevance of the data during training. For example, the training unit can prioritize highly relevant data and perform training quickly. The training unit can also postpone training on less relevant data. For example, the training unit can postpone training on less relevant data. Furthermore, the training unit can adjust the level of detail of the training according to the relevance of the data. For example, the training unit can adjust the level of detail of the training according to the relevance of the data. This allows the training order to be adjusted based on the relevance of the data. Some or all of the above processing in the training unit is performed using generative AI. For example, the training unit can input the relevance of the data into the generative AI and have the generative AI perform the adjustment of the training order.

[0104] The deployment unit can estimate the user's emotions and adjust the deployment method based on the estimated emotions. For example, if the user is relaxed, the deployment unit will deploy at a relaxed pace. The deployment unit can also complete the deployment quickly if the user is in a hurry. For example, if the user is excited, the deployment unit will complete the deployment quickly. Furthermore, if the user is excited, the deployment unit can provide a visually stimulating deployment method. For example, if the user is excited, the deployment unit will provide a visually stimulating deployment method. This allows the deployment method to be adjusted according to the user's emotions. 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 deployment unit is performed using generative AI. For example, the deployment unit can input user emotion data into the generative AI and have the generative AI perform emotion estimation.

[0105] The deployment unit can evaluate the reliability of AI models during deployment and prioritize the deployment of highly reliable models. For example, the deployment unit can check the test results of AI models and prioritize the deployment of highly reliable models. The deployment unit can also evaluate the past performance of AI models and prioritize the deployment of highly reliable models. Furthermore, the deployment unit can manage the versions of AI models and prioritize the deployment of highly reliable versions. For example, the deployment unit can manage the versions of AI models and prioritize the deployment of highly reliable versions. This improves the reliability of the system by prioritizing the deployment of highly reliable models. Some or all of the above processes in the deployment unit are performed using generative AI. For example, the deployment unit can input the reliability of the AI ​​models into the generative AI and have the generative AI perform the reliability evaluation.

[0106] The deployment unit can apply different deployment algorithms depending on the type of AI model during deployment. For example, in the case of an image recognition model, the deployment unit will use a specific deployment algorithm. The deployment unit can also use a different deployment algorithm for natural language processing models. Furthermore, the deployment unit can use yet another different deployment algorithm for reinforcement learning models. This improves deployment efficiency by applying a deployment algorithm appropriate to the type of AI model. Some or all of the above processing in the deployment unit is performed using a generative AI. For example, the deployment unit can input the type of AI model into the generative AI and have the generative AI execute the application of the deployment algorithm.

[0107] The deployment unit can estimate the user's emotions and determine deployment priorities based on those emotions. For example, if the user is stressed, the deployment unit will postpone less important deployments. The deployment unit can also prioritize more important deployments if the user is relaxed. Furthermore, if the user is in a hurry, the deployment unit can reduce the amount of deployment and complete it quickly. This allows for prioritizing deployments according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, 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 deployment unit is performed using generative AI. For example, the deployment unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0108] The deployment unit can determine deployment priorities based on the submission timing of AI models during deployment. For example, the deployment unit can prioritize the deployment of newly submitted AI models for quick processing. The deployment unit can also postpone the deployment of older AI models. Furthermore, the deployment unit can adjust the deployment method according to the submission timing. This allows the deployment priority to be determined based on the submission timing of AI models. Some or all of the above processing in the deployment unit is performed using a generative AI. For example, the deployment unit can input the submission timing of AI models into the generative AI and have the generative AI determine the deployment priority.

[0109] The deployment unit can adjust the deployment order based on the relevance of the AI ​​models during deployment. For example, the deployment unit can prioritize the deployment of highly relevant AI models for faster processing. The deployment unit can also postpone the deployment of less relevant AI models. Furthermore, the deployment unit can adjust the level of detail of the deployment according to the relevance of the AI ​​models. This allows the deployment order to be adjusted based on the relevance of the AI ​​models. Some or all of the above processing in the deployment unit is performed using a generative AI. For example, the deployment unit can input the relevance of the AI ​​models into the generative AI and have the generative AI perform the adjustment of the deployment order.

[0110] The monitoring unit can estimate the user's emotions and adjust its monitoring method based on the estimated emotions. For example, if the user is stressed, the monitoring unit can reduce the frequency of monitoring to alleviate the user's burden. The monitoring unit can also increase the frequency of monitoring and monitor more detailed data if the user is relaxed. Furthermore, if the user is in a hurry, the monitoring unit can temporarily stop monitoring and prioritize the user's work. This allows the monitoring method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 monitoring unit is performed using generative AI. For example, the monitoring unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0111] The monitoring unit can evaluate the performance of AI models during monitoring and prioritize monitoring high-performing models. For example, the monitoring unit can check the test results of AI models and prioritize monitoring high-performing models. The monitoring unit can also evaluate the past performance of AI models and prioritize monitoring high-performing models. Furthermore, the monitoring unit can manage the versions of AI models and prioritize monitoring high-performing versions. For example, the monitoring unit can manage the versions of AI models and prioritize monitoring high-performing versions. This improves system reliability by prioritizing the monitoring of high-performing models. Some or all of the above processes in the monitoring unit are performed using generative AI. For example, the monitoring unit can input the performance of AI models into the generative AI and have the generative AI perform the performance evaluation.

[0112] The monitoring unit can apply different monitoring algorithms depending on the type of AI model during monitoring. For example, in the case of an image recognition model, the monitoring unit will use a specific monitoring algorithm. The monitoring unit can also use a different monitoring algorithm for natural language processing models. Furthermore, the monitoring unit can use yet another different monitoring algorithm for reinforcement learning models. This improves the efficiency of monitoring by applying a monitoring algorithm appropriate to the type of AI model. Some or all of the above processing in the monitoring unit is performed using a generative AI. For example, the monitoring unit can input the type of AI model into the generative AI and have the generative AI execute the application of the monitoring algorithm.

[0113] The monitoring unit can estimate the user's emotions and determine monitoring priorities based on the estimated emotions. For example, if the user is stressed, the monitoring unit will postpone less important monitoring. The monitoring unit can also prioritize more important monitoring if the user is relaxed. For example, if the user is relaxed, the monitoring unit will prioritize more important monitoring. Furthermore, if the user is in a hurry, the monitoring unit can reduce the amount of monitoring and complete it quickly. For example, if the user is in a hurry, the monitoring unit will reduce the amount of monitoring and complete it quickly. This allows monitoring priorities to be determined according to the user's emotions. 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 monitoring unit is performed using generative AI. For example, the monitoring unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0114] The monitoring unit can determine monitoring priorities based on the submission timing of AI models during monitoring. For example, the monitoring unit can prioritize monitoring and quickly process newly submitted AI models. The monitoring unit can also postpone monitoring older AI models. For example, the monitoring unit can postpone monitoring older AI models. Furthermore, the monitoring unit can adjust its monitoring method according to the submission timing. For example, the monitoring unit can adjust its monitoring method according to the submission timing. This allows the monitoring unit to determine monitoring priorities based on the submission timing of AI models. Some or all of the above processing in the monitoring unit is performed using a generative AI. For example, the monitoring unit can input the submission timing of AI models into the generative AI and have the generative AI determine the monitoring priorities.

[0115] The monitoring unit can adjust the monitoring order based on the relevance of the AI ​​models during monitoring. For example, the monitoring unit can prioritize monitoring highly relevant AI models and process them quickly. The monitoring unit can also postpone monitoring less relevant AI models. For example, the monitoring unit can postpone monitoring less relevant AI models. Furthermore, the monitoring unit can adjust the level of detail of monitoring according to the relevance of the AI ​​models. For example, the monitoring unit can adjust the monitoring order based on the relevance of the AI ​​models. Some or all of the above processing in the monitoring unit is performed using a generating AI. For example, the monitoring unit can input the relevance of the AI ​​models into the generating AI and have the generating AI perform the adjustment of the monitoring order.

[0116] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0117] The data collection unit can analyze user behavior patterns during data collection and determine the optimal timing for collection. For example, the unit can learn the user's daily activities and collect data while avoiding busy times. It can also reduce the user's burden by collecting data while they are performing specific activities. Furthermore, the unit can adjust the frequency of data collection based on the user's behavior patterns. This enables efficient data collection tailored to user behavior.

[0118] The management department can prioritize data management based on its importance. For example, it can prioritize and process high-priority data quickly. It can also postpone the management of less important data. Furthermore, the management department can adjust the level of detail in management according to the importance of the data. This enables efficient data management based on data importance.

[0119] The training unit can select training data while considering data diversity during AI model training. For example, the training unit can use data from different sources in a balanced manner to improve the versatility of the AI ​​model. The training unit can also ensure data diversity to eliminate specific biases. Furthermore, the training unit can adjust the training algorithm based on data diversity. This enables the training of a well-balanced AI model.

[0120] The deployment unit can adjust the deployment method when deploying AI models, taking into account the user's network environment. For example, if the user is in a slow network environment, the deployment unit can reduce the amount of data deployed for efficient deployment. Conversely, if the user is in a high-speed network environment, the deployment unit can increase the amount of data deployed for more detailed deployment. Furthermore, the deployment unit can adjust the timing of deployment according to the user's network environment. This enables efficient deployment tailored to the user's network environment.

[0121] The monitoring unit can collect user feedback while monitoring AI models and improve its monitoring methods. For example, it can adjust the frequency and level of detail of monitoring based on user feedback. It can also analyze user feedback and improve its monitoring algorithms. Furthermore, it can determine monitoring priorities based on user feedback. This enables efficient monitoring that reflects user feedback.

[0122] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on those emotions. For example, if the user is stressed, the unit can reduce the frequency of data collection to lessen the user's burden. Conversely, if the user is relaxed, the unit can increase the frequency of data collection to collect more detailed data. Furthermore, if the user is in a hurry, the unit can temporarily stop data collection to prioritize the user's work. This allows the timing of data collection to be adjusted according to the user's emotions.

[0123] The management department can estimate the user's emotions and adjust data management methods based on those estimates. For example, if the management department is stressed, it can reduce the frequency of data management to lessen the user's burden. Conversely, if the user is relaxed, the management department can increase the frequency of data management and manage more detailed data. Furthermore, if the user is in a hurry, the management department can temporarily suspend data management to prioritize the user's work. This allows for adjustments to data management methods according to the user's emotions.

[0124] The training system can estimate the user's emotions and adjust the training method based on those estimates. For example, if the user is relaxed, the training system will conduct training at a relaxed pace. If the user is in a hurry, the training system can also conduct short, effective training sessions. Furthermore, if the user is excited, the training system can conduct visually stimulating training sessions. This allows the training method to be adjusted according to the user's emotions.

[0125] The deployment unit can estimate the user's emotions and adjust the deployment method based on those emotions. For example, if the user is relaxed, the deployment unit will deploy at a leisurely pace. Conversely, if the user is in a hurry, the deployment unit can complete the deployment quickly. Furthermore, if the user is excited, the deployment unit can provide a visually stimulating deployment method. This allows the deployment method to be adjusted according to the user's emotions.

[0126] The monitoring unit can estimate the user's emotions and adjust its monitoring methods based on those estimates. For example, if the user is stressed, the monitoring unit can reduce the frequency of monitoring to lessen the user's burden. Conversely, if the user is relaxed, the monitoring unit can increase the frequency of monitoring and monitor more detailed data. Furthermore, if the user is in a hurry, the monitoring unit can temporarily suspend monitoring to prioritize the user's work. This allows the monitoring method to be adjusted according to the user's emotions.

[0127] The following briefly describes the processing flow for example form 2.

[0128] Step 1: The data collection unit collects data. The data collection unit can collect various types of data, such as sensor data, text data, and image data. The data collection unit collects sensor data in real time and stores it in a database. The data collection unit can also collect text data using web scraping technology. Furthermore, the data collection unit can collect image data using a camera or smartphone. Step 2: The management department manages the data collected by the collection department. For example, the management department can manage how the data is stored and how access is controlled. The management department can centrally manage the data using a database and perform regular data backups. The management department can also set access permissions for the data to ensure security. Step 3: The training unit trains the AI ​​model based on the data managed by the management unit. The training unit can train the AI ​​model using algorithms such as neural networks and decision trees. The training unit can also train an image recognition model using a neural network and a classification model using a decision tree. Furthermore, the training unit can also train a reinforcement learning model using a reinforcement learning algorithm. Step 4: The deployment unit deploys the AI ​​model trained by the training unit. The deployment unit can deploy the AI ​​model to a cloud environment or an on-premises environment, for example. The deployment unit can deploy the AI ​​model to a cloud environment to ensure scalability. Alternatively, the deployment unit can deploy the AI ​​model to an on-premises environment to ensure security. Furthermore, the deployment unit can also deploy the AI ​​model using container technology. Step 5: The monitoring unit monitors the AI ​​model deployed by the deployment unit. The monitoring unit can, for example, monitor the performance and errors of the AI ​​model. The monitoring unit monitors the AI ​​model's performance in real time and detects anomalies. The monitoring unit can also collect error logs of the AI ​​model and identify the cause of errors. Furthermore, the monitoring unit can manage the versioning of the AI ​​model and always monitor for the latest model.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the collection unit, management unit, training unit, deployment unit, and monitoring unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects data using the camera 42 and sensors of the smart device 14 and stores it in a database by the control unit 46A. The management unit manages the data storage method and access control by, for example, the specific processing unit 290 of the data processing unit 12. The training unit trains the AI ​​model using algorithms such as neural networks and decision trees by, for example, the specific processing unit 290 of the data processing unit 12. The deployment unit deploys the AI ​​model to a cloud environment or on-premise environment by, for example, the specific processing unit 290 of the data processing unit 12. The monitoring unit monitors the performance and errors of the AI ​​model in real time by, for example, 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 can be changed in various ways.

[0133] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0134] 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.

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

[0136] The 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.

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

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

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

[0140] Figure 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.

[0141] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0143] In the 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.

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

[0145] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0147] The data processing system 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.

[0148] Each of the multiple elements described above, including the collection unit, management unit, training unit, deployment unit, and monitoring unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects data using the camera 42 and sensors of the smart glasses 214 and stores it in a database by the control unit 46A. The management unit manages the data storage method and access control by, for example, the specific processing unit 290 of the data processing unit 12. The training unit trains the AI ​​model using algorithms such as neural networks and decision trees by, for example, the specific processing unit 290 of the data processing unit 12. The deployment unit deploys the AI ​​model to a cloud environment or on-premise environment by, for example, the specific processing unit 290 of the data processing unit 12. The monitoring unit monitors the performance and errors of the AI ​​model in real time by, for example, 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 examples described above and can be changed in various ways.

[0149] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0150] 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.

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

[0152] The 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.

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

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (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).

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

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the collection unit, management unit, training unit, deployment unit, and monitoring unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects data using the camera 42 and sensors of the headset terminal 314 and stores it in a database by the control unit 46A. The management unit manages the data storage method and access control by, for example, the specific processing unit 290 of the data processing unit 12. The training unit trains the AI ​​model using algorithms such as neural networks and decision trees by, for example, the specific processing unit 290 of the data processing unit 12. The deployment unit deploys the AI ​​model to a cloud environment or on-premise environment by, for example, the specific processing unit 290 of the data processing unit 12. The monitoring unit monitors the performance and errors of the AI ​​model in real time by, for example, 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 examples described above and can be changed in various ways.

[0165] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.).

[0178] 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.

[0179] 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.

[0180] 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.

[0181] Each of the multiple elements described above, including the collection unit, management unit, training unit, deployment unit, and monitoring unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects data using the camera 42 and sensors of the robot 414 and stores it in a database by the control unit 46A. The management unit manages the data storage method and access control by, for example, the specific processing unit 290 of the data processing unit 12. The training unit trains the AI ​​model using algorithms such as neural networks and decision trees by, for example, the specific processing unit 290 of the data processing unit 12. The deployment unit deploys the AI ​​model to a cloud environment or on-premise environment by, for example, the specific processing unit 290 of the data processing unit 12. The monitoring unit monitors the performance and errors of the AI ​​model in real time by, for example, 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 examples described above and can be changed in various ways.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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."

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] (Note 1) A data collection unit that collects data, A management unit manages the data collected by the aforementioned collection unit, The training unit trains an AI model based on the data managed by the aforementioned management unit, A deployment unit deploys the AI ​​model trained by the aforementioned training unit, The system includes a monitoring unit that monitors the AI ​​model deployed by the aforementioned deployment unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Data Storage and Security Provider The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned management department, Centralized data management The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned training department, Train an AI model The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned deployment unit is Deploy the AI ​​model The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned monitoring unit, Configure monitoring of AI models The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned management department, Provides data scalability and flexibility. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned deployment unit is We provide services and UI / UX design. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned monitoring unit, Enables integration with existing systems and API utilization. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, evaluate the reliability of the data and prioritize collecting reliable data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting data, apply different collection methods depending on the type of data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned management department, We estimate user sentiment and adjust data management methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned management department, When managing data, 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 18) The aforementioned management department, When managing data, different management algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned management department, It estimates user sentiment and determines the priority of data to manage based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned management department, When managing data, prioritize management based on when the data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned management department, When managing data, adjust the order of management based on the relationships between the data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned training department, It estimates the user's emotions and adjusts the training method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned training department, During training, evaluate the reliability of the data and prioritize using the most reliable data. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned training department, During training, different training algorithms are applied depending on the type of AI model. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned training department, It estimates the user's emotions and determines training priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned training department, During training, training priorities are determined based on the timing of data submission. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned training department, During training, adjust the training sequence based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned deployment unit is It estimates user sentiment and adjusts the deployment method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned deployment unit is During deployment, the reliability of the AI ​​models is evaluated, and highly reliable models are prioritized for deployment. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned deployment unit is During deployment, different deployment algorithms are applied depending on the type of AI model. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned deployment unit is It estimates user sentiment and determines deployment priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned deployment unit is During deployment, deployment priorities are determined based on when the AI ​​models were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned deployment unit is During deployment, adjust the deployment order based on the relevance of the AI ​​models. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned monitoring unit, It estimates user sentiment and adjusts monitoring methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned monitoring unit, During monitoring, the performance of AI models is evaluated, and models with higher performance are prioritized for monitoring. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned monitoring unit, During monitoring, different monitoring algorithms are applied depending on the type of AI model. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned monitoring unit, It estimates user sentiment and determines monitoring priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned monitoring unit, During monitoring, the monitoring priority is determined based on when the AI ​​model was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned monitoring unit, During monitoring, the order of monitoring is adjusted based on the relevance of the AI ​​models. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0201] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data, A management unit manages the data collected by the aforementioned collection unit, A training unit that trains an AI model based on data managed by the aforementioned management unit, A deployment unit deploys the AI ​​model trained by the aforementioned training unit, The system includes a monitoring unit that monitors the AI ​​model deployed by the deployment unit. A system characterized by the following features.

2. The aforementioned collection unit is Data Storage and Security Provider The system according to feature 1.

3. The aforementioned management department, Centralized data management The system according to feature 1.

4. The aforementioned training department Training an AI model The system according to feature 1.

5. The aforementioned deployment unit is Deploy the AI ​​model. The system according to feature 1.

6. The aforementioned monitoring unit, Monitor the AI ​​model. The system according to feature 1.

7. The aforementioned management department, Provides data scalability and flexibility. The system according to feature 1.

8. The aforementioned deployment unit is We provide services and UI / UX design. The system according to feature 1.

9. The aforementioned monitoring unit, Enables integration with existing systems and API utilization. The system according to feature 1.

10. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

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