system

Abstract

Provide a system. 【Solution means】 A means by which an information processing device centrally manages a plurality of intelligent devices and immediately monitors the progress of their operations; A means for automatically optimizing resource allocation and task assignment in each intelligent device; A means for visually displaying the progress of operations to the user; A means for using a generation process for sharing and linking information between intelligent devices; A means for evaluating the performance of each device and generating opinions for efficiency improvement; A means for automatically generating a report and providing it in a form that can be visually analyzed by the user; A means for providing a visual operation screen integrated with a portable information terminal in order to improve inventory management and inbound / outbound operations in a logistics facility; A system including the above.

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 character of the chatbot, 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] Modern enterprises and individuals have introduced multiple artificial intelligence units to manage various tasks. However, when these units are operated individually, there is a problem that the overall efficiency and resource optimization are not fully exerted. In addition, due to insufficient information sharing between each unit, duplication of work and lack of cooperation occur, resulting in waste of time and cost for users.

Means for Solving the Problems

[0005] This invention provides a unified management system using an information processing device to comprehensively manage multiple artificial intelligence units. This allows for real-time monitoring of each unit's task progress and provides a means to automatically optimize resource allocation and task assignment. Furthermore, it improves overall operational efficiency by displaying visual progress to the user. In addition, it incorporates an information sharing function through generation processing, enabling seamless collaboration between units and promoting continuous operational efficiency through performance evaluation and feedback.

[0006] An "information processing device" is a hardware or software system that has the function of collecting, processing, storing, and transferring data, and managing an artificial intelligence unit.

[0007] An "artificial intelligence unit" is software or algorithms programmed to automatically handle specific tasks, assisting users in their work.

[0008] "Centralized management" refers to integrating multiple processes and systems to operate, monitor, and manage them from a single point of view.

[0009] "Real-time monitoring" refers to a process that allows for immediate and seamless monitoring of the current situation, enabling the instantaneous acquisition of necessary information.

[0010] "Resource allocation" refers to the effective distribution of available computing power, memory, and other system resources so that an artificial intelligence unit can function properly.

[0011] "Task assignment" refers to the process of allocating specific tasks to artificial intelligence units that are responsible for particular tasks or processes.

[0012] "Generative processing" refers to data processing activities that generate information and use that information to assist in the operation and decision-making of artificial intelligence units.

[0013] "Visual display" refers to methods of providing data and information to users in a graphical format to make it easier to understand.

[0014] "Performance evaluation" is the process of determining how effectively and efficiently an artificial intelligence unit or system performed a given task.

[0015] "Feedback" refers to the activity of providing information to improve the results of a system or process, and using that information to inform future actions and plans. [Brief explanation of the drawing]

[0016] [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. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), etc.

[0020] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] 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.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] As shown in Figure 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.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception 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 reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] The 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.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] The embodiment of this invention is based on a centralized management system built around an information processing device. This system is designed to allow users to efficiently manage multiple artificial intelligence units.

[0038] The server receives various task information entered by the user from the terminal and analyzes the capabilities and current load status of each artificial intelligence unit stored in the database. Based on this, the server allocates resources optimally and assigns tasks to the appropriate units. This process can be performed automatically to achieve optimal resource allocation according to the user's work.

[0039] Furthermore, the server monitors the task progress in real time and displays it visually on the user's terminal dashboard. Because users can easily access this information through their terminal, they can make quick decisions.

[0040] Furthermore, it features an information sharing function that utilizes generation processing. This function allows each artificial intelligence unit to exchange necessary information via the server and collaborate to perform tasks. In addition, the server evaluates the performance of each unit and generates feedback for improvement. Users can use the analytical information provided by this feedback to further improve the efficiency of their work.

[0041] To give a specific example, when a user manages the progress of multiple projects, the server assigns different tasks to the appropriate AI unit for each project and generates a dashboard that allows users to check the progress at a glance. Furthermore, it automatically generates reports based on the data generated as each project progresses, which the user can use to make subsequent decisions. This allows users to significantly improve work efficiency and effectively optimize resources.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] Users use a terminal to enter specific tasks and send them to the server. This includes details such as the task type, priority, and deadline.

[0045] Step 2:

[0046] The server analyzes the task information received from the terminal and retrieves performance data and current load status for each artificial intelligence unit from the database. Based on this, the server determines which unit is best suited to perform the task.

[0047] Step 3:

[0048] Once the server selects the most suitable artificial intelligence unit, it sends instructions to that unit. The unit receives the instructions and begins the specified task. Each unit automatically adjusts to efficiently use resources.

[0049] Step 4:

[0050] The server collects real-time task progress information from each unit and visualizes the data in a consistent format on a dashboard on the user's terminal. Users can then use this to understand the status of their tasks.

[0051] Step 5:

[0052] When information sharing is required between units, the server performs a generation process to ensure that relevant information is reliably exchanged. This allows units to work together collaboratively.

[0053] Step 6:

[0054] Once a task is completed, the server evaluates the performance of each unit in the execution, analyzes the results, and generates feedback. Users receive this feedback via their terminal and use it to improve future operations.

[0055] Step 7:

[0056] As needed, the server automatically generates and provides reports to the user based on the progress and results of each task. The user then uses this evidence to support their next planning and decision-making.

[0057] (Example 1)

[0058] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0059] In today's digital society, efficiently managing multiple intelligent processing units and monitoring work progress in real time is crucial for improving operational efficiency. However, conventional technologies have limitations in optimizing resource allocation, sharing information between units, and generating feedback for business improvement. A new system is needed to solve these problems.

[0060] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0061] In this invention, the server includes means for centrally managing multiple intelligent processing units and monitoring work processes in real time, means for automatically optimizing resource allocation and work assignments, means for visually displaying work progress to users, means for using generation processing to promote information sharing and cooperation among intelligent processing units, means for evaluating the operational performance of each unit and generating improvement measures, means for automatically providing reports to users, means for receiving work information from users, means for calculating optimized work assignments, and means for generating and notifying improvement suggestions. This enables efficient work management and optimization of operations.

[0062] An "information processing device" is a device used to receive, process, and analyze data, and to manage multiple intelligent processing units.

[0063] An "intelligent processing unit" is an artificial intelligence-based processing system designed to perform a specific task.

[0064] "Resource allocation" refers to assigning resources such as computing power and memory to intelligent processing units in order to efficiently perform tasks.

[0065] "Task assignment" refers to assigning specific tasks to each intelligent processing unit.

[0066] A "work process" refers to a process that outlines the steps in which multiple tasks are carried out.

[0067] "Generation processing" refers to processes used to standardize information and generate data among intelligent processing units.

[0068] "Business performance" is an indicator that shows the quality and efficiency of the work performed by the intelligent processing unit.

[0069] "Improvement measures" refer to specific strategies proposed to enhance business performance.

[0070] A "report" is a document that summarizes the progress and results of a project, and is provided in a visually analyzable format.

[0071] "User" refers to an individual or organization that operates or manages an information processing device or intelligent processing unit.

[0072] "Progress status" refers to the state of how far along a particular work process is.

[0073] This invention is a system based on an information processing device that efficiently manages multiple intelligent processing units and monitors the progress of tasks in real time. Users input various task information using a terminal and send it to the server. Based on the received information, the server analyzes the capabilities and current load status of the intelligent processing units in the database.

[0074] The server can use standard computing devices and cloud-based system environments as the hardware on which the program runs. The software is implemented in programming languages ​​such as Python and JavaScript (registered trademark), which are used to operate the task assignment algorithm. Resource allocation and work assignment for each unit are dynamically controlled by optimization algorithms. The server also sends progress information to user terminals via APIs and displays the information on dashboards built using HTML and CSS.

[0075] A concrete example is when a user manages a new product planning and development project. In this scenario, the server appropriately assigns each task included in the project to the appropriate intelligent processing unit and graphically displays the progress on a dashboard. In this way, the user can grasp the status of each task at a glance and make quick decisions.

[0076] An example of a prompt message is, "Check the progress of tasks related to the current project and reassign tasks if necessary." Based on this prompt, the server performs the necessary actions and provides feedback to the user.

[0077] Through this system, users can streamline complex project management and optimize their operations.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] User actions

[0081] Users log in to the system using their terminal and enter details of the tasks they want to manage (e.g., task content, deadline, priority). The entered data is structured in JSON format or similar and sent to the server.

[0082] Step 2:

[0083] Server operation

[0084] The server analyzes task information received from the user and stores it in a database. Next, it checks the current status of the intelligent processing units and collects data to evaluate the processing capacity and load status of each unit. In this process, the server executes efficient database queries to quickly retrieve the necessary information.

[0085] Step 3:

[0086] Server operation

[0087] The server performs calculations for optimal work assignment based on task information and unit evaluation data. Here, it uses in-program optimization algorithms (e.g., linear programming or heuristic methods) to select the most suitable unit for each task. Based on these results, it determines the task assignment to each unit.

[0088] Step 4:

[0089] Server operation

[0090] The server distributes the determined task assignments to intelligent processing units and adds them to each unit's queue. Based on the task IDs assigned at the input stage, efficient processing is ensured while avoiding duplication between units. This enables parallel processing of tasks.

[0091] Step 5:

[0092] Server operation

[0093] The server monitors each ongoing task in real time and collects progress data. This progress information is sent to the user's device via an API and displayed visually on a dashboard. This dashboard is built using HTML and JavaScript and is designed to be intuitive and easy to understand.

[0094] Step 6:

[0095] User actions

[0096] Users can check the dashboard on their device to understand the progress of each task. If necessary, they can be instructed to reassign tasks using prompt messages. Specifically, they might enter a prompt such as, "Review the task progress of the current project and reassign tasks to the most appropriate unit if necessary."

[0097] Step 7:

[0098] Server operation

[0099] Based on the user's reallocation instructions, the server performs calculations again using the optimization algorithm and makes any necessary adjustments. Furthermore, it logs the processing results in preparation for the next analysis and evaluation.

[0100] Step 8:

[0101] Server operation

[0102] The server evaluates the performance of the intelligent processing unit once all tasks are completed and provides the user with a report of the results. This report includes suggestions for improvement and feedback, providing the user with data that can be used for future project management.

[0103] (Application Example 1)

[0104] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0105] Inventory management and receiving / shipping operations in logistics facilities are time-consuming, labor-intensive, and prone to resource waste. Furthermore, a system is needed to instantly grasp the progress of operations and enable efficient decision-making. Traditional systems have struggled to comprehensively manage the progress of each operation and provide appropriate feedback. There is a need for solutions to these challenges and significantly improve operational efficiency within logistics facilities.

[0106] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0107] In this invention, the server includes means for centrally managing and immediately monitoring intelligent devices, means for optimizing resource allocation and task assignment, and means for providing a visual operation screen integrated with a portable information terminal. This enables more efficient inventory management and receiving / shipping operations in logistics facilities.

[0108] An "information processing device" is a foundational device that centrally manages multiple intelligent devices and provides real-time monitoring of the progress of work.

[0109] An "intelligent device" is a device that performs specific tasks and processes information, and operates based on instructions from a server.

[0110] "Resource allocation" refers to the optimal distribution of resources, such as computing power and data, necessary for each intelligent device to operate efficiently.

[0111] "Task assignment" refers to distributing specific tasks to each intelligent device to ensure their efficient execution.

[0112] "Immediate monitoring" means observing and recording the progress of work in real time to help make quick decisions.

[0113] A "portable information terminal" is a portable device that is primarily operated visually and is used to improve work efficiency within logistics facilities.

[0114] A "visual interface" is a screen interface that allows users to easily check information and perform operations.

[0115] To implement this invention, it is necessary to install a server as an information processing device and build a system that centrally manages multiple intelligent devices. The server automatically optimizes resource allocation and task assignment for each intelligent device in order to efficiently manage inventory and perform receiving and shipping operations within the logistics facility.

[0116] The server uses a real-time database (e.g., Firebase) to instantly monitor the progress of tasks and provides this information as a visual interface on mobile devices. This allows users to check the progress of tasks within the logistics facility at any time.

[0117] Intelligent devices share necessary information using generation processing and work collaboratively. Each intelligent device operates according to instructions from the server and feeds that information back to the server, enabling efficient and flexible work.

[0118] Mobile devices function as smartphones and tablets, providing users with a visually superior user interface. Through this interface, users can monitor their work status in real time and make appropriate decisions.

[0119] As a concrete example, consider picking operations in a logistics facility. The server uses an AI model to assign each picking task to the most suitable intelligent device and instantly calculates the priority of the tasks.

[0120] An example of a prompt for a generative AI model is: "Help design a generative AI model to optimize inventory tasks in a logistics facility. Specifically, it needs strategic resource allocation and real-time progress monitoring capabilities." This can dramatically improve operational efficiency within the logistics facility.

[0121] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0122] Step 1:

[0123] The server receives work task information from the user via a mobile device. Based on this input, the server performs data analysis to identify the type and priority of the task. As a result, the work items and urgency levels are obtained.

[0124] Step 2:

[0125] The server accesses a real-time database to obtain the current operating status and resource allocation of intelligent devices. The input obtained here is the amount of work each device can perform. Using this information, the server calculates the optimal resource allocation and task assignment based on the generated AI model.

[0126] Step 3:

[0127] The server uses the resource allocation and task assignments determined by the server to output specific work instructions to the intelligent devices. At this time, each intelligent device prepares to perform the received task and acquires the confirmed task assignment information as input.

[0128] Step 4:

[0129] The intelligent device begins the actual work according to the work instructions it receives. In this process, it performs execution processing according to the work instructions and generates progress data as a result. This data is fed back to the server.

[0130] Step 5:

[0131] The server receives progress data from the intelligent device and immediately displays it on the user interface of the mobile device to visualize the progress. This output allows the user to immediately check the progress of their work.

[0132] Step 6:

[0133] Users use their mobile devices to check the progress of their work and make necessary adjustments to their work plans or make decisions. During this process, users input new instructions and adjustment information.

[0134] Step 7:

[0135] Based on the results of the intelligent device's work and user feedback, the server improves the next resource allocation and task assignment by the AI ​​model. As a result of this process, task processing capacity is optimized.

[0136] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0137] One embodiment of the present invention is a centralized management system centered on an information processing device combined with an emotion engine. This system provides management of multiple artificial intelligence units, improvement of the user experience through emotion recognition, and real-time task optimization.

[0138] The server receives task information entered by the user from the terminal and, before starting normal task management processing, uses an emotion engine connected to the terminal to recognize the user's emotions. This emotion data is used as a parameter when the server assigns appropriate tasks to each artificial intelligence unit. Specifically, if the user is experiencing stress, the server adjusts task assignments to units and sets priorities to reduce the load.

[0139] Furthermore, the server stores data obtained from the emotion engine in a database and customizes the feedback based on this data. For example, if a user indicates high satisfaction, it can suggest more challenging tasks. This feedback is presented to the user via their device, contributing to improved work efficiency.

[0140] This system features real-time monitoring using emotion recognition results, which are displayed as visualized information on the user's dashboard. This allows users to efficiently manage tasks according to their emotional state. For example, if the artificial intelligence unit analyzing sales data determines, based on its emotion engine, that the user's concentration is declining, it can delay the processing of other lower-priority tasks. This operation enables optimal task execution while maintaining the user's concentration to the maximum extent.

[0141] By incorporating an emotion engine in this way, the system of the present invention achieves intuitive feedback and task management based on user experience, in addition to conventional management functions. As a result, it becomes possible to effectively and efficiently carry out users' daily tasks while making the most of the benefits of artificial intelligence technology.

[0142] The following describes the processing flow.

[0143] Step 1:

[0144] The user logs into the system using a terminal and selects a task to start. The task information is sent from the terminal to the server.

[0145] Step 2:

[0146] The device detects the user's current emotional state from their facial expressions, voice, or input actions via a built-in or connected emotion engine, and sends that data to a server.

[0147] Step 3:

[0148] The server analyzes task information and emotion data received from the terminal. Based on the analysis results, it decides to assign tasks to each artificial intelligence unit. For example, if the user is feeling stressed, the server will reduce the amount of tasks or prioritize easier tasks.

[0149] Step 4:

[0150] The server monitors task progress in real time, combines collected sentiment data, and visualizes the status for the user through a dashboard. Users can check task progress and sentiment status on the dashboard on their device.

[0151] Step 5:

[0152] The data obtained from the emotion engine is stored in a database on the server and used for subsequent task management and feedback generation. The server customizes the feedback according to the user's emotional state and provides it to the user through the terminal.

[0153] Step 6:

[0154] After a task is completed or interrupted, the server generates a report based on all process and sentiment data and presents it to the user. The user can use this report to gain a deeper understanding of their work and mental / physical state, and to use it for future improvements.

[0155] (Example 2)

[0156] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0157] Conventional information processing systems struggle to manage tasks while taking into account the user's emotional state, resulting in a failure to adequately respond to user stress and emotional fluctuations. Furthermore, efficient information sharing between intelligent units and optimized resource allocation remain challenges. As a result, user work efficiency and satisfaction have sometimes decreased.

[0158] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0159] In this invention, the server includes means for centrally managing multiple intelligent units and monitoring the progress of their tasks in real time, means for recognizing the user's emotions using an emotion analysis device connected to a terminal and dynamically adjusting task assignments using the emotion data, and means for automatically generating reports and providing them to the user in a format that can be visually analyzed. This makes it possible to appropriately adjust tasks according to the user's emotional state, thereby improving work efficiency and satisfaction.

[0160] An "information processing device" is a device that acquires and analyzes information from users and provides appropriate task management and feedback.

[0161] An "intelligent unit" is a functional unit that incorporates artificial intelligence technology designed to perform a specific task.

[0162] "Centralized management" is a method of maintaining efficient resource allocation and information consistency by centrally managing multiple intelligence units and related information.

[0163] "Monitoring in real time" means tracking the progress of tasks and operations in real time and always being aware of the latest status.

[0164] An "emotion analysis device" is a device that detects emotions based on data such as a user's facial expressions and voice, and transmits the results to an information processing device.

[0165] "Dynamically adjusting task assignments" means flexibly changing the priority and assignment of tasks based on the user's emotional state and circumstances.

[0166] "Generating reports and providing them in an analyzable format" means that the system automatically compiles data and visualizes it in a way that users can easily understand and analyze.

[0167] This system consists of an information processing unit that enables efficient task management based on the user's emotional state. The server receives task information transmitted by the user from a terminal and recognizes the user's emotions in real time using an emotion analysis device connected to the terminal. For this emotion analysis, analysis software such as Affectiva or IBM Watson® Tone Analyzer can be used.

[0168] The server inputs emotional data into an AI model that generates emotional data and dynamically adjusts task assignments based on the results. It also generates feedback based on task progress and emotional data, providing it visually to the user. Users can view this feedback on a dashboard on their device, allowing them to monitor their emotional state and task progress.

[0169] As a concrete example, if the server determines that a user is fatigued, it uses a generative AI model to prioritize assigning lower-difficulty tasks and postpone other tasks. In this way, it reduces the user's workload and provides an efficient work environment. It also provides feedback such as, "We recommend you learn new skills for future use."

[0170] An example of an input prompt for a generative AI model might be, "Given that the user is distracted, please advise on how to adjust lower-priority tasks." This prompt allows the system to provide the user with the optimal task management strategy.

[0171] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0172] Step 1:

[0173] The user inputs task information related to future work via a terminal. This information includes the task name, deadline, and importance level. The terminal then sends this input to the server.

[0174] Step 2:

[0175] The server stores the task information received from the terminal and prepares it as data for the next analysis. Here, the task information is formatted for use in subsequent processing steps.

[0176] Step 3:

[0177] An emotion analysis device connected to the user's terminal acquires the user's emotional data. During this process, facial recognition and voice analysis technologies are used to evaluate emotions in real time, and the results are sent from the terminal to a server. The analysis results include specific emotional states, such as whether the user is feeling stressed or relaxed.

[0178] Step 4:

[0179] The server inputs emotional data into an AI model, which then prioritizes tasks appropriate to the current emotional state based on the results. Here, a new task assignment is generated based on the emotional data and task information. At this stage, the AI ​​model performs appropriate data processing and calculations to determine the optimal task arrangement.

[0180] Step 5:

[0181] The server provides users with visual feedback based on the generated task priorities. This includes graphical representations on the dashboard and suggestions for specific actions. For example, if concentration is waning, it might advise, "We recommend taking a short break."

[0182] Step 6:

[0183] The user reviews the feedback provided and begins executing the task as needed. Based on the feedback, they revise their work schedule and make adjustments to work more efficiently.

[0184] This series of steps enables flexible task management based on the user's emotional state and improves work efficiency.

[0185] (Application Example 2)

[0186] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0187] Modern information processing systems require the efficient management of multiple learning devices and the dynamic adjustment of the environment based on the user's emotional state. However, conventional technologies often rely on fixed methods for resource allocation and task management without considering the user's emotional state, making it difficult to optimize the user experience.

[0188] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0189] In this invention, the server includes means for centrally managing multiple learning devices and monitoring the progress of their work in real time, means for automatically optimizing resource allocation and task assignment in each learning device, and means for recognizing the user's emotional state. This enables dynamic resource allocation and task adjustment in accordance with the user's emotions.

[0190] An "information processing system" is a technological system that manages multiple learning devices, recognizes user input and emotions, and optimizes resource allocation and tasks based on that information.

[0191] A "learning device" is a functional unit in an information processing system that performs a specific task and manages its progress and performance.

[0192] "Resource allocation" refers to the efficient and optimal distribution of available resources to multiple learning devices.

[0193] "Task assignment" is the process of appropriately distributing specific tasks to each learning device.

[0194] A "user" is an entity that directly or indirectly operates an information processing system and utilizes its functions.

[0195] "Emotional state" refers to the user's mental and psychological state, and is recognized in real time by the emotion engine.

[0196] "Dynamically adjusting the environment" means automatically changing the state of home appliances, lighting, automated devices, etc., based on the user's emotional state.

[0197] "Real-time monitoring" refers to the process of immediately checking the progress and status of work and taking necessary actions quickly.

[0198] The system for realizing this invention is centered around a server. The server centrally manages multiple learning devices and has the function of monitoring the progress of each device in real time. The terminal recognizes the user's emotional state using an emotion engine and transmits this data to the server. Based on the recognized emotional state, the server dynamically optimizes resource allocation and task assignment to each learning device.

[0199] Emotion recognition utilizes emotion engine software installed on the device, which analyzes the user's facial expressions and voice data. The analysis results are fed back in real time through data processing by a generative AI model, and the server adjusts the environment according to the user's emotional state. This process is used to create a home environment that provides enjoyment and comfort.

[0200] For example, if the system detects that a user is experiencing stress, the server automatically adjusts the lighting via the terminal and streams relaxing music. This allows the user to experience a relaxing environment.

[0201] Furthermore, as an example of a prompt message to the generative AI model to further enhance the effectiveness of this system, we can suggest: "Improve the following program and add features to further increase user satisfaction. How should the lighting settings be adjusted when the user is relaxed?"

[0202] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0203] Step 1:

[0204] The server receives facial expressions and voice data from the user's device. The input is emotion parameters in raw data format, and the output becomes input to the emotion engine. This data is acquired through the user's camera and microphone. The server converts the data to an appropriate format and prepares it for analysis by the emotion engine.

[0205] Step 2:

[0206] The device analyzes the received data using an emotion engine to recognize the user's emotional state. The input here consists of emotion parameters processed by the server from step 1. The output is data representing a specific emotional state, returned to the server in the form of "stress" or "relaxed," for example. This analysis is performed by an AI model, achieving emotion recognition through multi-dimensional data computation.

[0207] Step 3:

[0208] The server dynamically adjusts resource allocation and task assignments to each learning device based on emotion recognition results. The input is emotion state data from the terminal. The output is the adjusted task schedule, which is delivered to the learning devices. Specifically, if the emotion is determined to be "stress," the server adjusts resource priorities and configures settings to reduce the workload.

[0209] Step 4:

[0210] The server initiates appropriate environmental adjustment tasks via the terminal. The input is the task schedule generated in step 3. The output is the operation commands for home appliances and environmental adjustment devices. Specific examples include setting the lighting to relaxation mode and playing a specific playlist through a music service. The server sends this to the home system, which then adjusts the environment.

[0211] Step 5:

[0212] The server monitors all processes and stores user feedback in a database. Inputs include emotional states and device behavior. Outputs are statistical data for improving the user experience. This data will be used for future emotion recognition and system optimization. Specifically, it will also help optimize the prompts provided to the generative AI model.

[0213] 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.

[0214] Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0215] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0216] [Second Embodiment]

[0217] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0218] 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.

[0219] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0220] 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.

[0221] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0222] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0223] 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.

[0224] 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 using the processor 28. The storage 32 stores the specific processing program 56.

[0225] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0226] The 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.

[0227] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0228] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0229] The embodiment of this invention is based on a centralized management system built around an information processing device. This system is designed to allow users to efficiently manage multiple artificial intelligence units.

[0230] The server receives various task information entered by the user from the terminal and analyzes the capabilities and current load status of each artificial intelligence unit stored in the database. Based on this, the server allocates resources optimally and assigns tasks to the appropriate units. This process can be performed automatically to achieve optimal resource allocation according to the user's work.

[0231] Furthermore, the server monitors the task progress in real time and displays it visually on the user's terminal dashboard. Because users can easily access this information through their terminal, they can make quick decisions.

[0232] Furthermore, it features an information sharing function that utilizes generation processing. This function allows each artificial intelligence unit to exchange necessary information via the server and collaborate to perform tasks. In addition, the server evaluates the performance of each unit and generates feedback for improvement. Users can use the analytical information provided by this feedback to further improve the efficiency of their work.

[0233] To give a specific example, when a user manages the progress of multiple projects, the server assigns different tasks to the appropriate AI unit for each project and generates a dashboard that allows users to check the progress at a glance. Furthermore, it automatically generates reports based on the data generated as each project progresses, which the user can use to make subsequent decisions. This allows users to significantly improve work efficiency and effectively optimize resources.

[0234] The following describes the processing flow.

[0235] Step 1:

[0236] Users use a terminal to enter specific tasks and send them to the server. This includes details such as the task type, priority, and deadline.

[0237] Step 2:

[0238] The server analyzes the task information received from the terminal and retrieves performance data and current load status for each artificial intelligence unit from the database. Based on this, the server determines which unit is best suited to perform the task.

[0239] Step 3:

[0240] Once the server selects the most suitable artificial intelligence unit, it sends instructions to that unit. The unit receives the instructions and begins the specified task. Each unit automatically adjusts to efficiently use resources.

[0241] Step 4:

[0242] The server collects real-time task progress information from each unit and visualizes the data in a consistent format on a dashboard on the user's terminal. Users can then use this to understand the status of their tasks.

[0243] Step 5:

[0244] When information sharing is required between units, the server performs a generation process to ensure that relevant information is reliably exchanged. This allows units to work together collaboratively.

[0245] Step 6:

[0246] Once a task is completed, the server evaluates the performance of each unit in the execution, analyzes the results, and generates feedback. Users receive this feedback via their terminal and use it to improve future operations.

[0247] Step 7:

[0248] As needed, the server automatically generates and provides reports to the user based on the progress and results of each task. The user then uses this evidence to support their next planning and decision-making.

[0249] (Example 1)

[0250] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0251] In today's digital society, efficiently managing multiple intelligent processing units and monitoring work progress in real time is crucial for improving operational efficiency. However, conventional technologies have limitations in optimizing resource allocation, sharing information between units, and generating feedback for business improvement. A new system is needed to solve these problems.

[0252] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0253] In this invention, the server includes means for centrally managing multiple intelligent processing units and monitoring work processes in real time, means for automatically optimizing resource allocation and work assignments, means for visually displaying work progress to users, means for using generation processing to promote information sharing and cooperation among intelligent processing units, means for evaluating the operational performance of each unit and generating improvement measures, means for automatically providing reports to users, means for receiving work information from users, means for calculating optimized work assignments, and means for generating and notifying improvement suggestions. This enables efficient work management and optimization of operations.

[0254] An "information processing device" is a device used to receive, process, and analyze data, and to manage multiple intelligent processing units.

[0255] An "intelligent processing unit" is an artificial intelligence-based processing system designed to perform a specific task.

[0256] "Resource allocation" refers to assigning resources such as computing power and memory to intelligent processing units in order to efficiently perform tasks.

[0257] "Task assignment" refers to assigning specific tasks to each intelligent processing unit.

[0258] A "work process" refers to a process that outlines the steps in which multiple tasks are carried out.

[0259] "Generation processing" refers to processes used to standardize information and generate data among intelligent processing units.

[0260] "Business performance" is an indicator that shows the quality and efficiency of the work performed by the intelligent processing unit.

[0261] "Improvement measures" refer to specific strategies proposed to enhance business performance.

[0262] A "report" is a document that summarizes the progress and results of a project, and is provided in a visually analyzable format.

[0263] "User" refers to an individual or organization that operates or manages an information processing device or intelligent processing unit.

[0264] "Progress status" refers to the state of how far along a particular work process is.

[0265] This invention is a system based on an information processing device that efficiently manages multiple intelligent processing units and monitors the progress of tasks in real time. Users input various task information using a terminal and send it to the server. Based on the received information, the server analyzes the capabilities and current load status of the intelligent processing units in the database.

[0266] The server can use standard computing devices and cloud-based system environments as the hardware on which the program runs. The software is implemented in programming languages ​​such as Python and JavaScript, which are used to operate the task assignment algorithm. Resource allocation and work assignment for each unit are dynamically controlled by optimization algorithms. The server also sends progress information to user terminals via APIs and displays the information on dashboards built using HTML and CSS.

[0267] A concrete example is when a user manages a new product planning and development project. In this scenario, the server appropriately assigns each task included in the project to the appropriate intelligent processing unit and graphically displays the progress on a dashboard. In this way, the user can grasp the status of each task at a glance and make quick decisions.

[0268] An example of a prompt message is, "Check the progress of tasks related to the current project and reassign tasks if necessary." Based on this prompt, the server performs the necessary actions and provides feedback to the user.

[0269] Through this system, users can streamline complex project management and optimize their operations.

[0270] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0271] Step 1:

[0272] User actions

[0273] Users log in to the system using their terminal and enter details of the tasks they want to manage (e.g., task content, deadline, priority). The entered data is structured in JSON format or similar and sent to the server.

[0274] Step 2:

[0275] Server operation

[0276] The server analyzes task information received from the user and stores it in a database. Next, it checks the current status of the intelligent processing units and collects data to evaluate the processing capacity and load status of each unit. In this process, the server executes efficient database queries to quickly retrieve the necessary information.

[0277] Step 3:

[0278] Server operation

[0279] Based on the task information and the evaluation data of the units, the server performs calculations for optimal work allocation. Here, an optimization algorithm (e.g., linear programming method or heuristic method) within the program is used to select the most suitable unit for each task. Based on the results, the allocation of tasks to the units is determined.

[0280] Step 4:

[0281] Server operation

[0282] The server distributes the determined task allocation to the intelligent processing units and adds it to the queue of each unit. Based on the task ID assigned at the input stage, efficient processing is carried out while avoiding duplication between units. This enables parallel processing of tasks.

[0283] Step 5:

[0284] Server operation

[0285] The server monitors each ongoing task in real time and collects progress data. The progress information is sent to the user's terminal via the API and displayed in a visual format on the dashboard. This dashboard is constructed using HTML and JavaScript and is designed to enable intuitive understanding of the information.

[0286] Step 6:

[0287] User operation

[0288] Users can check the dashboard on their device to understand the progress of each task. If necessary, they can be instructed to reassign tasks using prompt messages. Specifically, they might enter a prompt such as, "Review the task progress of the current project and reassign tasks to the most appropriate unit if necessary."

[0289] Step 7:

[0290] Server operation

[0291] Based on the user's reallocation instructions, the server performs calculations again using the optimization algorithm and makes any necessary adjustments. Furthermore, it logs the processing results in preparation for the next analysis and evaluation.

[0292] Step 8:

[0293] Server operation

[0294] The server evaluates the performance of the intelligent processing unit once all tasks are completed and provides the user with a report of the results. This report includes suggestions for improvement and feedback, providing the user with data that can be used for future project management.

[0295] (Application Example 1)

[0296] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0297] Inventory management and receiving / shipping operations in logistics facilities are time-consuming, labor-intensive, and prone to resource waste. Furthermore, a system is needed to instantly grasp the progress of operations and enable efficient decision-making. Traditional systems have struggled to comprehensively manage the progress of each operation and provide appropriate feedback. There is a need for solutions to these challenges and significantly improve operational efficiency within logistics facilities.

[0298] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0299] In this invention, the server includes means for centrally managing and immediately monitoring intelligent devices, means for optimizing resource allocation and task assignment, and means for providing a visual operation screen integrated with a portable information terminal. This enables more efficient inventory management and receiving / shipping operations in logistics facilities.

[0300] An "information processing device" is a foundational device that centrally manages multiple intelligent devices and provides real-time monitoring of the progress of work.

[0301] An "intelligent device" is a device that performs specific tasks and processes information, and operates based on instructions from a server.

[0302] "Resource allocation" refers to the optimal distribution of resources, such as computing power and data, necessary for each intelligent device to operate efficiently.

[0303] "Task assignment" refers to distributing specific tasks to each intelligent device to ensure their efficient execution.

[0304] "Immediate monitoring" means observing and recording the progress of work in real time to help make quick decisions.

[0305] A "portable information terminal" is a portable device that is primarily operated visually and is used to improve work efficiency within logistics facilities.

[0306] A "visual interface" is a screen interface that allows users to easily check information and perform operations.

[0307] To implement this invention, it is necessary to install a server as an information processing device and construct a system for unified management of multiple intelligent devices. The server automatically optimizes resource allocation and task assignment for each intelligent device in order to efficiently perform inventory management and inbound / outbound operations within the logistics facility.

[0308] The server uses a real-time database (e.g., Firebase) to immediately monitor the progress of work in real time and provides this as a visual operation screen on the portable information terminal. By doing so, the user can always check the progress of tasks within the logistics facility.

[0309] Among the intelligent devices, necessary information is shared using generation processing to enable collaborative work. Each intelligent device operates according to instructions from the server and feeds back its information to the server, enabling work to proceed efficiently and flexibly.

[0310] The portable information terminal functions as a smartphone or tablet and provides the user with a visually excellent operation screen. Through this screen, the user can check the work status in real time and make appropriate decisions.

[0311] As a specific example, consider the picking operation in a logistics facility. The server uses an AI model to assign each picking operation to an optimal intelligent device and immediately calculates the priority of the work.

[0312] An example of a prompt sentence for the generation AI model is "Please assist in designing a generation AI model that optimizes inventory tasks in a logistics facility. Specifically, functions for strategic resource allocation and real-time progress monitoring are required." This can dramatically improve the business efficiency within the logistics facility.

[0313] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0314] Step 1:

[0315] The server receives work task information from the user via a mobile device. Based on this input, the server performs data analysis to identify the type and priority of the task. As a result, the work items and urgency levels are obtained.

[0316] Step 2:

[0317] The server accesses a real-time database to obtain the current operating status and resource allocation of intelligent devices. The input obtained here is the amount of work each device can perform. Using this information, the server calculates the optimal resource allocation and task assignment based on the generated AI model.

[0318] Step 3:

[0319] The server uses the resource allocation and task assignments determined by the server to output specific work instructions to the intelligent devices. At this time, each intelligent device prepares to perform the received task and acquires the confirmed task assignment information as input.

[0320] Step 4:

[0321] The intelligent device begins the actual work according to the work instructions it receives. In this process, it performs execution processing according to the work instructions and generates progress data as a result. This data is fed back to the server.

[0322] Step 5:

[0323] The server receives progress data from the intelligent device and immediately displays it on the user interface of the mobile device to visualize the progress. This output allows the user to immediately check the progress of their work.

[0324] Step 6:

[0325] Users use their mobile devices to check the progress of their work and make necessary adjustments to their work plans or make decisions. During this process, users input new instructions and adjustment information.

[0326] Step 7:

[0327] Based on the results of the intelligent device's work and user feedback, the server improves the next resource allocation and task assignment by the AI ​​model. As a result of this process, task processing capacity is optimized.

[0328] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0329] One embodiment of the present invention is a centralized management system centered on an information processing device combined with an emotion engine. This system provides management of multiple artificial intelligence units, improvement of the user experience through emotion recognition, and real-time task optimization.

[0330] The server receives task information entered by the user from the terminal and, before starting normal task management processing, uses an emotion engine connected to the terminal to recognize the user's emotions. This emotion data is used as a parameter when the server assigns appropriate tasks to each artificial intelligence unit. Specifically, if the user is experiencing stress, the server adjusts task assignments to units and sets priorities to reduce the load.

[0331] Furthermore, the server stores data obtained from the emotion engine in a database and customizes the feedback based on this data. For example, if a user indicates high satisfaction, it can suggest more challenging tasks. This feedback is presented to the user via their device, contributing to improved work efficiency.

[0332] This system features real-time monitoring using emotion recognition results, which are displayed as visualized information on the user's dashboard. This allows users to efficiently manage tasks according to their emotional state. For example, if the artificial intelligence unit analyzing sales data determines, based on its emotion engine, that the user's concentration is declining, it can delay the processing of other lower-priority tasks. This operation enables optimal task execution while maintaining the user's concentration to the maximum extent.

[0333] By incorporating an emotion engine in this way, the system of the present invention achieves intuitive feedback and task management based on user experience, in addition to conventional management functions. As a result, it becomes possible to effectively and efficiently carry out users' daily tasks while making the most of the benefits of artificial intelligence technology.

[0334] The following describes the processing flow.

[0335] Step 1:

[0336] The user logs into the system using a terminal and selects a task to start. The task information is sent from the terminal to the server.

[0337] Step 2:

[0338] The device detects the user's current emotional state from their facial expressions, voice, or input actions via a built-in or connected emotion engine, and sends that data to a server.

[0339] Step 3:

[0340] The server analyzes task information and emotion data received from the terminal. Based on the analysis results, it decides to assign tasks to each artificial intelligence unit. For example, if the user is feeling stressed, the server will reduce the amount of tasks or prioritize easier tasks.

[0341] Step 4:

[0342] The server monitors task progress in real time, combines collected sentiment data, and visualizes the status for the user through a dashboard. Users can check task progress and sentiment status on the dashboard on their device.

[0343] Step 5:

[0344] The data obtained from the emotion engine is stored in a database on the server and used for subsequent task management and feedback generation. The server customizes the feedback according to the user's emotional state and provides it to the user through the terminal.

[0345] Step 6:

[0346] After a task is completed or interrupted, the server generates a report based on all process and sentiment data and presents it to the user. The user can use this report to gain a deeper understanding of their work and mental / physical state, and to use it for future improvements.

[0347] (Example 2)

[0348] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0349] Conventional information processing systems struggle to manage tasks while taking into account the user's emotional state, resulting in a failure to adequately respond to user stress and emotional fluctuations. Furthermore, efficient information sharing between intelligent units and optimized resource allocation remain challenges. As a result, user work efficiency and satisfaction have sometimes decreased.

[0350] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0351] In this invention, the server includes means for centrally managing multiple intelligent units and monitoring the progress of their tasks in real time, means for recognizing the user's emotions using an emotion analysis device connected to a terminal and dynamically adjusting task assignments using the emotion data, and means for automatically generating reports and providing them to the user in a format that can be visually analyzed. This makes it possible to appropriately adjust tasks according to the user's emotional state, thereby improving work efficiency and satisfaction.

[0352] An "information processing device" is a device that acquires and analyzes information from users and provides appropriate task management and feedback.

[0353] An "intelligent unit" is a functional unit that incorporates artificial intelligence technology designed to perform a specific task.

[0354] "Centralized management" is a method of maintaining efficient resource allocation and information consistency by centrally managing multiple intelligence units and related information.

[0355] "Monitoring in real time" means tracking the progress of tasks and operations in real time and always being aware of the latest status.

[0356] An "emotion analysis device" is a device that detects emotions based on data such as a user's facial expressions and voice, and transmits the results to an information processing device.

[0357] "Dynamically adjusting task assignments" means flexibly changing the priority and assignment of tasks based on the user's emotional state and circumstances.

[0358] "Generating reports and providing them in an analyzable format" means that the system automatically compiles data and visualizes it in a way that users can easily understand and analyze.

[0359] This system consists of an information processing unit that enables efficient task management based on the user's emotional state. The server receives task information transmitted by the user from a terminal and recognizes the user's emotions in real time using an emotion analysis device connected to the terminal. For this emotion analysis, analysis software such as Affectiva or IBM Watson Tone Analyzer can be used.

[0360] The server inputs emotional data into an AI model that generates emotional data and dynamically adjusts task assignments based on the results. It also generates feedback based on task progress and emotional data, providing it visually to the user. Users can view this feedback on a dashboard on their device, allowing them to monitor their emotional state and task progress.

[0361] As a concrete example, if the server determines that a user is fatigued, it uses a generative AI model to prioritize assigning lower-difficulty tasks and postpone other tasks. In this way, it reduces the user's workload and provides an efficient work environment. It also provides feedback such as, "We recommend you learn new skills for future use."

[0362] An example of an input prompt for a generative AI model might be, "Given that the user is distracted, please advise on how to adjust lower-priority tasks." This prompt allows the system to provide the user with the optimal task management strategy.

[0363] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0364] Step 1:

[0365] The user inputs task information related to future work via a terminal. This information includes the task name, deadline, and importance level. The terminal then sends this input to the server.

[0366] Step 2:

[0367] The server stores the task information received from the terminal and prepares it as data for the next analysis. Here, the task information is formatted for use in subsequent processing steps.

[0368] Step 3:

[0369] An emotion analysis device connected to the user's terminal acquires the user's emotional data. During this process, facial recognition and voice analysis technologies are used to evaluate emotions in real time, and the results are sent from the terminal to a server. The analysis results include specific emotional states, such as whether the user is feeling stressed or relaxed.

[0370] Step 4:

[0371] The server inputs emotional data into an AI model, which then prioritizes tasks appropriate to the current emotional state based on the results. Here, a new task assignment is generated based on the emotional data and task information. At this stage, the AI ​​model performs appropriate data processing and calculations to determine the optimal task arrangement.

[0372] Step 5:

[0373] The server provides users with visual feedback based on the generated task priorities. This includes graphical representations on the dashboard and suggestions for specific actions. For example, if concentration is waning, it might advise, "We recommend taking a short break."

[0374] Step 6:

[0375] The user reviews the feedback provided and begins executing the task as needed. Based on the feedback, they revise their work schedule and make adjustments to work more efficiently.

[0376] This series of steps enables flexible task management based on the user's emotional state and improves work efficiency.

[0377] (Application Example 2)

[0378] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0379] Modern information processing systems require the efficient management of multiple learning devices and the dynamic adjustment of the environment based on the user's emotional state. However, conventional technologies often rely on fixed methods for resource allocation and task management without considering the user's emotional state, making it difficult to optimize the user experience.

[0380] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0381] In this invention, the server includes means for centrally managing multiple learning devices and monitoring the progress of their work in real time, means for automatically optimizing resource allocation and task assignment in each learning device, and means for recognizing the user's emotional state. This enables dynamic resource allocation and task adjustment in accordance with the user's emotions.

[0382] An "information processing system" is a technological system that manages multiple learning devices, recognizes user input and emotions, and optimizes resource allocation and tasks based on that information.

[0383] A "learning device" is a functional unit in an information processing system that performs a specific task and manages its progress and performance.

[0384] "Resource allocation" refers to the efficient and optimal distribution of available resources to multiple learning devices.

[0385] "Task assignment" is the process of appropriately distributing specific tasks to each learning device.

[0386] A "user" is an entity that directly or indirectly operates an information processing system and utilizes its functions.

[0387] "Emotional state" refers to the user's mental and psychological state, and is recognized in real time by the emotion engine.

[0388] "Dynamically adjusting the environment" means automatically changing the state of home appliances, lighting, automated devices, etc., based on the user's emotional state.

[0389] "Real-time monitoring" refers to the process of immediately checking the progress and status of work and taking necessary actions quickly.

[0390] The system for realizing this invention is centered around a server. The server centrally manages multiple learning devices and has the function of monitoring the progress of each device in real time. The terminal recognizes the user's emotional state using an emotion engine and transmits this data to the server. Based on the recognized emotional state, the server dynamically optimizes resource allocation and task assignment to each learning device.

[0391] Emotion recognition utilizes emotion engine software installed on the device, which analyzes the user's facial expressions and voice data. The analysis results are fed back in real time through data processing by a generative AI model, and the server adjusts the environment according to the user's emotional state. This process is used to create a home environment that provides enjoyment and comfort.

[0392] For example, if the system detects that a user is experiencing stress, the server automatically adjusts the lighting via the terminal and streams relaxing music. This allows the user to experience a relaxing environment.

[0393] Furthermore, as an example of a prompt message to the generative AI model to further enhance the effectiveness of this system, we can suggest: "Improve the following program and add features to further increase user satisfaction. How should the lighting settings be adjusted when the user is relaxed?"

[0394] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0395] Step 1:

[0396] The server receives facial expressions and voice data from the user's device. The input is emotion parameters in raw data format, and the output becomes input to the emotion engine. This data is acquired through the user's camera and microphone. The server converts the data to an appropriate format and prepares it for analysis by the emotion engine.

[0397] Step 2:

[0398] The device analyzes the received data using an emotion engine to recognize the user's emotional state. The input here consists of emotion parameters processed by the server from step 1. The output is data representing a specific emotional state, returned to the server in the form of "stress" or "relaxed," for example. This analysis is performed by an AI model, achieving emotion recognition through multi-dimensional data computation.

[0399] Step 3:

[0400] The server dynamically adjusts resource allocation and task assignments to each learning device based on emotion recognition results. The input is emotion state data from the terminal. The output is the adjusted task schedule, which is delivered to the learning devices. Specifically, if the emotion is determined to be "stress," the server adjusts resource priorities and configures settings to reduce the workload.

[0401] Step 4:

[0402] The server initiates appropriate environmental adjustment tasks via the terminal. The input is the task schedule generated in step 3. The output is the operation commands for home appliances and environmental adjustment devices. Specific examples include setting the lighting to relaxation mode and playing a specific playlist through a music service. The server sends this to the home system, which then adjusts the environment.

[0403] Step 5:

[0404] The server monitors all processes and stores user feedback in a database. Inputs include emotional states and device behavior. Outputs are statistical data for improving the user experience. This data will be used for future emotion recognition and system optimization. Specifically, it will also help optimize the prompts provided to the generative AI model.

[0405] 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.

[0406] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0407] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0408] [Third Embodiment]

[0409] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0410] 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.

[0411] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0412] 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.

[0413] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0414] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0415] 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.

[0416] 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.

[0417] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0418] The 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.

[0419] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0420] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0421] The embodiment of this invention is based on a centralized management system built around an information processing device. This system is designed to allow users to efficiently manage multiple artificial intelligence units.

[0422] The server receives various task information entered by the user from the terminal and analyzes the capabilities and current load status of each artificial intelligence unit stored in the database. Based on this, the server allocates resources optimally and assigns tasks to the appropriate units. This process can be performed automatically to achieve optimal resource allocation according to the user's work.

[0423] Furthermore, the server monitors the task progress in real time and displays it visually on the user's terminal dashboard. Because users can easily access this information through their terminal, they can make quick decisions.

[0424] Furthermore, it features an information sharing function that utilizes generation processing. This function allows each artificial intelligence unit to exchange necessary information via the server and collaborate to perform tasks. In addition, the server evaluates the performance of each unit and generates feedback for improvement. Users can use the analytical information provided by this feedback to further improve the efficiency of their work.

[0425] To give a specific example, when a user manages the progress of multiple projects, the server assigns different tasks to the appropriate AI unit for each project and generates a dashboard that allows users to check the progress at a glance. Furthermore, it automatically generates reports based on the data generated as each project progresses, which the user can use to make subsequent decisions. This allows users to significantly improve work efficiency and effectively optimize resources.

[0426] The following describes the processing flow.

[0427] Step 1:

[0428] Users use a terminal to enter specific tasks and send them to the server. This includes details such as the task type, priority, and deadline.

[0429] Step 2:

[0430] The server analyzes the task information received from the terminal and retrieves performance data and current load status for each artificial intelligence unit from the database. Based on this, the server determines which unit is best suited to perform the task.

[0431] Step 3:

[0432] Once the server selects the most suitable artificial intelligence unit, it sends instructions to that unit. The unit receives the instructions and begins the specified task. Each unit automatically adjusts to efficiently use resources.

[0433] Step 4:

[0434] The server collects real-time task progress information from each unit and visualizes the data in a consistent format on a dashboard on the user's terminal. Users can then use this to understand the status of their tasks.

[0435] Step 5:

[0436] When information sharing is required between units, the server performs a generation process to ensure that relevant information is reliably exchanged. This allows units to work together collaboratively.

[0437] Step 6:

[0438] Once a task is completed, the server evaluates the performance of each unit in the execution, analyzes the results, and generates feedback. Users receive this feedback via their terminal and use it to improve future operations.

[0439] Step 7:

[0440] As needed, the server automatically generates and provides reports to the user based on the progress and results of each task. The user then uses this evidence to support their next planning and decision-making.

[0441] (Example 1)

[0442] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0443] In today's digital society, efficiently managing multiple intelligent processing units and monitoring work progress in real time is crucial for improving operational efficiency. However, conventional technologies have limitations in optimizing resource allocation, sharing information between units, and generating feedback for business improvement. A new system is needed to solve these problems.

[0444] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0445] In this invention, the server includes means for centrally managing multiple intelligent processing units and monitoring work processes in real time, means for automatically optimizing resource allocation and work assignments, means for visually displaying work progress to users, means for using generation processing to promote information sharing and cooperation among intelligent processing units, means for evaluating the operational performance of each unit and generating improvement measures, means for automatically providing reports to users, means for receiving work information from users, means for calculating optimized work assignments, and means for generating and notifying improvement suggestions. This enables efficient work management and optimization of operations.

[0446] An "information processing device" is a device used to receive, process, and analyze data, and to manage multiple intelligent processing units.

[0447] An "intelligent processing unit" is an artificial intelligence-based processing system designed to perform a specific task.

[0448] "Resource allocation" refers to assigning resources such as computing power and memory to intelligent processing units in order to efficiently perform tasks.

[0449] "Task assignment" refers to assigning specific tasks to each intelligent processing unit.

[0450] A "work process" refers to a process that outlines the steps in which multiple tasks are carried out.

[0451] "Generation processing" refers to processes used to standardize information and generate data among intelligent processing units.

[0452] "Business performance" is an indicator that shows the quality and efficiency of the work performed by the intelligent processing unit.

[0453] "Improvement measures" refer to specific strategies proposed to enhance business performance.

[0454] A "report" is a document that summarizes the progress and results of a project, and is provided in a visually analyzable format.

[0455] "User" refers to an individual or organization that operates or manages an information processing device or intelligent processing unit.

[0456] "Progress status" refers to the state of how far along a particular work process is.

[0457] This invention is a system based on an information processing device that efficiently manages multiple intelligent processing units and monitors the progress of tasks in real time. Users input various task information using a terminal and send it to the server. Based on the received information, the server analyzes the capabilities and current load status of the intelligent processing units in the database.

[0458] The server can use standard computing devices and cloud-based system environments as the hardware on which the program runs. The software is implemented in programming languages ​​such as Python and JavaScript, which are used to operate the task assignment algorithm. Resource allocation and work assignment for each unit are dynamically controlled by optimization algorithms. The server also sends progress information to user terminals via APIs and displays the information on dashboards built using HTML and CSS.

[0459] A concrete example is when a user manages a new product planning and development project. In this scenario, the server appropriately assigns each task included in the project to the appropriate intelligent processing unit and graphically displays the progress on a dashboard. In this way, the user can grasp the status of each task at a glance and make quick decisions.

[0460] An example of a prompt message is, "Check the progress of tasks related to the current project and reassign tasks if necessary." Based on this prompt, the server performs the necessary actions and provides feedback to the user.

[0461] Through this system, users can streamline complex project management and optimize their operations.

[0462] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0463] Step 1:

[0464] User actions

[0465] Users log in to the system using their terminal and enter details of the tasks they want to manage (e.g., task content, deadline, priority). The entered data is structured in JSON format or similar and sent to the server.

[0466] Step 2:

[0467] Server operation

[0468] The server analyzes task information received from the user and stores it in a database. Next, it checks the current status of the intelligent processing units and collects data to evaluate the processing capacity and load status of each unit. In this process, the server executes efficient database queries to quickly retrieve the necessary information.

[0469] Step 3:

[0470] Server operation

[0471] The server performs calculations for optimal work assignment based on task information and unit evaluation data. Here, it uses in-program optimization algorithms (e.g., linear programming or heuristic methods) to select the most suitable unit for each task. Based on these results, it determines the task assignment to each unit.

[0472] Step 4:

[0473] Server operation

[0474] The server distributes the determined task assignments to intelligent processing units and adds them to each unit's queue. Based on the task IDs assigned at the input stage, efficient processing is ensured while avoiding duplication between units. This enables parallel processing of tasks.

[0475] Step 5:

[0476] Server operation

[0477] The server monitors each ongoing task in real time and collects progress data. This progress information is sent to the user's device via an API and displayed visually on a dashboard. This dashboard is built using HTML and JavaScript and is designed to be intuitive and easy to understand.

[0478] Step 6:

[0479] User actions

[0480] Users can check the dashboard on their device to understand the progress of each task. If necessary, they can be instructed to reassign tasks using prompt messages. Specifically, they might enter a prompt such as, "Review the task progress of the current project and reassign tasks to the most appropriate unit if necessary."

[0481] Step 7:

[0482] Server operation

[0483] Based on the user's reallocation instructions, the server performs calculations again using the optimization algorithm and makes any necessary adjustments. Furthermore, it logs the processing results in preparation for the next analysis and evaluation.

[0484] Step 8:

[0485] Server operation

[0486] The server evaluates the performance of the intelligent processing unit once all tasks are completed and provides the user with a report of the results. This report includes suggestions for improvement and feedback, providing the user with data that can be used for future project management.

[0487] (Application Example 1)

[0488] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0489] Inventory management and receiving / shipping operations in logistics facilities are time-consuming, labor-intensive, and prone to resource waste. Furthermore, a system is needed to instantly grasp the progress of operations and enable efficient decision-making. Traditional systems have struggled to comprehensively manage the progress of each operation and provide appropriate feedback. There is a need for solutions to these challenges and significantly improve operational efficiency within logistics facilities.

[0490] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0491] In this invention, the server includes means for centrally managing and immediately monitoring intelligent devices, means for optimizing resource allocation and task assignment, and means for providing a visual operation screen integrated with a portable information terminal. This enables more efficient inventory management and receiving / shipping operations in logistics facilities.

[0492] An "information processing device" is a foundational device that centrally manages multiple intelligent devices and provides real-time monitoring of the progress of work.

[0493] An "intelligent device" is a device that performs specific tasks and processes information, and operates based on instructions from a server.

[0494] "Resource allocation" refers to the optimal distribution of resources, such as computing power and data, necessary for each intelligent device to operate efficiently.

[0495] "Task assignment" refers to distributing specific tasks to each intelligent device to ensure their efficient execution.

[0496] "Immediate monitoring" means observing and recording the progress of work in real time to help make quick decisions.

[0497] A "portable information terminal" is a portable device that is primarily operated visually and is used to improve work efficiency within logistics facilities.

[0498] A "visual interface" is a screen interface that allows users to easily check information and perform operations.

[0499] To implement this invention, it is necessary to install a server as an information processing device and build a system that centrally manages multiple intelligent devices. The server automatically optimizes resource allocation and task assignment for each intelligent device in order to efficiently manage inventory and perform receiving and shipping operations within the logistics facility.

[0500] The server uses a real-time database (e.g., Firebase) to instantly monitor the progress of tasks and provides this information as a visual interface on mobile devices. This allows users to check the progress of tasks within the logistics facility at any time.

[0501] Intelligent devices share necessary information using generation processing and work collaboratively. Each intelligent device operates according to instructions from the server and feeds that information back to the server, enabling efficient and flexible work.

[0502] Mobile devices function as smartphones and tablets, providing users with a visually superior user interface. Through this interface, users can monitor their work status in real time and make appropriate decisions.

[0503] As a concrete example, consider picking operations in a logistics facility. The server uses an AI model to assign each picking task to the most suitable intelligent device and instantly calculates the priority of the tasks.

[0504] An example of a prompt for a generative AI model is: "Help design a generative AI model to optimize inventory tasks in a logistics facility. Specifically, it needs strategic resource allocation and real-time progress monitoring capabilities." This can dramatically improve operational efficiency within the logistics facility.

[0505] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0506] Step 1:

[0507] The server receives work task information from the user via a mobile device. Based on this input, the server performs data analysis to identify the type and priority of the task. As a result, the work items and urgency levels are obtained.

[0508] Step 2:

[0509] The server accesses a real-time database to obtain the current operating status and resource allocation of intelligent devices. The input obtained here is the amount of work each device can perform. Using this information, the server calculates the optimal resource allocation and task assignment based on the generated AI model.

[0510] Step 3:

[0511] The server uses the resource allocation and task assignments determined by the server to output specific work instructions to the intelligent devices. At this time, each intelligent device prepares to perform the received task and acquires the confirmed task assignment information as input.

[0512] Step 4:

[0513] The intelligent device begins the actual work according to the work instructions it receives. In this process, it performs execution processing according to the work instructions and generates progress data as a result. This data is fed back to the server.

[0514] Step 5:

[0515] The server receives progress data from the intelligent device and immediately displays it on the user interface of the mobile device to visualize the progress. This output allows the user to immediately check the progress of their work.

[0516] Step 6:

[0517] Users use their mobile devices to check the progress of their work and make necessary adjustments to their work plans or make decisions. During this process, users input new instructions and adjustment information.

[0518] Step 7:

[0519] Based on the results of the intelligent device's work and user feedback, the server improves the next resource allocation and task assignment by the AI ​​model. As a result of this process, task processing capacity is optimized.

[0520] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0521] One embodiment of the present invention is a centralized management system centered on an information processing device combined with an emotion engine. This system provides management of multiple artificial intelligence units, improvement of the user experience through emotion recognition, and real-time task optimization.

[0522] The server receives task information entered by the user from the terminal and, before starting normal task management processing, uses an emotion engine connected to the terminal to recognize the user's emotions. This emotion data is used as a parameter when the server assigns appropriate tasks to each artificial intelligence unit. Specifically, if the user is experiencing stress, the server adjusts task assignments to units and sets priorities to reduce the load.

[0523] Furthermore, the server stores data obtained from the emotion engine in a database and customizes the feedback based on this data. For example, if a user indicates high satisfaction, it can suggest more challenging tasks. This feedback is presented to the user via their device, contributing to improved work efficiency.

[0524] This system features real-time monitoring using emotion recognition results, which are displayed as visualized information on the user's dashboard. This allows users to efficiently manage tasks according to their emotional state. For example, if the artificial intelligence unit analyzing sales data determines, based on its emotion engine, that the user's concentration is declining, it can delay the processing of other lower-priority tasks. This operation enables optimal task execution while maintaining the user's concentration to the maximum extent.

[0525] By incorporating an emotion engine in this way, the system of the present invention achieves intuitive feedback and task management based on user experience, in addition to conventional management functions. As a result, it becomes possible to effectively and efficiently carry out users' daily tasks while making the most of the benefits of artificial intelligence technology.

[0526] The following describes the processing flow.

[0527] Step 1:

[0528] The user logs into the system using a terminal and selects a task to start. The task information is sent from the terminal to the server.

[0529] Step 2:

[0530] The device detects the user's current emotional state from their facial expressions, voice, or input actions via a built-in or connected emotion engine, and sends that data to a server.

[0531] Step 3:

[0532] The server analyzes task information and emotion data received from the terminal. Based on the analysis results, it decides to assign tasks to each artificial intelligence unit. For example, if the user is feeling stressed, the server will reduce the amount of tasks or prioritize easier tasks.

[0533] Step 4:

[0534] The server monitors task progress in real time, combines collected sentiment data, and visualizes the status for the user through a dashboard. Users can check task progress and sentiment status on the dashboard on their device.

[0535] Step 5:

[0536] The data obtained from the emotion engine is stored in a database on the server and used for subsequent task management and feedback generation. The server customizes the feedback according to the user's emotional state and provides it to the user through the terminal.

[0537] Step 6:

[0538] After a task is completed or interrupted, the server generates a report based on all process and sentiment data and presents it to the user. The user can use this report to gain a deeper understanding of their work and mental / physical state, and to use it for future improvements.

[0539] (Example 2)

[0540] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0541] Conventional information processing systems struggle to manage tasks while taking into account the user's emotional state, resulting in a failure to adequately respond to user stress and emotional fluctuations. Furthermore, efficient information sharing between intelligent units and optimized resource allocation remain challenges. As a result, user work efficiency and satisfaction have sometimes decreased.

[0542] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0543] In this invention, the server includes means for centrally managing multiple intelligent units and monitoring the progress of their tasks in real time, means for recognizing the user's emotions using an emotion analysis device connected to a terminal and dynamically adjusting task assignments using the emotion data, and means for automatically generating reports and providing them to the user in a format that can be visually analyzed. This makes it possible to appropriately adjust tasks according to the user's emotional state, thereby improving work efficiency and satisfaction.

[0544] An "information processing device" is a device that acquires and analyzes information from users and provides appropriate task management and feedback.

[0545] An "intelligent unit" is a functional unit that incorporates artificial intelligence technology designed to perform a specific task.

[0546] "Centralized management" is a method of maintaining efficient resource allocation and information consistency by centrally managing multiple intelligence units and related information.

[0547] "Monitoring in real time" means tracking the progress of tasks and operations in real time and always being aware of the latest status.

[0548] An "emotion analysis device" is a device that detects emotions based on data such as a user's facial expressions and voice, and transmits the results to an information processing device.

[0549] "Dynamically adjusting task assignments" means flexibly changing the priority and assignment of tasks based on the user's emotional state and circumstances.

[0550] "Generating reports and providing them in an analyzable format" means that the system automatically compiles data and visualizes it in a way that users can easily understand and analyze.

[0551] This system consists of an information processing unit that enables efficient task management based on the user's emotional state. The server receives task information transmitted by the user from a terminal and recognizes the user's emotions in real time using an emotion analysis device connected to the terminal. For this emotion analysis, analysis software such as Affectiva or IBM Watson Tone Analyzer can be used.

[0552] The server inputs emotional data into an AI model that generates emotional data and dynamically adjusts task assignments based on the results. It also generates feedback based on task progress and emotional data, providing it visually to the user. Users can view this feedback on a dashboard on their device, allowing them to monitor their emotional state and task progress.

[0553] As a concrete example, if the server determines that a user is fatigued, it uses a generative AI model to prioritize assigning lower-difficulty tasks and postpone other tasks. In this way, it reduces the user's workload and provides an efficient work environment. It also provides feedback such as, "We recommend you learn new skills for future use."

[0554] An example of an input prompt for a generative AI model might be, "Given that the user is distracted, please advise on how to adjust lower-priority tasks." This prompt allows the system to provide the user with the optimal task management strategy.

[0555] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0556] Step 1:

[0557] The user inputs task information related to future work via a terminal. This information includes the task name, deadline, and importance level. The terminal then sends this input to the server.

[0558] Step 2:

[0559] The server stores the task information received from the terminal and prepares it as data for the next analysis. Here, the task information is formatted for use in subsequent processing steps.

[0560] Step 3:

[0561] An emotion analysis device connected to the user's terminal acquires the user's emotional data. During this process, facial recognition and voice analysis technologies are used to evaluate emotions in real time, and the results are sent from the terminal to a server. The analysis results include specific emotional states, such as whether the user is feeling stressed or relaxed.

[0562] Step 4:

[0563] The server inputs emotional data into an AI model, which then prioritizes tasks appropriate to the current emotional state based on the results. Here, a new task assignment is generated based on the emotional data and task information. At this stage, the AI ​​model performs appropriate data processing and calculations to determine the optimal task arrangement.

[0564] Step 5:

[0565] The server provides users with visual feedback based on the generated task priorities. This includes graphical representations on the dashboard and suggestions for specific actions. For example, if concentration is waning, it might advise, "We recommend taking a short break."

[0566] Step 6:

[0567] The user reviews the feedback provided and begins executing the task as needed. Based on the feedback, they revise their work schedule and make adjustments to work more efficiently.

[0568] This series of steps enables flexible task management based on the user's emotional state and improves work efficiency.

[0569] (Application Example 2)

[0570] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0571] Modern information processing systems require the efficient management of multiple learning devices and the dynamic adjustment of the environment based on the user's emotional state. However, conventional technologies often rely on fixed methods for resource allocation and task management without considering the user's emotional state, making it difficult to optimize the user experience.

[0572] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0573] In this invention, the server includes means for centrally managing multiple learning devices and monitoring the progress of their work in real time, means for automatically optimizing resource allocation and task assignment in each learning device, and means for recognizing the user's emotional state. This enables dynamic resource allocation and task adjustment in accordance with the user's emotions.

[0574] An "information processing system" is a technological system that manages multiple learning devices, recognizes user input and emotions, and optimizes resource allocation and tasks based on that information.

[0575] A "learning device" is a functional unit in an information processing system that performs a specific task and manages its progress and performance.

[0576] "Resource allocation" refers to the efficient and optimal distribution of available resources to multiple learning devices.

[0577] "Task assignment" is the process of appropriately distributing specific tasks to each learning device.

[0578] A "user" is an entity that directly or indirectly operates an information processing system and utilizes its functions.

[0579] "Emotional state" refers to the user's mental and psychological state, and is recognized in real time by the emotion engine.

[0580] "Dynamically adjusting the environment" means automatically changing the state of home appliances, lighting, automated devices, etc., based on the user's emotional state.

[0581] "Real-time monitoring" refers to the process of immediately checking the progress and status of work and taking necessary actions quickly.

[0582] The system for realizing this invention is centered around a server. The server centrally manages multiple learning devices and has the function of monitoring the progress of each device in real time. The terminal recognizes the user's emotional state using an emotion engine and transmits this data to the server. Based on the recognized emotional state, the server dynamically optimizes resource allocation and task assignment to each learning device.

[0583] Emotion recognition utilizes emotion engine software installed on the device, which analyzes the user's facial expressions and voice data. The analysis results are fed back in real time through data processing by a generative AI model, and the server adjusts the environment according to the user's emotional state. This process is used to create a home environment that provides enjoyment and comfort.

[0584] For example, if the system detects that a user is experiencing stress, the server automatically adjusts the lighting via the terminal and streams relaxing music. This allows the user to experience a relaxing environment.

[0585] Furthermore, as an example of a prompt message to the generative AI model to further enhance the effectiveness of this system, we can suggest: "Improve the following program and add features to further increase user satisfaction. How should the lighting settings be adjusted when the user is relaxed?"

[0586] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0587] Step 1:

[0588] The server receives facial expressions and voice data from the user's device. The input is emotion parameters in raw data format, and the output becomes input to the emotion engine. This data is acquired through the user's camera and microphone. The server converts the data to an appropriate format and prepares it for analysis by the emotion engine.

[0589] Step 2:

[0590] The device analyzes the received data using an emotion engine to recognize the user's emotional state. The input here consists of emotion parameters processed by the server from step 1. The output is data representing a specific emotional state, returned to the server in the form of "stress" or "relaxed," for example. This analysis is performed by an AI model, achieving emotion recognition through multi-dimensional data computation.

[0591] Step 3:

[0592] The server dynamically adjusts resource allocation and task assignments to each learning device based on emotion recognition results. The input is emotion state data from the terminal. The output is the adjusted task schedule, which is delivered to the learning devices. Specifically, if the emotion is determined to be "stress," the server adjusts resource priorities and configures settings to reduce the workload.

[0593] Step 4:

[0594] The server initiates appropriate environmental adjustment tasks via the terminal. The input is the task schedule generated in step 3. The output is the operation commands for home appliances and environmental adjustment devices. Specific examples include setting the lighting to relaxation mode and playing a specific playlist through a music service. The server sends this to the home system, which then adjusts the environment.

[0595] Step 5:

[0596] The server monitors all processes and stores user feedback in a database. Inputs include emotional states and device behavior. Outputs are statistical data for improving the user experience. This data will be used for future emotion recognition and system optimization. Specifically, it will also help optimize the prompts provided to the generative AI model.

[0597] 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.

[0598] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0599] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0600] [Fourth Embodiment]

[0601] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0602] 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.

[0603] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0604] 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.

[0605] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0606] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0607] 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.

[0608] 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0609] 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.

[0610] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0611] The 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.

[0612] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0613] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0614] The embodiment of this invention is based on a centralized management system built around an information processing device. This system is designed to allow users to efficiently manage multiple artificial intelligence units.

[0615] The server receives various task information entered by the user from the terminal and analyzes the capabilities and current load status of each artificial intelligence unit stored in the database. Based on this, the server allocates resources optimally and assigns tasks to the appropriate units. This process can be performed automatically to achieve optimal resource allocation according to the user's work.

[0616] Furthermore, the server monitors the task progress in real time and displays it visually on the user's terminal dashboard. Because users can easily access this information through their terminal, they can make quick decisions.

[0617] Furthermore, it features an information sharing function that utilizes generation processing. This function allows each artificial intelligence unit to exchange necessary information via the server and collaborate to perform tasks. In addition, the server evaluates the performance of each unit and generates feedback for improvement. Users can use the analytical information provided by this feedback to further improve the efficiency of their work.

[0618] To give a specific example, when a user manages the progress of multiple projects, the server assigns different tasks to the appropriate AI unit for each project and generates a dashboard that allows users to check the progress at a glance. Furthermore, it automatically generates reports based on the data generated as each project progresses, which the user can use to make subsequent decisions. This allows users to significantly improve work efficiency and effectively optimize resources.

[0619] The following describes the processing flow.

[0620] Step 1:

[0621] Users use a terminal to enter specific tasks and send them to the server. This includes details such as the task type, priority, and deadline.

[0622] Step 2:

[0623] The server analyzes the task information received from the terminal and retrieves performance data and current load status for each artificial intelligence unit from the database. Based on this, the server determines which unit is best suited to perform the task.

[0624] Step 3:

[0625] Once the server selects the most suitable artificial intelligence unit, it sends instructions to that unit. The unit receives the instructions and begins the specified task. Each unit automatically adjusts to efficiently use resources.

[0626] Step 4:

[0627] The server collects real-time task progress information from each unit and visualizes the data in a consistent format on a dashboard on the user's terminal. Users can then use this to understand the status of their tasks.

[0628] Step 5:

[0629] When information sharing is required between units, the server performs a generation process to ensure that relevant information is reliably exchanged. This allows units to work together collaboratively.

[0630] Step 6:

[0631] Once a task is completed, the server evaluates the performance of each unit in the execution, analyzes the results, and generates feedback. Users receive this feedback via their terminal and use it to improve future operations.

[0632] Step 7:

[0633] As needed, the server automatically generates and provides reports to the user based on the progress and results of each task. The user then uses this evidence to support their next planning and decision-making.

[0634] (Example 1)

[0635] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0636] In today's digital society, efficiently managing multiple intelligent processing units and monitoring work progress in real time is crucial for improving operational efficiency. However, conventional technologies have limitations in optimizing resource allocation, sharing information between units, and generating feedback for business improvement. A new system is needed to solve these problems.

[0637] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0638] In this invention, the server includes means for centrally managing multiple intelligent processing units and monitoring work processes in real time, means for automatically optimizing resource allocation and work assignments, means for visually displaying work progress to users, means for using generation processing to promote information sharing and cooperation among intelligent processing units, means for evaluating the operational performance of each unit and generating improvement measures, means for automatically providing reports to users, means for receiving work information from users, means for calculating optimized work assignments, and means for generating and notifying improvement suggestions. This enables efficient work management and optimization of operations.

[0639] An "information processing device" is a device used to receive, process, and analyze data, and to manage multiple intelligent processing units.

[0640] An "intelligent processing unit" is an artificial intelligence-based processing system designed to perform a specific task.

[0641] "Resource allocation" refers to assigning resources such as computing power and memory to intelligent processing units in order to efficiently perform tasks.

[0642] "Task assignment" refers to assigning specific tasks to each intelligent processing unit.

[0643] A "work process" refers to a process that outlines the steps in which multiple tasks are carried out.

[0644] "Generation processing" refers to processes used to standardize information and generate data among intelligent processing units.

[0645] "Business performance" is an indicator that shows the quality and efficiency of the work performed by the intelligent processing unit.

[0646] "Improvement measures" refer to specific strategies proposed to enhance business performance.

[0647] A "report" is a document that summarizes the progress and results of a project, and is provided in a visually analyzable format.

[0648] "User" refers to an individual or organization that operates or manages an information processing device or intelligent processing unit.

[0649] "Progress status" refers to the state of how far along a particular work process is.

[0650] This invention is a system based on an information processing device that efficiently manages multiple intelligent processing units and monitors the progress of tasks in real time. Users input various task information using a terminal and send it to the server. Based on the received information, the server analyzes the capabilities and current load status of the intelligent processing units in the database.

[0651] The server can use standard computing devices and cloud-based system environments as the hardware on which the program runs. The software is implemented in programming languages ​​such as Python and JavaScript, which are used to operate the task assignment algorithm. Resource allocation and work assignment for each unit are dynamically controlled by optimization algorithms. The server also sends progress information to user terminals via APIs and displays the information on dashboards built using HTML and CSS.

[0652] A concrete example is when a user manages a new product planning and development project. In this scenario, the server appropriately assigns each task included in the project to the appropriate intelligent processing unit and graphically displays the progress on a dashboard. In this way, the user can grasp the status of each task at a glance and make quick decisions.

[0653] An example of a prompt message is, "Check the progress of tasks related to the current project and reassign tasks if necessary." Based on this prompt, the server performs the necessary actions and provides feedback to the user.

[0654] Through this system, users can streamline complex project management and optimize their operations.

[0655] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0656] Step 1:

[0657] User actions

[0658] Users log in to the system using their terminal and enter details of the tasks they want to manage (e.g., task content, deadline, priority). The entered data is structured in JSON format or similar and sent to the server.

[0659] Step 2:

[0660] Server operation

[0661] The server analyzes task information received from the user and stores it in a database. Next, it checks the current status of the intelligent processing units and collects data to evaluate the processing capacity and load status of each unit. In this process, the server executes efficient database queries to quickly retrieve the necessary information.

[0662] Step 3:

[0663] Server operation

[0664] The server performs calculations for optimal work assignment based on task information and unit evaluation data. Here, it uses in-program optimization algorithms (e.g., linear programming or heuristic methods) to select the most suitable unit for each task. Based on these results, it determines the task assignment to each unit.

[0665] Step 4:

[0666] Server operation

[0667] The server distributes the determined task assignments to intelligent processing units and adds them to each unit's queue. Based on the task IDs assigned at the input stage, efficient processing is ensured while avoiding duplication between units. This enables parallel processing of tasks.

[0668] Step 5:

[0669] Server operation

[0670] The server monitors each ongoing task in real time and collects progress data. This progress information is sent to the user's device via an API and displayed visually on a dashboard. This dashboard is built using HTML and JavaScript and is designed to be intuitive and easy to understand.

[0671] Step 6:

[0672] User actions

[0673] Users can check the dashboard on their device to understand the progress of each task. If necessary, they can be instructed to reassign tasks using prompt messages. Specifically, they might enter a prompt such as, "Review the task progress of the current project and reassign tasks to the most appropriate unit if necessary."

[0674] Step 7:

[0675] Server operation

[0676] Based on the user's reallocation instructions, the server performs calculations again using the optimization algorithm and makes any necessary adjustments. Furthermore, it logs the processing results in preparation for the next analysis and evaluation.

[0677] Step 8:

[0678] Server operation

[0679] The server evaluates the performance of the intelligent processing unit once all tasks are completed and provides the user with a report of the results. This report includes suggestions for improvement and feedback, providing the user with data that can be used for future project management.

[0680] (Application Example 1)

[0681] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0682] Inventory management and receiving / shipping operations in logistics facilities are time-consuming, labor-intensive, and prone to resource waste. Furthermore, a system is needed to instantly grasp the progress of operations and enable efficient decision-making. Traditional systems have struggled to comprehensively manage the progress of each operation and provide appropriate feedback. There is a need for solutions to these challenges and significantly improve operational efficiency within logistics facilities.

[0683] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0684] In this invention, the server includes means for centrally managing and immediately monitoring intelligent devices, means for optimizing resource allocation and task assignment, and means for providing a visual operation screen integrated with a portable information terminal. This enables more efficient inventory management and receiving / shipping operations in logistics facilities.

[0685] An "information processing device" is a foundational device that centrally manages multiple intelligent devices and provides real-time monitoring of the progress of work.

[0686] An "intelligent device" is a device that performs specific tasks and processes information, and operates based on instructions from a server.

[0687] "Resource allocation" refers to the optimal distribution of resources, such as computing power and data, necessary for each intelligent device to operate efficiently.

[0688] "Task assignment" refers to distributing specific tasks to each intelligent device to ensure their efficient execution.

[0689] "Immediate monitoring" means observing and recording the progress of work in real time to help make quick decisions.

[0690] A "portable information terminal" is a portable device that is primarily operated visually and is used to improve work efficiency within logistics facilities.

[0691] A "visual interface" is a screen interface that allows users to easily check information and perform operations.

[0692] To implement this invention, it is necessary to install a server as an information processing device and build a system that centrally manages multiple intelligent devices. The server automatically optimizes resource allocation and task assignment for each intelligent device in order to efficiently manage inventory and perform receiving and shipping operations within the logistics facility.

[0693] The server uses a real-time database (e.g., Firebase) to instantly monitor the progress of tasks and provides this information as a visual interface on mobile devices. This allows users to check the progress of tasks within the logistics facility at any time.

[0694] Intelligent devices share necessary information using generation processing and work collaboratively. Each intelligent device operates according to instructions from the server and feeds that information back to the server, enabling efficient and flexible work.

[0695] Mobile devices function as smartphones and tablets, providing users with a visually superior user interface. Through this interface, users can monitor their work status in real time and make appropriate decisions.

[0696] As a concrete example, consider picking operations in a logistics facility. The server uses an AI model to assign each picking task to the most suitable intelligent device and instantly calculates the priority of the tasks.

[0697] An example of a prompt for a generative AI model is: "Help design a generative AI model to optimize inventory tasks in a logistics facility. Specifically, it needs strategic resource allocation and real-time progress monitoring capabilities." This can dramatically improve operational efficiency within the logistics facility.

[0698] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0699] Step 1:

[0700] The server receives work task information from the user via a mobile device. Based on this input, the server performs data analysis to identify the type and priority of the task. As a result, the work items and urgency levels are obtained.

[0701] Step 2:

[0702] The server accesses a real-time database to obtain the current operating status and resource allocation of intelligent devices. The input obtained here is the amount of work each device can perform. Using this information, the server calculates the optimal resource allocation and task assignment based on the generated AI model.

[0703] Step 3:

[0704] The server uses the resource allocation and task assignments determined by the server to output specific work instructions to the intelligent devices. At this time, each intelligent device prepares to perform the received task and acquires the confirmed task assignment information as input.

[0705] Step 4:

[0706] The intelligent device begins the actual work according to the work instructions it receives. In this process, it performs execution processing according to the work instructions and generates progress data as a result. This data is fed back to the server.

[0707] Step 5:

[0708] The server receives progress data from the intelligent device and immediately displays it on the user interface of the mobile device to visualize the progress. This output allows the user to immediately check the progress of their work.

[0709] Step 6:

[0710] Users use their mobile devices to check the progress of their work and make necessary adjustments to their work plans or make decisions. During this process, users input new instructions and adjustment information.

[0711] Step 7:

[0712] Based on the results of the intelligent device's work and user feedback, the server improves the next resource allocation and task assignment by the AI ​​model. As a result of this process, task processing capacity is optimized.

[0713] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0714] One embodiment of the present invention is a centralized management system centered on an information processing device combined with an emotion engine. This system provides management of multiple artificial intelligence units, improvement of the user experience through emotion recognition, and real-time task optimization.

[0715] The server receives task information entered by the user from the terminal and, before starting normal task management processing, uses an emotion engine connected to the terminal to recognize the user's emotions. This emotion data is used as a parameter when the server assigns appropriate tasks to each artificial intelligence unit. Specifically, if the user is experiencing stress, the server adjusts task assignments to units and sets priorities to reduce the load.

[0716] Furthermore, the server stores data obtained from the emotion engine in a database and customizes the feedback based on this data. For example, if a user indicates high satisfaction, it can suggest more challenging tasks. This feedback is presented to the user via their device, contributing to improved work efficiency.

[0717] This system features real-time monitoring using emotion recognition results, which are displayed as visualized information on the user's dashboard. This allows users to efficiently manage tasks according to their emotional state. For example, if the artificial intelligence unit analyzing sales data determines, based on its emotion engine, that the user's concentration is declining, it can delay the processing of other lower-priority tasks. This operation enables optimal task execution while maintaining the user's concentration to the maximum extent.

[0718] By incorporating an emotion engine in this way, the system of the present invention achieves intuitive feedback and task management based on user experience, in addition to conventional management functions. As a result, it becomes possible to effectively and efficiently carry out users' daily tasks while making the most of the benefits of artificial intelligence technology.

[0719] The following describes the processing flow.

[0720] Step 1:

[0721] The user logs into the system using a terminal and selects a task to start. The task information is sent from the terminal to the server.

[0722] Step 2:

[0723] The device detects the user's current emotional state from their facial expressions, voice, or input actions via a built-in or connected emotion engine, and sends that data to a server.

[0724] Step 3:

[0725] The server analyzes task information and emotion data received from the terminal. Based on the analysis results, it decides to assign tasks to each artificial intelligence unit. For example, if the user is feeling stressed, the server will reduce the amount of tasks or prioritize easier tasks.

[0726] Step 4:

[0727] The server monitors task progress in real time, combines collected sentiment data, and visualizes the status for the user through a dashboard. Users can check task progress and sentiment status on the dashboard on their device.

[0728] Step 5:

[0729] The data obtained from the emotion engine is stored in a database on the server and used for subsequent task management and feedback generation. The server customizes the feedback according to the user's emotional state and provides it to the user through the terminal.

[0730] Step 6:

[0731] After a task is completed or interrupted, the server generates a report based on all process and sentiment data and presents it to the user. The user can use this report to gain a deeper understanding of their work and mental / physical state, and to use it for future improvements.

[0732] (Example 2)

[0733] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0734] Conventional information processing systems struggle to manage tasks while taking into account the user's emotional state, resulting in a failure to adequately respond to user stress and emotional fluctuations. Furthermore, efficient information sharing between intelligent units and optimized resource allocation remain challenges. As a result, user work efficiency and satisfaction have sometimes decreased.

[0735] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0736] In this invention, the server includes means for centrally managing multiple intelligent units and monitoring the progress of their tasks in real time, means for recognizing the user's emotions using an emotion analysis device connected to a terminal and dynamically adjusting task assignments using the emotion data, and means for automatically generating reports and providing them to the user in a format that can be visually analyzed. This makes it possible to appropriately adjust tasks according to the user's emotional state, thereby improving work efficiency and satisfaction.

[0737] An "information processing device" is a device that acquires and analyzes information from users and provides appropriate task management and feedback.

[0738] An "intelligent unit" is a functional unit that incorporates artificial intelligence technology designed to perform a specific task.

[0739] "Centralized management" is a method of maintaining efficient resource allocation and information consistency by centrally managing multiple intelligence units and related information.

[0740] "Monitoring in real time" means tracking the progress of tasks and operations in real time and always being aware of the latest status.

[0741] An "emotion analysis device" is a device that detects emotions based on data such as a user's facial expressions and voice, and transmits the results to an information processing device.

[0742] "Dynamically adjusting task assignments" means flexibly changing the priority and assignment of tasks based on the user's emotional state and circumstances.

[0743] "Generating reports and providing them in an analyzable format" means that the system automatically compiles data and visualizes it in a way that users can easily understand and analyze.

[0744] This system consists of an information processing unit that enables efficient task management based on the user's emotional state. The server receives task information transmitted by the user from a terminal and recognizes the user's emotions in real time using an emotion analysis device connected to the terminal. For this emotion analysis, analysis software such as Affectiva or IBM Watson Tone Analyzer can be used.

[0745] The server inputs emotional data into an AI model that generates emotional data and dynamically adjusts task assignments based on the results. It also generates feedback based on task progress and emotional data, providing it visually to the user. Users can view this feedback on a dashboard on their device, allowing them to monitor their emotional state and task progress.

[0746] As a concrete example, if the server determines that a user is fatigued, it uses a generative AI model to prioritize assigning lower-difficulty tasks and postpone other tasks. In this way, it reduces the user's workload and provides an efficient work environment. It also provides feedback such as, "We recommend you learn new skills for future use."

[0747] An example of an input prompt for a generative AI model might be, "Given that the user is distracted, please advise on how to adjust lower-priority tasks." This prompt allows the system to provide the user with the optimal task management strategy.

[0748] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0749] Step 1:

[0750] The user inputs task information related to future work via a terminal. This information includes the task name, deadline, and importance level. The terminal then sends this input to the server.

[0751] Step 2:

[0752] The server stores the task information received from the terminal and prepares it as data for the next analysis. Here, the task information is formatted for use in subsequent processing steps.

[0753] Step 3:

[0754] An emotion analysis device connected to the user's terminal acquires the user's emotional data. During this process, facial recognition and voice analysis technologies are used to evaluate emotions in real time, and the results are sent from the terminal to a server. The analysis results include specific emotional states, such as whether the user is feeling stressed or relaxed.

[0755] Step 4:

[0756] The server inputs emotional data into an AI model, which then prioritizes tasks appropriate to the current emotional state based on the results. Here, a new task assignment is generated based on the emotional data and task information. At this stage, the AI ​​model performs appropriate data processing and calculations to determine the optimal task arrangement.

[0757] Step 5:

[0758] The server provides users with visual feedback based on the generated task priorities. This includes graphical representations on the dashboard and suggestions for specific actions. For example, if concentration is waning, it might advise, "We recommend taking a short break."

[0759] Step 6:

[0760] The user reviews the feedback provided and begins executing the task as needed. Based on the feedback, they revise their work schedule and make adjustments to work more efficiently.

[0761] This series of steps enables flexible task management based on the user's emotional state and improves work efficiency.

[0762] (Application Example 2)

[0763] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0764] Modern information processing systems require the efficient management of multiple learning devices and the dynamic adjustment of the environment based on the user's emotional state. However, conventional technologies often rely on fixed methods for resource allocation and task management without considering the user's emotional state, making it difficult to optimize the user experience.

[0765] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0766] In this invention, the server includes means for centrally managing multiple learning devices and monitoring the progress of their work in real time, means for automatically optimizing resource allocation and task assignment in each learning device, and means for recognizing the user's emotional state. This enables dynamic resource allocation and task adjustment in accordance with the user's emotions.

[0767] An "information processing system" is a technological system that manages multiple learning devices, recognizes user input and emotions, and optimizes resource allocation and tasks based on that information.

[0768] A "learning device" is a functional unit in an information processing system that performs a specific task and manages its progress and performance.

[0769] "Resource allocation" refers to the efficient and optimal distribution of available resources to multiple learning devices.

[0770] "Task assignment" is the process of appropriately distributing specific tasks to each learning device.

[0771] A "user" is an entity that directly or indirectly operates an information processing system and utilizes its functions.

[0772] "Emotional state" refers to the user's mental and psychological state, and is recognized in real time by the emotion engine.

[0773] "Dynamically adjusting the environment" means automatically changing the state of home appliances, lighting, automated devices, etc., based on the user's emotional state.

[0774] "Real-time monitoring" refers to the process of immediately checking the progress and status of work and taking necessary actions quickly.

[0775] The system for realizing this invention is centered around a server. The server centrally manages multiple learning devices and has the function of monitoring the progress of each device in real time. The terminal recognizes the user's emotional state using an emotion engine and transmits this data to the server. Based on the recognized emotional state, the server dynamically optimizes resource allocation and task assignment to each learning device.

[0776] Emotion recognition utilizes emotion engine software installed on the device, which analyzes the user's facial expressions and voice data. The analysis results are fed back in real time through data processing by a generative AI model, and the server adjusts the environment according to the user's emotional state. This process is used to create a home environment that provides enjoyment and comfort.

[0777] For example, if the system detects that a user is experiencing stress, the server automatically adjusts the lighting via the terminal and streams relaxing music. This allows the user to experience a relaxing environment.

[0778] Furthermore, as an example of a prompt message to the generative AI model to further enhance the effectiveness of this system, we can suggest: "Improve the following program and add features to further increase user satisfaction. How should the lighting settings be adjusted when the user is relaxed?"

[0779] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0780] Step 1:

[0781] The server receives facial expressions and voice data from the user's device. The input is emotion parameters in raw data format, and the output becomes input to the emotion engine. This data is acquired through the user's camera and microphone. The server converts the data to an appropriate format and prepares it for analysis by the emotion engine.

[0782] Step 2:

[0783] The device analyzes the received data using an emotion engine to recognize the user's emotional state. The input here consists of emotion parameters processed by the server from step 1. The output is data representing a specific emotional state, returned to the server in the form of "stress" or "relaxed," for example. This analysis is performed by an AI model, achieving emotion recognition through multi-dimensional data computation.

[0784] Step 3:

[0785] The server dynamically adjusts resource allocation and task assignments to each learning device based on emotion recognition results. The input is emotion state data from the terminal. The output is the adjusted task schedule, which is delivered to the learning devices. Specifically, if the emotion is determined to be "stress," the server adjusts resource priorities and configures settings to reduce the workload.

[0786] Step 4:

[0787] The server initiates appropriate environmental adjustment tasks via the terminal. The input is the task schedule generated in step 3. The output is the operation commands for home appliances and environmental adjustment devices. Specific examples include setting the lighting to relaxation mode and playing a specific playlist through a music service. The server sends this to the home system, which then adjusts the environment.

[0788] Step 5:

[0789] The server monitors all processes and stores user feedback in a database. Inputs include emotional states and device behavior. Outputs are statistical data for improving the user experience. This data will be used for future emotion recognition and system optimization. Specifically, it will also help optimize the prompts provided to the generative AI model.

[0790] 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.

[0791] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0792] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0793] 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.

[0794] Figure 9 shows an 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.

[0795] 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.

[0796] 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.

[0797] 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, motorcycles, etc., 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, for example, based 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.

[0798] 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."

[0799] 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.

[0800] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0801] 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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 the like 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.

[0810] 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.

[0811] The following is further disclosed regarding the embodiments described above.

[0812] (Claim 1)

[0813] The information processing device provides a means for centrally managing multiple artificial intelligence units and monitoring the progress of these tasks in real time.

[0814] A means for automatically optimizing resource allocation and task assignment in each artificial intelligence unit,

[0815] A means of visually displaying the progress of a task to the user,

[0816] A means of using generation processing to share and link information between artificial intelligence units,

[0817] A means for evaluating the performance of each unit and generating feedback for efficiency improvements,

[0818] A system that includes means for automatically generating reports and providing them to users in a visually analyzable format.

[0819] (Claim 2)

[0820] The system according to claim 1, wherein the information processing device includes means for receiving instructions from a user using natural language processing technology and transmitting them to an artificial intelligence unit.

[0821] (Claim 3)

[0822] The system according to claim 1, wherein the information processing device includes means for collecting execution logs of each artificial intelligence unit and performing a detailed performance analysis based on these logs.

[0823] "Example 1"

[0824] (Claim 1)

[0825] The information processing device centrally manages multiple intelligent processing units and provides means for monitoring these work processes in real time.

[0826] A means for automatically optimizing resource allocation and task assignment in each intelligent processing unit,

[0827] A means of visually displaying the progress of the work to the user,

[0828] A means of using generation processing to share and coordinate information between intelligent processing units,

[0829] A means for evaluating the operational performance of each unit and generating improvement measures for operational efficiency,

[0830] A means of automatically generating reports and providing them in a format that allows users to visually analyze them,

[0831] A means by which a user inputs work information via a terminal and a processing unit receives it,

[0832] A means for calculating optimized work assignment results, taking into account the load status of each unit,

[0833] A means of generating improvement suggestions based on work results and notifying users,

[0834] A system that includes this.

[0835] (Claim 2)

[0836] The system according to claim 1, comprising means for receiving instructions from a user using natural language processing technology and transmitting them to an intelligent processing unit.

[0837] (Claim 3)

[0838] The system according to claim 1, comprising means for collecting execution records of each intelligent processing unit and performing a detailed business performance analysis based thereon.

[0839] "Application Example 1"

[0840] (Claim 1)

[0841] The information processing device provides a means for centrally managing multiple intelligent devices and immediately monitoring the progress of these operations.

[0842] A means for automatically optimizing resource allocation and task assignment in each intelligent device,

[0843] A means of visually displaying the progress of the work to the user,

[0844] A means of using generation processing to share and link information between intelligent devices,

[0845] A means for evaluating the performance of each device and generating suggestions for improving efficiency,

[0846] A means of automatically generating reports and providing them in a format that allows users to visually analyze them,

[0847] To streamline inventory management and receiving / shipping operations in logistics facilities, a means of providing a visually integrated operation screen on a mobile device is provided.

[0848] A system that includes this.

[0849] (Claim 2)

[0850] The system according to claim 1, comprising means for receiving instructions from a user using natural language processing technology and transmitting them to an intelligent device.

[0851] (Claim 3)

[0852] The system according to claim 1, comprising means for collecting execution records of each intelligent device and performing a detailed performance analysis based on these records.

[0853] "Example 2 of combining an emotion engine"

[0854] (Claim 1)

[0855] The information processing device centrally manages multiple intelligent units and provides a means to monitor the progress of these tasks in real time.

[0856] A means for automatically optimizing resource allocation and task assignment in each intelligent unit,

[0857] A means of recognizing the user's emotions using an emotion analysis device connected to the terminal, and dynamically adjusting task assignments using that emotion data,

[0858] A means to record the user's emotional state and customize feedback based on this information,

[0859] A means for evaluating the performance of each unit and generating feedback for efficiency improvements,

[0860] A means of generating and processing information using an artificial intelligence model and sharing and coordinating information among intelligent units,

[0861] A system that includes means for automatically generating reports and providing them to users in a visually analyzable format.

[0862] (Claim 2)

[0863] The system according to claim 1, comprising means for receiving instructions from a user using natural language processing technology and transmitting them to an intelligent unit.

[0864] (Claim 3)

[0865] The system according to claim 1, comprising means for collecting the execution history of each intelligent unit and performing a detailed performance analysis based on these.

[0866] "Application example 2 when combining with an emotional engine"

[0867] (Claim 1)

[0868] The information processing system provides a means for centrally managing multiple learning devices and monitoring the progress of these operations in real time.

[0869] A means for automatically optimizing resource allocation and task assignment in each learning device,

[0870] A means of visually displaying the progress of the work to the user,

[0871] A means of using generation processing to share and link information between learning devices,

[0872] A means for evaluating the operation of each device and generating suggestions for improving efficiency,

[0873] A means of automatically generating reports and providing them in a format that allows users to visually analyze them,

[0874] Means for recognizing the emotional state of the user,

[0875] A system that includes means for dynamically adjusting the environment based on emotional states.

[0876] (Claim 2)

[0877] The system according to claim 1, comprising means for receiving instructions from a user using natural language processing technology and transmitting them to a learning device.

[0878] (Claim 3)

[0879] The system according to claim 1, further comprising means for collecting the execution history of each learning device and performing a detailed operation analysis based thereon. [Explanation of symbols]

[0880] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. The information processing device provides a means for centrally managing multiple intelligent devices and immediately monitoring the progress of these operations. A means for automatically optimizing resource allocation and task assignment in each intelligent device, A means of visually displaying the progress of the work to the user, A means of using generation processing to share and link information between intelligent devices, A means for evaluating the performance of each device and generating suggestions for improving efficiency, A means of automatically generating reports and providing them in a format that allows users to visually analyze them, To streamline inventory management and receiving / shipping operations in logistics facilities, a means of providing a visually integrated operation screen on a mobile device is provided. A system that includes this.

2. The system according to claim 1, comprising means for receiving instructions from a user using natural language processing technology and transmitting them to an intelligent device.

3. The system according to claim 1, further comprising means for collecting execution records of each intelligent device and performing a detailed performance analysis based on these records.

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