system

Abstract

We provide the system. [Solution] A portal platform for managing multiple artificial intelligence agents, A means of receiving requests from users to use an artificial intelligence agent, Means for registering and managing information about the aforementioned artificial intelligence agent in a database, A means of searching for an artificial intelligence agent suitable for a specific function requested by the user, A system that includes means for coordinating information between selected artificial intelligence agents to perform processing.

Description

Technical Field

[0005] ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The present invention aims to solve the problem that it is not easy for a user to select agents that meet specific needs and efficiently cooperate them among various artificial intelligence agents. In addition, since each agent has different protocols and interfaces, there is also a problem that it is difficult to seamlessly integrate and link them. Furthermore, it is an object of the present invention to solve the problem that it is technically difficult to create and execute a custom workflow in the use of agents and it is inconvenient for general users.

Means for Solving the Problems

[0005] This invention provides a portal platform for managing multiple artificial intelligence agents, allowing users to easily search for and select the desired AI agent, and to link information between those agents. The platform features a smart search function to enable users to quickly find agents suitable for specific functions. Furthermore, the system has a means for linking information between selected agents and executing processes, enabling seamless integration and coordination even for agents with different protocols. In addition, it provides a means for users to easily build and execute custom workflows, lowering technical barriers and making agents practically usable by general users.

[0006] An "artificial intelligence agent" is a software program designed to provide specific functions or services, and it operates autonomously using artificial intelligence technology.

[0007] A "portal platform" is an integrated online environment that centrally manages multiple systems and services, allowing users to access, search for, and utilize them.

[0008] A "user" is an end-user who uses a system or service, and may include individuals or corporations.

[0009] A "smart search function" is a search system that quickly provides highly relevant results based on the entered keywords, enabling users to efficiently find the information and services they are looking for.

[0010] A "custom workflow" is a unique processing flow created by a user by combining multiple services and functions according to their own purpose and needs, and is intended to automate business processes and tasks. [Brief explanation of the drawing]

[0011] [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] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, when an emotion engine is combined. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] 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.

[0013] First, the terms used in the following description will be explained.

[0014] In the following embodiments, a 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), and the like.

[0015] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0017] 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).

[0018] 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."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] 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.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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".

[0032] The AI ​​agent portal platform of this invention is designed to manage a large number of artificial intelligence agents via the internet, enabling users to effectively utilize them. This allows users to easily find the optimal agent to meet their needs and even automate complex tasks by coordinating them.

[0033] System Configuration

[0034] User Interface: Operates on the device and provides a visual operating environment for users to register agents, search, configure integrations, and create workflows.

[0035] Server Infrastructure: Servers are responsible for key backend processes, including processing user requests, managing agent databases, executing search algorithms, and managing inter-agent communication.

[0036] Database: Holds information on all registered artificial intelligence agents, enabling efficient searching and management.

[0037] System operation

[0038] Agent Registration

[0039] When a user wants to add a new artificial intelligence agent to the portal, they use the registration interface to provide information such as the agent name, function overview, and API specifications.

[0040] Upon receiving this information, the server performs validation and then saves the agent information to the database.

[0041] Agent search and selection

[0042] When a user searches for an artificial intelligence agent to assist with a specific task, they enter relevant keywords on the portal site.

[0043] The server receives that information, searches the database for relevant agents, and returns the results to the user as a list.

[0044] The device displays this list to help the user select the most suitable agent.

[0045] Agent-to-agent collaboration

[0046] If the user wishes to coordinate multiple agents for a specific process, they can specify this.

[0047] The server configures communication between agents selected by the user, ensuring a seamless flow of data.

[0048] Create a custom workflow

[0049] Users design custom workflows tailored to their business processes, deploy the necessary agents, and configure the execution order and conditions.

[0050] The server receives this information and schedules the execution of automated tasks based on the designed flow.

[0051] The server monitors the progress and provides feedback to the user as needed.

[0052] In this way, the present invention enables users to utilize a variety of agents without hassle, thereby realizing an efficient work environment tailored to their individual needs.

[0053] The following describes the processing flow.

[0054] Step 1:

[0055] The user enters the required information into the registration form for the artificial intelligence agent through the portal site interface and presses the submit button.

[0056] Step 2:

[0057] The server receives an agent registration request from the user and performs initial validation, such as checking the format of the input information and required fields.

[0058] Step 3:

[0059] The server saves detailed agent information to the database for successful validations. This data includes the agent name, functions, and APIs provided.

[0060] Step 4:

[0061] The user enters keywords into the search bar on the portal site and performs an agent search.

[0062] Step 5:

[0063] The server receives keywords entered by the user and executes an algorithm to search the database for highly relevant artificial intelligence agents.

[0064] Step 6:

[0065] The server sends the search results to the user's terminal and provides a list of related agents.

[0066] Step 7:

[0067] The device displays search results on the screen, allowing the user to select a specific agent.

[0068] Step 8:

[0069] The user selects the agent to use and operates an interface to configure the coordination between agents.

[0070] Step 9:

[0071] The server receives the coordination settings between the selected agents and performs processing to configure data exchange and communication protocols between each agent.

[0072] Step 10:

[0073] Users use an interface to create custom workflows, arranging the order and conditions between agents and saving the workflow.

[0074] Step 11:

[0075] The server receives workflow information, saves it to the database, and prepares it for subsequent process management and execution.

[0076] Step 12:

[0077] The server initiates saved workflows based on the user's specified schedule and automates process execution through inter-agent coordination.

[0078] Step 13:

[0079] The server monitors the execution progress and provides real-time feedback and notifications to the user if necessary.

[0080] (Example 1)

[0081] 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."

[0082] In recent years, automated systems using intelligent agents have been increasing, but effectively managing these systems and achieving smooth coordination among multiple agents remains a challenging task. Furthermore, there is a need for agent combinations tailored to individual business workflows and efficient performance monitoring. To address these challenges, it is necessary to provide a platform that facilitates the management and coordination of intelligent agents and allows for customization to meet user needs.

[0083] 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.

[0084] In this invention, the server includes means for managing multiple intelligent agents, means for receiving usage requests from users, and means for registering and organizing information about the intelligent agents in a memory area. This enables effective management of intelligent agents and the realization of user-friendly automated processes.

[0085] An "intelligent agent" is a program designed to perform user instructions or automated tasks, and is software that has the ability to process information and output results under specific conditions.

[0086] A "network platform" is a system that provides a foundation for multiple intelligent agents and users to connect and exchange information.

[0087] A "usage request" refers to an instruction or request sent by a user to utilize the functions of a specific intelligent agent.

[0088] "Memory space" refers to a physical or virtual space used to store data and programs, and is a place where information is registered and organized.

[0089] "Exploration" is the process of searching a database to find information or intelligent agents that match the user's needs and then presenting the results.

[0090] A "generative knowledge model" is an algorithm or system that understands user requests in natural language and generates appropriate instructions based on that understanding.

[0091] "Means for creating and executing command statements" refers to a system that generates specific steps or commands based on the user's intent and then executes them.

[0092] "Providing information back" means clearly communicating the monitoring results to the user and providing feedback to evaluate and improve the agent's effectiveness.

[0093] This invention provides a network platform for efficiently managing and coordinating intelligent agents. The main components of the platform are a user interface, server infrastructure, and a database.

[0094] The user interface operates on the terminal and provides a visual operating environment that enables users to register, search for, configure integrations with, and create workflows for intelligent agents. Specifically, users use the interface to input agent names and function summaries to register new agents.

[0095] The server infrastructure receives user requests and performs the primary processing of retrieving relevant agent information from the database. As a backend, the server manages seamless communication between agents and parses and generates prompt messages using a generative AI model. The server incorporates validation functions to ensure data integrity, enabling accurate information management.

[0096] The database holds information on all registered intelligent agents and serves as the foundation for efficient searching. It contains each agent's functions, API specifications, and historical performance data, designed to allow users to quickly select the optimal agent.

[0097] For example, if a user wants to automate their marketing campaigns, they can use the platform to register an "email sending agent" and a "data analysis agent" and configure their integration. This integration allows for analysis of the effectiveness of emails sent and provides the results to the user as feedback.

[0098] As an example of a prompt, the AI ​​model can be programmed with the instruction, "Find and list the email sending agents to be used in the AI ​​agent portal," to retrieve a list of relevant agents. In this way, users can easily find agents with the necessary functions and streamline their work.

[0099] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0100] Step 1:

[0101] The user accesses the agent registration interface via a terminal. The user enters the agent name, function overview, and API specifications. The server receives the input data and performs validation, checking the data format and required fields. As a result, the verified information is output and stored in the storage area.

[0102] Step 2:

[0103] The user logs into a portal site via their device and searches for intelligent agents related to their task. The user enters relevant keywords, and the server queries the database using those keywords. The server calculates the relevance between the keywords and agent information, and outputs a list of highly relevant agent information as a result. The device receives the results sent from the server and displays them on the user interface.

[0104] Step 3:

[0105] The user configures communication between multiple agents using a terminal. The user specifies the agents they want to communicate with and the method of communication. The server configures the communication settings between the specified agents and adjusts the protocol and data format. This results in an environment where agents can smoothly exchange information.

[0106] Step 4:

[0107] Users design custom workflows based on their business processes on their terminals. They drag and drop agents to place them and set their execution order and conditions. The server receives this information, applies a scheduling algorithm, and outputs an efficient task execution order. Progress is monitored, and real-time feedback is provided to the user as needed.

[0108] (Application Example 1)

[0109] 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."

[0110] This invention aims to improve the efficiency of production processes in factories. Conventionally, scheduling tasks and allocating resources on factory production lines has relied on human judgment, making optimization difficult. Furthermore, effectively coordinating multiple intelligent processing units and robots is required, but managing such coordination has been extremely labor-intensive. Solving these problems is needed to improve productivity and streamline factory operations.

[0111] 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.

[0112] In this invention, the server includes means for providing a management platform for managing intelligent processing units, means for receiving requests from users to use intelligent processing units, and means for coordinating information between selected intelligent processing units to execute processing. This enables efficient task allocation and dynamic resource adjustment within the factory.

[0113] An "intelligent processing unit" is an artificial intelligence agent that has the ability to process information and perform specific functions.

[0114] A "management platform" is a foundation that integrates and manages multiple intelligent processing units, enabling users to operate them efficiently.

[0115] A "usage request" is a request or demand made by a user to an intelligent processing unit in order to perform a specific function.

[0116] An "information storage device" is a database or storage system that collects and stores information related to an intelligent processing unit and can retrieve it as needed.

[0117] "Dynamic adjustment" refers to the adjustment activities that change the allocation of resources and tasks in a timely manner according to the situation in order to achieve optimal processing.

[0118] "Efficient task allocation" is a process for improving overall production efficiency by assigning each task to the most suitable unit on a production line.

[0119] The system that realizes this invention is designed to maximize production efficiency in a factory by coordinating various intelligent processing units. The system configuration includes a management platform, main unit, peripherals, and a network.

[0120] The server uses a Python®-based program to build a management platform and provides an API using Flask to enable communication between intelligent processing units. This program uses information about intelligent processing units stored in a MySQL® database to search for the optimal unit and allocate tasks according to usage requests. Furthermore, a machine learning algorithm utilizing TENSORFLOW® analyzes the performance of intelligent processing units in real time, enabling efficient task scheduling and dynamic resource adjustment.

[0121] The terminal provides a user interface, enabling access from smartphones and tablets. This allows users to easily view the intelligent processing unit list and create and execute custom work procedures.

[0122] As a concrete example, in a certain factory, various types of products are assembled, and different robots operate depending on the product. By introducing this system, it is possible to improve production performance by monitoring the operating status of each robot in real time and, if necessary, reallocating tasks based on priorities.

[0123] An example of a prompt for a generative AI model is, "Based on the latest production data, please tell me the recommended intelligent processing unit configuration to improve the efficiency of the manufacturing process."

[0124] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0125] Step 1:

[0126] Users access the management platform using a terminal and input requests for the use of intelligent processing units. The input data includes the specific functions requested and related parameters.

[0127] Step 2:

[0128] The server receives a request, connects to the MySQL database, and searches for the most suitable intelligent processing unit for the user's request. The data retrieved from the database includes functional information and operating status for each unit. The server returns the search results to the terminal in list format.

[0129] Step 3:

[0130] The user views a list of intelligent processing units provided by the server on the terminal screen, selects multiple units as needed, and specifies the collaborative work procedure. The selected units are configured based on the specified order and conditions.

[0131] Step 4:

[0132] The server receives user selection information and uses TensorFlow to analyze the performance of each intelligent processing unit in real time. Historical performance data and current operational status are used as input for the analysis, and a scheduling optimization algorithm is executed to efficiently allocate tasks and adjust resources.

[0133] Step 5:

[0134] The server configures inter-unit coordination based on the analysis results and automatically assigns and executes tasks corresponding to each intelligent processing unit. The progress of the assigned tasks is monitored sequentially.

[0135] Step 6:

[0136] After execution is complete, the server saves the acquired performance data back to the MySQL database and provides the user with the results and analysis report. The user receives the results on their terminal and provides feedback or input for the next task as needed.

[0137] 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.

[0138] This invention aims to significantly improve the user experience by combining an emotion engine that recognizes user emotions with a portal platform that manages and coordinates artificial intelligence agents. This platform provides a user-accessible interface and has means for processing user requests. The emotion engine analyzes various input data, such as the user's voice, text, or facial expressions, to understand the user's emotional state.

[0139] System Configuration

[0140] User Interface: The interface displayed on the device allows for agent search, selection, integration configuration, and provision of sentiment data.

[0141] Server Infrastructure: Servers play a crucial role in managing the agent database, performing data analysis using the sentiment engine, and providing sentiment-based agent recommendations.

[0142] Database: Along with information about the artificial intelligence agent, it stores the user's emotional history and agent usage history, enabling personalized responses.

[0143] Emotion Engine: This is the core module for analyzing user emotions in real time based on diverse data inputs.

[0144] System operation

[0145] Emotion recognition and analysis

[0146] When a user inputs emotion-related data, for example, if it's voice data, the data is provided to the emotion engine via the device's microphone.

[0147] The server receives this data, activates the emotion engine, and identifies the user's emotions from the tone of voice, expressions in the text, or facial expression data.

[0148] Agent recommendation

[0149] The server searches the database for the most suitable artificial intelligence agent based on the identified user's emotional state and recommends it to the user.

[0150] Adjusting custom workflows

[0151] If the user is using the agent according to a previously configured custom workflow, the server will automatically adjust the flow conditions and actions based on the sentiment recognition results.

[0152] Providing user feedback

[0153] To evaluate how well the processes performed and services provided meet user expectations, the server uses sentiment analysis results to collect and store feedback.

[0154] The user experience can be optimized by providing recommendations for the following actions.

[0155] For example, if a user is showing signs of stress, the system can prioritize recommending agents that help with relaxation and guide the user to easily access them. In this way, it is possible to provide flexible responses tailored to the user's state and ensure the optimal use of agents.

[0156] The following describes the processing flow.

[0157] Step 1:

[0158] The user opens a portal site on their device and selects an option for inputting emotional data. For example, they might use their device's microphone to provide audio data.

[0159] Step 2:

[0160] The server transfers emotional data (voice, text, or facial expressions) received from the terminal to the emotion engine. The data is intended to be processed in real time.

[0161] Step 3:

[0162] The emotion engine analyzes data to identify the user's emotional state through voice tone, keywords in text, and facial expression analysis. For example, it can detect stress levels or relaxation levels from voice tone.

[0163] Step 4:

[0164] The server receives the analysis results from the emotion engine, searches the database, and identifies the AI ​​agent best suited to the user's current emotional state. For example, if the user is stressed, a stress reduction agent will be recommended.

[0165] Step 5:

[0166] Based on the emotions detected by the server, the server sends a request to the user's device recommending an appropriate agent. The user can then select the agent they need based on this information.

[0167] Step 6:

[0168] Based on the agent selected by the user, the terminal provides an operating interface for that agent. The user can then perform specific functions within this interface.

[0169] Step 7:

[0170] If a user has previously configured a custom workflow, the server will adjust the workflow content based on the sentiment analysis results and prepare it for automated execution. For example, it may change which agents to prioritize or what actions to take based on sentiment.

[0171] Step 8:

[0172] The server monitors the results of the agents or workflows that have been executed, and evaluates their performance and user response in real time.

[0173] Step 9:

[0174] Based on the data collected by the server and the sentiment analysis results, the system provides feedback to the user on their device regarding the next steps and improvement suggestions. It also offers recommendations to optimize the user experience for future use.

[0175] (Example 2)

[0176] 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 will be referred to as the "terminal."

[0177] While many artificial intelligence systems are currently available, methods for effectively utilizing them are not yet fully developed. Furthermore, there is a lack of means to select the appropriate AI system considering the user's emotional state. As a result, providing users with the best possible experience is difficult.

[0178] 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.

[0179] In this invention, the server includes data processing means for analyzing the user's emotional state, means for recommending artificial intelligence based on the emotional state, and means for providing a portal for managing multiple information processing devices. This enables the selection and use of the optimal artificial intelligence in accordance with the user's emotions.

[0180] "Data processing means for analyzing a user's emotional state" refers to a means that analyzes voice, text, and facial expression data input by the user and provides a function to determine that emotion in real time.

[0181] A "means for recommending artificial intelligence" refers to a method that selects the most suitable artificial intelligence based on the analyzed emotional state of the user and presents it to the user.

[0182] "Means for providing a portal for managing information processing devices" refers to means for centrally managing multiple information processing devices and providing an interface that enables users to efficiently utilize these devices.

[0183] An "information storage device" is a device for storing and managing information, and serves as a foundation for registering information related to artificial intelligence and user usage history.

[0184] "Means for creating and executing custom processing procedures" refers to means that have the function of combining multiple information processing devices to construct and execute a process that proceeds according to the sequence and conditions of those devices in order to perform a specific process requested by the user.

[0185] "Means for monitoring and evaluating performance and providing information" refers to means that have the function of providing feedback to the user by observing the operation of the information processing device and measuring its effects.

[0186] This invention provides a platform for users to interact smoothly with information processing devices. The following describes specific embodiments of this invention.

[0187] First, the device collects data related to the user's emotions through input devices such as microphones, cameras, and keyboards. This data includes voice, text, and facial expression information.

[0188] Next, the device uses internet communication to send the collected data to the server.

[0189] The server that receives the data performs data analysis using an emotion engine. The emotion engine determines the user's emotional state by, for example, analyzing voice tone, analyzing text using natural language processing, or recognizing facial expressions.

[0190] Based on the analysis results, the server searches for and selects the appropriate artificial intelligence from the database. This selection of information processing device allows for the recommendation of the agent most effective in the user's current emotional state.

[0191] Furthermore, the server manages custom workflows that combine multiple information processing devices, helping users achieve their goals flexibly and efficiently. These workflows are configured and executed automatically according to the order and conditions specified by the user.

[0192] For example, if a user is showing signs of stress, an agent that provides relaxation music might be recommended as an information processing device. This selection is important for providing the optimal experience tailored to the user's emotional state.

[0193] An example of a prompt to the generative AI model is, "Please tell me what agent would be helpful when I'm feeling stressed." Based on this prompt, the server searches for and recommends an appropriate information processing device.

[0194] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0195] Step 1:

[0196] The user inputs emotion-related data into the device. This includes inputting voice data using the device's microphone, inputting text data from the keyboard, or acquiring facial expression data through the camera. The input in this step is voice, text, and facial expression information from the user, and the output is that the device internally stores this data.

[0197] Step 2:

[0198] The device sends the emotion data acquired in Step 1 to the server. This transmission takes place in real time via internet communication. The input is the data collected in Step 1, and the output is the data sent to the server.

[0199] Step 3:

[0200] The server passes the data received from the terminal to the emotion engine. The emotion engine analyzes the input data to identify the user's emotional state. This analysis includes, for example, analyzing the intonation of sounds in audio data, natural language processing of text data, and recognition of facial expressions. The input is the emotional data sent to the server, and the output is information about the identified emotional state.

[0201] Step 4:

[0202] The server searches a database for the appropriate artificial intelligence based on the analyzed emotional state. The input is information about the emotional state, and the output is a list of selected artificial intelligences. From this list, the server selects the agent best suited to the user and generates a recommendation.

[0203] Step 5:

[0204] The server adjusts the selected artificial intelligence agent based on the user's custom workflow. This adjustment includes automatically modifying the workflow's operating conditions and sequence based on the user's emotional state. The input is the user's workflow settings and emotion analysis results, and the output is the details of the adjusted workflow.

[0205] Step 6:

[0206] The server provides feedback to the user based on the workflow results adjusted in Step 5 and the agent's performance information. The feedback includes recommendations and suggestions for improvement to help increase user satisfaction. The input is the adjusted workflow outcomes and agent evaluation data, and the output is the feedback and recommendations for the next steps provided to the user.

[0207] (Application Example 2)

[0208] 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 device 14 will be referred to as the "terminal."

[0209] In recent years, systems utilizing artificial intelligence agents have been used in various fields, but providing personalized responses that respond to user emotions remains challenging. Furthermore, systems capable of accurately identifying user emotions and recommending the optimal AI agent or dynamically adjusting custom workflows based on those emotions are not yet sufficiently developed. Therefore, there is a need for efficient and flexible interfaces and processes to improve the user experience.

[0210] 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.

[0211] In this invention, the server includes means for providing an interface incorporating an engine for analyzing user emotions, means for receiving requests from users to use artificial intelligence agents, and means for coordinating information among selected artificial intelligence agents to perform processing and adjust responses based on the user's emotions. This makes it possible to build a flexible system that analyzes user emotions in real time, recommends the most suitable artificial intelligence agent based on that analysis, and enables personalized responses.

[0212] The "user emotion analysis engine" is a module that identifies the user's emotional state in real time from various data such as voice, text, and facial expressions.

[0213] An "interface" is a point of contact, whether visual or auditory, that a user directly interacts with to search for or request the use of an artificial intelligence agent.

[0214] An "artificial intelligence agent" is an autonomous software entity programmed to provide specific functions or services.

[0215] A "server" is a central computing device responsible for receiving, processing, and managing data.

[0216] A "database" is a data management system for systematically storing information about artificial intelligence agents and user sentiment history.

[0217] A "custom workflow" is a set of procedures and steps used to automate a specific process according to the user's needs.

[0218] "Monitoring and evaluation" is the process of observing the behavior and performance of registered artificial intelligence agents and evaluating their quality and effectiveness.

[0219] "Feedback" refers to information collected from users regarding their evaluations and reactions to services and suggestions they have received.

[0220] This invention is a system that utilizes artificial intelligence agents to recommend the most suitable agent according to the user's emotions, thereby aiming to improve the user experience. This system mainly consists of the following components.

[0221] First, a user interface is displayed on the client terminal. This interface is accessible to the user in a graphical or voice-based format and serves as the user's tool for searching for artificial intelligence agents and inputting emotional data. This interface is also connected to an engine that analyzes emotions and collects the user's voice and text data in real time.

[0222] The server performs sentiment analysis based on data received from the user interface. This process uses a sentiment analysis engine to analyze voice and text data to identify the user's emotional state. This engine identifies emotions using voice analysis libraries (e.g., Librosa, PyDub) and machine learning libraries (e.g., TensorFlow, Keras).

[0223] Subsequently, the server searches its database for the most suitable artificial intelligence agent based on the sentiment analysis results and recommends it to the user. In this process, an agent tailored to the user's emotional state is suggested, providing the best possible support for the user's needs.

[0224] For example, if a user shows signs of stress while working at their desk, an agent providing relaxation music or a stress management program could be suggested as a tool.

[0225] By utilizing a generative AI model, the sentiment analysis engine assists in selecting the optimal agent using prompt text. For example, a prompt such as "Identify the emotion from the user's tone of voice and select the appropriate robot response" might be used. This prompt provides specific user context as input to the generative AI model, serving as a guide for optimizing the response.

[0226] The implementation of this system will enable the provision of a more deeply personalized user experience, and is therefore expected to have applications in various fields.

[0227] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0228] Step 1:

[0229] The terminal displays an interface for user access. Here, the user can provide voice commands or input text data. The input data is sent directly to the server, providing data for sentiment analysis.

[0230] Step 2:

[0231] The server passes the user's voice and text data received from the terminal to the sentiment analysis engine. In this step, the voice data is analyzed for tone and pitch using a speech analysis library, and the text data is evaluated using a natural language processing library. The output identifies the user's current emotional state.

[0232] Step 3:

[0233] The server searches the database for the most suitable artificial intelligence agent based on the emotional state generated by the emotion analysis engine. Using a generative AI model, it creates prompt statements to recommend appropriate agents based on the emotions, and uses these as search queries. The output is a list of candidate agents.

[0234] Step 4:

[0235] The server selects the most suitable agent from the generated list of candidate agents and sends the recommended information to the user's terminal. This process prioritizes providing services that resonate with the user's emotions, ensuring that the recommended agent provides specific programs or services. The agent information is then displayed on the terminal as output.

[0236] Step 5:

[0237] The user reviews the recommended agent displayed on the device and utilizes the selected agent as needed. The device then launches the user's selected agent and starts the functions it provides. This executes the services of the chosen agent.

[0238] Step 6:

[0239] The server monitors the performance of executed agents and user feedback, evaluating them along with sentiment data. The evaluation results are stored in a database and used for future agent recommendations. The output includes updated feedback data regarding the user's service experience.

[0240] 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.

[0241] 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.

[0242] 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.

[0243] [Second Embodiment]

[0244] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0245] 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.

[0246] 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).

[0247] 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.

[0248] 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.

[0249] 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).

[0250] 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.

[0251] 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.

[0252] 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.

[0253] 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.

[0254] 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.

[0255] 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".

[0256] The AI ​​agent portal platform of this invention is designed to manage a large number of artificial intelligence agents via the internet, enabling users to effectively utilize them. This allows users to easily find the optimal agent to meet their needs and even automate complex tasks by coordinating them.

[0257] System Configuration

[0258] User Interface: Operates on the device and provides a visual operating environment for users to register agents, search, configure integrations, and create workflows.

[0259] Server Infrastructure: Servers are responsible for key backend processes, including processing user requests, managing agent databases, executing search algorithms, and managing inter-agent communication.

[0260] Database: Holds information on all registered artificial intelligence agents, enabling efficient searching and management.

[0261] System operation

[0262] Agent Registration

[0263] When a user wants to add a new artificial intelligence agent to the portal, they use the registration interface to provide information such as the agent name, function overview, and API specifications.

[0264] Upon receiving this information, the server performs validation and then saves the agent information to the database.

[0265] Agent search and selection

[0266] When a user searches for an artificial intelligence agent to assist with a specific task, they enter relevant keywords on the portal site.

[0267] The server receives that information, searches the database for relevant agents, and returns the results to the user as a list.

[0268] The device displays this list to help the user select the most suitable agent.

[0269] Agent-to-agent collaboration

[0270] If the user wishes to coordinate multiple agents for a specific process, they can specify this.

[0271] The server configures communication between agents selected by the user, ensuring a seamless flow of data.

[0272] Create a custom workflow

[0273] Users design custom workflows tailored to their business processes, deploy the necessary agents, and configure the execution order and conditions.

[0274] The server receives this information and schedules the execution of automated tasks based on the designed flow.

[0275] The server monitors the progress and provides feedback to the user as needed.

[0276] In this way, the present invention enables users to utilize a variety of agents without hassle, thereby realizing an efficient work environment tailored to their individual needs.

[0277] The following describes the processing flow.

[0278] Step 1:

[0279] The user enters the required information into the registration form for the artificial intelligence agent through the portal site interface and presses the submit button.

[0280] Step 2:

[0281] The server receives an agent registration request from the user and performs initial validation, such as checking the format of the input information and required fields.

[0282] Step 3:

[0283] For the information on which the server has successfully validated, the agent's detailed information is saved in the database. This data includes the agent name, function, provided APIs, etc.

[0284] Step 4:

[0285] The user enters keywords in the search bar on the portal site and executes an agent search.

[0286] Step 5:

[0287] The server receives the keywords entered by the user and executes an algorithm to search for relevant artificial intelligence agents from the database.

[0288] Step 6:

[0289] The server sends the search results to the user's terminal and provides a list of relevant agents.

[0290] Step 7:

[0291] The terminal displays the search results on the screen, enabling the user to select a specific agent.

[0292] Step 8:

[0293] The user selects the agent to use and operates an interface for setting up cooperation between agents.

[0294] Step 9:

[0295] The server receives the cooperation settings between the selected agents and performs processing to set up data exchange and communication protocols between each agent.

[0296] Step 10:

[0297] The user uses an interface for creating a custom workflow, arranges the order and conditions between agents, and saves the workflow.

[0298] Step 11:

[0299] The server receives the workflow information, saves it in the database, and prepares for subsequent process management and execution.

[0300] Step 12:

[0301] Based on the schedule specified by the user, the server starts the saved workflow and automatically executes the process through agent cooperation.

[0302] Step 13:

[0303] The server monitors the progress of the execution and provides real-time feedback and notifications to the user if necessary.

[0304] (Example 1)

[0305] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0306] In recent years, the number of automated systems using intelligent agents has been increasing. However, it is a difficult task to effectively manage these systems and achieve smooth cooperation between multiple agents. In addition, combinations of agents according to individual business flows and efficient performance monitoring are also required. To solve these problems, it is necessary to provide a platform that facilitates the management and cooperation of intelligent agents and enables customization according to user needs.

[0307] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. ​ In this invention, the server includes means for managing multiple intelligent agents, means for receiving usage requests from users, and means for registering and organizing information about the intelligent agents in a memory area. This enables effective management of intelligent agents and the realization of user-friendly automated processes.

[0309] An "intelligent agent" is a program designed to perform user instructions or automated tasks, and is software that has the ability to process information and output results under specific conditions.

[0310] A "network platform" is a system that provides a foundation for multiple intelligent agents and users to connect and exchange information.

[0311] A "usage request" refers to an instruction or request sent by a user to utilize the functions of a specific intelligent agent.

[0312] "Memory space" refers to a physical or virtual space used to store data and programs, and is a place where information is registered and organized.

[0313] "Exploration" is the process of searching a database to find information or intelligent agents that match the user's needs and then presenting the results.

[0314] A "generative knowledge model" is an algorithm or system that understands user requests in natural language and generates appropriate instructions based on that understanding.

[0315] "Means for creating and executing command statements" refers to a system that generates specific steps or commands based on the user's intent and then executes them.

[0316] "Providing information back" means clearly communicating the monitoring results to the user and providing feedback to evaluate and improve the agent's effectiveness.

[0317] This invention provides a network platform for efficiently managing and coordinating intelligent agents. The main components of the platform are a user interface, server infrastructure, and a database.

[0318] The user interface operates on the terminal and provides a visual operating environment that enables users to register, search for, configure integrations with, and create workflows for intelligent agents. Specifically, users use the interface to input agent names and function summaries to register new agents.

[0319] The server infrastructure receives user requests and performs the primary processing of retrieving relevant agent information from the database. As a backend, the server manages seamless communication between agents and parses and generates prompt messages using a generative AI model. The server incorporates validation functions to ensure data integrity, enabling accurate information management.

[0320] The database holds information on all registered intelligent agents and serves as the foundation for efficient searching. It contains each agent's functions, API specifications, and historical performance data, designed to allow users to quickly select the optimal agent.

[0321] For example, if a user wants to automate their marketing campaigns, they can use the platform to register an "email sending agent" and a "data analysis agent" and configure their integration. This integration allows for analysis of the effectiveness of emails sent and provides the results to the user as feedback.

[0322] As an example of a prompt, the AI ​​model can be programmed with the instruction, "Find and list the email sending agents to be used in the AI ​​agent portal," to retrieve a list of relevant agents. In this way, users can easily find agents with the necessary functions and streamline their work.

[0323] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0324] Step 1:

[0325] The user accesses the agent registration interface via a terminal. The user enters the agent name, function overview, and API specifications. The server receives the input data and performs validation, checking the data format and required fields. As a result, the verified information is output and stored in the storage area.

[0326] Step 2:

[0327] The user logs into a portal site via their device and searches for intelligent agents related to their task. The user enters relevant keywords, and the server queries the database using those keywords. The server calculates the relevance between the keywords and agent information, and outputs a list of highly relevant agent information as a result. The device receives the results sent from the server and displays them on the user interface.

[0328] Step 3:

[0329] The user configures communication between multiple agents using a terminal. The user specifies the agents they want to communicate with and the method of communication. The server configures the communication settings between the specified agents and adjusts the protocol and data format. This results in an environment where agents can smoothly exchange information.

[0330] Step 4:

[0331] Users design custom workflows based on their business processes on their terminals. They drag and drop agents to place them and set their execution order and conditions. The server receives this information, applies a scheduling algorithm, and outputs an efficient task execution order. Progress is monitored, and real-time feedback is provided to the user as needed.

[0332] (Application Example 1)

[0333] 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."

[0334] This invention aims to improve the efficiency of production processes in factories. Conventionally, scheduling tasks and allocating resources on factory production lines has relied on human judgment, making optimization difficult. Furthermore, effectively coordinating multiple intelligent processing units and robots is required, but managing such coordination has been extremely labor-intensive. Solving these problems is needed to improve productivity and streamline factory operations.

[0335] 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.

[0336] In this invention, the server includes means for providing a management platform for managing intelligent processing units, means for receiving requests from users to use intelligent processing units, and means for coordinating information between selected intelligent processing units to execute processing. This enables efficient task allocation and dynamic resource adjustment within the factory.

[0337] An "intelligent processing unit" is an artificial intelligence agent that has the ability to process information and perform specific functions.

[0338] A "management platform" is a foundation that integrates and manages multiple intelligent processing units, enabling users to operate them efficiently.

[0339] A "usage request" is a request or demand made by a user to an intelligent processing unit in order to perform a specific function.

[0340] An "information storage device" is a database or storage system that collects and stores information related to an intelligent processing unit and can retrieve it as needed.

[0341] "Dynamic adjustment" refers to the adjustment activities that change the allocation of resources and tasks in a timely manner according to the situation in order to achieve optimal processing.

[0342] "Efficient task allocation" is a process for improving overall production efficiency by assigning each task to the most suitable unit on a production line.

[0343] The system that realizes this invention is designed to maximize production efficiency in a factory by coordinating various intelligent processing units. The system configuration includes a management platform, main unit, peripherals, and a network.

[0344] The server uses a Python-based program to build a management platform and provides an API using Flask to enable communication between intelligent processing units. This program uses information about intelligent processing units stored in a MySQL database to search for the optimal unit and allocate tasks according to usage requests. Furthermore, machine learning algorithms utilizing TensorFlow analyze the performance of intelligent processing units in real time, enabling efficient task scheduling and dynamic resource adjustment.

[0345] The terminal provides a user interface, enabling access from smartphones and tablets. This allows users to easily view the intelligent processing unit list and create and execute custom work procedures.

[0346] As a concrete example, in a certain factory, various types of products are assembled, and different robots operate depending on the product. By introducing this system, it is possible to improve production performance by monitoring the operating status of each robot in real time and, if necessary, reallocating tasks based on priorities.

[0347] An example of a prompt for a generative AI model is, "Based on the latest production data, please tell me the recommended intelligent processing unit configuration to improve the efficiency of the manufacturing process."

[0348] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0349] Step 1:

[0350] Users access the management platform using a terminal and input requests for the use of intelligent processing units. The input data includes the specific functions requested and related parameters.

[0351] Step 2:

[0352] The server receives a request, connects to the MySQL database, and searches for the most suitable intelligent processing unit for the user's request. The data retrieved from the database includes functional information and operating status for each unit. The server returns the search results to the terminal in list format.

[0353] Step 3:

[0354] The user views a list of intelligent processing units provided by the server on the terminal screen, selects multiple units as needed, and specifies the collaborative work procedure. The selected units are configured based on the specified order and conditions.

[0355] Step 4:

[0356] The server receives user selection information and uses TensorFlow to analyze the performance of each intelligent processing unit in real time. Historical performance data and current operational status are used as input for the analysis, and a scheduling optimization algorithm is executed to efficiently allocate tasks and adjust resources.

[0357] Step 5:

[0358] The server configures inter-unit coordination based on the analysis results and automatically assigns and executes tasks corresponding to each intelligent processing unit. The progress of the assigned tasks is monitored sequentially.

[0359] Step 6:

[0360] After execution is complete, the server saves the acquired performance data back to the MySQL database and provides the user with the results and analysis report. The user receives the results on their terminal and provides feedback or input for the next task as needed.

[0361] 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.

[0362] This invention aims to significantly improve the user experience by combining an emotion engine that recognizes user emotions with a portal platform that manages and coordinates artificial intelligence agents. This platform provides a user-accessible interface and has means for processing user requests. The emotion engine analyzes various input data, such as the user's voice, text, or facial expressions, to understand the user's emotional state.

[0363] System Configuration

[0364] User Interface: The interface displayed on the device allows for agent search, selection, integration configuration, and provision of sentiment data.

[0365] Server Infrastructure: Servers play a crucial role in managing the agent database, performing data analysis using the sentiment engine, and providing sentiment-based agent recommendations.

[0366] Database: Along with information about the artificial intelligence agent, it stores the user's emotional history and agent usage history, enabling personalized responses.

[0367] Emotion Engine: This is the core module for analyzing user emotions in real time based on diverse data inputs.

[0368] System operation

[0369] Emotion recognition and analysis

[0370] When a user inputs emotion-related data, for example, if it's voice data, the data is provided to the emotion engine via the device's microphone.

[0371] The server receives this data, activates the emotion engine, and identifies the user's emotions from the tone of voice, expressions in the text, or facial expression data.

[0372] Agent recommendation

[0373] The server searches the database for the most suitable artificial intelligence agent based on the identified user's emotional state and recommends it to the user.

[0374] Adjusting custom workflows

[0375] If the user is using the agent according to a previously configured custom workflow, the server will automatically adjust the flow conditions and actions based on the sentiment recognition results.

[0376] Providing user feedback

[0377] To evaluate how well the processes performed and services provided meet user expectations, the server uses sentiment analysis results to collect and store feedback.

[0378] The user experience can be optimized by providing recommendations for the following actions.

[0379] For example, if a user is showing signs of stress, the system can prioritize recommending agents that help with relaxation and guide the user to easily access them. In this way, it is possible to provide flexible responses tailored to the user's state and ensure the optimal use of agents.

[0380] The following describes the processing flow.

[0381] Step 1:

[0382] The user opens a portal site on their device and selects an option for inputting emotional data. For example, they might use their device's microphone to provide audio data.

[0383] Step 2:

[0384] The server transfers emotional data (voice, text, or facial expressions) received from the terminal to the emotion engine. The data is intended to be processed in real time.

[0385] Step 3:

[0386] The emotion engine analyzes data to identify the user's emotional state through voice tone, keywords in text, and facial expression analysis. For example, it can detect stress levels or relaxation levels from voice tone.

[0387] Step 4:

[0388] The server receives the analysis results from the emotion engine, searches the database, and identifies the AI ​​agent best suited to the user's current emotional state. For example, if the user is stressed, a stress reduction agent will be recommended.

[0389] Step 5:

[0390] Based on the emotions detected by the server, the server sends a request to the user's device recommending an appropriate agent. The user can then select the agent they need based on this information.

[0391] Step 6:

[0392] Based on the agent selected by the user, the terminal provides an operating interface for that agent. The user can then perform specific functions within this interface.

[0393] Step 7:

[0394] If a user has previously configured a custom workflow, the server will adjust the workflow content based on the sentiment analysis results and prepare it for automated execution. For example, it may change which agents to prioritize or what actions to take based on sentiment.

[0395] Step 8:

[0396] The server monitors the results of the agents or workflows that have been executed, and evaluates their performance and user response in real time.

[0397] Step 9:

[0398] Based on the data collected by the server and the sentiment analysis results, the system provides feedback to the user on their device regarding the next steps and improvement suggestions. It also offers recommendations to optimize the user experience for future use.

[0399] (Example 2)

[0400] 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".

[0401] While many artificial intelligence systems are currently available, methods for effectively utilizing them are not yet fully developed. Furthermore, there is a lack of means to select the appropriate AI system considering the user's emotional state. As a result, providing users with the best possible experience is difficult.

[0402] 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.

[0403] In this invention, the server includes data processing means for analyzing the user's emotional state, means for recommending artificial intelligence based on the emotional state, and means for providing a portal for managing multiple information processing devices. This enables the selection and use of the optimal artificial intelligence in accordance with the user's emotions.

[0404] "Data processing means for analyzing a user's emotional state" refers to a means that analyzes voice, text, and facial expression data input by the user and provides a function to determine that emotion in real time.

[0405] A "means for recommending artificial intelligence" refers to a method that selects the most suitable artificial intelligence based on the analyzed emotional state of the user and presents it to the user.

[0406] "Means of providing a portal for managing information processing devices" refers to means of providing an interface that allows for the centralized management of multiple information processing devices and enables users to efficiently utilize these devices.

[0407] An "information storage device" is a device for storing and managing information, and serves as a foundation for registering information related to artificial intelligence and user usage history.

[0408] "Means for creating and executing custom processing procedures" refers to means that have the function of combining multiple information processing devices to construct and execute a process that proceeds according to the order and conditions of those devices in order to perform a specific process requested by the user.

[0409] "Means for monitoring and evaluating performance and providing information" refers to means that have the function of providing feedback to the user by observing the operation of the information processing device and measuring its effects.

[0410] This invention provides a platform for users to interact smoothly with information processing devices. The following describes specific embodiments of this invention.

[0411] First, the device collects data related to the user's emotions through input devices such as microphones, cameras, and keyboards. This data includes voice, text, and facial expression information.

[0412] Next, the device uses internet communication to send the collected data to the server.

[0413] The server that receives the data performs data analysis using an emotion engine. The emotion engine determines the user's emotional state by, for example, analyzing voice tone, analyzing text using natural language processing, or recognizing facial expressions.

[0414] Based on the analysis results, the server searches for and selects the appropriate artificial intelligence from the database. This selection of information processing device allows for the recommendation of the agent most effective in the user's current emotional state.

[0415] Furthermore, the server manages custom workflows that combine multiple information processing devices, helping users achieve their goals flexibly and efficiently. These workflows are configured and executed automatically according to the order and conditions specified by the user.

[0416] For example, if a user is showing signs of stress, an agent that provides relaxation music might be recommended as an information processing device. This selection is important for providing the optimal experience tailored to the user's emotional state.

[0417] An example of a prompt to the generative AI model is, "Please tell me which agent can help me when I'm feeling stressed." Based on this prompt, the server searches for and recommends an appropriate information processing device.

[0418] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0419] Step 1:

[0420] The user inputs emotion-related data into the device. This includes inputting voice data using the device's microphone, inputting text data from the keyboard, or acquiring facial expression data through the camera. The input in this step is voice, text, and facial expression information from the user, and the output is that the device internally stores this data.

[0421] Step 2:

[0422] The device sends the emotion data acquired in Step 1 to the server. This transmission takes place in real time via internet communication. The input is the data collected in Step 1, and the output is the data sent to the server.

[0423] Step 3:

[0424] The server passes the data received from the terminal to the emotion engine. The emotion engine analyzes the input data to identify the user's emotional state. This analysis includes, for example, analyzing the intonation of sounds in audio data, natural language processing of text data, and recognition of facial expressions. The input is the emotional data sent to the server, and the output is information about the identified emotional state.

[0425] Step 4:

[0426] The server searches a database for the appropriate artificial intelligence based on the analyzed emotional state. The input is information about the emotional state, and the output is a list of selected artificial intelligences. From this list, the server selects the agent best suited to the user and generates a recommendation.

[0427] Step 5:

[0428] The server adjusts the selected artificial intelligence agent based on the user's custom workflow. This adjustment includes automatically modifying the workflow's operating conditions and sequence based on the user's emotional state. The input is the user's workflow settings and emotion analysis results, and the output is the details of the adjusted workflow.

[0429] Step 6:

[0430] The server provides feedback to the user based on the workflow results adjusted in Step 5 and the agent's performance information. The feedback includes recommendations and suggestions for improvement to help increase user satisfaction. The input is the adjusted workflow outcomes and agent evaluation data, and the output is the feedback and recommendations for the next steps provided to the user.

[0431] (Application Example 2)

[0432] 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."

[0433] In recent years, systems utilizing artificial intelligence agents have been used in various fields, but providing personalized responses that respond to user emotions remains challenging. Furthermore, systems capable of accurately identifying user emotions and recommending the optimal AI agent or dynamically adjusting custom workflows based on those emotions are not yet sufficiently developed. Therefore, there is a need for efficient and flexible interfaces and processes to improve the user experience.

[0434] 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.

[0435] In this invention, the server includes means for providing an interface incorporating an engine for analyzing user emotions, means for receiving requests from users to use artificial intelligence agents, and means for coordinating information among selected artificial intelligence agents to perform processing and adjust responses based on the user's emotions. This makes it possible to build a flexible system that analyzes user emotions in real time, recommends the most suitable artificial intelligence agent based on that analysis, and enables personalized responses.

[0436] The "user emotion analysis engine" is a module that identifies the user's emotional state in real time from various data such as voice, text, and facial expressions.

[0437] An "interface" is a point of contact, whether visual or auditory, that a user directly interacts with to search for or request the use of an artificial intelligence agent.

[0438] An "artificial intelligence agent" is an autonomous software entity programmed to provide specific functions or services.

[0439] A "server" is a central computing device responsible for receiving, processing, and managing data.

[0440] A "database" is a data management system for systematically storing information about artificial intelligence agents and user sentiment history.

[0441] A "custom workflow" is a set of procedures and steps used to automate a specific process according to the user's needs.

[0442] "Monitoring and evaluation" is the process of observing the behavior and performance of registered artificial intelligence agents and evaluating their quality and effectiveness.

[0443] "Feedback" refers to information collected from users regarding their evaluations and reactions to services and suggestions they have received.

[0444] This invention is a system that utilizes artificial intelligence agents to recommend the most suitable agent according to the user's emotions, thereby aiming to improve the user experience. This system mainly consists of the following components.

[0445] First, a user interface is displayed on the client terminal. This interface is accessible to the user in a graphical or voice-based format and serves as the user's tool for searching for artificial intelligence agents and inputting emotional data. This interface is also connected to an engine that analyzes emotions and collects the user's voice and text data in real time.

[0446] The server performs sentiment analysis based on data received from the user interface. This process uses a sentiment analysis engine to analyze voice and text data to identify the user's emotional state. This engine identifies emotions using voice analysis libraries (e.g., Librosa, PyDub) and machine learning libraries (e.g., TensorFlow, Keras).

[0447] Subsequently, the server searches the database for the most suitable artificial intelligence agent based on the sentiment analysis results and recommends it to the user. In this process, an agent tailored to the user's emotional state is suggested, providing the best possible support for the user's needs.

[0448] For example, if a user shows signs of stress while working at their desk, an agent providing relaxation music or a stress management program could be suggested as a tool.

[0449] By utilizing a generative AI model, the sentiment analysis engine assists in selecting the optimal agent using prompt text. For example, a prompt such as "Identify the emotion from the user's tone of voice and select the appropriate robot response" might be used. This prompt provides specific user context as input to the generative AI model, serving as a guide for optimizing the response.

[0450] The implementation of this system will enable the provision of a more deeply personalized user experience, and is therefore expected to have applications in various fields.

[0451] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0452] Step 1:

[0453] The terminal displays an interface for user access. Here, the user can input voice commands or text data. The input data is sent directly to the server, providing data for sentiment analysis.

[0454] Step 2:

[0455] The server passes the user's voice and text data received from the terminal to the sentiment analysis engine. In this step, the voice data is analyzed for tone and pitch using a speech analysis library, and the text data is evaluated using a natural language processing library. The output identifies the user's current emotional state.

[0456] Step 3:

[0457] The server searches the database for the most suitable artificial intelligence agent based on the emotional state generated by the emotion analysis engine. Using a generative AI model, it creates prompt statements to recommend appropriate agents based on the emotions, and uses these as search queries. The output is a list of candidate agents.

[0458] Step 4:

[0459] The server selects the most suitable agent from the generated list of candidate agents and sends the recommended information to the user's terminal. This process prioritizes providing services that resonate with the user's emotions, ensuring that the recommended agent provides specific programs or services. The agent information is then displayed on the terminal as output.

[0460] Step 5:

[0461] The user reviews the recommended agent displayed on the device and utilizes the selected agent as needed. The device then launches the user's selected agent and starts the functions it provides. This executes the services of the chosen agent.

[0462] Step 6:

[0463] The server monitors the performance of executed agents and user feedback, evaluating them along with sentiment data. The evaluation results are stored in a database and used for future agent recommendations. The output includes updated feedback data regarding the user's service experience.

[0464] 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.

[0465] 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.

[0466] 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.

[0467] [Third Embodiment]

[0468] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0469] 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.

[0470] 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).

[0471] 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.

[0472] 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.

[0473] 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).

[0474] 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.

[0475] 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.

[0476] 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.

[0477] 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.

[0478] 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.

[0479] 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".

[0480] The AI ​​agent portal platform of this invention is designed to manage a large number of artificial intelligence agents via the internet, enabling users to effectively utilize them. This allows users to easily find the optimal agent to meet their needs and even automate complex tasks by coordinating them.

[0481] System Configuration

[0482] User Interface: Operates on the device and provides a visual operating environment for users to register agents, search, configure integrations, and create workflows.

[0483] Server Infrastructure: Servers are responsible for key backend processes, including processing user requests, managing agent databases, executing search algorithms, and managing inter-agent communication.

[0484] Database: Holds information on all registered artificial intelligence agents, enabling efficient searching and management.

[0485] System operation

[0486] Agent Registration

[0487] When a user wants to add a new artificial intelligence agent to the portal, they use the registration interface to provide information such as the agent name, function overview, and API specifications.

[0488] Upon receiving this information, the server performs validation and then saves the agent information to the database.

[0489] Agent search and selection

[0490] When a user searches for an artificial intelligence agent to assist with a specific task, they enter relevant keywords on the portal site.

[0491] The server receives that information, searches the database for relevant agents, and returns the results to the user as a list.

[0492] The device displays this list to help the user select the most suitable agent.

[0493] Agent-to-agent collaboration

[0494] If the user wishes to coordinate multiple agents for a specific process, they can specify this.

[0495] The server configures communication between agents selected by the user, ensuring a seamless flow of data.

[0496] Create a custom workflow

[0497] Users design custom workflows tailored to their business processes, deploy the necessary agents, and configure the execution order and conditions.

[0498] The server receives this information and schedules the execution of automated tasks based on the designed flow.

[0499] The server monitors the progress and provides feedback to the user as needed.

[0500] In this way, the present invention enables users to utilize a variety of agents without hassle, thereby realizing an efficient work environment tailored to their individual needs.

[0501] The following describes the processing flow.

[0502] Step 1:

[0503] The user enters the required information into the registration form for the artificial intelligence agent through the portal site interface and presses the submit button.

[0504] Step 2:

[0505] The server receives an agent registration request from the user and performs initial validation, such as checking the format of the input information and required fields.

[0506] Step 3:

[0507] The server saves detailed agent information to the database for successful validations. This data includes the agent name, functions, and APIs provided.

[0508] Step 4:

[0509] The user enters keywords into the search bar on the portal site and performs an agent search.

[0510] Step 5:

[0511] The server receives keywords entered by the user and executes an algorithm to search the database for highly relevant artificial intelligence agents.

[0512] Step 6:

[0513] The server sends the search results to the user's terminal and provides a list of related agents.

[0514] Step 7:

[0515] The device displays search results on the screen, allowing the user to select a specific agent.

[0516] Step 8:

[0517] The user selects the agent to use and operates an interface to configure the coordination between agents.

[0518] Step 9:

[0519] The server receives the coordination settings between the selected agents and performs processing to configure data exchange and communication protocols between each agent.

[0520] Step 10:

[0521] Users use an interface to create custom workflows, arranging the order and conditions between agents and saving the workflow.

[0522] Step 11:

[0523] The server receives workflow information, saves it to the database, and prepares it for subsequent process management and execution.

[0524] Step 12:

[0525] The server initiates saved workflows based on the user's specified schedule and automates process execution through inter-agent coordination.

[0526] Step 13:

[0527] The server monitors the execution progress and provides real-time feedback and notifications to the user if necessary.

[0528] (Example 1)

[0529] 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."

[0530] In recent years, automated systems using intelligent agents have been increasing, but effectively managing these systems and achieving smooth coordination among multiple agents remains a challenging task. Furthermore, there is a need for agent combinations tailored to individual business workflows and efficient performance monitoring. To address these challenges, it is necessary to provide a platform that facilitates the management and coordination of intelligent agents and allows for customization to meet user needs.

[0531] 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.

[0532] In this invention, the server includes means for managing multiple intelligent agents, means for receiving usage requests from users, and means for registering and organizing information about the intelligent agents in a memory area. This enables effective management of intelligent agents and the realization of user-friendly automated processes.

[0533] An "intelligent agent" is a program designed to perform user instructions or automated tasks, and is software that has the ability to process information and output results under specific conditions.

[0534] A "network platform" is a system that provides a foundation for multiple intelligent agents and users to connect and exchange information.

[0535] A "usage request" refers to an instruction or request sent by a user to utilize the functions of a specific intelligent agent.

[0536] "Memory space" refers to a physical or virtual space used to store data and programs, and is a place where information is registered and organized.

[0537] "Exploration" is the process of searching a database to find information or intelligent agents that match the user's needs and then presenting the results.

[0538] A "generative knowledge model" is an algorithm or system that understands user requests in natural language and generates appropriate instructions based on that understanding.

[0539] "Means for creating and executing command statements" refers to a system that generates specific steps or commands based on the user's intent and then executes them.

[0540] "Providing information back" means clearly communicating the monitoring results to the user and providing feedback to evaluate and improve the agent's effectiveness.

[0541] This invention provides a network platform for efficiently managing and coordinating intelligent agents. The main components of the platform are a user interface, server infrastructure, and a database.

[0542] The user interface operates on the terminal and provides a visual operating environment that enables users to register, search for, configure integrations with, and create workflows for intelligent agents. Specifically, users use the interface to input agent names and function summaries to register new agents.

[0543] The server infrastructure receives user requests and performs the primary processing of retrieving relevant agent information from the database. As a backend, the server manages seamless communication between agents and parses and generates prompt messages using a generative AI model. The server incorporates validation functions to ensure data integrity, enabling accurate information management.

[0544] The database holds information on all registered intelligent agents and serves as the foundation for efficient searching. It contains each agent's functions, API specifications, and historical performance data, designed to allow users to quickly select the optimal agent.

[0545] For example, if a user wants to automate their marketing campaigns, they can use the platform to register an "email sending agent" and a "data analysis agent" and configure their integration. This integration allows for analysis of the effectiveness of emails sent and provides the results to the user as feedback.

[0546] As an example of a prompt, the AI ​​model can be programmed with the instruction, "Find and list the email sending agents to be used in the AI ​​agent portal," to retrieve a list of relevant agents. In this way, users can easily find agents with the necessary functions and streamline their work.

[0547] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0548] Step 1:

[0549] The user accesses the agent registration interface via a terminal. The user enters the agent name, function overview, and API specifications. The server receives the input data and performs validation, checking the data format and required fields. As a result, the verified information is output and stored in the storage area.

[0550] Step 2:

[0551] The user logs into a portal site via their device and searches for intelligent agents related to their task. The user enters relevant keywords, and the server queries the database using those keywords. The server calculates the relevance between the keywords and agent information, and outputs a list of highly relevant agent information as a result. The device receives the results sent from the server and displays them on the user interface.

[0552] Step 3:

[0553] The user configures communication between multiple agents using a terminal. The user specifies the agents they want to communicate with and the method of communication. The server configures the communication settings between the specified agents and adjusts the protocol and data format. This results in an environment where agents can smoothly exchange information.

[0554] Step 4:

[0555] Users design custom workflows based on their business processes on their terminals. They drag and drop agents to place them and set their execution order and conditions. The server receives this information, applies a scheduling algorithm, and outputs an efficient task execution order. Progress is monitored, and real-time feedback is provided to the user as needed.

[0556] (Application Example 1)

[0557] 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."

[0558] This invention aims to improve the efficiency of production processes in factories. Conventionally, scheduling tasks and allocating resources on factory production lines has relied on human judgment, making optimization difficult. Furthermore, effectively coordinating multiple intelligent processing units and robots is required, but managing such coordination has been extremely labor-intensive. Solving these problems is needed to improve productivity and streamline factory operations.

[0559] 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.

[0560] In this invention, the server includes means for providing a management platform for managing intelligent processing units, means for receiving requests from users to use intelligent processing units, and means for coordinating information between selected intelligent processing units to execute processing. This enables efficient task allocation and dynamic resource adjustment within the factory.

[0561] An "intelligent processing unit" is an artificial intelligence agent that has the ability to process information and perform specific functions.

[0562] A "management platform" is a foundation that integrates and manages multiple intelligent processing units, enabling users to operate them efficiently.

[0563] A "usage request" is a request or demand made by a user to an intelligent processing unit in order to perform a specific function.

[0564] An "information storage device" is a database or storage system that collects and stores information related to an intelligent processing unit and can retrieve it as needed.

[0565] "Dynamic adjustment" refers to the adjustment activities that change the allocation of resources and tasks in a timely manner according to the situation in order to achieve optimal processing.

[0566] "Efficient task allocation" is a process for improving overall production efficiency by assigning each task to the most suitable unit on a production line.

[0567] The system that realizes this invention is designed to maximize production efficiency in a factory by coordinating various intelligent processing units. The system configuration includes a management platform, main unit, peripherals, and a network.

[0568] The server uses a Python-based program to build a management platform and provides an API using Flask to enable communication between intelligent processing units. This program uses information about intelligent processing units stored in a MySQL database to search for the optimal unit and allocate tasks according to usage requests. Furthermore, machine learning algorithms utilizing TensorFlow analyze the performance of intelligent processing units in real time, enabling efficient task scheduling and dynamic resource adjustment.

[0569] The terminal provides a user interface, enabling access from smartphones and tablets. This allows users to easily view the intelligent processing unit list and create and execute custom work procedures.

[0570] As a concrete example, in a certain factory, various types of products are assembled, and different robots operate depending on the product. By introducing this system, it is possible to improve production performance by monitoring the operating status of each robot in real time and, if necessary, reallocating tasks based on priorities.

[0571] An example of a prompt for a generative AI model is, "Based on the latest production data, please tell me the recommended intelligent processing unit configuration to improve the efficiency of the manufacturing process."

[0572] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0573] Step 1:

[0574] Users access the management platform using a terminal and input requests for the use of intelligent processing units. The input data includes the specific functions requested and related parameters.

[0575] Step 2:

[0576] The server receives a request, connects to the MySQL database, and searches for the most suitable intelligent processing unit for the user's request. The data retrieved from the database includes functional information and operating status for each unit. The server returns the search results to the terminal in list format.

[0577] Step 3:

[0578] The user views a list of intelligent processing units provided by the server on the terminal screen, selects multiple units as needed, and specifies the collaborative work procedure. The selected units are configured based on the specified order and conditions.

[0579] Step 4:

[0580] The server receives user selection information and uses TensorFlow to analyze the performance of each intelligent processing unit in real time. Historical performance data and current operational status are used as input for the analysis, and a scheduling optimization algorithm is executed to efficiently allocate tasks and adjust resources.

[0581] Step 5:

[0582] The server configures inter-unit coordination based on the analysis results and automatically assigns and executes tasks corresponding to each intelligent processing unit. The progress of the assigned tasks is monitored sequentially.

[0583] Step 6:

[0584] After execution is complete, the server saves the acquired performance data back to the MySQL database and provides the user with the results and analysis report. The user receives the results on their terminal and provides feedback or input for the next task as needed.

[0585] 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.

[0586] This invention aims to significantly improve the user experience by combining an emotion engine that recognizes user emotions with a portal platform that manages and coordinates artificial intelligence agents. This platform provides a user-accessible interface and has means for processing user requests. The emotion engine analyzes various input data, such as the user's voice, text, or facial expressions, to understand the user's emotional state.

[0587] System Configuration

[0588] User Interface: The interface displayed on the device allows for agent search, selection, integration configuration, and provision of sentiment data.

[0589] Server Infrastructure: Servers play a crucial role in managing the agent database, performing data analysis using the sentiment engine, and providing sentiment-based agent recommendations.

[0590] Database: Along with information about the artificial intelligence agent, it stores the user's emotional history and agent usage history, enabling personalized responses.

[0591] Emotion Engine: This is the core module for analyzing user emotions in real time based on diverse data inputs.

[0592] System operation

[0593] Emotion recognition and analysis

[0594] When a user inputs emotion-related data, for example, if it's voice data, the data is provided to the emotion engine via the device's microphone.

[0595] The server receives this data, activates the emotion engine, and identifies the user's emotions from the tone of voice, expressions in the text, or facial expression data.

[0596] Agent recommendation

[0597] The server searches the database for the most suitable artificial intelligence agent based on the identified user's emotional state and recommends it to the user.

[0598] Adjusting custom workflows

[0599] If the user is using the agent according to a previously configured custom workflow, the server will automatically adjust the flow conditions and actions based on the sentiment recognition results.

[0600] Providing user feedback

[0601] To evaluate how well the processes performed and services provided meet user expectations, the server uses sentiment analysis results to collect and store feedback.

[0602] The user experience can be optimized by providing recommendations for the following actions.

[0603] For example, if a user is showing signs of stress, the system can prioritize recommending agents that help with relaxation and guide the user to easily access them. In this way, it is possible to provide flexible responses tailored to the user's state and ensure the optimal use of agents.

[0604] The following describes the processing flow.

[0605] Step 1:

[0606] The user opens a portal site on their device and selects an option for inputting emotional data. For example, they might use their device's microphone to provide audio data.

[0607] Step 2:

[0608] The server transfers emotional data (voice, text, or facial expressions) received from the terminal to the emotion engine. The data is intended to be processed in real time.

[0609] Step 3:

[0610] The emotion engine analyzes data to identify the user's emotional state through voice tone, keywords in text, and facial expression analysis. For example, it can detect stress levels or relaxation levels from voice tone.

[0611] Step 4:

[0612] The server receives the analysis results from the emotion engine, searches the database, and identifies the AI ​​agent best suited to the user's current emotional state. For example, if the user is stressed, a stress reduction agent will be recommended.

[0613] Step 5:

[0614] Based on the emotions detected by the server, the server sends a request to the user's device recommending an appropriate agent. The user can then select the agent they need based on this information.

[0615] Step 6:

[0616] Based on the agent selected by the user, the terminal provides an operating interface for that agent. The user can then perform specific functions within this interface.

[0617] Step 7:

[0618] If a user has previously configured a custom workflow, the server will adjust the workflow content based on the sentiment analysis results and prepare it for automated execution. For example, it may change which agents to prioritize or what actions to take based on sentiment.

[0619] Step 8:

[0620] The server monitors the results of the agents or workflows that have been executed, and evaluates their performance and user response in real time.

[0621] Step 9:

[0622] Based on the data collected by the server and the sentiment analysis results, the system provides feedback to the user on their device regarding the next steps and improvement suggestions. It also offers recommendations to optimize the user experience for future use.

[0623] (Example 2)

[0624] 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."

[0625] While many artificial intelligence systems are currently available, methods for effectively utilizing them are not yet fully developed. Furthermore, there is a lack of means to select the appropriate AI system considering the user's emotional state. As a result, providing users with the best possible experience is difficult.

[0626] 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.

[0627] In this invention, the server includes data processing means for analyzing the user's emotional state, means for recommending artificial intelligence based on the emotional state, and means for providing a portal for managing multiple information processing devices. This enables the selection and use of the optimal artificial intelligence in accordance with the user's emotions.

[0628] "Data processing means for analyzing a user's emotional state" refers to a means that analyzes voice, text, and facial expression data input by the user and provides a function to determine that emotion in real time.

[0629] A "means for recommending artificial intelligence" refers to a method that selects the most suitable artificial intelligence based on the analyzed emotional state of the user and presents it to the user.

[0630] "Means for providing a portal for managing information processing devices" refers to means for centrally managing multiple information processing devices and providing an interface that enables users to efficiently utilize these devices.

[0631] An "information storage device" is a device for storing and managing information, and serves as a foundation for registering information related to artificial intelligence and user usage history.

[0632] "Means for creating and executing custom processing procedures" refers to means that have the function of combining multiple information processing devices to construct and execute a process that proceeds according to the sequence and conditions of those devices in order to perform a specific process requested by the user.

[0633] "Means for monitoring and evaluating performance and providing information" refers to means that have the function of providing feedback to the user by observing the operation of the information processing device and measuring its effects.

[0634] This invention provides a platform for users to interact smoothly with information processing devices. The following describes specific embodiments of this invention.

[0635] First, the device collects data related to the user's emotions through input devices such as microphones, cameras, and keyboards. This data includes voice, text, and facial expression information.

[0636] Next, the device uses internet communication to send the collected data to the server.

[0637] The server that receives the data performs data analysis using an emotion engine. The emotion engine determines the user's emotional state by, for example, analyzing voice tone, analyzing text using natural language processing, or recognizing facial expressions.

[0638] Based on the analysis results, the server searches for and selects the appropriate artificial intelligence from the database. This selection of information processing device allows for the recommendation of the agent most effective in the user's current emotional state.

[0639] Furthermore, the server manages custom workflows that combine multiple information processing devices, helping users achieve their goals flexibly and efficiently. These workflows are configured and executed automatically according to the order and conditions specified by the user.

[0640] For example, if a user is showing signs of stress, an agent that provides relaxation music might be recommended as an information processing device. This selection is important for providing the optimal experience tailored to the user's emotional state.

[0641] An example of a prompt to the generative AI model is, "Please tell me what agent would be helpful when I'm feeling stressed." Based on this prompt, the server searches for and recommends an appropriate information processing device.

[0642] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0643] Step 1:

[0644] The user inputs emotion-related data into the device. This includes inputting voice data using the device's microphone, inputting text data from the keyboard, or acquiring facial expression data through the camera. The input in this step is voice, text, and facial expression information from the user, and the output is that the device internally stores this data.

[0645] Step 2:

[0646] The device sends the emotion data acquired in Step 1 to the server. This transmission takes place in real time via internet communication. The input is the data collected in Step 1, and the output is the data sent to the server.

[0647] Step 3:

[0648] The server passes the data received from the terminal to the emotion engine. The emotion engine analyzes the input data to identify the user's emotional state. This analysis includes, for example, analyzing the intonation of sounds in audio data, natural language processing of text data, and recognition of facial expressions. The input is the emotional data sent to the server, and the output is information about the identified emotional state.

[0649] Step 4:

[0650] The server searches a database for the appropriate artificial intelligence based on the analyzed emotional state. The input is information about the emotional state, and the output is a list of selected artificial intelligences. From this list, the server selects the agent best suited to the user and generates a recommendation.

[0651] Step 5:

[0652] The server adjusts the selected artificial intelligence agent based on the user's custom workflow. This adjustment includes automatically modifying the workflow's operating conditions and sequence based on the user's emotional state. The input is the user's workflow settings and emotion analysis results, and the output is the details of the adjusted workflow.

[0653] Step 6:

[0654] The server provides feedback to the user based on the workflow results adjusted in Step 5 and the agent's performance information. The feedback includes recommendations and suggestions for improvement to help increase user satisfaction. The input is the adjusted workflow outcomes and agent evaluation data, and the output is the feedback and recommendations for the next steps provided to the user.

[0655] (Application Example 2)

[0656] 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."

[0657] In recent years, systems utilizing artificial intelligence agents have been used in various fields, but providing personalized responses that respond to user emotions remains challenging. Furthermore, systems capable of accurately identifying user emotions and recommending the optimal AI agent or dynamically adjusting custom workflows based on those emotions are not yet sufficiently developed. Therefore, there is a need for efficient and flexible interfaces and processes to improve the user experience.

[0658] 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.

[0659] In this invention, the server includes means for providing an interface incorporating an engine for analyzing user emotions, means for receiving requests from users to use artificial intelligence agents, and means for coordinating information among selected artificial intelligence agents to perform processing and adjust responses based on the user's emotions. This makes it possible to build a flexible system that analyzes user emotions in real time, recommends the most suitable artificial intelligence agent based on that analysis, and enables personalized responses.

[0660] The "user emotion analysis engine" is a module that identifies the user's emotional state in real time from various data such as voice, text, and facial expressions.

[0661] An "interface" is a point of contact, whether visual or auditory, that a user directly interacts with to search for or request the use of an artificial intelligence agent.

[0662] An "artificial intelligence agent" is an autonomous software entity programmed to provide specific functions or services.

[0663] A "server" is a central computing device responsible for receiving, processing, and managing data.

[0664] A "database" is a data management system for systematically storing information about artificial intelligence agents and user sentiment history.

[0665] A "custom workflow" is a set of procedures and steps used to automate a specific process according to the user's needs.

[0666] "Monitoring and evaluation" is the process of observing the behavior and performance of registered artificial intelligence agents and evaluating their quality and effectiveness.

[0667] "Feedback" refers to information collected from users regarding their evaluations and reactions to services and suggestions they have received.

[0668] This invention is a system that utilizes artificial intelligence agents to recommend the most suitable agent according to the user's emotions, thereby aiming to improve the user experience. This system mainly consists of the following components.

[0669] First, a user interface is displayed on the client terminal. This interface is accessible to the user in a graphical or voice-based format and serves as the user's tool for searching for artificial intelligence agents and inputting emotional data. This interface is also connected to an engine that analyzes emotions and collects the user's voice and text data in real time.

[0670] The server performs sentiment analysis based on data received from the user interface. This process uses a sentiment analysis engine to analyze voice and text data to identify the user's emotional state. This engine identifies emotions using voice analysis libraries (e.g., Librosa, PyDub) and machine learning libraries (e.g., TensorFlow, Keras).

[0671] Subsequently, the server searches the database for the most suitable artificial intelligence agent based on the sentiment analysis results and recommends it to the user. In this process, an agent tailored to the user's emotional state is suggested, providing the best possible support for the user's needs.

[0672] For example, if a user shows signs of stress while working at their desk, an agent providing relaxation music or a stress management program could be suggested as a tool.

[0673] By utilizing a generative AI model, the sentiment analysis engine assists in selecting the optimal agent using prompt text. For example, a prompt such as "Identify the emotion from the user's tone of voice and select the appropriate robot response" might be used. This prompt provides specific user context as input to the generative AI model, serving as a guide for optimizing the response.

[0674] The implementation of this system will enable the provision of a more deeply personalized user experience, and is therefore expected to have applications in various fields.

[0675] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0676] Step 1:

[0677] The terminal displays an interface for user access. Here, the user can provide voice commands or input text data. The input data is sent directly to the server, providing data for sentiment analysis.

[0678] Step 2:

[0679] The server passes the user's voice and text data received from the terminal to the sentiment analysis engine. In this step, the voice data is analyzed for tone and pitch using a speech analysis library, and the text data is evaluated using a natural language processing library. The output identifies the user's current emotional state.

[0680] Step 3:

[0681] The server searches the database for the most suitable artificial intelligence agent based on the emotional state generated by the emotion analysis engine. Using a generative AI model, it creates prompt statements to recommend appropriate agents based on the emotions, and uses these as search queries. The output is a list of candidate agents.

[0682] Step 4:

[0683] The server selects the most suitable agent from the generated list of candidate agents and sends the recommended information to the user's terminal. This process prioritizes providing services that resonate with the user's emotions, ensuring that the recommended agent provides specific programs or services. The agent information is then displayed on the terminal as output.

[0684] Step 5:

[0685] The user reviews the recommended agent displayed on the device and utilizes the selected agent as needed. The device then launches the user's selected agent and starts the functions it provides. This executes the services of the chosen agent.

[0686] Step 6:

[0687] The server monitors the performance of executed agents and user feedback, evaluating them along with sentiment data. The evaluation results are stored in a database and used for future agent recommendations. The output includes updated feedback data regarding the user's service experience.

[0688] 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.

[0689] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0690] 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.

[0691] [Fourth Embodiment]

[0692] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0693] 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.

[0694] 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).

[0695] 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.

[0696] 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.

[0697] 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).

[0698] 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.

[0699] 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.

[0700] 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.

[0701] 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.

[0702] 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.

[0703] 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.

[0704] 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".

[0705] The AI ​​agent portal platform of this invention is designed to manage a large number of artificial intelligence agents via the internet, enabling users to effectively utilize them. This allows users to easily find the optimal agent to meet their needs and even automate complex tasks by coordinating them.

[0706] System Configuration

[0707] User Interface: Operates on the device and provides a visual operating environment for users to register agents, search, configure integrations, and create workflows.

[0708] Server Infrastructure: Servers are responsible for key backend processes, including processing user requests, managing agent databases, executing search algorithms, and managing inter-agent communication.

[0709] Database: Holds information on all registered artificial intelligence agents, enabling efficient searching and management.

[0710] System operation

[0711] Agent Registration

[0712] When a user wants to add a new artificial intelligence agent to the portal, they use the registration interface to provide information such as the agent name, function overview, and API specifications.

[0713] Upon receiving this information, the server performs validation and then saves the agent information to the database.

[0714] Agent search and selection

[0715] When a user searches for an artificial intelligence agent to assist with a specific task, they enter relevant keywords on the portal site.

[0716] The server receives that information, searches the database for relevant agents, and returns the results to the user as a list.

[0717] The device displays this list to help the user select the most suitable agent.

[0718] Agent-to-agent collaboration

[0719] If the user wishes to coordinate multiple agents for a specific process, they can specify this.

[0720] The server configures communication between agents selected by the user, ensuring a seamless flow of data.

[0721] Create a custom workflow

[0722] Users design custom workflows tailored to their business processes, deploy the necessary agents, and configure the execution order and conditions.

[0723] The server receives this information and schedules the execution of automated tasks based on the designed flow.

[0724] The server monitors the progress and provides feedback to the user as needed.

[0725] In this way, the present invention enables users to utilize a variety of agents without hassle, thereby realizing an efficient work environment tailored to their individual needs.

[0726] The following describes the processing flow.

[0727] Step 1:

[0728] The user enters the required information into the registration form for the artificial intelligence agent through the portal site interface and presses the submit button.

[0729] Step 2:

[0730] The server receives an agent registration request from the user and performs initial validation, such as checking the format of the input information and required fields.

[0731] Step 3:

[0732] The server saves detailed agent information to the database for successful validations. This data includes the agent name, functions, and APIs provided.

[0733] Step 4:

[0734] The user enters keywords into the search bar on the portal site and performs an agent search.

[0735] Step 5:

[0736] The server receives keywords entered by the user and executes an algorithm to search the database for highly relevant artificial intelligence agents.

[0737] Step 6:

[0738] The server sends the search results to the user's terminal and provides a list of related agents.

[0739] Step 7:

[0740] The device displays search results on the screen, allowing the user to select a specific agent.

[0741] Step 8:

[0742] The user selects the agent to use and operates an interface to configure the coordination between agents.

[0743] Step 9:

[0744] The server receives the coordination settings between the selected agents and performs processing to configure data exchange and communication protocols between each agent.

[0745] Step 10:

[0746] Users use an interface to create custom workflows, arranging the order and conditions between agents and saving the workflow.

[0747] Step 11:

[0748] The server receives workflow information, saves it to the database, and prepares it for subsequent process management and execution.

[0749] Step 12:

[0750] The server initiates saved workflows based on the user's specified schedule and automates process execution through inter-agent coordination.

[0751] Step 13:

[0752] The server monitors the execution progress and provides real-time feedback and notifications to the user if necessary.

[0753] (Example 1)

[0754] 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".

[0755] In recent years, automated systems using intelligent agents have been increasing, but effectively managing these systems and achieving smooth coordination among multiple agents remains a challenging task. Furthermore, there is a need for agent combinations tailored to individual business workflows and efficient performance monitoring. To address these challenges, it is necessary to provide a platform that facilitates the management and coordination of intelligent agents and allows for customization to meet user needs.

[0756] 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.

[0757] In this invention, the server includes means for managing multiple intelligent agents, means for receiving usage requests from users, and means for registering and organizing information about the intelligent agents in a memory area. This enables effective management of intelligent agents and the realization of user-friendly automated processes.

[0758] An "intelligent agent" is a program designed to perform user instructions or automated tasks, and is software that has the ability to process information and output results under specific conditions.

[0759] A "network platform" is a system that provides a foundation for multiple intelligent agents and users to connect and exchange information.

[0760] A "usage request" refers to an instruction or request sent by a user to utilize the functions of a specific intelligent agent.

[0761] "Memory space" refers to a physical or virtual space used to store data and programs, and is a place where information is registered and organized.

[0762] "Exploration" is the process of searching a database to find information or intelligent agents that match the user's needs and then presenting the results.

[0763] A "generative knowledge model" is an algorithm or system that understands user requests in natural language and generates appropriate instructions based on that understanding.

[0764] "Means for creating and executing command statements" refers to a system that generates specific steps or commands based on the user's intent and then executes them.

[0765] "Providing information back" means clearly communicating the monitoring results to the user and providing feedback to evaluate and improve the agent's effectiveness.

[0766] This invention provides a network platform for efficiently managing and coordinating intelligent agents. The main components of the platform are a user interface, server infrastructure, and a database.

[0767] The user interface operates on the terminal and provides a visual operating environment that enables users to register, search for, configure integrations with, and create workflows for intelligent agents. Specifically, users use the interface to input agent names and function summaries to register new agents.

[0768] The server infrastructure receives user requests and performs the primary processing of retrieving relevant agent information from the database. As a backend, the server manages seamless communication between agents and parses and generates prompt messages using a generative AI model. The server incorporates validation functions to ensure data integrity, enabling accurate information management.

[0769] The database holds information on all registered intelligent agents and serves as the foundation for efficient searching. It contains each agent's functions, API specifications, and historical performance data, designed to allow users to quickly select the optimal agent.

[0770] For example, if a user wants to automate their marketing campaigns, they can use the platform to register an "email sending agent" and a "data analysis agent" and configure their integration. This integration allows for analysis of the effectiveness of emails sent and provides the results to the user as feedback.

[0771] As an example of a prompt, the AI ​​model can be programmed with the instruction, "Find and list the email sending agents to be used in the AI ​​agent portal," to retrieve a list of relevant agents. In this way, users can easily find agents with the necessary functions and streamline their work.

[0772] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0773] Step 1:

[0774] The user accesses the agent registration interface via a terminal. The user enters the agent name, function overview, and API specifications. The server receives the input data and performs validation, checking the data format and required fields. As a result, the verified information is output and stored in the storage area.

[0775] Step 2:

[0776] The user logs into a portal site via their device and searches for intelligent agents related to their task. The user enters relevant keywords, and the server queries the database using those keywords. The server calculates the relevance between the keywords and agent information, and outputs a list of highly relevant agent information as a result. The device receives the results sent from the server and displays them on the user interface.

[0777] Step 3:

[0778] The user configures communication between multiple agents using a terminal. The user specifies the agents they want to communicate with and the method of communication. The server configures the communication settings between the specified agents and adjusts the protocol and data format. This results in an environment where agents can smoothly exchange information.

[0779] Step 4:

[0780] Users design custom workflows based on their business processes on their terminals. They drag and drop agents to place them and set their execution order and conditions. The server receives this information, applies a scheduling algorithm, and outputs an efficient task execution order. Progress is monitored, and real-time feedback is provided to the user as needed.

[0781] (Application Example 1)

[0782] 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".

[0783] This invention aims to improve the efficiency of production processes in factories. Conventionally, scheduling tasks and allocating resources on factory production lines has relied on human judgment, making optimization difficult. Furthermore, effectively coordinating multiple intelligent processing units and robots is required, but managing such coordination has been extremely labor-intensive. Solving these problems is needed to improve productivity and streamline factory operations.

[0784] 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.

[0785] In this invention, the server includes means for providing a management platform for managing intelligent processing units, means for receiving requests from users to use intelligent processing units, and means for coordinating information between selected intelligent processing units to execute processing. This enables efficient task allocation and dynamic resource adjustment within the factory.

[0786] An "intelligent processing unit" is an artificial intelligence agent that has the ability to process information and perform specific functions.

[0787] A "management platform" is a foundation that integrates and manages multiple intelligent processing units, enabling users to operate them efficiently.

[0788] A "usage request" is a request or demand made by a user to an intelligent processing unit in order to perform a specific function.

[0789] An "information storage device" is a database or storage system that collects and stores information related to an intelligent processing unit and can retrieve it as needed.

[0790] "Dynamic adjustment" refers to the adjustment activities that change the allocation of resources and tasks in a timely manner according to the situation in order to achieve optimal processing.

[0791] "Efficient task allocation" is a process for improving overall production efficiency by assigning each task to the most suitable unit on a production line.

[0792] The system that realizes this invention is designed to maximize production efficiency in a factory by coordinating various intelligent processing units. The system configuration includes a management platform, main unit, peripherals, and a network.

[0793] The server uses a Python-based program to build a management platform and provides an API using Flask to enable communication between intelligent processing units. This program uses information about intelligent processing units stored in a MySQL database to search for the optimal unit and allocate tasks according to usage requests. Furthermore, machine learning algorithms utilizing TensorFlow analyze the performance of intelligent processing units in real time, enabling efficient task scheduling and dynamic resource adjustment.

[0794] The terminal provides a user interface, enabling access from smartphones and tablets. This allows users to easily view the intelligent processing unit list and create and execute custom work procedures.

[0795] As a concrete example, in a certain factory, various types of products are assembled, and different robots operate depending on the product. By introducing this system, it is possible to improve production performance by monitoring the operating status of each robot in real time and, if necessary, reallocating tasks based on priorities.

[0796] An example of a prompt for a generative AI model is, "Based on the latest production data, please tell me the recommended intelligent processing unit configuration to improve the efficiency of the manufacturing process."

[0797] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0798] Step 1:

[0799] Users access the management platform using a terminal and input requests for the use of intelligent processing units. The input data includes the specific functions requested and related parameters.

[0800] Step 2:

[0801] The server receives a request, connects to the MySQL database, and searches for the most suitable intelligent processing unit for the user's request. The data retrieved from the database includes functional information and operating status for each unit. The server returns the search results to the terminal in list format.

[0802] Step 3:

[0803] The user views a list of intelligent processing units provided by the server on the terminal screen, selects multiple units as needed, and specifies the collaborative work procedure. The selected units are configured based on the specified order and conditions.

[0804] Step 4:

[0805] The server receives user selection information and uses TensorFlow to analyze the performance of each intelligent processing unit in real time. Historical performance data and current operational status are used as input for the analysis, and a scheduling optimization algorithm is executed to efficiently allocate tasks and adjust resources.

[0806] Step 5:

[0807] The server configures inter-unit coordination based on the analysis results and automatically assigns and executes tasks corresponding to each intelligent processing unit. The progress of the assigned tasks is monitored sequentially.

[0808] Step 6:

[0809] After execution is complete, the server saves the acquired performance data back to the MySQL database and provides the user with the results and analysis report. The user receives the results on their terminal and provides feedback or input for the next task as needed.

[0810] 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.

[0811] This invention aims to significantly improve the user experience by combining an emotion engine that recognizes user emotions with a portal platform that manages and coordinates artificial intelligence agents. This platform provides a user-accessible interface and has means for processing user requests. The emotion engine analyzes various input data, such as the user's voice, text, or facial expressions, to understand the user's emotional state.

[0812] System Configuration

[0813] User Interface: The interface displayed on the device allows for agent search, selection, integration configuration, and provision of sentiment data.

[0814] Server Infrastructure: Servers play a crucial role in managing the agent database, performing data analysis using the sentiment engine, and providing sentiment-based agent recommendations.

[0815] Database: Along with information about the artificial intelligence agent, it stores the user's emotional history and agent usage history, enabling personalized responses.

[0816] Emotion Engine: This is the core module for analyzing user emotions in real time based on diverse data inputs.

[0817] System operation

[0818] Emotion recognition and analysis

[0819] When a user inputs emotion-related data, for example, if it's voice data, the data is provided to the emotion engine via the device's microphone.

[0820] The server receives this data, activates the emotion engine, and identifies the user's emotions from the tone of voice, expressions in the text, or facial expression data.

[0821] Agent recommendation

[0822] The server searches the database for the most suitable artificial intelligence agent based on the identified user's emotional state and recommends it to the user.

[0823] Adjusting custom workflows

[0824] If the user is using the agent according to a previously configured custom workflow, the server will automatically adjust the flow conditions and actions based on the sentiment recognition results.

[0825] Providing user feedback

[0826] To evaluate how well the processes performed and services provided meet user expectations, the server uses sentiment analysis results to collect and store feedback.

[0827] The user experience can be optimized by providing recommendations for the following actions.

[0828] For example, if a user is showing signs of stress, the system can prioritize recommending agents that help with relaxation and guide the user to easily access them. In this way, it is possible to provide flexible responses tailored to the user's state and ensure the optimal use of agents.

[0829] The following describes the processing flow.

[0830] Step 1:

[0831] The user opens a portal site on their device and selects an option for inputting emotional data. For example, they might use their device's microphone to provide audio data.

[0832] Step 2:

[0833] The server transfers emotional data (voice, text, or facial expressions) received from the terminal to the emotion engine. The data is intended to be processed in real time.

[0834] Step 3:

[0835] The emotion engine analyzes data to identify the user's emotional state through voice tone, keywords in text, and facial expression analysis. For example, it can detect stress levels or relaxation levels from voice tone.

[0836] Step 4:

[0837] The server receives the analysis results from the emotion engine, searches the database, and identifies the AI ​​agent best suited to the user's current emotional state. For example, if the user is stressed, a stress reduction agent will be recommended.

[0838] Step 5:

[0839] Based on the emotions detected by the server, the server sends a request to the user's device recommending an appropriate agent. The user can then select the agent they need based on this information.

[0840] Step 6:

[0841] Based on the agent selected by the user, the terminal provides an operating interface for that agent. The user can then perform specific functions within this interface.

[0842] Step 7:

[0843] If a user has previously configured a custom workflow, the server will adjust the workflow content based on the sentiment analysis results and prepare it for automated execution. For example, it may change which agents to prioritize or what actions to take based on sentiment.

[0844] Step 8:

[0845] The server monitors the results of the agents or workflows that have been executed, and evaluates their performance and user response in real time.

[0846] Step 9:

[0847] Based on the data collected by the server and the sentiment analysis results, the system provides feedback to the user on their device regarding the next steps and improvement suggestions. It also offers recommendations to optimize the user experience for future use.

[0848] (Example 2)

[0849] 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".

[0850] While many artificial intelligence systems are currently available, methods for effectively utilizing them are not yet fully developed. Furthermore, there is a lack of means to select the appropriate AI system considering the user's emotional state. As a result, providing users with the best possible experience is difficult.

[0851] 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.

[0852] In this invention, the server includes data processing means for analyzing the user's emotional state, means for recommending artificial intelligence based on the emotional state, and means for providing a portal for managing multiple information processing devices. This enables the selection and use of the optimal artificial intelligence in accordance with the user's emotions.

[0853] "Data processing means for analyzing a user's emotional state" refers to a means that analyzes voice, text, and facial expression data input by the user and provides a function to determine that emotion in real time.

[0854] A "means for recommending artificial intelligence" refers to a method that selects the most suitable artificial intelligence based on the analyzed emotional state of the user and presents it to the user.

[0855] "Means for providing a portal for managing information processing devices" refers to means for centrally managing multiple information processing devices and providing an interface that enables users to efficiently utilize these devices.

[0856] An "information storage device" is a device for storing and managing information, and serves as a foundation for registering information related to artificial intelligence and user usage history.

[0857] "Means for creating and executing custom processing procedures" refers to means that have the function of combining multiple information processing devices to construct and execute a process that proceeds according to the sequence and conditions of those devices in order to perform a specific process requested by the user.

[0858] "Means for monitoring and evaluating performance and providing information" refers to means that have the function of providing feedback to the user by observing the operation of the information processing device and measuring its effects.

[0859] This invention provides a platform for users to interact smoothly with information processing devices. The following describes specific embodiments of this invention.

[0860] First, the device collects data related to the user's emotions through input devices such as microphones, cameras, and keyboards. This data includes voice, text, and facial expression information.

[0861] Next, the device uses internet communication to send the collected data to the server.

[0862] The server that receives the data performs data analysis using an emotion engine. The emotion engine determines the user's emotional state by, for example, analyzing voice tone, analyzing text using natural language processing, or recognizing facial expressions.

[0863] Based on the analysis results, the server searches for and selects the appropriate artificial intelligence from the database. This selection of information processing device allows for the recommendation of the agent most effective in the user's current emotional state.

[0864] Furthermore, the server manages custom workflows that combine multiple information processing devices, helping users achieve their goals flexibly and efficiently. These workflows are configured and executed automatically according to the order and conditions specified by the user.

[0865] For example, if a user is showing signs of stress, an agent that provides relaxation music might be recommended as an information processing device. This selection is important for providing the optimal experience tailored to the user's emotional state.

[0866] An example of a prompt to the generative AI model is, "Please tell me what agent would be helpful when I'm feeling stressed." Based on this prompt, the server searches for and recommends an appropriate information processing device.

[0867] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0868] Step 1:

[0869] The user inputs emotion-related data into the device. This includes inputting voice data using the device's microphone, inputting text data from the keyboard, or acquiring facial expression data through the camera. The input in this step is voice, text, and facial expression information from the user, and the output is that the device internally stores this data.

[0870] Step 2:

[0871] The device sends the emotion data acquired in Step 1 to the server. This transmission takes place in real time via internet communication. The input is the data collected in Step 1, and the output is the data sent to the server.

[0872] Step 3:

[0873] The server passes the data received from the terminal to the emotion engine. The emotion engine analyzes the input data to identify the user's emotional state. This analysis includes, for example, analyzing the intonation of sounds in audio data, natural language processing of text data, and recognition of facial expressions. The input is the emotional data sent to the server, and the output is information about the identified emotional state.

[0874] Step 4:

[0875] The server searches a database for the appropriate artificial intelligence based on the analyzed emotional state. The input is information about the emotional state, and the output is a list of selected artificial intelligences. From this list, the server selects the agent best suited to the user and generates a recommendation.

[0876] Step 5:

[0877] The server adjusts the selected artificial intelligence agent based on the user's custom workflow. This adjustment includes automatically modifying the workflow's operating conditions and sequence based on the user's emotional state. The input is the user's workflow settings and emotion analysis results, and the output is the details of the adjusted workflow.

[0878] Step 6:

[0879] The server provides feedback to the user based on the workflow results adjusted in Step 5 and the agent's performance information. The feedback includes recommendations and suggestions for improvement to help increase user satisfaction. The input is the adjusted workflow outcomes and agent evaluation data, and the output is the feedback and recommendations for the next steps provided to the user.

[0880] (Application Example 2)

[0881] 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".

[0882] In recent years, systems utilizing artificial intelligence agents have been used in various fields, but providing personalized responses that respond to user emotions remains challenging. Furthermore, systems capable of accurately identifying user emotions and recommending the optimal AI agent or dynamically adjusting custom workflows based on those emotions are not yet sufficiently developed. Therefore, there is a need for efficient and flexible interfaces and processes to improve the user experience.

[0883] 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.

[0884] In this invention, the server includes means for providing an interface incorporating an engine for analyzing user emotions, means for receiving requests from users to use artificial intelligence agents, and means for coordinating information among selected artificial intelligence agents to perform processing and adjust responses based on the user's emotions. This makes it possible to build a flexible system that analyzes user emotions in real time, recommends the most suitable artificial intelligence agent based on that analysis, and enables personalized responses.

[0885] The "user emotion analysis engine" is a module that identifies the user's emotional state in real time from various data such as voice, text, and facial expressions.

[0886] An "interface" is a point of contact, whether visual or auditory, that a user directly interacts with to search for or request the use of an artificial intelligence agent.

[0887] An "artificial intelligence agent" is an autonomous software entity programmed to provide specific functions or services.

[0888] A "server" is a central computing device responsible for receiving, processing, and managing data.

[0889] A "database" is a data management system for systematically storing information about artificial intelligence agents and user sentiment history.

[0890] A "custom workflow" is a set of procedures and steps used to automate a specific process according to the user's needs.

[0891] "Monitoring and evaluation" is the process of observing the behavior and performance of registered artificial intelligence agents and evaluating their quality and effectiveness.

[0892] "Feedback" refers to information collected from users regarding their evaluations and reactions to services and suggestions they have received.

[0893] This invention is a system that utilizes artificial intelligence agents to recommend the most suitable agent according to the user's emotions, thereby aiming to improve the user experience. This system mainly consists of the following components.

[0894] First, a user interface is displayed on the client terminal. This interface is accessible to the user in a graphical or voice-based format and serves as the user's tool for searching for artificial intelligence agents and inputting emotional data. This interface is also connected to an engine that analyzes emotions and collects the user's voice and text data in real time.

[0895] The server performs sentiment analysis based on data received from the user interface. This process uses a sentiment analysis engine to analyze voice and text data to identify the user's emotional state. This engine identifies emotions using voice analysis libraries (e.g., Librosa, PyDub) and machine learning libraries (e.g., TensorFlow, Keras).

[0896] Subsequently, the server searches the database for the most suitable artificial intelligence agent based on the sentiment analysis results and recommends it to the user. In this process, an agent tailored to the user's emotional state is suggested, providing the best possible support for the user's needs.

[0897] For example, if a user shows signs of stress while working at their desk, an agent providing relaxation music or a stress management program could be suggested as a tool.

[0898] By utilizing a generative AI model, the sentiment analysis engine assists in selecting the optimal agent using prompt text. For example, a prompt such as "Identify the emotion from the user's tone of voice and select the appropriate robot response" might be used. This prompt provides specific user context as input to the generative AI model, serving as a guide for optimizing the response.

[0899] The implementation of this system will enable the provision of a more deeply personalized user experience, and is therefore expected to have applications in various fields.

[0900] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0901] Step 1:

[0902] The terminal displays an interface for user access. Here, the user can provide voice commands or input text data. The input data is sent directly to the server, providing data for sentiment analysis.

[0903] Step 2:

[0904] The server passes the user's voice and text data received from the terminal to the sentiment analysis engine. In this step, the voice data is analyzed for tone and pitch using a speech analysis library, and the text data is evaluated using a natural language processing library. The output identifies the user's current emotional state.

[0905] Step 3:

[0906] The server searches the database for the most suitable artificial intelligence agent based on the emotional state generated by the emotion analysis engine. Using a generative AI model, it creates prompt statements to recommend appropriate agents based on the emotions, and uses these as search queries. The output is a list of candidate agents.

[0907] Step 4:

[0908] The server selects the most suitable agent from the generated list of candidate agents and sends the recommended information to the user's terminal. This process prioritizes providing services that resonate with the user's emotions, ensuring that the recommended agent provides specific programs or services. The agent information is then displayed on the terminal as output.

[0909] Step 5:

[0910] The user reviews the recommended agent displayed on the device and utilizes the selected agent as needed. The device then launches the user's selected agent and starts the functions it provides. This executes the services of the chosen agent.

[0911] Step 6:

[0912] The server monitors the performance of executed agents and user feedback, evaluating them along with sentiment data. The evaluation results are stored in a database and used for future agent recommendations. The output includes updated feedback data regarding the user's service experience.

[0913] 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.

[0914] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0915] 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.

[0916] 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.

[0917] 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.

[0918] 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.

[0919] 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.

[0920] 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.

[0921] 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."

[0922] 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.

[0923] 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.

[0924] 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.

[0925] 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.

[0926] 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.

[0927] 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.

[0928] 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.

[0929] 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.

[0930] 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.

[0931] 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.

[0932] 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.

[0933] 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.

[0934] The following is further disclosed regarding the embodiments described above.

[0935] (Claim 1)

[0936] A portal platform for managing multiple artificial intelligence agents,

[0937] A means of receiving requests from users to use an artificial intelligence agent,

[0938] Means for registering and managing information about the aforementioned artificial intelligence agent in a database,

[0939] A means of searching for an artificial intelligence agent suitable for a specific function requested by the user,

[0940] A system that includes means for coordinating information between selected artificial intelligence agents to perform processing.

[0941] (Claim 2)

[0942] The system according to claim 1, further comprising means for creating and executing a custom workflow by combining multiple artificial intelligence agents based on a specified order and conditions.

[0943] (Claim 3)

[0944] The system according to claim 1, comprising means for monitoring and evaluating the performance of registered artificial intelligence agents and providing feedback to the user.

[0945] "Example 1"

[0946] (Claim 1)

[0947] A network platform for managing multiple intelligent agents,

[0948] A means of receiving requests from users to use an intelligent agent,

[0949] Means for registering and organizing information about the intelligent agent in a memory area,

[0950] A means of searching for an intelligent agent suitable for a specific function requested by the user,

[0951] A means of coordinating information among selected intelligent agents to perform processing,

[0952] A means for creating and using command statements using a generative knowledge model,

[0953] A means of managing communication between multiple agents and ensuring the flow of data,

[0954] A system that includes means for verifying and accurately storing information provided during agent registration.

[0955] (Claim 2)

[0956] The system according to claim 1, further comprising means for combining multiple intelligent agents based on a specified order and conditions to create and execute individual workflows.

[0957] (Claim 3)

[0958] The system according to claim 1, comprising means for observing and evaluating the performance of registered intelligent agents and providing information to users.

[0959] "Application Example 1"

[0960] (Claim 1)

[0961] A management platform for managing multiple intelligent processing units,

[0962] A means for receiving requests from users to use an intelligent processing unit,

[0963] Means for registering and managing information relating to the aforementioned intelligent processing unit in an information storage device,

[0964] A means for searching for an intelligent processing unit suitable for a specific function requested by the user,

[0965] A means of coordinating information between selected intelligent processing units to perform processing,

[0966] A means of efficiently allocating tasks and dynamically adjusting resources in a work process using an intelligent processing unit.

[0967] A system that includes this.

[0968] (Claim 2)

[0969] The system according to claim 1, further comprising means for creating and executing custom work procedures by combining multiple intelligent processing units based on a specified order and conditions.

[0970] (Claim 3)

[0971] The system according to claim 1, comprising means for monitoring and evaluating the performance of registered intelligent processing units and providing reports to users, and means for performing performance analysis of intelligent processing units and optimizing work procedures.

[0972] "Example 2 of combining an emotion engine"

[0973] (Claim 1)

[0974] A data processing method for analyzing the emotional state of a user,

[0975] A means for recommending artificial intelligence based on the aforementioned emotional state,

[0976] A means of providing a portal for managing multiple information processing devices,

[0977] A means for receiving a request from a user to use an information processing device,

[0978] Means for registering and managing information related to the information processing device in an information storage device,

[0979] A means for searching for an information processing device suitable for a specific function requested by the user,

[0980] A system that includes means for coordinating information between selected information processing devices to perform processing.

[0981] (Claim 2)

[0982] The system according to claim 1, further comprising means for creating and executing a custom processing procedure by combining a plurality of information processing devices based on a specified order and conditions.

[0983] (Claim 3)

[0984] The system according to claim 1, comprising means for monitoring and evaluating the performance of registered information processing devices and providing information to users.

[0985] "Application example 2 when combining with an emotional engine"

[0986] (Claim 1)

[0987] It provides an interface that incorporates an engine for analyzing user emotions.

[0988] A means of receiving requests from users to use an artificial intelligence agent,

[0989] Means for registering and managing information regarding the aforementioned artificial intelligence agent in a centralized management device,

[0990] A means of searching for and recommending an artificial intelligence agent suitable for the specific function requested by the user,

[0991] A system that includes means for coordinating information between selected artificial intelligence agents to perform processing and adjusting responses based on the user's emotions.

[0992] (Claim 2)

[0993] The system according to claim 1, further comprising means for dynamically adjusting a specified order and conditions based on emotion recognition results, and for combining multiple artificial intelligence agents to create and execute a custom workflow.

[0994] (Claim 3)

[0995] The system according to claim 1, comprising means for monitoring and evaluating the performance of registered artificial intelligence agents and for providing feedback based on user sentiment analysis data. [Explanation of Symbols]

[0996] 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. A portal platform for managing multiple artificial intelligence agents, A means of receiving requests from users to use an artificial intelligence agent, Means for registering and managing information about the aforementioned artificial intelligence agent in a database, A means of searching for an artificial intelligence agent suitable for a specific function requested by the user, A system that includes means for coordinating information between selected artificial intelligence agents to perform processing.

2. The system according to claim 1, further comprising means for creating and executing a custom workflow by combining multiple artificial intelligence agents based on a specified order and conditions.

3. The system according to claim 1, comprising means for monitoring and evaluating the performance of registered artificial intelligence agents and providing feedback to the user.

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