system

Abstract

We provide the system. [Solution] A means of monitoring the operational information of multiple artificial intelligence agents within a company, A means for optimizing the functions of each artificial intelligence agent based on the operational information, A means of communication for coordinating information between multiple artificial intelligence agents, Means for managing norms and security, A means of presenting the optimal way to utilize artificial intelligence agents, A means for receiving and visually displaying suggestions from an artificial intelligence agent via a device, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Currently, a plurality of artificial intelligence agents have been independently introduced within a company, resulting in fragmentation of data utilization. In addition, it is impossible to keep up with the rapid technological evolution, and the update of old agents may be delayed, which hinders the efficiency of business operations. Furthermore, the autonomous activities of each agent lead to an increase in the cost of compliance and security management, making it difficult to effectively utilize management resources.

Means for Solving the Problems

[0005] This invention provides a system that centrally monitors multiple artificial intelligence agents operating within an enterprise and optimizes the role of each agent based on their operational status. Furthermore, it facilitates information exchange between agents via APIs and achieves efficient business execution by analyzing and optimizing business processes using process mining algorithms. It also solves challenges by centralizing compliance and security management and proposing the most appropriate use of artificial intelligence agents as needed.

[0006] An "artificial intelligence agent" is a software program designed to automatically handle specific tasks or operations, and capable of learning and making decisions.

[0007] "Operating status" refers to the state of how frequently and for how long an artificial intelligence agent is operating.

[0008] "Optimization" refers to adjusting something to operate most effectively or efficiently under specific conditions.

[0009] "Communication methods" refer to technologies and protocols for transmitting information between multiple artificial intelligence agents.

[0010] "Compliance" means acting in accordance with laws, regulations, and internal company standards.

[0011] "Security" refers to protecting data and information assets and keeping them safe from unauthorized access and leaks.

[0012] "Process mining" is a technique that analyzes the actual state of business processes from data to gain insights for improving operational efficiency.

[0013] An "API" stands for "API," which refers to an interface that enables different software to share functions and exchange data. [Brief explanation of the drawing]

[0014] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

MODE FOR CARRYING OUT THE INVENTION

[0015] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, the numbered 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.

[0018] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, the numbered 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, etc.

[0020] 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).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention is a system for efficiently controlling and optimizing multiple artificial intelligence agents operating within an enterprise. A server plays a central role, monitoring the operational status of each AI agent and analyzing business processes. Based on these analysis results, the system optimizes the roles and tasks of each agent to improve operational efficiency. Furthermore, communication between agents is conducted via APIs, enabling proactive collaboration. This prevents data fragmentation and promotes a seamless flow of information.

[0036] Regarding compliance and security, the server centrally manages these aspects, granting access permissions and encrypting information based on corporate policies. The server constantly monitors communication logs and takes immediate action if any abnormal access attempts are detected.

[0037] Users can request support for their work through a dedicated client. In response, the server, based on analysis results obtained through process mining, suggests the most suitable AI agent for the task. For example, a user planning a promotion for a new product might be shown by the server which agent is the most suitable tool for market analysis, enabling effective information gathering.

[0038] Furthermore, terminals are deployed in each department, and each communicates with the server or agents in other departments via a dedicated API. This facilitates smooth cross-departmental work execution and ensures data integrity.

[0039] Thus, the present invention enables artificial intelligence agents in various departments within a company to collaborate efficiently, optimizing the organization's overall business processes. For example, a sales department terminal can adjust inventory data in real time, and a manufacturing department agent can immediately utilize that data to revise the production schedule. This process leads to faster customer response and simultaneously optimizes internal resource management.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The server collects operational logs from each artificial intelligence agent. This creates a dataset to understand which agents are running and to what extent.

[0043] Step 2:

[0044] The server applies process mining algorithms using the collected operational data. This analyzes the current state of business processes and visualizes inefficient parts and areas with long processing times.

[0045] Step 3:

[0046] Based on process mining results, the server generates instructions to optimize each agent's tasks. Following these instructions, agents have their roles and tasks reconfigured, streamlining business processes.

[0047] Step 4:

[0048] The terminal updates its API settings according to the server's instructions and prepares to collaborate with other departmental agents. Each agent then begins exchanging information using the new API.

[0049] Step 5:

[0050] The server monitors communication between all agents, immediately issues an alert if an anomaly occurs, and blocks communication as necessary.

[0051] Step 6:

[0052] When a user requests assistance with their work, the server uses insights gained from process mining to suggest the most suitable agent usage for the user. This allows the user to perform their tasks effectively.

[0053] (Example 1)

[0054] 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."

[0055] As the need for effective management and optimization of intelligent agents within enterprises increases, current systems fail to adequately monitor the operational status of each agent, optimize their functions, and ensure standards and security. Furthermore, insufficient information sharing between agents leads to decreased operational efficiency. Additionally, a lack of guidance on how users can best utilize intelligent agents remains a challenge.

[0056] 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.

[0057] In this invention, the server includes means for observing the operating status of intelligent agents, means for optimizing the functions of each intelligent agent, means for dialogue, means for analyzing and optimizing business processes using analytical techniques, and means for using generative technology models. This makes it possible to provide usage methods that contribute to the efficient management and optimization of agents within an enterprise, the promotion of a seamless flow of information, and the improvement of users' work efficiency.

[0058] An "intelligent agent" is a program used within a company to efficiently perform specific tasks or business processes.

[0059] "Operating status" refers to data related to the state and performance of an intelligent agent while it is running.

[0060] "Optimization" is the process of improving the arrangement of roles and tasks so that intelligent agents can perform at their best.

[0061] A "dialogue mechanism" is a communication mechanism that allows intelligent agents to exchange information with each other.

[0062] "Standards" refer to a set of rules related to compliance and standards established within a company.

[0063] "Security" refers to the security of data and systems within a company, and includes measures to prevent unauthorized access and data breaches.

[0064] "Analytical techniques" refer to methods and tools for collecting and processing data to gain insights into business processes.

[0065] A "generative technology model" refers to AI technology that generates appropriate output or advice based on specific input information (e.g., a prompt).

[0066] This invention embodies a system aimed at managing and optimizing intelligent agents within an enterprise. The server first monitors the operational status of the intelligent agents. This involves using common monitoring tools to collect data on how each agent operates and how much resources it uses. For example, the operational status is recorded in a database in real time.

[0067] Next, the server uses analytical techniques to analyze the entire business process. This analysis utilizes a data streaming platform and process mining tools to identify bottlenecks and inefficiencies in the business flow. The optimized business flow is then fed back to each agent by the server, updating their respective roles and tasks.

[0068] Furthermore, the server facilitates smooth information exchange between agents through dialogue mechanisms. APIs are utilized for this purpose, providing a system that allows each agent to utilize each other's output data in a timely manner.

[0069] The server centrally manages standards and security. The server implements authentication protocols and encryption of communications to ensure data and communication security. This enables agent operation in an environment protected from unauthorized access while complying with standards.

[0070] Users can use a dedicated client application to request instructions and advice regarding their work. The server utilizes a generative technology model to suggest how to use the agent based on the prompts entered by the user. For example, if a user wants to analyze market data when launching a new product, they can use prompts like the following:

[0071] Example prompt:

[0072] "Before launching our new product X, please analyze sales data from the past five years and current market trends to advise us on which regions have the greatest potential for sales."

[0073] Terminals are deployed in each department and support cross-departmental collaboration by communicating with servers and agents in other departments. This communication allows each department to share data in real time and make quick decisions based on accurate information. For example, a terminal in the sales department can update inventory data, and the manufacturing department can then revise its production schedule based on those results.

[0074] Thus, the present invention provides specific means for achieving efficient management of intelligent agents and optimization of business processes.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The server collects operational data from intelligent agents. Input data includes CPU usage, memory usage, and network activity for each agent. The server analyzes this data and records it in a database as performance metrics. Output data includes a dashboard that visualizes the operational status of each agent. Specific operations include a process of periodically collecting data using monitoring tools.

[0078] Step 2:

[0079] The server analyzes business processes using a data streaming platform. The input consists of various data streams related to the business. The server uses process mining tools to analyze the data and identify bottlenecks and inefficient processes. The output is a visualized report of the business flow, including improvement suggestions. Specific operations include real-time data processing and reporting of analysis results.

[0080] Step 3:

[0081] The server uses a generative AI model to generate advice in response to user prompts. The input is a prompt entered by the user through a dedicated client application. The server uses the generative AI model to generate appropriate advice and information for this prompt. The output is a suggestion of how to use the agent, presented to the user. Specific operations include parsing the prompt and providing appropriate output.

[0082] Step 4:

[0083] The terminal exchanges information with each department via APIs with servers or agents in other departments. Inputs include departmental business data and API calls. The terminal processes this information quickly and communicates it to the relevant departments and agents. Outputs provide data for real-time information sharing and decision-making between departments. Specific operations include sending and receiving data via APIs.

[0084] (Application Example 1)

[0085] 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."

[0086] In today's business environment, there is a demand for increased work efficiency and elimination of waste. Conventional artificial intelligence agent systems suffer from insufficient coordination between agents and difficulties in centralizing information, hindering the optimization of business processes. Furthermore, the difficulty in integrating suggestions from different agents makes it difficult for on-site workers to respond quickly and accurately. It is necessary to solve these problems, further improve operational efficiency within factories, and realize a smooth data flow.

[0087] 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.

[0088] In this invention, the server includes means for monitoring the operational information of multiple artificial intelligence agents within a company, means for optimizing the functions of each artificial intelligence agent based on said operational information, and communication means for coordinating information between the multiple artificial intelligence agents. This enables each agent to cooperate efficiently, improves on-site work efficiency, and realizes a seamless flow of information.

[0089] An "artificial intelligence agent" is an intelligent program designed to automate and streamline specific tasks or operations.

[0090] "Operational information" refers to data about the agent's work status and operating condition, and serves as basic data for monitoring and optimization.

[0091] "Function optimization" is the process of enabling agents to perform their roles more effectively in order to improve operational efficiency.

[0092] "Communication methods" refer to the technical techniques and infrastructure used to exchange information and coordinate among multiple artificial intelligence agents.

[0093] "Means of visual display" refers to methods of using devices to display data and proposals in a format that is easy for users to understand.

[0094] A "processing activity mining algorithm" is a data analysis method used to analyze business procedures and gain insights for improvement.

[0095] The AI ​​agent system of this invention aims to improve operational efficiency within factories. The server continuously monitors the operational information of each artificial intelligence agent and optimizes its functions as needed. This optimization process is based on a process mining algorithm, analyzing work procedures and providing insights to enable efficient operation. Furthermore, the server utilizes an application program interface as a communication means to facilitate information exchange between agents. This allows each agent to exchange information in real time and strengthen their collaboration.

[0096] On the terminal side, suggestions from the agent can be visually displayed to the user using specific devices, such as smart glasses. This feature allows users to receive optimized suggestions based on real-time data within the factory, improving the speed and accuracy of decision-making.

[0097] As a concrete example, consider a manufacturing plant. The server collects and analyzes operational data from each production line, and then proposes resource reallocation for lines that are deemed inefficient. Upon receiving this information, workers can take quick action based on the suggestions presented through smart glasses. This improves overall productivity and reduces unnecessary waiting time. An example of a prompt using a generative AI model is, "Please provide a method to analyze the efficiency of each production line in the factory in real time and identify lines with high potential for improvement."

[0098] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0099] Step 1:

[0100] The server collects operational information from each artificial intelligence agent. The input is data indicating the operational status of each agent, and the output is overall operational data that integrates these. Specifically, the server acquires data from each agent in real time using communication methods and stores that data in a central database.

[0101] Step 2:

[0102] The server analyzes the collected operational data using a process mining algorithm. The input is the integrated operational data obtained in step 1, and the output is the analysis results regarding the optimization of business processes. At this stage, the server detects data patterns and identifies process bottlenecks. Specifically, it calculates indicators that show inefficient lines or imbalances in resource allocation.

[0103] Step 3:

[0104] The server redistributes optimized tasks and roles to each agent based on the analysis results. The input is the analysis results obtained in step 2, and the output is the new instructions for the agents. The server sends the optimized roles to the agents via the application programming interface, and the agents adjust their actions accordingly.

[0105] Step 4:

[0106] The user receives action suggestions from the agent through smart glasses. The input is suggestion data sent from the server, and the output is specific action guidelines displayed on the visual display. Specifically, the user takes action according to resource reallocation and task priorities displayed using the smart glasses.

[0107] Step 5:

[0108] The terminal updates field data based on user actions and sends feedback to the server. The input is the result of the user's actions, and the output is the updated field data. The terminal records the completion and changes of tasks and sends this information to the server, allowing the entire process to be continuously optimized in real time.

[0109] 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.

[0110] This invention provides a system for efficiently managing and optimizing multiple artificial intelligence agents operating within a company, and incorporates an emotion engine that recognizes user emotions. The server functions as a central management device, monitoring the operational status of each artificial intelligence agent and receiving the user's emotional state via the emotion engine.

[0111] The emotion engine analyzes the user's text input, voice, and facial expression data to understand their emotional state in real time. This emotional data is sent to the server and used to optimize agent roles based on the current task performance and the user's emotions. For example, if the user is stressed, the server will activate additional agents to simplify information delivery in order to reduce the user's burden.

[0112] The device receives instructions from the emotion engine and modifies the agent's settings or configures new APIs. This allows the artificial intelligence agent to dynamically change its role in response to the user's emotions, enabling flexible responses. This situation is centrally reflected in the agent's process flow, guaranteeing a response appropriate to the user's emotions.

[0113] Furthermore, the system can suggest the most suitable agent usage based on the user's business requirements. For example, if a user is feeling anxious about a project deadline, the server will recommend a task management agent to support effective schedule management. In this way, by integrating with the emotion engine, comprehensive business support is achieved that goes beyond mere operational efficiency and also takes into account the user's psychological burden.

[0114] The following describes the processing flow.

[0115] Step 1:

[0116] The user provides text input, voice, or facial expression data through the device. This allows the device to collect data to detect the user's emotional state.

[0117] Step 2:

[0118] The device uses an emotion engine to analyze the user's emotions. The analysis identifies the user's current emotional state. This information is then used for subsequent processing.

[0119] Step 3:

[0120] The device sends the results of the emotion engine's analysis to the server. The server receives this data and plans the optimal actions of the artificial intelligence agent based on the user's emotional state.

[0121] Step 4:

[0122] The server makes necessary adjustments based on the operational status of each artificial intelligence agent and the user's emotional state. For example, if the user is stressed, it activates agents that simplify tasks or provide more support.

[0123] Step 5:

[0124] The server sends the newly adjusted agent configuration to the terminal. The terminal receives this information, updates the agent settings locally, and modifies the API settings. This results in an agent running that is optimized for the user's situation.

[0125] Step 6:

[0126] When a user utilizes the agent, the server suggests an agent usage method appropriate to the user's current emotional state, based on process mining results and sentiment data. This suggestion allows the user to perform their tasks more comfortably and efficiently.

[0127] (Example 2)

[0128] 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."

[0129] Efficient management and optimization of multiple intelligent agents within a company require flexible responses that take into account the emotional state of the user. However, conventional systems fix the roles of agents without adequately considering the user's psychological state, and therefore do not always achieve optimal responses. This leads to increased user burden and decreased work efficiency.

[0130] 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.

[0131] In this invention, the server includes means for monitoring the operational status of multiple intelligent agents within a company, means including an emotion analysis device for analyzing the user's emotional state, and means for dynamically adjusting the roles of the intelligent agents according to the user's emotional state. This makes it possible for the intelligent agents to flexibly change their roles according to the user's emotions, thereby reducing the user's psychological burden while improving work efficiency.

[0132] An "intelligent agent" is software that has the ability to perform a variety of tasks in response to user instructions and changes in the environment.

[0133] "Operating status" refers to information indicating the current state of activities and processes being performed by the intelligent agent.

[0134] "Role optimization" refers to the process where an intelligent agent adjusts its functions and tasks according to the user's needs and emotional state.

[0135] "Communication infrastructure" is a general term for the infrastructure that allows multiple systems and devices to exchange information with each other.

[0136] An "emotion analysis device" is a technology or software that detects and analyzes a user's emotional state from data such as text, voice, and facial expressions.

[0137] "Dynamic adjustment" refers to a system autonomously changing its settings or operation in response to specific conditions or circumstances.

[0138] "Compliance" refers to measures and means to ensure operations are conducted in accordance with laws, regulations, guidelines, etc.

[0139] "Security" refers to the means and policies taken to protect information and systems from unauthorized access and data breaches.

[0140] The system of this invention aims to improve operational efficiency by managing multiple intelligent agents operating within a company, analyzing the user's emotional state in real time, and dynamically adjusting the roles of the agents.

[0141] The server acts as the core of this system, monitoring the operational status of the intelligent agents. The server receives emotion data sent from users and generates instructions to optimize the agents' roles based on that data.

[0142] The device receives text input, voice, and facial expression data from the user and analyzes it via an emotion analysis device. This analysis uses natural language processing libraries (e.g., spaCy) and speech analysis tools (e.g., Google® Cloud Speech-to-Text). This data allows the user's emotional state to be understood in real time and transmitted to the server accordingly.

[0143] For example, if a user is experiencing stress, the emotion analyzer sends that data to a server, which then adjusts the agent's role to provide information that helps reduce stress. For instance, if a user feels overwhelmed with tasks, the task management agent is activated to help with scheduling and prioritizing.

[0144] Examples of prompt statements for generative AI models include the following:

[0145] "Please explain how the task management agent is activated when the user is feeling stressed."

[0146] "Please explain in detail the process by which the emotion engine analyzes the user's emotions."

[0147] In this way, the system supports the efficient progress of tasks while reducing the user's psychological burden through interaction with the user.

[0148] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0149] Step 1:

[0150] The user inputs text, voice, or facial expression data from their device into the system. This is the input data. This data is first captured by the device.

[0151] Step 2:

[0152] The device uses an emotion analysis device to analyze the user's input data. In this step, data processing is performed using natural language processing libraries and speech analysis tools, such as tokenizing text, transcribing speech into text, and quantifying facial expression data. As a result of this analysis, the user's emotional state (e.g., high stress, joy, anxiety) is output.

[0153] Step 3:

[0154] The device sends analyzed emotional data to the server. The data sent includes the user's emotional state, along with recommended actions based on that state.

[0155] Step 4:

[0156] The server analyzes and optimizes the role of each intelligent agent based on the received emotion data. Specifically, it updates agent schedules and adjusts loads based on the data. As a result, a new role configuration for each agent is output.

[0157] Step 5:

[0158] The server sends instructions to the terminal specifying the role of the optimized agent. This involves the execution of necessary API calls and configurations to activate the corresponding agent. This ensures that the agent on the terminal takes the most appropriate action based on the user's current situation.

[0159] Step 6:

[0160] Users receive responses and information from the system, including stress reduction information and recommendations for new agents for task management. Based on this output, users can perform their tasks more effectively.

[0161] (Application Example 2)

[0162] 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."

[0163] In modern organizations, the demanding work environment places stress and burden on employees, posing a significant obstacle to efficient work performance. Furthermore, when operating multiple autonomous knowledge processing systems, it is necessary to appropriately optimize the role of each system. However, conventional systems struggle to dynamically adjust roles while adequately considering the user's emotional state, making it difficult to alleviate psychological burden and improve work efficiency and staff satisfaction.

[0164] 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.

[0165] In this invention, the server includes means for monitoring the operational status of multiple autonomous knowledge processing devices within an organization, means for optimizing the role of each autonomous knowledge processing device based on its operational status and the emotional state of an individual, and emotion analysis means for analyzing an individual's emotions. This enables appropriate workload reduction and role adjustment according to the user's emotional state, thereby improving work efficiency while reducing the psychological burden on employees.

[0166] An "autonomous knowledge processing device" is an information processing device that automatically executes specific business processes within an organization and assists in decision-making based on user input.

[0167] "Emotional state" refers to the emotional state and psychological reactions that individual users experience, and is a factor that influences their ability to perform their work.

[0168] "Optimizing roles" means automatically adjusting the functions and tasks of each autonomous knowledge processing unit so that it can perform its duties efficiently and accurately.

[0169] "Emotion analysis means" refers to technology that analyzes data such as the user's text, voice, and facial expressions to identify the user's emotions in real time.

[0170] "Load reduction" refers to reducing the mental and physical stress and effort placed on the user.

[0171] This invention realizes a system that optimizes the role of autonomous knowledge processing devices within an organization according to the emotional state of users. A server manages multiple autonomous knowledge processing devices within the organization and continuously monitors the operating status of each device. The emotion analysis means used here receives text data, voice data, and facial expression data from individual users within the organization in real time and determines the emotional state of the users. Cloud services such as Google Cloud AI's natural language processing tools, Google Cloud Vision API, and Google Cloud Speech-to-Text are used for this data analysis.

[0172] Based on the analysis results, the server dynamically adjusts the roles of each autonomous knowledge processing unit and implements appropriate load reduction measures as needed. For example, in a security center, if a user is experiencing high levels of stress, the server automatically activates additional information-providing devices to support the user's work. This improves work efficiency and reduces psychological burden.

[0173] As a specific example, if the smart glasses being used detect that a security staff member is feeling anxious through emotion analysis, they may display a notification saying, "Please relax. We have initiated support to help you cope with this new situation," and then offer helpful instructions.

[0174] An example of a prompt sentence to input into the generating AI model is, "How can you support and stabilize the emotions of staff who are feeling anxious while preparing for a safety event?"

[0175] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0176] Step 1:

[0177] When users perform daily tasks through their smart devices, the devices acquire voice data, text data, and facial expression data. This input data is collected to evaluate the user's current emotional state. Specifically, the device's microphone and camera capture voice and facial expressions, and if text is entered, its content is also captured.

[0178] Step 2:

[0179] The device sends the acquired data to the server, which then analyzes the user's emotional state using emotion analysis tools on a cloud service. In this process, Google Cloud Speech-to-Text is used to convert speech to text, and cloud-based natural language processing tools analyze the text data to determine the emotion. The output is a label indicating the user's emotional state (e.g., stressed, excited, calm).

[0180] Step 3:

[0181] The server receives the analysis results and optimizes the role of the autonomous knowledge processing unit according to the individual's emotional state. Specifically, if the user is under stress, it activates support devices to reduce the load. The output here is the optimized device configuration information, and the processing unit modifies its operation based on this information.

[0182] Step 4:

[0183] The server sends optimized information to the terminal and displays notifications to the user in an easy-to-understand format. Specific procedures and navigation may be presented using smart glasses, or advice to reduce the burden may be conveyed via voice or text. The terminal's role is to intuitively convey data from the server to the user.

[0184] Step 5:

[0185] The user proceeds with their tasks based on the information presented, and the server continues to optimize in real time by acquiring new data. This process is updated whenever the user's emotional state changes, enabling effective and flexible support for their work. This forms a feedback loop after the user receives the information.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] [Second Embodiment]

[0190] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0191] 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.

[0192] 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).

[0193] 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.

[0194] 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.

[0195] 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).

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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".

[0202] This invention is a system for efficiently controlling and optimizing multiple artificial intelligence agents operating within an enterprise. A server plays a central role, monitoring the operational status of each AI agent and analyzing business processes. Based on these analysis results, the system optimizes the roles and tasks of each agent to improve operational efficiency. Furthermore, communication between agents is conducted via APIs, enabling proactive collaboration. This prevents data fragmentation and promotes a seamless flow of information.

[0203] Regarding compliance and security, the server centrally manages these aspects, granting access permissions and encrypting information based on corporate policies. The server constantly monitors communication logs and takes immediate action if any abnormal access attempts are detected.

[0204] Users can request support for their work through a dedicated client. In response, the server, based on analysis results obtained through process mining, suggests the most suitable AI agent for the task. For example, a user planning a promotion for a new product might be shown by the server which agent is the most suitable tool for market analysis, enabling effective information gathering.

[0205] Furthermore, terminals are deployed in each department, and each communicates with the server or agents in other departments via a dedicated API. This facilitates smooth cross-departmental work execution and ensures data integrity.

[0206] Thus, the present invention enables artificial intelligence agents in various departments within a company to collaborate efficiently, optimizing the organization's overall business processes. For example, a sales department terminal can adjust inventory data in real time, and a manufacturing department agent can immediately utilize that data to revise the production schedule. This process leads to faster customer response and simultaneously optimizes internal resource management.

[0207] The following describes the processing flow.

[0208] Step 1:

[0209] The server collects operational logs from each artificial intelligence agent. This creates a dataset to understand which agents are running and to what extent.

[0210] Step 2:

[0211] The server applies process mining algorithms using the collected operational data. This analyzes the current state of business processes and visualizes inefficient parts and areas with long processing times.

[0212] Step 3:

[0213] Based on process mining results, the server generates instructions to optimize each agent's tasks. Following these instructions, agents have their roles and tasks reconfigured, streamlining business processes.

[0214] Step 4:

[0215] The terminal updates its API settings according to the server's instructions and prepares to collaborate with other departmental agents. Each agent then begins exchanging information using the new API.

[0216] Step 5:

[0217] The server monitors communication between all agents, immediately issues an alert if an anomaly occurs, and blocks communication as necessary.

[0218] Step 6:

[0219] When a user requests assistance with their work, the server uses insights gained from process mining to suggest the most suitable agent usage for the user. This allows the user to perform their tasks effectively.

[0220] (Example 1)

[0221] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0222] As the need for effective management and optimization of intelligent agents within enterprises increases, current systems fail to adequately monitor the operational status of each agent, optimize their functions, and ensure standards and security. Furthermore, insufficient information sharing between agents leads to decreased operational efficiency. Additionally, a lack of guidance on how users can best utilize intelligent agents remains a challenge.

[0223] 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.

[0224] In this invention, the server includes means for observing the operating status of intelligent agents, means for optimizing the functions of each intelligent agent, means for dialogue, means for analyzing and optimizing business processes using analytical techniques, and means for using generative technology models. This makes it possible to provide usage methods that contribute to the efficient management and optimization of agents within an enterprise, the promotion of a seamless flow of information, and the improvement of users' work efficiency.

[0225] An "intelligent agent" is a program used within a company to efficiently perform specific tasks or business processes.

[0226] "Operating status" refers to data related to the state and performance of an intelligent agent while it is running.

[0227] "Optimization" is the process of improving the arrangement of roles and tasks so that intelligent agents can perform at their best.

[0228] A "dialogue mechanism" is a communication mechanism that allows intelligent agents to exchange information with each other.

[0229] "Standards" refer to a set of rules related to compliance and standards established within a company.

[0230] "Security" refers to the security of data and systems within a company, and includes measures to prevent unauthorized access and data breaches.

[0231] "Analytical techniques" refer to methods and tools for collecting and processing data to gain insights into business processes.

[0232] A "generative technology model" refers to AI technology that generates appropriate output or advice based on specific input information (e.g., a prompt).

[0233] This invention embodies a system aimed at managing and optimizing intelligent agents within an enterprise. The server first monitors the operational status of the intelligent agents. This involves using common monitoring tools to collect data on how each agent operates and how much resources it uses. For example, the operational status is recorded in a database in real time.

[0234] Next, the server uses analytical techniques to analyze the entire business process. This analysis utilizes a data streaming platform and process mining tools to identify bottlenecks and inefficiencies in the business flow. The optimized business flow is then fed back to each agent by the server, updating their respective roles and tasks.

[0235] Furthermore, the server facilitates smooth information exchange between agents through dialogue mechanisms. APIs are utilized for this purpose, providing a system that allows each agent to utilize each other's output data in a timely manner.

[0236] The server centrally manages standards and security. The server implements authentication protocols and encryption of communications to ensure data and communication security. This enables agent operation in an environment protected from unauthorized access while complying with standards.

[0237] Users can use a dedicated client application to request instructions and advice regarding their work. The server utilizes a generative technology model to suggest how to use the agent based on the prompts entered by the user. For example, if a user wants to analyze market data when launching a new product, they can use prompts like the following:

[0238] Example prompt:

[0239] "Before launching our new product X, please analyze sales data from the past five years and current market trends to advise us on which regions have the greatest potential for sales."

[0240] Terminals are deployed in each department and support cross-departmental collaboration by communicating with servers and agents in other departments. This communication allows each department to share data in real time and make quick decisions based on accurate information. For example, a terminal in the sales department can update inventory data, and the manufacturing department can then revise its production schedule based on those results.

[0241] Thus, the present invention provides specific means for achieving efficient management of intelligent agents and optimization of business processes.

[0242] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0243] Step 1:

[0244] The server collects operational data from intelligent agents. Input data includes CPU usage, memory usage, and network activity for each agent. The server analyzes this data and records it in a database as performance metrics. Output data includes a dashboard that visualizes the operational status of each agent. Specific operations include a process of periodically collecting data using monitoring tools.

[0245] Step 2:

[0246] The server analyzes business processes using a data streaming platform. The input consists of various data streams related to the business. The server uses process mining tools to analyze the data and identify bottlenecks and inefficient processes. The output is a visualized report of the business flow, including improvement suggestions. Specific operations include real-time data processing and reporting of analysis results.

[0247] Step 3:

[0248] The server uses a generative AI model to generate advice in response to user prompts. The input is a prompt entered by the user through a dedicated client application. The server uses the generative AI model to generate appropriate advice and information for this prompt. The output is a suggestion of how to use the agent, presented to the user. Specific operations include parsing the prompt and providing appropriate output.

[0249] Step 4:

[0250] The terminal exchanges information with each department via APIs with servers or agents in other departments. Inputs include departmental business data and API calls. The terminal processes this information quickly and communicates it to the relevant departments and agents. Outputs provide data for real-time information sharing and decision-making between departments. Specific operations include sending and receiving data via APIs.

[0251] (Application Example 1)

[0252] 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."

[0253] In today's business environment, there is a demand for increased work efficiency and elimination of waste. Conventional artificial intelligence agent systems suffer from insufficient coordination between agents and difficulties in centralizing information, hindering the optimization of business processes. Furthermore, the difficulty in integrating suggestions from different agents makes it difficult for on-site workers to respond quickly and accurately. It is necessary to solve these problems, further improve operational efficiency within factories, and realize a smooth data flow.

[0254] 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.

[0255] In this invention, the server includes means for monitoring the operational information of multiple artificial intelligence agents within a company, means for optimizing the functions of each artificial intelligence agent based on said operational information, and communication means for coordinating information between the multiple artificial intelligence agents. This enables each agent to cooperate efficiently, improves on-site work efficiency, and realizes a seamless flow of information.

[0256] An "artificial intelligence agent" is an intelligent program designed to automate and streamline specific tasks or operations.

[0257] "Operational information" refers to data about the agent's work status and operating condition, and serves as basic data for monitoring and optimization.

[0258] "Function optimization" is the process of enabling agents to perform their roles more effectively in order to improve operational efficiency.

[0259] "Communication methods" refer to the technical techniques and infrastructure used to exchange information and coordinate among multiple artificial intelligence agents.

[0260] "Means of visual display" refers to methods of using devices to display data and proposals in a format that is easy for users to understand.

[0261] A "processing activity mining algorithm" is a data analysis method used to analyze business procedures and gain insights for improvement.

[0262] The AI ​​agent system of this invention aims to improve operational efficiency within factories. The server continuously monitors the operational information of each artificial intelligence agent and optimizes its functions as needed. This optimization process is based on a process mining algorithm, analyzing work procedures and providing insights to enable efficient operation. Furthermore, the server utilizes an application program interface as a communication means to facilitate information exchange between agents. This allows each agent to exchange information in real time and strengthen their collaboration.

[0263] On the terminal side, suggestions from the agent can be visually displayed to the user using specific devices, such as smart glasses. This feature allows users to receive optimized suggestions based on real-time data within the factory, improving the speed and accuracy of decision-making.

[0264] As a concrete example, consider a manufacturing plant. The server collects and analyzes operational data from each production line, and then proposes resource reallocation for lines that are deemed inefficient. Upon receiving this information, workers can take quick action based on the suggestions presented through smart glasses. This improves overall productivity and reduces unnecessary waiting time. An example of a prompt using a generative AI model is, "Please provide a method to analyze the efficiency of each production line in the factory in real time and identify lines with high potential for improvement."

[0265] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0266] Step 1:

[0267] The server collects operational information from each artificial intelligence agent. The input is data indicating the operational status of each agent, and the output is overall operational data that integrates these. Specifically, the server acquires data from each agent in real time using communication methods and stores that data in a central database.

[0268] Step 2:

[0269] The server analyzes the collected operational data using a process mining algorithm. The input is the integrated operational data obtained in step 1, and the output is the analysis results regarding the optimization of business processes. At this stage, the server detects data patterns and identifies process bottlenecks. Specifically, it calculates indicators that show inefficient lines or imbalances in resource allocation.

[0270] Step 3:

[0271] The server redistributes optimized tasks and roles to each agent based on the analysis results. The input is the analysis results obtained in step 2, and the output is the new instructions for the agents. The server sends the optimized roles to the agents via the application programming interface, and the agents adjust their actions accordingly.

[0272] Step 4:

[0273] The user receives action suggestions from the agent through smart glasses. The input is suggestion data sent from the server, and the output is specific action guidelines displayed on the visual display. Specifically, the user takes action according to resource reallocation and task priorities displayed using the smart glasses.

[0274] Step 5:

[0275] The terminal updates field data based on user actions and sends feedback to the server. The input is the result of the user's actions, and the output is the updated field data. The terminal records the completion and changes of tasks and sends this information to the server, allowing the entire process to be continuously optimized in real time.

[0276] 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.

[0277] This invention provides a system for efficiently managing and optimizing multiple artificial intelligence agents operating within a company, and incorporates an emotion engine that recognizes user emotions. The server functions as a central management device, monitoring the operational status of each artificial intelligence agent and receiving the user's emotional state via the emotion engine.

[0278] The emotion engine analyzes the user's text input, voice, and facial expression data to understand their emotional state in real time. This emotional data is sent to the server and used to optimize agent roles based on the current task performance and the user's emotions. For example, if the user is stressed, the server will activate additional agents to simplify information delivery in order to reduce the user's burden.

[0279] The device receives instructions from the emotion engine and modifies the agent's settings or configures new APIs. This allows the artificial intelligence agent to dynamically change its role in response to the user's emotions, enabling flexible responses. This situation is centrally reflected in the agent's process flow, guaranteeing a response appropriate to the user's emotions.

[0280] Furthermore, according to the user's business requirements, an optimal agent usage method can be proposed. For example, when the user is pressed for time and anxious about a project deadline, the server recommends a task management agent to assist with effective schedule management. In this way, by collaborating with the emotion engine, comprehensive business support that not only improves business efficiency but also takes into account the user's psychological burden can be achieved.

[0281] The processing flow will be described below.

[0282] Step 1:

[0283] The user provides text input, voice, or facial expression data through the terminal. As a result, the terminal collects data for detecting the user's emotional state.

[0284] Step 2:

[0285] The terminal uses the emotion engine to analyze the user's emotions. As an analysis result, it is determined what emotional state the user is currently in. This information is used for subsequent processing.

[0286] Step 3:

[0287] The terminal sends the analysis result of the emotion engine to the server. The server receives this data and plans the operation of the optimal artificial intelligence agent based on the user's emotional state.

[0288] Step 4:

[0289] Based on the operating status of each artificial intelligence agent and the user's emotional state, the server performs necessary adjustments. For example, if the user is in a stressed state, an agent that simplifies tasks or increases support is activated.

[0290] Step 5:

[0291] The server sends the newly adjusted agent configuration to the terminal. The terminal receives this information, updates the agent settings locally, and modifies the API settings. This results in an agent running that is optimized for the user's situation.

[0292] Step 6:

[0293] When a user utilizes the agent, the server suggests an agent usage method appropriate to the user's current emotional state, based on process mining results and sentiment data. This suggestion allows the user to perform their tasks more comfortably and efficiently.

[0294] (Example 2)

[0295] 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".

[0296] Efficient management and optimization of multiple intelligent agents within a company require flexible responses that take into account the emotional state of the user. However, conventional systems fix the roles of agents without adequately considering the user's psychological state, and therefore do not always achieve optimal responses. This leads to increased user burden and decreased work efficiency.

[0297] 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.

[0298] In this invention, the server includes means for monitoring the operational status of multiple intelligent agents within a company, means including an emotion analysis device for analyzing the user's emotional state, and means for dynamically adjusting the roles of the intelligent agents according to the user's emotional state. This makes it possible for the intelligent agents to flexibly change their roles according to the user's emotions, thereby reducing the user's psychological burden while improving work efficiency.

[0299] An "intelligent agent" is software that has the ability to perform various tasks according to the instructions of the user or changes in the environment.

[0300] The "operating status" is information indicating the state of the activities and processes currently being carried out by the intelligent agent.

[0301] "Role optimization" refers to the intelligent agent adjusting its functions and tasks according to the user's needs and emotional state.

[0302] The "communication means" is a general term for the infrastructure for multiple systems and devices to exchange information with each other.

[0303] The "emotion analysis device" is technology or software for detecting and analyzing the user's emotional state from data such as text, voice, and expressions.

[0304] "Dynamic adjustment" refers to the system autonomously changing its settings and operations according to specific conditions and situations.

[0305] "Compliance" refers to the policies and means for ensuring operation in accordance with laws, regulations, guidelines, etc.

[0306] "Security" refers to the means and policies for protecting information and systems from unauthorized access and data leakage.

[0307] The system of this invention manages multiple intelligent agents operating within an enterprise, analyzes the user's emotional state in real time, and dynamically adjusts the roles of the agents to improve business efficiency.

[0308] The server functions as the core of this system and monitors the operating status of the intelligent agents. The server receives the emotion data sent from the user and generates instructions for optimizing the roles of the agents based on that data.

[0309] The device receives text input, voice, and facial expression data from the user and analyzes it via an emotion analysis device. This analysis uses natural language processing libraries (e.g., spaCy) and speech analysis tools (e.g., Google Cloud Speech-to-Text). This data allows the user's emotional state to be understood in real time and transmitted to the server accordingly.

[0310] For example, if a user is experiencing stress, the emotion analyzer sends that data to a server, which then adjusts the agent's role to provide information that helps reduce stress. For instance, if a user feels overwhelmed with tasks, the task management agent is activated to help with scheduling and prioritizing.

[0311] Examples of prompt statements for generative AI models include the following:

[0312] "Please explain how the task management agent is activated when the user is feeling stressed."

[0313] "Please explain in detail the process by which the emotion engine analyzes the user's emotions."

[0314] In this way, the system supports the efficient progress of tasks while reducing the user's psychological burden through interaction with the user.

[0315] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0316] Step 1:

[0317] The user inputs text, voice, or facial expression data from their device into the system. This is the input data. This data is first captured by the device.

[0318] Step 2:

[0319] The device uses an emotion analysis device to analyze the user's input data. In this step, data processing is performed using natural language processing libraries and speech analysis tools, such as tokenizing text, transcribing speech into text, and quantifying facial expression data. As a result of this analysis, the user's emotional state (e.g., high stress, joy, anxiety) is output.

[0320] Step 3:

[0321] The device sends analyzed emotional data to the server. The data sent includes the user's emotional state, along with recommended actions based on that state.

[0322] Step 4:

[0323] The server analyzes and optimizes the role of each intelligent agent based on the received emotion data. Specifically, it updates agent schedules and adjusts loads based on the data. As a result, a new role configuration for each agent is output.

[0324] Step 5:

[0325] The server sends instructions to the terminal specifying the role of the optimized agent. This involves the execution of necessary API calls and configurations to activate the corresponding agent. This ensures that the agent on the terminal takes the most appropriate action based on the user's current situation.

[0326] Step 6:

[0327] Users receive responses and information from the system, including stress reduction information and recommendations for new agents for task management. Based on this output, users can perform their tasks more effectively.

[0328] (Application Example 2)

[0329] 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."

[0330] In modern organizations, the demanding work environment places stress and burden on employees, posing a significant obstacle to efficient work performance. Furthermore, when operating multiple autonomous knowledge processing systems, it is necessary to appropriately optimize the role of each system. However, conventional systems struggle to dynamically adjust roles while adequately considering the user's emotional state, making it difficult to alleviate psychological burden and improve work efficiency and staff satisfaction.

[0331] 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.

[0332] In this invention, the server includes means for monitoring the operational status of multiple autonomous knowledge processing devices within an organization, means for optimizing the role of each autonomous knowledge processing device based on its operational status and the emotional state of an individual, and emotion analysis means for analyzing an individual's emotions. This enables appropriate workload reduction and role adjustment according to the user's emotional state, thereby improving work efficiency while reducing the psychological burden on employees.

[0333] An "autonomous knowledge processing device" is an information processing device that automatically executes specific business processes within an organization and assists in decision-making based on user input.

[0334] "Emotional state" refers to the emotional state and psychological reactions that individual users experience, and is a factor that influences their ability to perform their work.

[0335] "Optimizing roles" means automatically adjusting the functions and tasks of each autonomous knowledge processing unit so that it can perform its duties efficiently and accurately.

[0336] "Emotion analysis means" refers to technology that analyzes data such as the user's text, voice, and facial expressions to identify the user's emotions in real time.

[0337] "Load reduction" refers to reducing the mental and physical stress and effort placed on the user.

[0338] This invention realizes a system that optimizes the role of autonomous knowledge processing devices within an organization according to the emotional state of users. A server manages multiple autonomous knowledge processing devices within the organization and continuously monitors the operating status of each device. The emotion analysis means used here receives text data, voice data, and facial expression data from individual users within the organization in real time and determines the emotional state of the users. Cloud services such as Google Cloud AI's natural language processing tools, Google Cloud Vision API, and Google Cloud Speech-to-Text are used for this data analysis.

[0339] Based on the analysis results, the server dynamically adjusts the roles of each autonomous knowledge processing unit and implements appropriate load reduction measures as needed. For example, in a security center, if a user is experiencing high levels of stress, the server automatically activates additional information-providing devices to support the user's work. This improves work efficiency and reduces psychological burden.

[0340] As a specific example, if the smart glasses being used detect that a security staff member is feeling anxious through emotion analysis, they may display a notification saying, "Please relax. We have initiated support to help you cope with this new situation," and then offer helpful instructions.

[0341] An example of a prompt sentence to input into the generating AI model is, "How can you support and stabilize the emotions of staff who are feeling anxious while preparing for a safety event?"

[0342] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0343] Step 1:

[0344] When users perform daily tasks through their smart devices, the devices acquire voice data, text data, and facial expression data. This input data is collected to evaluate the user's current emotional state. Specifically, the device's microphone and camera capture voice and facial expressions, and if text is entered, its content is also captured.

[0345] Step 2:

[0346] The device sends the acquired data to the server, which then analyzes the user's emotional state using emotion analysis tools on a cloud service. In this process, Google Cloud Speech-to-Text is used to convert speech to text, and cloud-based natural language processing tools analyze the text data to determine the emotion. The output is a label indicating the user's emotional state (e.g., stressed, excited, calm).

[0347] Step 3:

[0348] The server receives the analysis results and optimizes the role of the autonomous knowledge processing unit according to the individual's emotional state. Specifically, if the user is under stress, it activates support devices to reduce the load. The output here is the optimized device configuration information, and the processing unit modifies its operation based on this information.

[0349] Step 4:

[0350] The server sends optimized information to the terminal and displays notifications to the user in an easy-to-understand format. Specific procedures and navigation may be presented using smart glasses, or advice to reduce the burden may be conveyed via voice or text. The terminal's role is to intuitively convey data from the server to the user.

[0351] Step 5:

[0352] The user proceeds with their tasks based on the information presented, and the server continues to optimize in real time by acquiring new data. This process is updated whenever the user's emotional state changes, enabling effective and flexible support for their work. This forms a feedback loop after the user receives the information.

[0353] 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.

[0354] 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.

[0355] 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.

[0356] [Third Embodiment]

[0357] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0358] 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.

[0359] 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).

[0360] 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.

[0361] 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.

[0362] 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).

[0363] 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.

[0364] 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.

[0365] 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.

[0366] 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.

[0367] 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.

[0368] 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".

[0369] This invention is a system for efficiently controlling and optimizing multiple artificial intelligence agents operating within an enterprise. A server plays a central role, monitoring the operational status of each AI agent and analyzing business processes. Based on these analysis results, the system optimizes the roles and tasks of each agent to improve operational efficiency. Furthermore, communication between agents is conducted via APIs, enabling proactive collaboration. This prevents data fragmentation and promotes a seamless flow of information.

[0370] Regarding compliance and security, the server centrally manages these aspects, granting access permissions and encrypting information based on corporate policies. The server constantly monitors communication logs and takes immediate action if any abnormal access attempts are detected.

[0371] Users can request support for their work through a dedicated client. In response, the server, based on analysis results obtained through process mining, suggests the most suitable AI agent for the task. For example, a user planning a promotion for a new product might be shown by the server which agent is the most suitable tool for market analysis, enabling effective information gathering.

[0372] Furthermore, terminals are deployed in each department, and each communicates with the server or agents in other departments via a dedicated API. This facilitates smooth cross-departmental work execution and ensures data integrity.

[0373] Thus, the present invention enables artificial intelligence agents in various departments within a company to collaborate efficiently, optimizing the organization's overall business processes. For example, a sales department terminal can adjust inventory data in real time, and a manufacturing department agent can immediately utilize that data to revise the production schedule. This process leads to faster customer response and simultaneously optimizes internal resource management.

[0374] The following describes the processing flow.

[0375] Step 1:

[0376] The server collects operational logs from each artificial intelligence agent. This creates a dataset to understand which agents are running and to what extent.

[0377] Step 2:

[0378] The server applies process mining algorithms using the collected operational data. This analyzes the current state of business processes and visualizes inefficient parts and areas with long processing times.

[0379] Step 3:

[0380] Based on process mining results, the server generates instructions to optimize each agent's tasks. Following these instructions, agents have their roles and tasks reconfigured, streamlining business processes.

[0381] Step 4:

[0382] The terminal updates its API settings according to the server's instructions and prepares to collaborate with other departmental agents. Each agent then begins exchanging information using the new API.

[0383] Step 5:

[0384] The server monitors communication between all agents, immediately issues an alert if an anomaly occurs, and blocks communication as necessary.

[0385] Step 6:

[0386] When a user requests assistance with their work, the server uses insights gained from process mining to suggest the most suitable agent usage for the user. This allows the user to perform their tasks effectively.

[0387] (Example 1)

[0388] 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."

[0389] As the need for effective management and optimization of intelligent agents within enterprises increases, current systems fail to adequately monitor the operational status of each agent, optimize their functions, and ensure standards and security. Furthermore, insufficient information sharing between agents leads to decreased operational efficiency. Additionally, a lack of guidance on how users can best utilize intelligent agents remains a challenge.

[0390] 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.

[0391] In this invention, the server includes means for observing the operating status of intelligent agents, means for optimizing the functions of each intelligent agent, means for dialogue, means for analyzing and optimizing business processes using analytical techniques, and means for using generative technology models. This makes it possible to provide usage methods that contribute to the efficient management and optimization of agents within an enterprise, the promotion of a seamless flow of information, and the improvement of users' work efficiency.

[0392] An "intelligent agent" is a program used within a company to efficiently perform specific tasks or business processes.

[0393] "Operating status" refers to data related to the state and performance of an intelligent agent while it is running.

[0394] "Optimization" is the process of improving the arrangement of roles and tasks so that intelligent agents can perform at their best.

[0395] A "dialogue mechanism" is a communication mechanism that allows intelligent agents to exchange information with each other.

[0396] "Standards" refer to a set of rules related to compliance and standards established within a company.

[0397] "Security" refers to the security of data and systems within a company, and includes measures to prevent unauthorized access and data breaches.

[0398] "Analytical techniques" refer to methods and tools for collecting and processing data to gain insights into business processes.

[0399] A "generative technology model" refers to AI technology that generates appropriate output or advice based on specific input information (e.g., a prompt).

[0400] This invention embodies a system aimed at managing and optimizing intelligent agents within an enterprise. The server first monitors the operational status of the intelligent agents. This involves using common monitoring tools to collect data on how each agent operates and how much resources it uses. For example, the operational status is recorded in a database in real time.

[0401] Next, the server uses analytical techniques to analyze the entire business process. This analysis utilizes a data streaming platform and process mining tools to identify bottlenecks and inefficiencies in the business flow. The optimized business flow is then fed back to each agent by the server, updating their respective roles and tasks.

[0402] Furthermore, the server facilitates smooth information exchange between agents through dialogue mechanisms. APIs are utilized for this purpose, providing a system that allows each agent to utilize each other's output data in a timely manner.

[0403] The server centrally manages standards and security. The server implements authentication protocols and encryption of communications to ensure data and communication security. This enables agent operation in an environment protected from unauthorized access while complying with standards.

[0404] Users can use a dedicated client application to request instructions and advice regarding their work. The server utilizes a generative technology model to suggest how to use the agent based on the prompts entered by the user. For example, if a user wants to analyze market data when launching a new product, they can use prompts like the following:

[0405] Example prompt:

[0406] "Before launching our new product X, please analyze sales data from the past five years and current market trends to advise us on which regions have the greatest potential for sales."

[0407] Terminals are deployed in each department and support cross-departmental collaboration by communicating with servers and agents in other departments. This communication allows each department to share data in real time and make quick decisions based on accurate information. For example, a terminal in the sales department can update inventory data, and the manufacturing department can then revise its production schedule based on those results.

[0408] Thus, the present invention provides specific means for achieving efficient management of intelligent agents and optimization of business processes.

[0409] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0410] Step 1:

[0411] The server collects operational data from intelligent agents. Input data includes CPU usage, memory usage, and network activity for each agent. The server analyzes this data and records it in a database as performance metrics. Output data includes a dashboard that visualizes the operational status of each agent. Specific operations include a process of periodically collecting data using monitoring tools.

[0412] Step 2:

[0413] The server analyzes business processes using a data streaming platform. The input consists of various data streams related to the business. The server uses process mining tools to analyze the data and identify bottlenecks and inefficient processes. The output is a visualized report of the business flow, including improvement suggestions. Specific operations include real-time data processing and reporting of analysis results.

[0414] Step 3:

[0415] The server uses a generative AI model to generate advice in response to user prompts. The input is a prompt entered by the user through a dedicated client application. The server uses the generative AI model to generate appropriate advice and information for this prompt. The output is a suggestion of how to use the agent, presented to the user. Specific operations include parsing the prompt and providing appropriate output.

[0416] Step 4:

[0417] The terminal exchanges information with each department via APIs with servers or agents in other departments. Inputs include departmental business data and API calls. The terminal processes this information quickly and communicates it to the relevant departments and agents. Outputs provide data for real-time information sharing and decision-making between departments. Specific operations include sending and receiving data via APIs.

[0418] (Application Example 1)

[0419] 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."

[0420] In today's business environment, there is a demand for increased work efficiency and elimination of waste. Conventional artificial intelligence agent systems suffer from insufficient coordination between agents and difficulties in centralizing information, hindering the optimization of business processes. Furthermore, the difficulty in integrating suggestions from different agents makes it difficult for on-site workers to respond quickly and accurately. It is necessary to solve these problems, further improve operational efficiency within factories, and realize a smooth data flow.

[0421] 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.

[0422] In this invention, the server includes means for monitoring the operational information of multiple artificial intelligence agents within a company, means for optimizing the functions of each artificial intelligence agent based on said operational information, and communication means for coordinating information between the multiple artificial intelligence agents. This enables each agent to cooperate efficiently, improves on-site work efficiency, and realizes a seamless flow of information.

[0423] An "artificial intelligence agent" is an intelligent program designed to automate and streamline specific tasks or operations.

[0424] "Operational information" refers to data about the agent's work status and operating condition, and serves as basic data for monitoring and optimization.

[0425] "Function optimization" is the process of enabling agents to perform their roles more effectively in order to improve operational efficiency.

[0426] "Communication methods" refer to the technical techniques and infrastructure used to exchange information and coordinate among multiple artificial intelligence agents.

[0427] "Means of visual display" refers to methods of using devices to display data and proposals in a format that is easy for users to understand.

[0428] A "processing activity mining algorithm" is a data analysis method used to analyze business procedures and gain insights for improvement.

[0429] The AI ​​agent system of this invention aims to improve operational efficiency within factories. The server continuously monitors the operational information of each artificial intelligence agent and optimizes its functions as needed. This optimization process is based on a process mining algorithm, analyzing work procedures and providing insights to enable efficient operation. Furthermore, the server utilizes an application program interface as a communication means to facilitate information exchange between agents. This allows each agent to exchange information in real time and strengthen their collaboration.

[0430] On the terminal side, suggestions from the agent can be visually displayed to the user using specific devices, such as smart glasses. This feature allows users to receive optimized suggestions based on real-time data within the factory, improving the speed and accuracy of decision-making.

[0431] As a concrete example, consider a manufacturing plant. The server collects and analyzes operational data from each production line, and then proposes resource reallocation for lines that are deemed inefficient. Upon receiving this information, workers can take quick action based on the suggestions presented through smart glasses. This improves overall productivity and reduces unnecessary waiting time. An example of a prompt using a generative AI model is, "Please provide a method to analyze the efficiency of each production line in the factory in real time and identify lines with high potential for improvement."

[0432] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0433] Step 1:

[0434] The server collects operational information from each artificial intelligence agent. The input is data indicating the operational status of each agent, and the output is overall operational data that integrates these. Specifically, the server acquires data from each agent in real time using communication methods and stores that data in a central database.

[0435] Step 2:

[0436] The server analyzes the collected operational data using a process mining algorithm. The input is the integrated operational data obtained in step 1, and the output is the analysis results regarding the optimization of business processes. At this stage, the server detects data patterns and identifies process bottlenecks. Specifically, it calculates indicators that show inefficient lines or imbalances in resource allocation.

[0437] Step 3:

[0438] The server redistributes optimized tasks and roles to each agent based on the analysis results. The input is the analysis results obtained in step 2, and the output is the new instructions for the agents. The server sends the optimized roles to the agents via the application programming interface, and the agents adjust their actions accordingly.

[0439] Step 4:

[0440] The user receives action suggestions from the agent through smart glasses. The input is suggestion data sent from the server, and the output is specific action guidelines displayed on the visual display. Specifically, the user takes action according to resource reallocation and task priorities displayed using the smart glasses.

[0441] Step 5:

[0442] The terminal updates field data based on user actions and sends feedback to the server. The input is the result of the user's actions, and the output is the updated field data. The terminal records the completion and changes of tasks and sends this information to the server, allowing the entire process to be continuously optimized in real time.

[0443] 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.

[0444] This invention provides a system for efficiently managing and optimizing multiple artificial intelligence agents operating within a company, and incorporates an emotion engine that recognizes user emotions. The server functions as a central management device, monitoring the operational status of each artificial intelligence agent and receiving the user's emotional state via the emotion engine.

[0445] The emotion engine analyzes the user's text input, voice, and facial expression data to understand their emotional state in real time. This emotional data is sent to the server and used to optimize agent roles based on the current task performance and the user's emotions. For example, if the user is stressed, the server will activate additional agents to simplify information delivery in order to reduce the user's burden.

[0446] The device receives instructions from the emotion engine and modifies the agent's settings or configures new APIs. This allows the artificial intelligence agent to dynamically change its role in response to the user's emotions, enabling flexible responses. This situation is centrally reflected in the agent's process flow, guaranteeing a response appropriate to the user's emotions.

[0447] Furthermore, the system can suggest the most suitable agent usage based on the user's business requirements. For example, if a user is feeling anxious about a project deadline, the server will recommend a task management agent to support effective schedule management. In this way, by integrating with the emotion engine, comprehensive business support is achieved that goes beyond mere operational efficiency and also takes into account the user's psychological burden.

[0448] The following describes the processing flow.

[0449] Step 1:

[0450] The user provides text input, voice, or facial expression data through the device. This allows the device to collect data to detect the user's emotional state.

[0451] Step 2:

[0452] The device uses an emotion engine to analyze the user's emotions. The analysis identifies the user's current emotional state. This information is then used for subsequent processing.

[0453] Step 3:

[0454] The device sends the results of the emotion engine's analysis to the server. The server receives this data and plans the optimal actions of the artificial intelligence agent based on the user's emotional state.

[0455] Step 4:

[0456] The server makes necessary adjustments based on the operational status of each artificial intelligence agent and the user's emotional state. For example, if the user is stressed, it activates agents that simplify tasks or provide more support.

[0457] Step 5:

[0458] The server sends the newly adjusted agent configuration to the terminal. The terminal receives this information, updates the agent settings locally, and modifies the API settings. This results in an agent running that is optimized for the user's situation.

[0459] Step 6:

[0460] When a user utilizes the agent, the server suggests an agent usage method appropriate to the user's current emotional state, based on process mining results and sentiment data. This suggestion allows the user to perform their tasks more comfortably and efficiently.

[0461] (Example 2)

[0462] 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."

[0463] Efficient management and optimization of multiple intelligent agents within a company require flexible responses that take into account the emotional state of the user. However, conventional systems fix the roles of agents without adequately considering the user's psychological state, and therefore do not always achieve optimal responses. This leads to increased user burden and decreased work efficiency.

[0464] 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.

[0465] In this invention, the server includes means for monitoring the operational status of multiple intelligent agents within a company, means including an emotion analysis device for analyzing the user's emotional state, and means for dynamically adjusting the roles of the intelligent agents according to the user's emotional state. This makes it possible for the intelligent agents to flexibly change their roles according to the user's emotions, thereby reducing the user's psychological burden while improving work efficiency.

[0466] An "intelligent agent" is software that has the ability to perform a variety of tasks in response to user instructions and changes in the environment.

[0467] "Operating status" refers to information indicating the current state of activities and processes being performed by the intelligent agent.

[0468] "Role optimization" refers to the process where an intelligent agent adjusts its functions and tasks according to the user's needs and emotional state.

[0469] "Communication infrastructure" is a general term for the infrastructure that allows multiple systems and devices to exchange information with each other.

[0470] An "emotion analysis device" is a technology or software that detects and analyzes a user's emotional state from data such as text, voice, and facial expressions.

[0471] "Dynamic adjustment" refers to a system autonomously changing its settings or operation in response to specific conditions or circumstances.

[0472] "Compliance" refers to measures and means to ensure operations are conducted in accordance with laws, regulations, guidelines, etc.

[0473] "Security" refers to the means and policies taken to protect information and systems from unauthorized access and data breaches.

[0474] The system of this invention aims to improve operational efficiency by managing multiple intelligent agents operating within a company, analyzing the user's emotional state in real time, and dynamically adjusting the roles of the agents.

[0475] The server acts as the core of this system, monitoring the operational status of the intelligent agents. The server receives emotion data sent from users and generates instructions to optimize the agents' roles based on that data.

[0476] The device receives text input, voice, and facial expression data from the user and analyzes it via an emotion analysis device. This analysis uses natural language processing libraries (e.g., spaCy) and speech analysis tools (e.g., Google Cloud Speech-to-Text). This data allows the user's emotional state to be understood in real time and transmitted to the server accordingly.

[0477] For example, if a user is experiencing stress, the emotion analyzer sends that data to a server, which then adjusts the agent's role to provide information that helps reduce stress. For instance, if a user feels overwhelmed with tasks, the task management agent is activated to help with scheduling and prioritizing.

[0478] Examples of prompt statements for generative AI models include the following:

[0479] "Please explain how the task management agent is activated when the user is feeling stressed."

[0480] "Please explain in detail the process by which the emotion engine analyzes the user's emotions."

[0481] In this way, the system supports the efficient progress of tasks while reducing the user's psychological burden through interaction with the user.

[0482] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0483] Step 1:

[0484] The user inputs text, voice, or facial expression data from their device into the system. This is the input data. This data is first captured by the device.

[0485] Step 2:

[0486] The device uses an emotion analysis device to analyze the user's input data. In this step, data processing is performed using natural language processing libraries and speech analysis tools, such as tokenizing text, transcribing speech into text, and quantifying facial expression data. As a result of this analysis, the user's emotional state (e.g., high stress, joy, anxiety) is output.

[0487] Step 3:

[0488] The device sends analyzed emotional data to the server. The data sent includes the user's emotional state, along with recommended actions based on that state.

[0489] Step 4:

[0490] The server analyzes and optimizes the role of each intelligent agent based on the received emotion data. Specifically, it updates agent schedules and adjusts loads based on the data. As a result, a new role configuration for each agent is output.

[0491] Step 5:

[0492] The server sends instructions to the terminal specifying the role of the optimized agent. This involves the execution of necessary API calls and configurations to activate the corresponding agent. This ensures that the agent on the terminal takes the most appropriate action based on the user's current situation.

[0493] Step 6:

[0494] Users receive responses and information from the system, including stress reduction information and recommendations for new agents for task management. Based on this output, users can perform their tasks more effectively.

[0495] (Application Example 2)

[0496] 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."

[0497] In modern organizations, the demanding work environment places stress and burden on employees, posing a significant obstacle to efficient work performance. Furthermore, when operating multiple autonomous knowledge processing systems, it is necessary to appropriately optimize the role of each system. However, conventional systems struggle to dynamically adjust roles while adequately considering the user's emotional state, making it difficult to alleviate psychological burden and improve work efficiency and staff satisfaction.

[0498] 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.

[0499] In this invention, the server includes means for monitoring the operational status of multiple autonomous knowledge processing devices within an organization, means for optimizing the role of each autonomous knowledge processing device based on its operational status and the emotional state of an individual, and emotion analysis means for analyzing an individual's emotions. This enables appropriate workload reduction and role adjustment according to the user's emotional state, thereby improving work efficiency while reducing the psychological burden on employees.

[0500] An "autonomous knowledge processing device" is an information processing device that automatically executes specific business processes within an organization and assists in decision-making based on user input.

[0501] "Emotional state" refers to the emotional state and psychological reactions that individual users experience, and is a factor that influences their ability to perform their work.

[0502] "Optimizing roles" means automatically adjusting the functions and tasks of each autonomous knowledge processing unit so that it can perform its duties efficiently and accurately.

[0503] "Emotion analysis means" refers to technology that analyzes data such as the user's text, voice, and facial expressions to identify the user's emotions in real time.

[0504] "Load reduction" refers to reducing the mental and physical stress and effort placed on the user.

[0505] This invention realizes a system that optimizes the role of autonomous knowledge processing devices within an organization according to the emotional state of users. A server manages multiple autonomous knowledge processing devices within the organization and continuously monitors the operating status of each device. The emotion analysis means used here receives text data, voice data, and facial expression data from individual users within the organization in real time and determines the emotional state of the users. Cloud services such as Google Cloud AI's natural language processing tools, Google Cloud Vision API, and Google Cloud Speech-to-Text are used for this data analysis.

[0506] Based on the analysis results, the server dynamically adjusts the roles of each autonomous knowledge processing unit and implements appropriate load reduction measures as needed. For example, in a security center, if a user is experiencing high levels of stress, the server automatically activates additional information-providing devices to support the user's work. This improves work efficiency and reduces psychological burden.

[0507] As a specific example, if the smart glasses being used detect that a security staff member is feeling anxious through emotion analysis, they may display a notification saying, "Please relax. We have initiated support to help you cope with this new situation," and then offer helpful instructions.

[0508] An example of a prompt sentence to input into the generating AI model is, "How can you support and stabilize the emotions of staff who are feeling anxious while preparing for a safety event?"

[0509] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0510] Step 1:

[0511] When users perform daily tasks through their smart devices, the devices acquire voice data, text data, and facial expression data. This input data is collected to evaluate the user's current emotional state. Specifically, the device's microphone and camera capture voice and facial expressions, and if text is entered, its content is also captured.

[0512] Step 2:

[0513] The device sends the acquired data to the server, which then analyzes the user's emotional state using emotion analysis tools on a cloud service. In this process, Google Cloud Speech-to-Text is used to convert speech to text, and cloud-based natural language processing tools analyze the text data to determine the emotion. The output is a label indicating the user's emotional state (e.g., stressed, excited, calm).

[0514] Step 3:

[0515] The server receives the analysis results and optimizes the role of the autonomous knowledge processing unit according to the individual's emotional state. Specifically, if the user is under stress, it activates support devices to reduce the load. The output here is the optimized device configuration information, and the processing unit modifies its operation based on this information.

[0516] Step 4:

[0517] The server sends optimized information to the terminal and displays notifications to the user in an easy-to-understand format. Specific procedures and navigation may be presented using smart glasses, or advice to reduce the burden may be conveyed via voice or text. The terminal's role is to intuitively convey data from the server to the user.

[0518] Step 5:

[0519] The user proceeds with their tasks based on the information presented, and the server continues to optimize in real time by acquiring new data. This process is updated whenever the user's emotional state changes, enabling effective and flexible support for their work. This forms a feedback loop after the user receives the information.

[0520] 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.

[0521] 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.

[0522] 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.

[0523] [Fourth Embodiment]

[0524] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0525] 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.

[0526] 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).

[0527] 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.

[0528] 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.

[0529] 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).

[0530] 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.

[0531] 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.

[0532] 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.

[0533] 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.

[0534] 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.

[0535] 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.

[0536] 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".

[0537] This invention is a system for efficiently controlling and optimizing multiple artificial intelligence agents operating within an enterprise. A server plays a central role, monitoring the operational status of each AI agent and analyzing business processes. Based on these analysis results, the system optimizes the roles and tasks of each agent to improve operational efficiency. Furthermore, communication between agents is conducted via APIs, enabling proactive collaboration. This prevents data fragmentation and promotes a seamless flow of information.

[0538] Regarding compliance and security, the server centrally manages these aspects, granting access permissions and encrypting information based on corporate policies. The server constantly monitors communication logs and takes immediate action if any abnormal access attempts are detected.

[0539] Users can request support for their work through a dedicated client. In response, the server, based on analysis results obtained through process mining, suggests the most suitable AI agent for the task. For example, a user planning a promotion for a new product might be shown by the server which agent is the most suitable tool for market analysis, enabling effective information gathering.

[0540] Furthermore, terminals are deployed in each department, and each communicates with the server or agents in other departments via a dedicated API. This facilitates smooth cross-departmental work execution and ensures data integrity.

[0541] Thus, the present invention enables artificial intelligence agents in various departments within a company to collaborate efficiently, optimizing the organization's overall business processes. For example, a sales department terminal can adjust inventory data in real time, and a manufacturing department agent can immediately utilize that data to revise the production schedule. This process leads to faster customer response and simultaneously optimizes internal resource management.

[0542] The following describes the processing flow.

[0543] Step 1:

[0544] The server collects operational logs from each artificial intelligence agent. This creates a dataset to understand which agents are running and to what extent.

[0545] Step 2:

[0546] The server applies process mining algorithms using the collected operational data. This analyzes the current state of business processes and visualizes inefficient parts and areas with long processing times.

[0547] Step 3:

[0548] Based on process mining results, the server generates instructions to optimize each agent's tasks. Following these instructions, agents have their roles and tasks reconfigured, streamlining business processes.

[0549] Step 4:

[0550] The terminal updates its API settings according to the server's instructions and prepares to collaborate with other departmental agents. Each agent then begins exchanging information using the new API.

[0551] Step 5:

[0552] The server monitors communication between all agents, immediately issues an alert if an anomaly occurs, and blocks communication as necessary.

[0553] Step 6:

[0554] When a user requests assistance with their work, the server uses insights gained from process mining to suggest the most suitable agent usage for the user. This allows the user to perform their tasks effectively.

[0555] (Example 1)

[0556] 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".

[0557] As the need for effective management and optimization of intelligent agents within enterprises increases, current systems fail to adequately monitor the operational status of each agent, optimize their functions, and ensure standards and security. Furthermore, insufficient information sharing between agents leads to decreased operational efficiency. Additionally, a lack of guidance on how users can best utilize intelligent agents remains a challenge.

[0558] 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.

[0559] In this invention, the server includes means for observing the operating status of intelligent agents, means for optimizing the functions of each intelligent agent, means for dialogue, means for analyzing and optimizing business processes using analytical techniques, and means for using generative technology models. This makes it possible to provide usage methods that contribute to the efficient management and optimization of agents within an enterprise, the promotion of a seamless flow of information, and the improvement of users' work efficiency.

[0560] An "intelligent agent" is a program used within a company to efficiently perform specific tasks or business processes.

[0561] "Operating status" refers to data related to the state and performance of an intelligent agent while it is running.

[0562] "Optimization" is the process of improving the arrangement of roles and tasks so that intelligent agents can perform at their best.

[0563] A "dialogue mechanism" is a communication mechanism that allows intelligent agents to exchange information with each other.

[0564] "Standards" refer to a set of rules related to compliance and standards established within a company.

[0565] "Security" refers to the security of data and systems within a company, and includes measures to prevent unauthorized access and data breaches.

[0566] "Analytical techniques" refer to methods and tools for collecting and processing data to gain insights into business processes.

[0567] A "generative technology model" refers to AI technology that generates appropriate output or advice based on specific input information (e.g., a prompt).

[0568] This invention embodies a system aimed at managing and optimizing intelligent agents within an enterprise. The server first monitors the operational status of the intelligent agents. This involves using common monitoring tools to collect data on how each agent operates and how much resources it uses. For example, the operational status is recorded in a database in real time.

[0569] Next, the server uses analytical techniques to analyze the entire business process. This analysis utilizes a data streaming platform and process mining tools to identify bottlenecks and inefficiencies in the business flow. The optimized business flow is then fed back to each agent by the server, updating their respective roles and tasks.

[0570] Furthermore, the server facilitates smooth information exchange between agents through dialogue mechanisms. APIs are utilized for this purpose, providing a system that allows each agent to utilize each other's output data in a timely manner.

[0571] The server centrally manages standards and security. The server implements authentication protocols and encryption of communications to ensure data and communication security. This enables agent operation in an environment protected from unauthorized access while complying with standards.

[0572] Users can use a dedicated client application to request instructions and advice regarding their work. The server utilizes a generative technology model to suggest how to use the agent based on the prompts entered by the user. For example, if a user wants to analyze market data when launching a new product, they can use prompts like the following:

[0573] Example prompt:

[0574] "Before launching our new product X, please analyze sales data from the past five years and current market trends to advise us on which regions have the greatest potential for sales."

[0575] Terminals are deployed in each department and support cross-departmental collaboration by communicating with servers and agents in other departments. This communication allows each department to share data in real time and make quick decisions based on accurate information. For example, a terminal in the sales department can update inventory data, and the manufacturing department can then revise its production schedule based on those results.

[0576] Thus, the present invention provides specific means for achieving efficient management of intelligent agents and optimization of business processes.

[0577] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0578] Step 1:

[0579] The server collects operational data from intelligent agents. Input data includes CPU usage, memory usage, and network activity for each agent. The server analyzes this data and records it in a database as performance metrics. Output data includes a dashboard that visualizes the operational status of each agent. Specific operations include a process of periodically collecting data using monitoring tools.

[0580] Step 2:

[0581] The server analyzes business processes using a data streaming platform. The input consists of various data streams related to the business. The server uses process mining tools to analyze the data and identify bottlenecks and inefficient processes. The output is a visualized report of the business flow, including improvement suggestions. Specific operations include real-time data processing and reporting of analysis results.

[0582] Step 3:

[0583] The server uses a generative AI model to generate advice in response to user prompts. The input is a prompt entered by the user through a dedicated client application. The server uses the generative AI model to generate appropriate advice and information for this prompt. The output is a suggestion of how to use the agent, presented to the user. Specific operations include parsing the prompt and providing appropriate output.

[0584] Step 4:

[0585] The terminal exchanges information with each department via APIs with servers or agents in other departments. Inputs include departmental business data and API calls. The terminal processes this information quickly and communicates it to the relevant departments and agents. Outputs provide data for real-time information sharing and decision-making between departments. Specific operations include sending and receiving data via APIs.

[0586] (Application Example 1)

[0587] 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".

[0588] In today's business environment, there is a demand for increased work efficiency and elimination of waste. Conventional artificial intelligence agent systems suffer from insufficient coordination between agents and difficulties in centralizing information, hindering the optimization of business processes. Furthermore, the difficulty in integrating suggestions from different agents makes it difficult for on-site workers to respond quickly and accurately. It is necessary to solve these problems, further improve operational efficiency within factories, and realize a smooth data flow.

[0589] 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.

[0590] In this invention, the server includes means for monitoring the operational information of multiple artificial intelligence agents within a company, means for optimizing the functions of each artificial intelligence agent based on said operational information, and communication means for coordinating information between the multiple artificial intelligence agents. This enables each agent to cooperate efficiently, improves on-site work efficiency, and realizes a seamless flow of information.

[0591] An "artificial intelligence agent" is an intelligent program designed to automate and streamline specific tasks or operations.

[0592] "Operational information" refers to data about the agent's work status and operating condition, and serves as basic data for monitoring and optimization.

[0593] "Function optimization" is the process of enabling agents to perform their roles more effectively in order to improve operational efficiency.

[0594] "Communication methods" refer to the technical techniques and infrastructure used to exchange information and coordinate among multiple artificial intelligence agents.

[0595] "Means of visual display" refers to methods of using devices to display data and proposals in a format that is easy for users to understand.

[0596] A "processing activity mining algorithm" is a data analysis method used to analyze business procedures and gain insights for improvement.

[0597] The AI ​​agent system of this invention aims to improve operational efficiency within factories. The server continuously monitors the operational information of each artificial intelligence agent and optimizes its functions as needed. This optimization process is based on a process mining algorithm, analyzing work procedures and providing insights to enable efficient operation. Furthermore, the server utilizes an application program interface as a communication means to facilitate information exchange between agents. This allows each agent to exchange information in real time and strengthen their collaboration.

[0598] On the terminal side, suggestions from the agent can be visually displayed to the user using specific devices, such as smart glasses. This feature allows users to receive optimized suggestions based on real-time data within the factory, improving the speed and accuracy of decision-making.

[0599] As a concrete example, consider a manufacturing plant. The server collects and analyzes operational data from each production line, and then proposes resource reallocation for lines that are deemed inefficient. Upon receiving this information, workers can take quick action based on the suggestions presented through smart glasses. This improves overall productivity and reduces unnecessary waiting time. An example of a prompt using a generative AI model is, "Please provide a method to analyze the efficiency of each production line in the factory in real time and identify lines with high potential for improvement."

[0600] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0601] Step 1:

[0602] The server collects operational information from each artificial intelligence agent. The input is data indicating the operational status of each agent, and the output is overall operational data that integrates these. Specifically, the server acquires data from each agent in real time using communication methods and stores that data in a central database.

[0603] Step 2:

[0604] The server analyzes the collected operational data using a process mining algorithm. The input is the integrated operational data obtained in step 1, and the output is the analysis results regarding the optimization of business processes. At this stage, the server detects data patterns and identifies process bottlenecks. Specifically, it calculates indicators that show inefficient lines or imbalances in resource allocation.

[0605] Step 3:

[0606] The server redistributes optimized tasks and roles to each agent based on the analysis results. The input is the analysis results obtained in step 2, and the output is the new instructions for the agents. The server sends the optimized roles to the agents via the application programming interface, and the agents adjust their actions accordingly.

[0607] Step 4:

[0608] The user receives action suggestions from the agent through smart glasses. The input is suggestion data sent from the server, and the output is specific action guidelines displayed on the visual display. Specifically, the user takes action according to resource reallocation and task priorities displayed using the smart glasses.

[0609] Step 5:

[0610] The terminal updates field data based on user actions and sends feedback to the server. The input is the result of the user's actions, and the output is the updated field data. The terminal records the completion and changes of tasks and sends this information to the server, allowing the entire process to be continuously optimized in real time.

[0611] 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.

[0612] This invention provides a system for efficiently managing and optimizing multiple artificial intelligence agents operating within a company, and incorporates an emotion engine that recognizes user emotions. The server functions as a central management device, monitoring the operational status of each artificial intelligence agent and receiving the user's emotional state via the emotion engine.

[0613] The emotion engine analyzes the user's text input, voice, and facial expression data to understand their emotional state in real time. This emotional data is sent to the server and used to optimize agent roles based on the current task performance and the user's emotions. For example, if the user is stressed, the server will activate additional agents to simplify information delivery in order to reduce the user's burden.

[0614] The device receives instructions from the emotion engine and modifies the agent's settings or configures new APIs. This allows the artificial intelligence agent to dynamically change its role in response to the user's emotions, enabling flexible responses. This situation is centrally reflected in the agent's process flow, guaranteeing a response appropriate to the user's emotions.

[0615] Furthermore, the system can suggest the most suitable agent usage based on the user's business requirements. For example, if a user is feeling anxious about a project deadline, the server will recommend a task management agent to support effective schedule management. In this way, by integrating with the emotion engine, comprehensive business support is achieved that goes beyond mere operational efficiency and also takes into account the user's psychological burden.

[0616] The following describes the processing flow.

[0617] Step 1:

[0618] The user provides text input, voice, or facial expression data through the device. This allows the device to collect data to detect the user's emotional state.

[0619] Step 2:

[0620] The device uses an emotion engine to analyze the user's emotions. The analysis identifies the user's current emotional state. This information is then used for subsequent processing.

[0621] Step 3:

[0622] The device sends the results of the emotion engine's analysis to the server. The server receives this data and plans the optimal actions of the artificial intelligence agent based on the user's emotional state.

[0623] Step 4:

[0624] The server makes necessary adjustments based on the operational status of each artificial intelligence agent and the user's emotional state. For example, if the user is stressed, it activates agents that simplify tasks or provide more support.

[0625] Step 5:

[0626] The server sends the newly adjusted agent configuration to the terminal. The terminal receives this information, updates the agent settings locally, and modifies the API settings. This results in an agent running that is optimized for the user's situation.

[0627] Step 6:

[0628] When a user utilizes the agent, the server suggests an agent usage method appropriate to the user's current emotional state, based on process mining results and sentiment data. This suggestion allows the user to perform their tasks more comfortably and efficiently.

[0629] (Example 2)

[0630] 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".

[0631] Efficient management and optimization of multiple intelligent agents within a company require flexible responses that take into account the emotional state of the user. However, conventional systems fix the roles of agents without adequately considering the user's psychological state, and therefore do not always achieve optimal responses. This leads to increased user burden and decreased work efficiency.

[0632] 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.

[0633] In this invention, the server includes means for monitoring the operational status of multiple intelligent agents within a company, means including an emotion analysis device for analyzing the user's emotional state, and means for dynamically adjusting the roles of the intelligent agents according to the user's emotional state. This makes it possible for the intelligent agents to flexibly change their roles according to the user's emotions, thereby reducing the user's psychological burden while improving work efficiency.

[0634] An "intelligent agent" is software that has the ability to perform a variety of tasks in response to user instructions and changes in the environment.

[0635] "Operating status" refers to information indicating the current state of activities and processes being performed by the intelligent agent.

[0636] "Role optimization" refers to the process where an intelligent agent adjusts its functions and tasks according to the user's needs and emotional state.

[0637] "Communication infrastructure" is a general term for the infrastructure that allows multiple systems and devices to exchange information with each other.

[0638] An "emotion analysis device" is a technology or software that detects and analyzes a user's emotional state from data such as text, voice, and facial expressions.

[0639] "Dynamic adjustment" refers to a system autonomously changing its settings or operation in response to specific conditions or circumstances.

[0640] "Compliance" refers to measures and means to ensure operations are conducted in accordance with laws, regulations, guidelines, etc.

[0641] "Security" refers to the means and policies taken to protect information and systems from unauthorized access and data breaches.

[0642] The system of this invention aims to improve operational efficiency by managing multiple intelligent agents operating within a company, analyzing the user's emotional state in real time, and dynamically adjusting the roles of the agents.

[0643] The server acts as the core of this system, monitoring the operational status of the intelligent agents. The server receives emotion data sent from users and generates instructions to optimize the agents' roles based on that data.

[0644] The device receives text input, voice, and facial expression data from the user and analyzes it via an emotion analysis device. This analysis uses natural language processing libraries (e.g., spaCy) and speech analysis tools (e.g., Google Cloud Speech-to-Text). This data allows the user's emotional state to be understood in real time and transmitted to the server accordingly.

[0645] For example, if a user is experiencing stress, the emotion analyzer sends that data to a server, which then adjusts the agent's role to provide information that helps reduce stress. For instance, if a user feels overwhelmed with tasks, the task management agent is activated to help with scheduling and prioritizing.

[0646] Examples of prompt statements for generative AI models include the following:

[0647] "Please explain how the task management agent is activated when the user is feeling stressed."

[0648] "Please explain in detail the process by which the emotion engine analyzes the user's emotions."

[0649] In this way, the system supports the efficient progress of tasks while reducing the user's psychological burden through interaction with the user.

[0650] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0651] Step 1:

[0652] The user inputs text, voice, or facial expression data from their device into the system. This is the input data. This data is first captured by the device.

[0653] Step 2:

[0654] The device uses an emotion analysis device to analyze the user's input data. In this step, data processing is performed using natural language processing libraries and speech analysis tools, such as tokenizing text, transcribing speech into text, and quantifying facial expression data. As a result of this analysis, the user's emotional state (e.g., high stress, joy, anxiety) is output.

[0655] Step 3:

[0656] The device sends analyzed emotional data to the server. The data sent includes the user's emotional state, along with recommended actions based on that state.

[0657] Step 4:

[0658] The server analyzes and optimizes the role of each intelligent agent based on the received emotion data. Specifically, it updates agent schedules and adjusts loads based on the data. As a result, a new role configuration for each agent is output.

[0659] Step 5:

[0660] The server sends instructions to the terminal specifying the role of the optimized agent. This involves the execution of necessary API calls and configurations to activate the corresponding agent. This ensures that the agent on the terminal takes the most appropriate action based on the user's current situation.

[0661] Step 6:

[0662] Users receive responses and information from the system, including stress reduction information and recommendations for new agents for task management. Based on this output, users can perform their tasks more effectively.

[0663] (Application Example 2)

[0664] 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".

[0665] In modern organizations, the demanding work environment places stress and burden on employees, posing a significant obstacle to efficient work performance. Furthermore, when operating multiple autonomous knowledge processing systems, it is necessary to appropriately optimize the role of each system. However, conventional systems struggle to dynamically adjust roles while adequately considering the user's emotional state, making it difficult to alleviate psychological burden and improve work efficiency and staff satisfaction.

[0666] 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.

[0667] In this invention, the server includes means for monitoring the operational status of multiple autonomous knowledge processing devices within an organization, means for optimizing the role of each autonomous knowledge processing device based on its operational status and the emotional state of an individual, and emotion analysis means for analyzing an individual's emotions. This enables appropriate workload reduction and role adjustment according to the user's emotional state, thereby improving work efficiency while reducing the psychological burden on employees.

[0668] An "autonomous knowledge processing device" is an information processing device that automatically executes specific business processes within an organization and assists in decision-making based on user input.

[0669] "Emotional state" refers to the emotional state and psychological reactions that individual users experience, and is a factor that influences their ability to perform their work.

[0670] "Optimizing roles" means automatically adjusting the functions and tasks of each autonomous knowledge processing unit so that it can perform its duties efficiently and accurately.

[0671] "Emotion analysis means" refers to technology that analyzes data such as the user's text, voice, and facial expressions to identify the user's emotions in real time.

[0672] "Load reduction" refers to reducing the mental and physical stress and effort placed on the user.

[0673] This invention realizes a system that optimizes the role of autonomous knowledge processing devices within an organization according to the emotional state of users. A server manages multiple autonomous knowledge processing devices within the organization and continuously monitors the operating status of each device. The emotion analysis means used here receives text data, voice data, and facial expression data from individual users within the organization in real time and determines the emotional state of the users. Cloud services such as Google Cloud AI's natural language processing tools, Google Cloud Vision API, and Google Cloud Speech-to-Text are used for this data analysis.

[0674] Based on the analysis results, the server dynamically adjusts the roles of each autonomous knowledge processing unit and implements appropriate load reduction measures as needed. For example, in a security center, if a user is experiencing high levels of stress, the server automatically activates additional information-providing devices to support the user's work. This improves work efficiency and reduces psychological burden.

[0675] As a specific example, if the smart glasses being used detect that a security staff member is feeling anxious through emotion analysis, they may display a notification saying, "Please relax. We have initiated support to help you cope with this new situation," and then offer helpful instructions.

[0676] An example of a prompt sentence to input into the generating AI model is, "How can you support and stabilize the emotions of staff who are feeling anxious while preparing for a safety event?"

[0677] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0678] Step 1:

[0679] When users perform daily tasks through their smart devices, the devices acquire voice data, text data, and facial expression data. This input data is collected to evaluate the user's current emotional state. Specifically, the device's microphone and camera capture voice and facial expressions, and if text is entered, its content is also captured.

[0680] Step 2:

[0681] The device sends the acquired data to the server, which then analyzes the user's emotional state using emotion analysis tools on a cloud service. In this process, Google Cloud Speech-to-Text is used to convert speech to text, and cloud-based natural language processing tools analyze the text data to determine the emotion. The output is a label indicating the user's emotional state (e.g., stressed, excited, calm).

[0682] Step 3:

[0683] The server receives the analysis results and optimizes the role of the autonomous knowledge processing unit according to the individual's emotional state. Specifically, if the user is under stress, it activates support devices to reduce the load. The output here is the optimized device configuration information, and the processing unit modifies its operation based on this information.

[0684] Step 4:

[0685] The server sends optimized information to the terminal and displays notifications to the user in an easy-to-understand format. Specific procedures and navigation may be presented using smart glasses, or advice to reduce the burden may be conveyed via voice or text. The terminal's role is to intuitively convey data from the server to the user.

[0686] Step 5:

[0687] The user proceeds with their tasks based on the information presented, and the server continues to optimize in real time by acquiring new data. This process is updated whenever the user's emotional state changes, enabling effective and flexible support for their work. This forms a feedback loop after the user receives the information.

[0688] 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.

[0689] 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.

[0690] 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.

[0691] 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.

[0692] 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.

[0693] 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.

[0694] 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.

[0695] 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.

[0696] 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."

[0697] 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.

[0698] 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.

[0699] 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.

[0700] 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.

[0701] 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.

[0702] 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.

[0703] 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.

[0704] 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.

[0705] 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.

[0706] 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.

[0707] 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.

[0708] 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.

[0709] The following is further disclosed regarding the embodiments described above.

[0710] (Claim 1)

[0711] A means of monitoring the operational status of multiple artificial intelligence agents within a company,

[0712] A means for optimizing the role of each artificial intelligence agent based on its operating status,

[0713] A means of communication for connecting information between multiple artificial intelligence agents,

[0714] Means for managing compliance and security,

[0715] A means of suggesting the optimal way to use an artificial intelligence agent,

[0716] A system that includes this.

[0717] (Claim 2)

[0718] The system according to claim 1, further comprising means for communicating between each artificial intelligence agent via an API.

[0719] (Claim 3)

[0720] The system according to claim 1, further comprising means for analyzing and optimizing business processes using process mining algorithms.

[0721] "Example 1"

[0722] (Claim 1)

[0723] A means of observing the operational status of numerous intelligent agents within a company,

[0724] A means for optimizing the function of each intelligent agent based on the operating status,

[0725] A means of dialogue for connecting information between multiple intelligent agents,

[0726] Means for managing norms and safety,

[0727] A means of proposing the optimal way to utilize intelligent agents,

[0728] A means of analyzing and optimizing business processes using analytical techniques,

[0729] A system that includes this.

[0730] (Claim 2)

[0731] The system according to claim 1, further comprising means for conducting dialogue between each intelligent agent via a programmatic interface.

[0732] (Claim 3)

[0733] The system according to claim 1, comprising means for providing advice to specific questions from users using a generative technology model.

[0734] "Application Example 1"

[0735] (Claim 1)

[0736] A means of monitoring the operational information of multiple artificial intelligence agents within a company,

[0737] A means for optimizing the functions of each artificial intelligence agent based on the operational information,

[0738] A means of communication for coordinating information between multiple artificial intelligence agents,

[0739] Means for managing norms and security,

[0740] A means of presenting the optimal way to utilize artificial intelligence agents,

[0741] A means for receiving and visually displaying suggestions from an artificial intelligence agent via a device,

[0742] A system that includes this.

[0743] (Claim 2)

[0744] The system according to claim 1, further comprising means for exchanging information between each artificial intelligence agent via an application program interface.

[0745] (Claim 3)

[0746] The system according to claim 1, further comprising means for analyzing and optimizing business procedures using a processing activity mining algorithm.

[0747] "Example 2 of combining an emotion engine"

[0748] (Claim 1)

[0749] A means of monitoring the operational status of multiple intelligent agents within a company,

[0750] A means for optimizing the role of each intelligent agent based on the operating status,

[0751] A means of communication for connecting information between multiple intelligent agents,

[0752] Means including an emotion analysis device for analyzing the emotional state of a user,

[0753] A means of dynamically adjusting the role of an intelligent agent according to the user's emotional state,

[0754] A means of proposing the optimal way to utilize intelligent agents,

[0755] Means for managing compliance and security,

[0756] A system that includes this.

[0757] (Claim 2)

[0758] The system according to claim 1, further comprising means for communicating between each intelligent agent via an applied programming interface.

[0759] (Claim 3)

[0760] The system according to claim 1, further comprising means for analyzing and optimizing business processes using process analysis algorithms.

[0761] "Application example 2 when combining with an emotional engine"

[0762] (Claim 1)

[0763] A means of monitoring the operational status of multiple autonomous knowledge processing devices within an organization,

[0764] A means for optimizing the role of each autonomous knowledge processing device based on its operating status and the emotional state of the individual,

[0765] A communication means for connecting information between multiple autonomous knowledge processing devices,

[0766] Means for managing internal regulations and safety,

[0767] A means of proposing the optimal use of an autonomous knowledge processing device,

[0768] A means to provide users with optimal notifications and reduce their burden,

[0769] A means of analyzing an individual's emotions,

[0770] A system that includes this.

[0771] (Claim 2)

[0772] The system according to claim 1, further comprising means for communicating between each autonomous knowledge processing device via an integrated interface.

[0773] (Claim 3)

[0774] The system according to claim 1, further comprising means for analyzing and optimizing business processes using a processing task discovery method. [Explanation of Symbols]

[0775] 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 means of monitoring the operational information of multiple artificial intelligence agents within a company, A means for optimizing the functions of each artificial intelligence agent based on the operational information, A means of communication for coordinating information between multiple artificial intelligence agents, Means for managing norms and security, A means of presenting the optimal way to utilize artificial intelligence agents, A means for receiving and visually displaying suggestions from an artificial intelligence agent via a device, A system that includes this.

2. The system according to claim 1, further comprising means for exchanging information between each artificial intelligence agent via an application program interface.

3. The system according to claim 1, further comprising means for analyzing and optimizing business procedures using a processing activity mining algorithm.

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