System

Abstract

To provide a system.SOLUTION: A system comprises: means for receiving and analyzing organization chart data of a company; means for arranging in each section, a generative AI coping with each section in the company indicated by the analyzed organization chart data; means for connecting the arranged generative AI through a network for sharing information in the respective section; means where the generative AI processes the data in the respective sections of which the generative AI takes charge, for generating information related to management of the company; and means for summarizing the generated information related to management and outputting the same.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Currently, humans are at the center of corporate operations, with AI playing a supporting role. However, there are limits to human judgment, and it is difficult to quickly process large amounts of information and make appropriate decisions. Furthermore, human-centered operations are subject to efficiency problems due to human capabilities and time constraints. To solve these issues, a new management system is needed in which AI takes the lead and humans are used in a supporting role. [Means for solving the problem]

[0005] This invention provides a means for generative AI to give commands and hold discussions with other generative AI, and a means for recreating a company's organizational structure using a large number of generative AIs. This will enable companies to become semi-automated, freeing them from the constraints of human ability and time, and enabling more efficient operations. Furthermore, by having generative AI run the company according to a business plan and calling on humans as needed, it will evolve from an era of "humans using generative AI" to an era of "generative AI using humans." [Brief description of the drawings]

[0006] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 2 is a sequence diagram showing a process flow of the data processing system in the first embodiment of the first form example. [Figure 12]FIG. 11 is a sequence diagram showing a process flow of the data processing system in the application example 1 of the first embodiment. [Figure 13] FIG. 11 is a sequence diagram showing a process flow of the data processing system in the second embodiment of the second embodiment. [Figure 14] FIG. 13 is a sequence diagram showing a process flow of the data processing system in application example 2 of embodiment 2. [Figure 15] FIG. 13 is a sequence diagram showing a process flow of a data processing system in a third embodiment of the present invention. [Figure 16] FIG. 13 is a sequence diagram showing a process flow of the data processing system in the third application example of the third embodiment. [Figure 17] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when combined with an emotion engine. [Figure 19] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system according to the embodiment 2 of the second embodiment in the case where an emotion engine is combined. [Figure 20] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when combined with an emotion engine. [Figure 21] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system in the embodiment 3 of the form example 3 in the case where an emotion engine is combined. [Figure 22] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3 when combined with an emotion engine. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0007] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0008] First, the terms used in the following description will be explained.

[0009] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices 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), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0011] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0012] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0013] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

[0014] [First embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] 1, a 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.

[0017] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a wide area network (WAN) and / or a local area network (LAN).

[0018] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0019] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0020] 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 of expression that the user 20 can perceive (e.g., voice 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 voice according to instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0021] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

[0022] FIG. 2 shows an example of main functions of the data processing device 12 and the smart device 14.

[0023] As shown in Fig. 2, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific process program 56 executed on the RAM 30.

[0024] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0025] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores a reception output program 60. The reception output program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads out 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 the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0026] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0027] "Example 1"

[0028] As one embodiment of the present invention, a means is provided for a generative AI to command and discuss with other generative AIs. Specifically, a generative AI issues instructions to other generative AIs, so that each generative AI is responsible for a specific task. For example, some generative AIs are responsible for financial management of a company, while other generative AIs are responsible for human resources management, and so on, with each generative AI taking charge of a company's departments.

[0029] "Example 2"

[0030] Furthermore, a means is provided for reproducing the organizational structure of a company using a large number of generative AIs. Specifically, a network of generative AIs that mimics the organizational structure of a company is constructed. For example, generative AIs are arranged based on a company's organizational chart, with each generative AI in charge of a different department of the company.

[0031] "Example 3"

[0032] Furthermore, a means is provided for generative AI to operate a company in accordance with a business plan. Specifically, generative AI understands the business plan and operates the company based on it. For example, based on the business plan, generative AI appropriately performs tasks such as financial management and human resource management.

[0033] "Example 4"

[0034] Furthermore, a means will be provided for the generative AI to call on humans when necessary. Specifically, when a matter arises that the generative AI cannot decide on or that requires human judgment, the generative AI will call on humans. For example, the generative AI will call on humans when a legal decision is required or when human sensibility is required.

[0035] The process flow of each embodiment will be described below.

[0036] "Example 1"

[0037] Step 1: A generative AI issues instructions to other generative AIs. These instructions are set up so that each generative AI is responsible for a specific task.

[0038] Step 2: Each generative AI performs tasks according to instructions. For example, some generative AIs are responsible for managing the company's finances, while others are responsible for managing human resources.

[0039] "Example 2"

[0040] Step 1: Build a generative AI network that mimics a company's organizational structure.

[0041] Step 2: Place the generative AIs based on the company's organizational chart. Set each generative AI to be in charge of a different department in the company.

[0042] "Example 3"

[0043] Step 1: Generative AI understands the business plan.

[0044] Step 2: The generative AI runs the company based on the business plan. For example, based on the business plan, the generative AI appropriately performs tasks such as financial management and human resources management.

[0045] "Example 4"

[0046] Step 1: When an issue arises that the generative AI cannot decide or that requires human judgment, the generative AI calls in a human.

[0047] Step 2: If a human is called in, that human makes the necessary decisions. For example, when a legal decision is required or when human sensibility is required, the generative AI calls in a human.

[0048] Example 1

[0049] Next, a description will be given of Example 1 of the embodiment 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal".

[0050] Traditional business operations required a lot of manpower, making it difficult to carry out business efficiently. Even when generative AI was used, each individual AI operated independently, making it difficult to coordinate and collaborate as a whole, and optimizing the entire company. Furthermore, generative AI could not give instructions to humans, making it impossible to make the most of its capabilities.

[0051] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0052] In this invention, the server includes a means for a generative AI to give commands to and discuss with other generative AIs, a means for reproducing the organizational structure of a company using a large number of generative AIs, a means for aiming for semi-automation of a company, a means for a user to input a prompt sentence for the generative AI, a means for the server to receive the prompt sentence and give instructions to an appropriate generative AI, a means for the generative AI to receive the instruction and perform processing, a means for the generative AI to return the processing result to the server, and a means for the server to display the processing result to the user. This makes it possible to efficiently carry out the business of a company, strengthen cooperation between generative AIs, and optimize the entire company. In addition, since the generative AI can give instructions to humans, the capabilities of the generative AI can be utilized to the maximum extent.

[0053] "Generative AI" is a system that uses artificial intelligence technology to generate data and perform specific tasks.

[0054] An "instruction" is an act of instructing to perform a particular action or process.

[0055] "Discussion" is a process in which multiple generative AIs exchange opinions and information with each other to find the optimal solution.

[0056] A "corporate organizational structure" is a structure that shows the arrangement of departments and positions within a company and their interrelationships.

[0057] "Semi-automated" refers to a state in which human intervention is minimized and tasks are carried out automatically by systems and machines.

[0058] A "prompt" is an instruction given to a generative AI to carry out a specific task.

[0059] A "server" is a computer system that provides services to other computers over a network.

[0060] "Processing results" are data and information generated as a result of a task performed by a generative AI based on instructions.

[0061] The "user" is the human who operates the system and issues instructions to the generative AI.

[0062] This invention is a system in which generative AI issues commands and holds discussions with other generative AIs, and aims to reproduce the organizational structure of a company and make the company semi-automated. A specific embodiment of this system is described below.

[0063] First, a user logs into the system using a terminal. The user enters a user name and password on the login screen and is authenticated. If the authentication is successful, the user is redirected to the system's main screen.

[0064] Next, the user finds a form on the main screen to enter a prompt for the generative AI. The user enters a prompt containing specific tasks and instructions, and clicks the send button. For example, the user can enter the following prompt:

[0065] "Prepare a proposed budget for the coming year based on the most recent financial data. Specifically, include revenue projections and cost-cutting proposals."

[0066] The server receives the prompt sent by the user. The server analyzes the contents of the prompt and issues instructions to the appropriate generative AI. For example, if the prompt is about financial management, it issues instructions to the generative AI in charge of financial management. In this case, the server uses a generative AI model such as GPT-4 (registered trademark) from OpenAI (registered trademark).

[0067] The generative AI initiates processing based on instructions received from the server. For example, a generative AI in charge of financial management would analyze the latest financial data and create a budget proposal with sales forecasts and cost-cutting suggestions.

[0068] When the generative AI completes the process, it returns the results to the server. For example, this may include the generated budget proposal or analysis results. The server displays the processing results received from the generative AI to the user. The user checks the results on the device screen and takes the next action if necessary.

[0069] This system will enable companies to efficiently carry out their business, strengthen cooperation between generative AIs, and optimize the entire company. In addition, generative AI can give instructions to humans, making the most of its capabilities.

[0070] As a concrete example, consider instructions for a generative AI responsible for managing a company's financial affairs. The user inputs the following prompt sentence into the generative AI model:

[0071] "Prepare a proposed budget for the coming year based on the most recent financial data. Specifically, include revenue projections and cost-cutting proposals."

[0072] The generative AI that receives this prompt will analyze the financial data and create a budget proposal that includes sales forecasts and cost-cutting proposals. Similarly, the generative AI responsible for human resources management will be given the following prompt:

[0073] "Perform performance evaluations of employees and select candidates for promotion. Evaluation criteria will be business results, teamwork, and leadership skills."

[0074] The generative AI receives this prompt and performs an evaluation based on employee performance data to select promotion candidates.

[0075] The above is a specific embodiment for carrying out the present invention.

[0076] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0077] Step 1:

[0078] A user logs into the system.

[0079] A user accesses the login screen of the system using a terminal. The user enters a user name and password to perform authentication. If the authentication is successful, the user transitions to the main screen of the system.

[0080] Input: Username, Password

[0081] Output: Access to the main screen

[0082] Specific operation: The user opens a browser, enters the system URL, and accesses the system. On the login screen, the user enters "User name: user123" and "Password: password123" and clicks the login button.

[0083] Step 2:

[0084] The user inputs a prompt for the generative AI.

[0085] On the main screen, the user finds a form for entering a prompt for the generative AI. The user enters a prompt containing specific tasks and instructions, and clicks the send button.

[0086] Input: Prompt statement

[0087] Output: Prompt sent to server

[0088] Specific actions: The user enters the following into the form on the main screen: "Please prepare a budget proposal for the next fiscal year based on the latest financial data. In particular, please include sales forecasts and cost reduction proposals." and clicks the submit button.

[0089] Step 3:

[0090] The server receives the prompt and issues instructions to the appropriate generative AI.

[0091] The server receives the prompt sent by the user, analyzes the contents of the prompt, and issues instructions to the appropriate AI generator.

[0092] Input: Prompt statement

[0093] Output: Instructions for the generative AI

[0094] Specific operation: The server analyzes the prompt text and instructs the "Financial Management AI" to "prepare the next fiscal year's budget proposal based on the latest financial data."

[0095] Step 4:

[0096] The generative AI receives instructions and carries out the processing.

[0097] The generative AI initiates processing based on instructions received from the server. For example, a generative AI in charge of financial management would analyze the latest financial data and create a budget proposal with sales forecasts and cost-cutting suggestions.

[0098] Input: Instructions from the server, financial data

[0099] Output: Budget proposal

[0100] How it works: The financial management AI retrieves the latest financial data from the database and creates a budget proposal with sales forecasts and cost-cutting suggestions.

[0101] Step 5:

[0102] The generative AI returns the processing results to the server.

[0103] Once the generative AI completes its processing, it returns the results to the server, including the generated budget proposal and analysis results, for example.

[0104] Input: Budget proposal

[0105] Output: Sending the results of processing to the server

[0106] Specific operation: The financial management AI returns the budget proposal it has created to the server. The budget proposal includes a sales forecast graph and specific proposals for cost reduction.

[0107] Step 6:

[0108] The server displays the processing results to the user.

[0109] The server displays the processing results received from the generative AI to the user, who then checks the results on the device screen and takes the next action if necessary.

[0110] Input: Processing results from generative AI

[0111] Output: Displaying the results to the user

[0112] Specific operation: The server displays the received budget proposal on the user's terminal. The user can check the details of the budget proposal on the screen and download or print it as necessary.

[0113] (Application example 1)

[0114] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0115] In modern factories, improving the efficiency of production lines and automating management are important issues. In particular, there is a demand for collecting real-time data from each process and issuing instructions quickly based on that data. However, in conventional systems, the management of each process is decentralized, making it difficult to optimize the entire system. In addition, there are limited means for factory managers to grasp the status of each process and intervene as necessary. This can lead to reduced production efficiency and variations in quality.

[0116] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0117] In this invention, the server includes a means for a generative AI to give commands to and discuss with other generative AIs, a means for reproducing the organizational structure of a company using a large number of generative AIs, a means for aiming for semi-automation of the company, a means for a generative AI to manage each process on the production line in a factory, collect data in real time, and give commands to other generative AIs, and a means for a factory manager to monitor the activity status of each generative AI through a smartphone application and intervene as necessary. This makes it possible to improve the efficiency of the entire production line and automate management.

[0118] "Generative AI" is a type of artificial intelligence that generates new information and content based on data.

[0119] An "instruction" is an act of instructing to perform a particular action or process.

[0120] "Discussion" is a process in which multiple generative AIs exchange opinions and find the optimal solution.

[0121] A "corporate organizational structure" is a structure that shows the arrangement and relationships of each department and position within a company.

[0122] "Semi-automated" refers to a state in which systems and machines operate automatically with minimal human intervention.

[0123] A "production line" is a line in which a series of steps are arranged in succession to manufacture a product.

[0124] "Collecting data in real time" refers to instantly obtaining data about ongoing events or situations.

[0125] "Giving instructions" means the act of ordering something to perform a particular action or process.

[0126] A "factory manager" is a person responsible for managing production activities and employees within a factory.

[0127] A "smartphone application" is a software program that runs on a smartphone.

[0128] "Monitoring" is the act of observing the status of a particular system or process and responding as necessary.

[0129] "Intervention" is the act of actively engaging in and influencing a particular situation or process.

[0130] A system for implementing this invention includes means for a generative AI to give commands and hold discussions with other generative AI, means for recreating the organizational structure of a company using a large number of generative AI, means for aiming to make a company semi-automated, means for a generative AI to manage each process on a production line in a factory, collect data in real time, and issue instructions to other generative AI, and means for a factory manager to monitor the activity status of each generative AI through a smartphone application and intervene as necessary.

[0131] System Programming

[0132] The server implements a generative AI model and its coordinator using Python (registered trademark). The generative AI model is responsible for each process in the factory (e.g., material supply, assembly, quality inspection), collects data in real time, and issues instructions to other generative AIs. The coordinator monitors the status of each generative AI and issues appropriate instructions.

[0133] Hardware and Software

[0134] Hardware: Smartphones, IoT devices in factories

[0135] Software: Python, Flask (as API server), SQLite (database)

[0136] Data processing and calculation

[0137] Generative AI collects real-time data from IoT devices in factories and processes it based on that data. For example, a material supply AI collects material inventory data, and when inventory is low, it instructs the assembly AI to "require material supply." The assembly AI collects progress data on the assembly process, and when progress is delayed, it instructs the quality inspection AI to "delay quality inspection because assembly is delayed."

[0138] Examples

[0139] The material supply AI collects material inventory data, and when inventory is low, it instructs the assembly AI that "material supply is required."

[0140] The assembly AI collects progress data on the assembly process, and if progress is delayed, it instructs the quality inspection AI to "delay quality inspection because assembly is delayed."

[0141] Examples of prompt statements

[0142] The material supply AI collects material inventory data, and when inventory is low, it instructs the assembly AI that "material supply is required."

[0143] In this way, generative AI can issue instructions to other generative AI, allowing efficient management of the production line in the factory. Factory managers can monitor the activity of each generative AI through a smartphone application and intervene if necessary.

[0144] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0145] Step 1:

[0146] The server collects real-time data from IoT devices in the factory. Specifically, it obtains data related to the processes that each generative AI model is responsible for (e.g., material supply, assembly, quality inspection). The input is sensor data from the IoT devices, and the output is raw data passed to the generative AI model.

[0147] Step 2:

[0148] The server passes the collected real-time data to each generative AI model. The generative AI model analyzes the current situation based on the received data. The input is the raw data collected in step 1, and the output is the analysis result. In concrete terms, the material supply AI analyzes the inventory data and generates an alert if inventory is low.

[0149] Step 3:

[0150] A generative AI model issues instructions to other generative AI models based on the analysis results. For example, if a material supply AI detects an inventory shortage, it issues an instruction to an assembly AI that "materials need to be supplied." The input is the analysis result, and the output is the instruction content. In terms of specific operations, the instructions are generated in the form of a prompt sentence.

[0151] Step 4:

[0152] The server sends the generated instructions to other generative AI models. The generative AI models that receive the instructions begin to operate according to the instructions. The input is the instructions, and the output is the execution result. In concrete terms, the assembly AI receives instructions from the material supply AI and requests replenishment of materials.

[0153] Step 5:

[0154] The server monitors the status of each generative AI model and notifies the factory manager as necessary. The input is the status data of each generative AI model, and the output is the notification to the manager. Specifically, the server reports the status to the manager in real time via a smartphone application.

[0155] Step 6:

[0156] The user (factory manager) monitors the activity of each generative AI model through a smartphone application and intervenes as necessary. The input is notifications from the server, and the output is instructions from the manager. In concrete terms, the manager can operate the application to issue new instructions to specific generative AI models.

[0157] In this way, it is possible to efficiently manage the production line within the factory through the specific operations and data flow at each step.

[0158] Example 2

[0159] Next, a second embodiment of the second embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0160] In traditional corporate operations, data processing and decision-making in each department was carried out separately, which led to problems with overall efficiency. In addition, since there was no system that could mimic the organizational structure of a company, it was difficult to achieve semi-automation. Furthermore, there was a lack of means for generative AI to work with other generative AI to operate the company, making it difficult to optimize the entire company.

[0161] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0162] In this invention, the server includes a means for a generative AI to give commands to and discuss with other generative AIs, a means for reproducing the organizational structure of a company using a large number of generative AIs, a means for aiming for semi-automation of the company, a means for receiving and analyzing organizational chart data of the company, a means for arranging generative AIs corresponding to each department, a means for connecting each generative AI via a network and sharing information, a means for each generative AI to process data of the department in charge and generate necessary information, and a means for aggregating the results generated by each generative AI and providing them to the user. This makes it possible to mimic the organizational structure of a company, streamline data processing and decision-making in each department, and optimize and semi-automate the entire company.

[0163] "Generative AI" is artificial intelligence that generates new information and content based on data.

[0164] An "order" is the act of giving instructions to perform a particular action or process.

[0165] A "discussion" is the act of multiple generative AIs exchanging opinions and information and engaging in dialogue to find the optimal solution.

[0166] A "corporate organizational structure" is a structure that shows the arrangement of departments and positions within a company and the relationships between them.

[0167] "Semi-automated" refers to a state in which human intervention is minimized and systems and machines operate and manage automatically.

[0168] "Corporate organization chart data" refers to data showing the arrangement of departments and positions within a company and the relationships between them.

[0169] "Analysis" is the act of examining data in detail and understanding its meaning and structure.

[0170] "Deployment" is the act of assigning a generative AI to a specific location or role.

[0171] A "network" is a connection structure that allows multiple generative AIs to communicate with each other and share information.

[0172] "Information sharing" refers to the act of exchanging data and knowledge between generative AIs and using them jointly.

[0173] "Processing data" is the act of performing calculations and analysis based on input data to generate necessary information.

[0174] "Aggregating results" refers to the act of combining information generated by multiple generative AIs into one.

[0175] A "user" is a human or other entity that uses the system.

[0176] This invention is a system that mimics the organizational structure of a company, improves the efficiency of data processing and decision-making in each department, and realizes optimization and semi-automation of the entire company. A specific embodiment of this system will be described below.

[0177] 1. Program Generation

[0178] The server generates a program that constructs a generative AI network to mimic the organizational structure of a company. The program receives the company's organizational chart as input and includes an algorithm for placing generative AIs corresponding to each department.

[0179] 2. Program Processing Explanation

[0180] The server executes the generated program and performs the following processes.

[0181] Data Entry

[0182] The server receives organizational chart data for the company. This data can come from a CSV file or a database. For example, the CSV file contains the names of each department and the relationships between the departments.

[0183] Generative AI placement

[0184] The server assigns generative AIs to each department based on the organizational chart. For example, the sales department assigns a generative AI specialized in sales, and the marketing department assigns a generative AI specialized in marketing.

[0185] Network Construction

[0186] The server will connect each generative AI to a network, mimicking the organizational structure of a company. This network will be designed so that each generative AI can cooperate with each other and share information.

[0187] Data processing and calculation

[0188] Each generative AI processes data for the department it is responsible for and generates the necessary information. For example, the generative AI for the sales department analyzes sales data and generates future sales forecasts.

[0189] The hardware used will be high-performance cloud servers (e.g., AWS (registered trademark) EC2 instances) as servers, and the software will be a generative AI model (e.g., OpenAI's GPT-4).

[0190] 3. Examples and prompts

[0191] As a concrete example, consider the following corporate organizational chart.

[0192] Sales Department

[0193] Marketing Department

[0194] Human resources department

[0195] The server will place the generative AI based on this organizational chart as follows:

[0196] The sales department will have generative AI to analyze sales data.

[0197] The marketing department needs generative AI to generate marketing strategies.

[0198] HR departments need generative AI to generate recruitment plans

[0199] An example of a prompt sentence might be:

[0200] "Analyze the sales data from the sales department and generate sales forecasts for the next quarter."

[0201] By inputting this prompt sentence into the generative AI model, the sales department's generative AI will generate a sales forecast.

[0202] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0203] Step 1: Data entry

[0204] The server receives the organizational chart data of the company. This data can come from a CSV file or a database. For example, the CSV file contains the names of each department and the relationships between the departments. The server reads the CSV file from the specified file path and parses the information of each department. In case of using a database, the server executes an SQL query to get the required data. The input is the organizational chart data of the company and the output is the parsed department information.

[0205] Step 2: Placing the generative AI

[0206] The server assigns a generative AI to each department based on the organizational chart. For example, the sales department assigns a generative AI specialized in sales, and the marketing department assigns a generative AI specialized in marketing. Based on the information of each department, the server selects an appropriate generative AI model (e.g. GPT-4) and assigns it to each department. The input is the analyzed department information, and the output is the generative AI assigned to each department.

[0207] Step 3: Networking

[0208] The server connects each generative AI to a network, mimicking the organizational structure of a company. This network is designed to allow each generative AI to work together and share information. The server sets the communication protocol between each generative AI, enabling data to be sent and received. For example, sales forecast data generated by an AI in the sales department is sent to an AI in the marketing department, and used to generate a marketing strategy. The input is the generative AI placed in each department, and the output is the generative AI connected via the network.

[0209] Step 4: Data processing and calculations

[0210] Each generative AI processes data from its assigned department and generates the necessary information. For example, the sales department's generative AI analyzes sales data and generates future sales forecasts. The sales department's generative AI takes past sales data as input and predicts future sales using statistical methods and machine learning algorithms. Similarly, the marketing department's generative AI analyzes advertising campaign data and proposes optimal marketing strategies. The input is the data from each department, and the output is the generated information.

[0211] Step 5: Output the results

[0212] The server aggregates the results generated by each generative AI and provides them to the user. The user makes decisions based on these results. The server collects output data from each generative AI and displays it to the user in the form of a dashboard or report. For example, it visualizes sales forecasts and marketing strategy proposals in the form of graphs or tables. The input is the generated information, and the output is the aggregated results provided to the user.

[0213] (Application example 2)

[0214] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0215] In traditional business and factory operations, human managers were required to oversee each department, analyze data, and make decisions. However, this required a great deal of time and effort, making efficient operation difficult. In addition, it was difficult to analyze data in real time and provide optimal instructions, which led to problems with overall operational efficiency.

[0216] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means. In this invention, the server includes a means for a generative AI to give commands to and discuss with other generative AIs, a means for reproducing the organizational structure of a company using a large number of generative AIs, a means for aiming for semi-automation of a company, a means for optimizing the operation of the entire factory using generative AIs that mimic each department in the factory, and a means for each generative AI to take on the role of its respective department, analyze data in real time, and issue optimal instructions. This makes it possible to streamline the operation of companies and factories, and to perform data analysis and make optimal decisions in real time.

[0217] "Generative AI" refers to artificial intelligence that generates new information and content based on data.

[0218] An "order" is an instruction to perform a particular action or process.

[0219] "Discussion" is a process in which multiple generative AIs exchange opinions and find the optimal solution.

[0220] "Corporate organizational structure" refers to the arrangement and relationships of departments and positions within a company.

[0221] "Semi-automated" refers to a state in which systems and machines operate automatically with minimal human intervention.

[0222] "Each department within the factory" refers to departments that have specific roles within the factory, such as production, quality control, and logistics.

[0223] "Optimizing operations" means maximizing overall performance through efficient use of resources and improved processes.

[0224] "Analyzing data in real time" means analyzing the data as it is generated and obtaining instant results.

[0225] "Giving optimal instructions" means giving instructions for the most effective actions or processes based on the results of analysis.

[0226] In order to put this invention into practice, it is first necessary to build a generative AI that mimics each department in a factory. The server includes a means for a generative AI to give commands and discuss with other generative AIs, a means for reproducing the organizational structure of a company using a large number of generative AIs, a means for aiming for semi-automation of a company, a means for optimizing the operation of the entire factory using generative AIs that mimic each department in a factory, and a means for each generative AI to take on the role of its respective department, analyze data in real time, and give optimal instructions.

[0227] Specifically, the server uses programming languages ​​such as Python to build generative AI for each department. Each generative AI analyzes data collected from sensors and robots in the factory in real time and issues optimal instructions. For example, the generative AI in the production department analyzes production data and decides whether to increase or decrease production volume. The generative AI in the quality control department analyzes quality data and issues instructions for improving quality. The generative AI in the logistics department analyzes logistics data and decides the optimal logistics route.

[0228] The hardware used is a robot or sensor in the factory that collects data and runs the program on a central server. The software used is Python and its libraries (e.g. NumPy, Pandas).

[0229] For example, when the production department in a factory inputs "current production data," the generative AI may make the decision to "increase production." The quality control department may make the decision to "improve quality."

[0230] Example of a prompt to be input to a generative AI model:

[0231] Please enter your current production data.

[0232] This will streamline operations throughout the factory, enabling real-time data analysis and optimal decision-making.

[0233] The flow of the specific process in the application example 2 will be described with reference to FIG.

[0234] Step 1:

[0235] The server builds a generative AI that mimics each department in the factory. Specifically, it uses a programming language such as Python to program the generative AI in charge of each department. The input includes the role and necessary data of each department. The output is a model of the generative AI in charge of each department.

[0236] Step 2:

[0237] The server collects data from sensors and robots in the factory. Specifically, each sensor and robot transmits data to the server in real time. The input includes data from the sensors and robots. The output is the collected data stored in the server.

[0238] Step 3:

[0239] The server inputs the collected data to each generative AI. Specifically, it filters the data corresponding to the department that each generative AI is responsible for, converts it into an appropriate format, and inputs it. The input includes the collected data. The output includes the data input to the generative AI.

[0240] Step 4:

[0241] Each generative AI analyzes the input data in real time. Specifically, it uses data analysis algorithms to detect trends and anomalies in the data. The inputs include the data that was input to the generative AI. The output is the analysis results.

[0242] Step 5:

[0243] Each generative AI issues optimal instructions based on the analysis results. Specifically, it instructs each department on specific actions and processes based on the analysis results. The input includes the analysis results. The output is instructions for each department.

[0244] Step 6:

[0245] Each department acts according to instructions from the generative AI. Specifically, the production department adjusts production volume, the quality control department takes measures to improve quality, and the logistics department optimizes logistics routes. The input includes instructions from the generative AI. The output is the result of each department's actions.

[0246] Step 7:

[0247] The server again collects the action results of each department and uses them as data for the next cycle. Specifically, it collects feedback data from each department and uses it for the next data analysis. The action results of each department are included as input. The data for the next cycle is generated as output.

[0248] Example 3

[0249] Next, a third embodiment of the third embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0250] Modern business operations require efficient business management and rapid decision-making. However, conventional systems do not adequately handle operations based on business plans or when human judgment is required. Furthermore, the use of generative AI is essential to achieve semi-automation in companies, but current technology makes this difficult to achieve. To solve these issues, a new system using generative AI is needed.

[0251] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means. In this invention, the server includes a means for a generative AI to command and discuss with other generative AIs, a means for reproducing the organizational structure of a company using a large number of generative AIs, a means for aiming at semi-automation of the company, a means for receiving business plan data, a means for analyzing business plan data, a means for generating work instructions, a means for executing work instructions, and a means for calling a human when an issue arises that the generative AI cannot judge or that requires human judgment. This enables a flexible response that combines efficient business management based on a business plan with human judgment as needed.

[0252] "Generative AI" is an artificial intelligence technology that generates business plans and work instructions to support corporate operations.

[0253] "Commands and means of discussion" is a function that enables a generative AI to give instructions to other generative AIs, exchange information, and hold discussions.

[0254] "Means of reproducing a company's organizational structure" refers to a function that uses a large amount of generative AI to imitate the actual organizational structure of a company and perform simulations.

[0255] "Measures aimed at making companies semi-automated" refers to functions that utilize generative AI to automate corporate operations and minimize human intervention.

[0256] The "means for receiving business plan data" is a function that enables the server to receive data relating to business plans provided by companies.

[0257] "Means of analyzing business plan data" refers to a function that enables the generative AI to analyze the received business plan data and extract the necessary information.

[0258] The "means for generating work instructions" is a function for generating specific work instructions based on the analysis results.

[0259] The "means for executing work instructions" is a function for actually executing the generated work instructions.

[0260] The "means of summoning a human" is a function that sends a notification to a human and requests that he or she respond when an issue arises that the generative AI cannot decide or that requires human decision-making.

[0261] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0262] This invention is a system that aims to streamline corporate operations and achieve semi-automation by using generative AI. A specific embodiment of this system will be described below.

[0263] System Configuration

[0264] This system is mainly composed of three elements: a server, a terminal, and a user. The server hosts the generative AI model and receives and analyzes business plan data and generates work instructions. The terminal receives work instructions from the server and executes specific tasks. The user is called upon when there are matters that the generative AI cannot decide or when human judgment is required.

[0265] Hardware and Software Used

[0266] The server is a high-performance computer with the computational power to host a generative AI model (e.g., GPT-4). The terminal is a device for executing business instructions, such as using accounting software. The user is a device for receiving notifications (e.g., a smartphone or PC).

[0267] Data processing and calculation

[0268] The server receives business plan data provided by companies and stores it in JSON format. It then uses a generative AI model to analyze the business plan data and extract the necessary information. Based on the analysis results, it generates specific work instructions. For example, it generates instructions such as "Increase the marketing budget by 20% to achieve this month's sales target."

[0269] The terminal executes the work instructions received from the server, for example, updating a budget using accounting software, by accessing the accounting software through an API and updating the necessary data.

[0270] The user is called upon when there is a matter that the generative AI cannot decide or when human judgment is required. For example, if a legal decision is required, the server will notify the user, who will then make the professional judgment. Notifications are sent via email or chat app.

[0271] Examples

[0272] For example, a company may have a business plan to bring a new product to market. A generative AI can analyze this business plan and manage the progress of product development and develop a marketing strategy. If the product development progress is delayed, the generative AI can notify the project manager and instruct him or her to take necessary measures.

[0273] Examples of prompt statements

[0274] "Analyze the business plan for the new product launch, monitor the product development progress and develop the marketing strategy. If there is a delay in the progress, notify the project manager."

[0275] In this way, the generative AI manages the business based on the business plan and calls humans as necessary, thereby realizing efficient business management. The flow of the specific process in the third embodiment will be described with reference to FIG.

[0276] Step 1:

[0277] The server receives business plan data from the company. The input is the business plan data provided by the company, and the output is the business plan data stored in JSON format. In concrete terms, the server receives the HTTP request and stores the business plan data in a database.

[0278] Step 2:

[0279] The server analyzes the received business plan data. The input is the stored business plan data, and the output is the analysis result. In concrete terms, the server inputs the business plan data into a generative AI model (e.g., GPT-4), which then analyzes the data using natural language processing technology and extracts the necessary information.

[0280] Step 3:

[0281] The server generates specific business instructions based on the analysis results. The input is the analysis results, and the output is business instructions. In concrete terms, the server inputs a prompt sentence into the generative AI model and obtains the generated business instructions. For example, it generates an instruction such as "Increase the marketing budget by 20% to achieve this month's sales target."

[0282] Step 4:

[0283] The terminal executes the work instructions received from the server. The input is the work instructions from the server, and the output is the executed work. In concrete terms, the terminal accesses the accounting software through the API and updates the necessary data, for example updating the budget or recording expenditures.

[0284] Step 5:

[0285] The server calls the user when a matter arises that the generative AI cannot decide or when a matter that requires human judgment arises. The input is the matter that requires judgment, and the output is a notification to the user. Specifically, the server sends a notification to the user via email or a chat app. For example, if a legal decision is required, the server sends a notification to the user, and the user makes a professional judgment.

[0286] In this way, by clarifying the specific actions and data flow at each step, it is possible to achieve more efficient and semi-automated corporate operations using generative AI.

[0287] (Application example 3)

[0288] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0289] In conventional business operations, production schedule optimization, inventory management, and quality control had to be led by humans, which had limitations in efficiency and accuracy. In addition, when it came to matters that were difficult to judge or required human sensitivity, it was difficult to call on humans at the right time. This resulted in issues such as reduced business efficiency and increased costs.

[0290] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0291] In this invention, the server includes a means for generative AI to give commands to and discuss with other generative AI, a means for reproducing the organizational structure of a company using a large number of generative AIs, a means for aiming for semi-automation of the company, a means for generative AI to optimize production schedules, manage inventory, and manage quality based on business plans, and a means for calling humans when matters that generative AI cannot decide or when human decision is required. This makes it possible to significantly improve the operational efficiency of a company and reduce costs.

[0292] "Generative AI" refers to artificial intelligence that generates new information and content based on data.

[0293] An "instruction" is an instruction given to perform a particular action or process.

[0294] "Discussion" is a process in which multiple generative AIs exchange opinions and information to find the optimal solution.

[0295] A "corporate organizational structure" refers to the structure that shows the arrangement and relationships of each department and position within a company.

[0296] "Semi-automated" refers to a state in which human intervention is minimized and the facility is primarily operated by automated systems and robots.

[0297] A business plan is a plan that outlines the goals a company needs to achieve and the specific strategies it will use to achieve them.

[0298] "Production schedule optimization" is the process of optimizing the timing and sequence of production activities to achieve efficient production.

[0299] "Inventory management" is the management activity of maintaining appropriate inventory of products and raw materials and supplying the appropriate amounts when needed.

[0300] "Quality control" is a management activity for maintaining the quality of products and services at a certain standard.

[0301] "Undeterminable matters" are problems or situations that are difficult or impossible for a generative AI to handle automatically.

[0302] "When human judgment is required" refers to situations where generative AI cannot make appropriate decisions and human knowledge and sensibility are required.

[0303] "Invoking a human" refers to a generative AI requesting human intervention for a particular problem or situation.

[0304] A system for implementing this invention includes means for a generative AI to give commands and discuss with other generative AI, means for recreating a company's organizational structure using a large number of generative AI, means for aiming to make a company semi-automated, means for a generative AI to optimize production schedules, manage inventory, and control quality based on business plans, and means for calling humans when there are matters that the generative AI cannot decide or when human decision is required.

[0305] The server uses a generative AI model (e.g., GPT-4) to optimize production schedules based on business plans. Specifically, it schedules production tasks according to the goals of the business plan to achieve efficient production. In inventory management, it analyzes inventory data and generates instructions to maintain appropriate inventory levels. In quality control, it analyzes product quality data to detect quality problems early and take measures.

[0306] Furthermore, when there is a matter that the generative AI cannot decide or when human judgment is required, the system will have the function of calling the appropriate person in charge. For example, when legal judgment or human sensibility is required, the generative AI will automatically send a notification to the person in charge.

[0307] The hardware used includes robots used in factories that operate according to the instructions of the generative AI, while the software uses a Python program to implement the generative AI model and analyze data and generate instructions.

[0308] As a concrete example, the prompt text for optimizing the production schedule within a factory is shown below.

[0309] Example prompt:

[0310] "Based on the business plan, please optimize the production schedule for the next week. The goal is to improve production efficiency by 20%."

[0311] In this way, the server can use generative AI models to make business operations more efficient and reduce costs.

[0312] The flow of the specific process in the application example 3 will be described with reference to FIG.

[0313] Step 1:

[0314] The server receives business plan data as input. The business plan data includes company goals and goals for improving production efficiency. The server analyzes this data and converts it into a format suitable for the generative AI model. Specifically, it extracts the goals of the business plan and generates a prompt sentence to input to the generative AI model.

[0315] Step 2:

[0316] The server inputs prompt statements into a generative AI model (e.g., GPT-4) to optimize the production schedule. The generative AI model generates an optimal production schedule based on the business plan. Specifically, it optimizes the timing and order of each production task and outputs a schedule for achieving efficient production.

[0317] Step 3:

[0318] The server receives the generated production schedule and sends instructions to the robots in the factory. Specifically, it instructs each robot which task to perform and when. The robots operate according to these instructions and carry out production activities.

[0319] Step 4:

[0320] The server takes inventory data as input and performs inventory management. Specifically, it analyzes the inventory data and generates instructions to maintain appropriate stock levels. It uses a generative AI model to predict inventory surpluses and shortages and makes the necessary adjustments.

[0321] Step 5:

[0322] The server receives quality data as input and performs quality control. Specifically, it analyzes the quality data to detect quality problems early. It uses a generative AI model to identify the cause of the quality problem and generate instructions for taking countermeasures.

[0323] Step 6:

[0324] The server calls on humans when the generative AI cannot make a decision or when human judgment is required. Specifically, when legal judgment or human sensitivity is required, the server sends a notification to the appropriate person. The notification includes details of the problem and the required action.

[0325] Step 7:

[0326] The server monitors the overall operational status and adjusts the parameters of the generative AI model as necessary. Specifically, it evaluates the performance of the generative AI model against operational efficiency and cost reduction goals and makes necessary adjustments.

[0327] Furthermore, an emotion engine that estimates the emotion of the user may be combined. That is, the identification processing unit 290 may estimate the emotion of the user using the emotion identification model 59, and perform identification processing using the emotion of the user.

[0328] "Example 1"

[0329] One embodiment of the present invention is a system including an emotion engine in which a generative AI recognizes the emotions of a user. This emotion engine analyzes the emotions of the user from the tone of voice, facial expressions, and language used, and feeds the results back to the generative AI. For example, if the user feels angry, the generative AI adjusts its response to the user. This enables a more appropriate response according to the user's emotions.

[0330] "Example 2"

[0331] Furthermore, some systems include a means for the emotion engine to adjust the behavior of the generative AI based on the user's emotions. In this system, the generative AI adjusts its behavior based on the user's emotions analyzed by the emotion engine. For example, if the generative AI senses that the user is happy, it will respond to the user more proactively. This allows for a more appropriate response according to the user's emotions.

[0332] "Example 3"

[0333] There are also systems that include a means for the emotion engine to adjust the timing at which the generative AI calls a human based on the user's emotions. In this system, the generative AI adjusts the timing at which it calls a human based on the user's emotions analyzed by the emotion engine. For example, if the user feels confused, the generative AI leaves it up to the human to respond to the user. This allows for a more appropriate response according to the user's emotions.

[0334] The process flow of each embodiment will be described below.

[0335] "Example 1"

[0336] Step 1: An interaction with a user begins. This occurs when the user takes some action on the system, or when the system periodically checks the user's status.

[0337] Step 2: The emotion engine analyzes the user's emotions based on their tone of voice, facial expressions, and language.

[0338] Step 3: The emotion engine feeds back the analysis results to the generative AI.

[0339] Step 4: The generative AI uses the feedback to adjust its response to the user.

[0340] "Example 2"

[0341] Step 1: User interaction begins.

[0342] Step 2: The emotion engine analyzes the user's emotions.

[0343] Step 3: The emotion engine adjusts the generative AI’s behavior based on the analysis results.

[0344] Step 4: The generative AI executes the adjusted actions.

[0345] "Example 3"

[0346] Step 1: User interaction begins.

[0347] Step 2: The emotion engine analyzes the user's emotions.

[0348] Step 3: Based on the analysis results, the emotion engine adjusts the timing at which the generative AI will call the human.

[0349] Step 4: The generative AI summons a human at the adjusted timing.

[0350] Example 1

[0351] Next, a description will be given of Example 1 of the embodiment 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal".

[0352] In conventional business operations, humans had to perform many tasks manually, which was inefficient and prone to human error. It was also difficult to respond to users' emotions, making it difficult to improve customer satisfaction. Furthermore, there was a lack of ways for generative AI to work with other generative AI to perform tasks efficiently.

[0353] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0354] In this invention, the server includes a means for a generative AI to give commands to and discuss with other generative AIs, a means for reproducing the organizational structure of a company using a large number of generative AIs, a means for aiming for semi-automation of the company, a means including an emotion engine for analyzing the user's emotions, and a means for the generative AI to adjust its response based on the analysis results of the emotion engine. This significantly improves the business efficiency of the company and enables appropriate responses according to the user's emotions, which is expected to improve customer satisfaction.

[0355] "Generative AI" is artificial intelligence that generates new information and content based on data.

[0356] An "instruction" is an act of instructing to perform a particular action or process.

[0357] "Discussion" is a process in which multiple generative AIs exchange opinions and find the optimal solution.

[0358] A "corporate organizational structure" is a structure that shows the arrangement and relationships of each department and position within a company.

[0359] "Semi-automated" refers to a state in which human intervention is minimized and systems and machines perform tasks automatically.

[0360] An "emotion engine" is a technology that analyzes a user's tone of voice, facial expressions, choice of words, etc. to recognize emotions.

[0361] "Analysis results" refers to data or information obtained after the emotion engine analyzes the user's emotions.

[0362] "Adjusting the response" means that the generative AI changes its response or behavior toward the user based on the results of the analysis.

[0363] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0364] This invention is a system that aims to provide a means for generative AI to give commands and hold discussions with other generative AI, reproduce the organizational structure of a company, and become semi-automated. It also includes an emotion engine that analyzes the user's emotions, and includes a means for the generative AI to adjust its response based on the analysis results.

[0365] Generating a Program

[0366] First, the generative AI generates a program to give commands and hold discussions with other generative AIs. This program has the function of issuing instructions to the generative AI in charge of each department of the company to carry out specific tasks efficiently.

[0367] Program Processing

[0368] The server receives instructions from the generative AI and transmits them to other generative AIs. For example, the server instructs a generative AI responsible for a company's financial management to analyze specific financial data. The server also instructs a generative AI responsible for human resources management to evaluate employee performance.

[0369] The device is equipped with an emotion engine that recognizes the user's emotions. This emotion engine analyzes the user's tone of voice, facial expressions, and choice of words, and feeds the results back to the generative AI. For example, if the emotion engine determines that the user is angry, the device sends that information to the generative AI.

[0370] The generative AI receives feedback from the emotion engine and adjusts its response based on the user's emotions. For example, if the user is angry, the generative AI is programmed to respond calmly and politely.

[0371] Examples

[0372] As a concrete example, consider the case where a generative AI in charge of financial management of a company is instructed to create a monthly report. The server collects financial data and instructs the generative AI to analyze it. The generative AI creates a monthly report based on the collected data and sends the results back to the server.

[0373] In addition, when a user makes an inquiry to the generative AI through a device, the emotion engine analyzes the user's tone of voice and facial expressions and determines that the user is dissatisfied. Based on that information, the generative AI responds to the user in a more polite manner.

[0374] Examples of prompt statements

[0375] Examples of prompt sentences to be input to a generative AI model include the following:

[0376] "Tell your generative AI, responsible for managing your company's finances, to create an up-to-date monthly report."

[0377] "If the user is angry, explain how the generative AI would adjust its response."

[0378] This makes it clearer the overall picture of a system in which generative AI issues instructions to other generative AIs and responds according to the user's emotions.

[0379] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0380] Program processing flow

[0381] Step 1:

[0382] A user accesses the system through a terminal.

[0383] Input: User request (e.g. request to create monthly report)

[0384] Output: User request data

[0385] Specific operation: A user uses the terminal interface to request a specific task (e.g., preparing a monthly report). This request is entered into the terminal.

[0386] Step 2:

[0387] The device analyzes the user's emotions using an emotion engine.

[0388] Input: User tone of voice, facial expressions, and language

[0389] Output: Sentiment analysis result (e.g. user is angry)

[0390] How it works: The device uses a built-in emotion engine to analyze the user's tone of voice, facial expressions, and speech to determine the user's emotional state.

[0391] Step 3:

[0392] The server issues instructions to the generation AI.

[0393] Input: User request data, sentiment analysis results

[0394] Output: Instructions for the generative AI (e.g., create a monthly report)

[0395] Specific operation: Based on the user's request and the results of emotion analysis, the server issues specific instructions to the generative AI. For example, it might instruct a generative AI in charge of financial management to "create the latest monthly report."

[0396] Step 4:

[0397] The generative AI carries out processing based on instructions.

[0398] Input: Instructions for the generative AI, required data (e.g. financial data)

[0399] Output: Processing results (e.g. monthly report)

[0400] Specific operation: The generative AI receives instructions from the server, collects and analyzes the necessary data, and performs processing. For example, it creates a monthly report based on financial data.

[0401] Step 5:

[0402] The generative AI sends the processing results back to the server.

[0403] Input: Processing result (e.g. monthly report)

[0404] Output: Sending the results of processing to the server

[0405] Specific operation: Once the generative AI has completed the processing, it sends the results back to the server. For example, it sends the monthly report it created to the server.

[0406] Step 6:

[0407] The server transmits the processing result to the terminal.

[0408] Input: Processing results from generative AI (e.g. monthly report)

[0409] Output: Send the results of the operation to the terminal.

[0410] Specific operation: The server sends the processing results received from the generative AI to the terminal. For example, it sends a monthly report to the terminal.

[0411] Step 7:

[0412] The terminal displays the results to the user.

[0413] Input: Processing results from the server (e.g. monthly report)

[0414] Output: Displaying the results to the user

[0415] Specific operation: The terminal displays the processing results received from the server to the user, for example, displaying a monthly report to the user for the user to check.

[0416] This allows efficient business processing in response to user requests, and enables appropriate responses in response to the user's emotions.

[0417] (Application example 1)

[0418] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0419] Improving the quality of customer service is an important issue in modern business operations. However, with conventional systems, it was difficult to analyze customer emotions in real time and respond appropriately accordingly. Furthermore, as companies aimed to become semi-automated, there was a lack of means for generative AI to give orders and hold discussions with other generative AI and share tasks efficiently. This could lead to a decline in customer satisfaction and a decline in business efficiency.

[0420] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0421] In this invention, the server includes a means for a generative AI to give commands to and discuss with other generative AIs, a means for reproducing the organizational structure of a company using a large number of generative AIs, a means for aiming for semi-automation of the company, a means for analyzing the emotions of a user using an emotion recognition engine, a means for the generative AI to instruct an appropriate response based on the analysis results, and a means for analyzing the emotions of a user in real time using a smart device and instructing a response. This enables appropriate responses according to the emotions of customers, and improves the business efficiency of the company.

[0422] "Generative AI" refers to artificial intelligence that generates new information and content based on data.

[0423] An "instruction" is an act of instructing to perform a particular action or process.

[0424] "Discussion" refers to the process in which multiple generative AIs exchange opinions and find the optimal solution.

[0425] "Massive generative AI" refers to a system in which a large number of generative AIs work together.

[0426] A "corporate organizational structure" refers to the structure that shows the arrangement and relationships of each department and role within a company.

[0427] "Semi-automated" refers to a state in which human intervention is kept to a minimum and tasks are carried out primarily by machines and artificial intelligence.

[0428] An "emotion recognition engine" is software or hardware for analyzing a user's emotions from facial expressions, tone of voice, etc.

[0429] "User emotion" refers to the emotional state, such as happiness, anger, sadness, etc., felt by the user.

[0430] "Analysis results" refers to the data obtained after the emotion recognition engine analyzes the user's emotions.

[0431] "Appropriate response" means taking the most effective action or reaction depending on the user's emotions.

[0432] A "smart device" is an electronic device with Internet connectivity and advanced computing capabilities.

[0433] "Real-time" means that processing or response is immediate, without delay.

[0434] As an embodiment of the present invention, a customer service support system for brick-and-mortar stores will be described as an example. This system uses smart devices to analyze customer emotions in real time, and a generative AI instructs appropriate responses.

[0435] System Configuration

[0436] 1. Hardware:

[0437] Smart glasses: Equipped with a camera and microphone to capture the customer's facial expressions and tone of voice.

[0438] Server: Manages data processing and generative AI instructions.

[0439] 2. Software:

[0440] Emotion recognition engine: Analyzes customer facial expressions and tone of voice to recognize emotions.

[0441] Generative AI: Prescribes appropriate responses based on the analysis results of the emotion recognition engine.

[0442] Data analysis module: processes camera video and audio data in real time.

[0443] Processing flow

[0444] 1. Data Acquisition:

[0445] The smart glasses' cameras and microphones capture the customer's facial expressions and tone of voice.

[0446] The acquired data is sent to the server.

[0447] 2. Emotion analysis:

[0448] An emotion recognition engine on the server analyzes camera footage and audio data to recognize the customer's emotions.

[0449] The analysis results are fed back to the generative AI.

[0450] 3. Instructions:

[0451] Based on the analysis results, the generative AI will instruct the appropriate response.

[0452] The instructions are displayed on the smart glasses' display and communicated to the store clerk.

[0453] Examples

[0454] For example, if a customer is angry, the emotion recognition engine will recognize that emotion, and the generative AI will instruct the store clerk to "apologize to the customer and provide specific suggestions to resolve the issue." This instruction will be displayed on the smart glasses' display, allowing the store clerk to respond immediately.

[0455] Examples of prompt statements

[0456] It analyzes the customer's facial expressions and tone of voice to recognize emotions: if the customer is angry, the generative AI will instruct the store assistant to apologize and offer specific suggestions to resolve the issue.

[0457] In this way, customer service at physical stores can be carried out more effectively. By having the entire system work together, we can expect to improve customer satisfaction and operational efficiency.

[0458] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0459] Step 1:

[0460] The smart glasses' cameras and microphones capture the customer's facial expressions and tone of voice.

[0461] Input: Customer's facial video and audio.

[0462] Output: Video and audio data.

[0463] How it works: The camera in the smart glasses captures the customer's face and the microphone records the customer's voice, and these data are sent to the server in real time.

[0464] Step 2:

[0465] The server passes the received video and audio data to an emotion recognition engine.

[0466] Input: Video and audio data.

[0467] Output: Emotion analysis results.

[0468] How it works: The server inputs video and audio data into the emotion recognition engine, which then analyzes the data to recognize the customer's emotions. For example, the facial expression analysis algorithm analyzes facial features, and the voice analysis algorithm evaluates the tone of voice.

[0469] Step 3:

[0470] The emotion recognition engine feeds back the analysis results to the generative AI.

[0471] Input: Sentiment analysis results.

[0472] Output: Generative AI instructions.

[0473] Specific behavior: The emotion recognition engine sends the analysis results to the generative AI, which then uses the results to determine the appropriate response. For example, if the customer is angry, the generative AI will instruct the AI ​​to "apologize and make specific suggestions to resolve the issue."

[0474] Step 4:

[0475] The generative AI displays the instructions on the smart glasses' display.

[0476] Input: Generative AI instructions.

[0477] Output: Instructions to be displayed on the smart glasses display.

[0478] Specific operation: The instructions determined by the generative AI are sent to the smart glasses and displayed on the screen. The store clerk checks these instructions and takes appropriate action for the customer.

[0479] Step 5:

[0480] Store clerks respond to customers according to instructions from the smart glasses.

[0481] Input: Instructions displayed on the smart glasses display.

[0482] Output: Implementing customer interaction.

[0483] Specific behavior: The salesperson checks the instructions displayed on the smart glasses display and apologizes to the customer or suggests ways to solve the problem. For example, if the customer is angry, the salesperson will ask, "I'm sorry. What problem did you encounter?"

[0484] In this way, the entire system works in tandem to enable appropriate responses according to the customer's emotions.

[0485] Example 2

[0486] Next, a second embodiment of the second embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0487] Conventional corporate management systems have not fully realized the reproduction of corporate organizational structures using generative AI or semi-automation. In addition, it is difficult to respond appropriately according to the user's emotions, and improving the user experience is a challenge.

[0488] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for a generative AI to give commands to and discuss with other generative AIs, a means for reproducing the organizational structure of a company using a large number of generative AIs, a means for aiming for semi-automation of a company, a means for acquiring user emotion data, a means for analyzing the emotion data, and a means for adjusting the behavior of the generative AI based on the analyzed emotion data. This makes it possible to respond appropriately to the user's emotions while reproducing the organizational structure of a company.

[0489] "Generative AI" is a system that uses artificial intelligence technology to generate new data and information.

[0490] An "instruction" is an instruction given to perform a particular action or process.

[0491] A "discussion" is a process in which multiple generative AIs exchange information and exchange opinions.

[0492] A "corporate organizational structure" is a structure that shows the arrangement and relationships of each department and position within a company.

[0493] "Semi-autonomous" refers to a state in which human intervention is minimized and the system operates autonomously.

[0494] "User emotion data" is information about emotions extracted from the user's facial expressions, voice, and the like.

[0495] "Analyzing emotion data" refers to the process of identifying the user's emotional state based on the acquired emotion data.

[0496] "Adjusting behavior" means changing the generative AI's responses and behavior based on analyzed emotional data.

[0497] This invention is a system that builds a generative AI network that mimics the organizational structure of a company and adjusts the behavior of the generative AI based on the emotions of the user. A specific embodiment of this system is described below.

[0498] The server retrieves organizational chart data for a company. This data includes information about the structure and role of each department in the company. The server retrieves the organizational chart data by querying the company's database. For example, the server uses a SQL query such as "SELECT FROM organization_chart WHERE company_id = '12345'".

[0499] Next, the server assigns generative AI to each department based on the acquired organizational chart data. Specifically, it uses OpenAI's GPT-4 model to set up generative AI corresponding to each department. For example, it assigns a "sales-specialized AI" to the sales department and a "marketing-specialized AI" to the marketing department.

[0500] The device acquires the user's emotion data. The data is extracted from the user's facial expressions and voice. The device uses emotion recognition software to analyze the user's facial expressions and voice in real time and extract the emotion data.

[0501] The device analyzes the acquired emotion data and identifies the user's emotional state. It uses emotion recognition software algorithms to identify the user's emotional state (happiness, sadness, anger, etc.). For example, if it detects the user's smile, it analyzes it as "happiness."

[0502] The server adjusts the behavior of the generative AI based on the analyzed emotion data. The server sends a prompt to the generative AI. For example, if the analysis finds that the user is happy, the server sends a prompt saying, "The user seems happy. Please respond more proactively."

[0503] The generative AI responds to the user based on the adjusted behavior. The generative AI responds to the user according to the prompt. For example, a sales-focused AI might respond by "suggesting special offers or additional services."

[0504] As a concrete example, consider a scenario in which a user makes a query to a generative AI in the sales department. If the emotion engine analyzes the emotion of happiness from the user's facial expression, the server sends the following prompt to the generative AI in the sales department:

[0505] Example prompt:

[0506] Your users seem happy, so be more proactive with them, for example by offering them special offers or additional services.

[0507] The generative AI that receives this prompt can then respond by suggesting special offers or additional services to the user, allowing for more appropriate responses based on the user's emotions.

[0508] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0509] Step 1:

[0510] The server retrieves organizational chart data for a company. As input, it queries the organizational chart data from the company database. Specifically, it uses a SQL query to perform an operation such as "SELECT FROM organization_chart WHERE company_id = '12345'". As output, it obtains organizational chart data showing the structure and role of each department in the company.

[0511] Step 2:

[0512] The server assigns generative AI to each department based on the acquired organizational chart data. The organizational chart data is used as input. Specifically, OpenAI's GPT-4 model is used to set up a generative AI corresponding to each department. For example, a "sales-specialized AI" is assigned to the sales department, and a "marketing-specialized AI" is assigned to the marketing department. The output is a network of generative AI that mimics the corporate organizational structure.

[0513] Step 3:

[0514] The device obtains the user's emotion data. As input, the device captures the user's facial expressions and voice in real time. Specifically, emotion recognition software is used to analyze the user's facial expressions and voice and extract emotion data. As output, data on the user's emotions is obtained.

[0515] Step 4:

[0516] The terminal analyzes the acquired emotion data and identifies the user's emotional state. The emotion data is used as input. Specifically, an emotion recognition software algorithm is used to identify the user's emotional state (happiness, sadness, anger, etc.). For example, if the terminal detects a user's smile, it analyzes it as "happiness." The analyzed emotional state is obtained as output.

[0517] Step 5:

[0518] The server adjusts the behavior of the generative AI based on the analyzed emotion data. As input, it uses the analyzed emotion state. Specifically, it sends a prompt sentence to the generative AI. For example, if the user is analyzed as happy, the server sends a prompt sentence saying "The user seems happy. Please be more proactive." As output, the adjusted prompt sentence is sent to the generative AI.

[0519] Step 6:

[0520] The generative AI responds to the user based on the adjusted behavior. As input, it uses the prompt sent from the server. Specifically, it responds to the user according to the prompt. For example, a sales-specific AI responds by "proposing special offers and additional services." As output, an appropriate response is executed for the user.

[0521] (Application example 2)

[0522] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0523] Traditional business operations are heavily dependent on human labor, and there are limitations to efficiency and responsiveness. Customer service also depends on the experience and skills of staff, making it difficult to improve customer satisfaction. Furthermore, it is difficult to grasp customer emotions in real time and respond accordingly. To solve these issues, a system that uses a generative AI network that mimics the organizational structure of a company and an emotion engine is needed.

[0524] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0525] In this invention, the server includes a means for a generative AI to give commands to and discuss with other generative AIs, a means for reproducing the organizational structure of a company using a large number of generative AIs, a means for aiming for semi-automation of the company, a means for an emotion engine to adjust the behavior of the generative AI based on the user's emotions, and a means for a smart device to analyze the emotions of customers and for the generative AI to propose appropriate ways of responding. This significantly improves the operational efficiency of the company and enables appropriate responses according to the emotions of customers, thereby improving customer satisfaction.

[0526] "Generative AI" is artificial intelligence that generates new information and content based on large amounts of data.

[0527] An "instruction" is an instruction given to perform a particular action or process.

[0528] A "discussion" is a dialogue to exchange opinions on a particular topic and reach a conclusion.

[0529] A "corporate organizational structure" is a system that shows the allocation of departments and positions within a company and the relationships between them.

[0530] "Semi-automated" refers to a state in which human intervention is minimized and operations are carried out automatically by machines and artificial intelligence.

[0531] An "emotion engine" is a technology for analyzing a user's emotions and adjusting the system's operation based on the results.

[0532] "User" means an individual or organization that uses the system or services.

[0533] "Adjusting behavior" means changing how you act or respond in response to specific situations or conditions.

[0534] A "smart device" is an electronic device with Internet connectivity and advanced computing capabilities.

[0535] "Customer" means an individual or entity that purchases or uses products or services.

[0536] "Analysis" means examining data or information in detail to understand its meaning and structure.

[0537] A "response" is an action or measure to be taken in response to a particular situation or problem.

[0538] A "suggestion" is something that suggests a specific action or option and encourages implementation.

[0539] The system for implementing this invention is configured by combining a generative AI, an emotion engine, and a smart device. A specific embodiment is shown below.

[0540] System Configuration

[0541] The server includes means for generative AI to give commands and hold discussions with other generative AI, means for recreating a company's organizational structure using a large number of generative AI, means for aiming for semi-automation of a company, means for an emotion engine to adjust the behavior of generative AI based on user emotions, and means for analyzing customer emotions using smart devices and for generative AI to suggest appropriate ways of responding.

[0542] Hardware and Software Used

[0543] Hardware: Smart glasses (e.g. Google® Glass®), camera (built into smart glasses)

[0544] Software: OpenCV (image processing library), EmotionEngine (emotion analysis engine), AINetwork (generative AI network)

[0545] Data processing and calculation

[0546] The server uses OpenCV to process the customer's facial image captured by the smart glasses' camera and analyzes the customer's emotions using EmotionEngine. The analysis results are sent to a generative AI network, which generates an appropriate response method. The generated response method is displayed on the smart glasses' display and suggested to the staff.

[0547] Examples

[0548] For example, if a customer is smiling, the generative AI will suggest to the staff to "actively introduce new products." On the other hand, if the customer looks dissatisfied, the generative AI will suggest to the staff to "carefully listen to the problem and deal with it promptly."

[0549] Examples of prompt statements

[0550] "If the customer is smiling, what response would the generative AI suggest?"

[0551] "If a customer has a dissatisfied expression, how would the generative AI suggest a response?"

[0552] In this way, the server can improve customer satisfaction by suggesting appropriate responses to staff based on the customer's emotions.

[0553] The flow of the identification process in the application example 2 will be described with reference to FIG.

[0554] Step 1:

[0555] The server captures a facial image of the customer using the camera in the smart glasses.

[0556] Input: Real-time video from the smart glasses camera.

[0557] Output: The captured face image of the customer.

[0558] Specific operation: The camera in the smart glasses detects the customer's face and sends the image data to the server.

[0559] Step 2:

[0560] The server pre-processes the captured face images using OpenCV.

[0561] Input: A captured face image of the customer.

[0562] Output: A preprocessed face image.

[0563] Specific operation: Use OpenCV to convert face images to grayscale and remove noise.

[0564] Step 3:

[0565] The server analyzes the customer's emotions from facial images preprocessed using the EmotionEngine.

[0566] Input: Preprocessed face image.

[0567] Output: Parsed customer sentiment data (e.g. happy, sad, anger, etc.).

[0568] How it works: The EmotionEngine analyzes facial features and classifies emotions.

[0569] Step 4:

[0570] The server sends the analyzed emotion data to a generative AI network to generate an appropriate response.

[0571] Input: Parsed customer sentiment data.

[0572] Output: The generated response.

[0573] Specific operation: The generative AI network generates prompt sentences based on emotion data and determines the appropriate response.

[0574] Step 5:

[0575] The server displays the generated response method on the display of the smart glasses.

[0576] Input: The generated response method.

[0577] Output: The corresponding method shown on the smart glasses display.

[0578] Specific operation: The server sends the response method in text format to the smart glasses, which then display it on the display.

[0579] Step 6:

[0580] The user (staff member) responds to the customer according to the method of response displayed on the display of the smart glasses.

[0581] Input: The corresponding instructions displayed on the smart glasses display.

[0582] Output: Appropriate response to the customer.

[0583] Specific actions: Staff follow the instructions from the smart glasses and respond appropriately to customers.

[0584] Example 3

[0585] Next, a third embodiment of the third embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0586] Conventional business management systems have had difficulty efficiently performing complex tasks such as analyzing business plans, generating work instructions, and detecting items that are difficult to judge. In addition, they were unable to call up humans at the appropriate time based on the user's emotions, making it difficult to improve the user experience. Furthermore, there was a lack of means for generative AI to work with other generative AI and aim for semi-automated enterprises.

[0587] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0588] In this invention, the server includes a means for a generative AI to give commands to and discuss with other generative AIs, a means for reproducing the organizational structure of a company using a large number of generative AIs, a means for aiming for semi-automation of a company, a means for receiving and analyzing data on a business plan, a means for a generative AI to generate work instructions based on the analysis results, a means for a generative AI to detect matters that are difficult to judge and to call a human, and a means for an emotion engine to analyze user emotion data and adjust the timing for the generative AI to call a human. This enables the efficiency of business operations based on a business plan and appropriate responses according to user emotions.

[0589] "Generative AI" refers to artificial intelligence that generates new information and instructions based on data.

[0590] "Commands and means of discussion" is a function that allows a generative AI to give instructions to other generative AIs and exchange opinions.

[0591] "Means of reproducing a company's organizational structure" refers to a function that uses a large amount of generative AI to imitate the departments and roles of a company and simulate actual corporate operations.

[0592] "Measures aimed at making companies semi-automated" refers to functions that utilize generative AI to automate corporate operations and minimize human intervention.

[0593] "Means for receiving and analyzing business plan data" refers to a function that enables the server to receive business plan data provided by a company and analyze it.

[0594] "Means for generating work instructions" refers to a function that enables the generative AI to create specific work instructions based on the analysis results.

[0595] "Means of detecting difficult issues and calling in humans" refers to a function that detects complex problems that the generative AI cannot automatically determine and calls in humans to solve them.

[0596] An "emotion engine" is software that analyzes a user's emotional data and takes appropriate action based on the results.

[0597] "A means of analyzing user emotional data and adjusting the timing for the generative AI to call on humans" refers to a function in which the emotion engine analyzes the user's emotions and, based on the results, determines the optimal timing for the generative AI to call on humans.

[0598] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0599] The present invention is a system for supporting business operations using generative AI. A specific embodiment of this system will be described below.

[0600] 1. System Configuration

[0601] The system consists of three main components: a server, a terminal, and a user. The server is responsible for running the generative AI model and analyzing the business plan data. The terminal receives instructions from the generative AI and executes appropriate actions for each department of the company. The user operates the system and provides the necessary data.

[0602] 2. Hardware and Software Used

[0603] Server: A server with a powerful processor and large memory capacity is required, specifically, a server with an Intel Xeon processor or AMD EPYC processor is suitable.

[0604] Generative AI model: Natural language processing models such as GPT-3 (registered trademark) and BERT are used as generative AI models.

[0605] OCR Software: Use OCR software to convert the business plan PDF file into text data.

[0606] Financial Management Software: Financial management software is used to execute the work instructions generated by the generative AI.

[0607] Emotion Engine: Uses emotion analysis software such as IBM Watson® Tone Analyzer to analyze user emotion data.

[0608] Notification system: Use a notification system, such as the Communications Tool API, to page a human.

[0609] 3. System Operation

[0610] The server receives business plan data from companies and converts it into text data using OCR software. The text data is then input into a generative AI model, which analyzes the contents of the business plan. The generative AI generates specific work instructions based on the analysis results and sends them to the terminal.

[0611] The terminal receives work instructions from the generative AI and executes appropriate actions for each department of the company. For example, the generative AI generates a prompt such as "Please propose the budget allocation for the next quarter" and sends it to the financial management software.

[0612] When a problem occurs that is difficult to judge or requires human judgment, the generative AI detects it and notifies the server. The server then sends a notification to a designated human using the communication tool API.

[0613] The emotion engine analyzes the user's emotion data and adjusts the timing of when the generative AI should call a human based on the results. For example, if the generative AI determines that the user is confused, it will contact a user support representative.

[0614] 4. Examples of prompts

[0615] Below are examples of prompts to be input to the generative AI model.

[0616] "Please suggest budget allocation for next quarter based on business plan."

[0617] "Do I need to seek an expert opinion on this legal issue?"

[0618] "User appears to be confused. Please contact your support representative."

[0619] In this way, the system manages the business based on the business plan and calls humans as necessary to realize more appropriate responses. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0620] Program processing flow

[0621] Step 1: Receive business plan data

[0622] The server receives PDF files of business plans from companies and stores the PDF files uploaded by users in the file system.

[0623] Input: Business plan PDF file

[0624] Output: PDF file saved to the file system

[0625] Specific behavior:

[0626] The server receives the PDF file uploaded by the user and saves it in the specified directory.

[0627] Step 2: Converting the business plan into text data

[0628] The server converts the PDF file into text data using OCR software.

[0629] Input: A PDF file stored in the file system.

[0630] Output: Text data

[0631] Specific behavior:

[0632] The server calls the OCR software to convert the PDF file into text data and holds the converted text data in memory.

[0633] Step 3: Analyze the business plan

[0634] The server inputs the text data into a generative AI model and analyzes the contents of the business plan.

[0635] Input: Text data

[0636] Output: Analysis results

[0637] Specific behavior:

[0638] The server sends text data to the generative AI model and calls an API to analyze the contents of the business plan.

[0639] Step 4: Generate work instructions

[0640] The generative AI generates specific work instructions based on the analysis results.

[0641] Input: Analysis results

[0642] Output: Work instructions

[0643] Specific behavior:

[0644] The generative AI generates a prompt such as, "Please propose budget allocation for the next quarter," and sends it to the financial management software.

[0645] Step 5: Executing work orders

[0646] The terminal receives work instructions from the generative AI and takes appropriate action for each department within the company.

[0647] Input: Work Instructions

[0648] Output: The action that was performed.

[0649] Specific behavior:

[0650] The terminal inputs the budget allocation instructions received from the generative AI into the financial management software.

[0651] Step 6: Finding tricky decisions

[0652] Generative AI detects when issues arise that are difficult to judge or require human judgment.

[0653] Input: Business data

[0654] Output: Detection results

[0655] Specific behavior:

[0656] When faced with a complex legal issue, the generative AI can generate a prompt such as, "Do you need to seek an expert opinion on this legal issue?"

[0657] Step 7: Human Invocation

[0658] The server receives instructions from the generative AI and sends notifications to designated humans.

[0659] Input: Detection results

[0660] Output: Notification

[0661] Specific behavior:

[0662] The server sends a notification to the legal professional using a communication tool API.

[0663] Step 8: Analyze the sentiment data

[0664] The emotion engine analyzes the user's emotion data, specifically, by analyzing voice data and text data to determine the user's emotion.

[0665] Input: User emotion data

[0666] Output: Emotion analysis results

[0667] Specific behavior:

[0668] The emotion engine sends the user's voice data to the IBM Watson Tone Analyzer to analyze emotions.

[0669] Step 9: Adjusting human call timing based on emotion

[0670] The generative AI receives instructions from the emotion engine and summons humans at the appropriate time.

[0671] Input: Sentiment analysis results

[0672] Output: Call instructions

[0673] Specific behavior:

[0674] The generative AI receives an instruction from the emotion engine that "the user is confused" and contacts a user support representative.

[0675] In this way, the system runs the company based on business plans and, when necessary, calls on humans to respond more appropriately.

[0676] (Application example 3)

[0677] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0678] Modern business operations require efficient production planning, inventory management, and quality control, but these tasks require a large number of manpower, which places a heavy burden in terms of cost and time. It is also important to properly manage the emotions and stress of employees, but there is a lack of effective means to achieve this. Another issue is that it is difficult for generative AI to determine the appropriate time to call on humans.

[0679] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means. In this invention, the server includes a means for a generative AI to give commands to and discuss with other generative AIs, a means for reproducing the organizational structure of a company using a large number of generative AIs, a means for aiming for semi-automation of a company, a means for the generative AI to automatically perform production planning, inventory management, and quality control for a factory, and a means for analyzing the user's emotions using an emotion engine and calling a human at an appropriate time. This makes it possible to improve the efficiency of company operations and manage the emotions of employees.

[0680] "Generative AI" refers to artificial intelligence that generates new information and content based on data.

[0681] An "instruction" is an instruction given to perform a particular action or process.

[0682] "Discussion" is a process in which multiple generative AIs exchange opinions and information to find the optimal solution.

[0683] A "corporate organizational structure" refers to the structure that shows the arrangement and relationships of each department and position within a company.

[0684] "Semi-automated" refers to a state in which human intervention is minimized and operations are carried out automatically by machines and artificial intelligence.

[0685] "Production planning" means planning the schedule and procedures for producing a product.

[0686] "Inventory management" is the process of properly managing inventory of products and raw materials and ensuring the needed amount when needed.

[0687] "Quality control" is a management activity for maintaining and improving the quality of products and services.

[0688] An "emotion engine" is a system that analyzes a user's emotions and takes appropriate action based on the results.

[0689] A "user" is someone who uses a system or service.

[0690] "Analysis" means examining data or information in detail to clarify its meaning and structure.

[0691] "Timing" refers to the appropriate time for a particular action or event to occur.

[0692] "Calling a human" means asking a human to handle matters that the generative AI cannot decide or that require human judgment.

[0693] As an embodiment of the present invention, a smart factory management system using generative AI will be described as an example. This system automatically performs production planning, inventory management, and quality control in the factory, and has the function of seeking human judgment when necessary. In addition, it can use an emotion engine to detect stress or confusion of factory staff and call humans at the appropriate time.

[0694] System Configuration

[0695] 1. Hardware:

[0696] Factory robots: Robots used to automate tasks on production lines.

[0697] Sensors: Sensors for monitoring production line conditions and inventory status in real time.

[0698] Cameras: Cameras to monitor the condition of the product for quality control.

[0699] 2. Software:

[0700] OpenAI API: An API for optimizing production planning, inventory management, and quality control using generative AI models.

[0701] EmotionEngine: An emotion engine that analyzes user emotions and calls humans at the appropriate time.

[0702] Program Processing

[0703] The server first initializes a generative AI model using the OpenAI API. This generative AI model operates based on a prompt sentence to optimize the factory's production plan. For example, by inputting a prompt sentence such as "A machine has broken down on the factory's production line. Please propose the optimal repair plan," the optimal repair plan is generated.

[0704] Next, the EmotionEngine is used to analyze the user's emotions. Based on data obtained from sensors and cameras, it determines whether the user is confused or stressed. For example, if the generative AI senses that the user is confused, it leaves it up to the human to respond to the user.

[0705] Examples

[0706] As a concrete example, consider a machine malfunction on a factory production line: In this case, generative AI would suggest an optimal repair plan and an emotion engine would summon a human if it sensed staff stress.

[0707] Example prompt:

[0708] A machine failure has occurred on the factory production line. Please suggest the optimal repair plan.

[0709] In this way, a smart factory management system that combines generative AI and an emotion engine will be realized, which will improve the efficiency of business operations and manage employee emotions.

[0710] The flow of the specific process in the application example 3 will be described with reference to FIG.

[0711] Step 1:

[0712] The server initializes the generative AI model using the OpenAI API. Specifically, it sets an API key and inputs a prompt sentence to optimize the factory production plan. As input, it uses the prompt sentence "Please tell me how to optimize the factory production plan." As output, it gets a proposal for an optimized production plan.

[0713] Step 2:

[0714] The server optimizes the production plan using a generative AI model. Specifically, the generative AI generates an optimal production plan based on a prompt statement. The input is the prompt statement, "A machine has broken down on the factory's production line. Please suggest the optimal repair plan.", and the output is a proposal for an optimal repair plan.

[0715] Step 3:

[0716] The server initializes the EmotionEngine and analyzes the user's emotions. Specifically, it inputs data obtained from sensors and cameras into the EmotionEngine and analyzes the user's emotional state. As input, it uses real-time data from sensors and cameras, and as output, it obtains the user's emotional state (e.g., confusion, stress).

[0717] Step 4:

[0718] The server calls a human as necessary based on the analysis results of the Emotion Engine. Specifically, if the user is confused or stressed, the server requests a human to respond. The input is the analysis results of the Emotion Engine, and the output is to generate instructions to call a human.

[0719] Step 5:

[0720] The server integrates the results of the generative AI model and the Emotion Engine to optimize factory operations. Specifically, it formulates an optimal operation plan based on the production plan obtained from the generative AI model and the user's emotional state obtained from the Emotion Engine. The output of the generative AI model and the output of the Emotion Engine are used as inputs, and the optimal operation plan is obtained as output.

[0721] Step 6:

[0722] The terminal controls the factory robots, sensors, and cameras based on instructions from the server. Specifically, it controls the operation of the factory robots according to an optimal operation plan and monitors data from the sensors and cameras in real time. It uses instructions from the server as input and generates control signals for the factory robots, sensors, and cameras as output.

[0723] Step 7:

[0724] The user responds to the call from the server and takes the necessary action. Specifically, humans respond to matters that the generative AI model and EmotionEngine cannot determine. The input is the call instruction from the server, and the output is the appropriate response.

[0725] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the 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 voice data.

[0726] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0727] Another example of generative AI is Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0728] In the above embodiment, an example was given in which the specific process was performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0729] [Second embodiment]

[0730] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0731] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0732] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a wide area network (WAN) and / or a local area network (LAN).

[0733] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0734] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0735] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0736] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0737] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0738] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0739] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0740] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0741] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0742] "Example 1"

[0743] As one embodiment of the present invention, a means is provided for a generative AI to command and discuss with other generative AIs. Specifically, a generative AI issues instructions to other generative AIs, so that each generative AI is responsible for a specific task. For example, some generative AIs are responsible for financial management of a company, while other generative AIs are responsible for human resources management, and so on, with each generative AI taking charge of a company's departments.

[0744] "Example 2"

[0745] Furthermore, a means is provided for reproducing the organizational structure of a company using a large number of generative AIs. Specifically, a network of generative AIs that mimics the organizational structure of a company is constructed. For example, generative AIs are arranged based on a company's organizational chart, with each generative AI in charge of a different department of the company.

[0746] "Example 3"

[0747] Furthermore, a means is provided for generative AI to operate a company in accordance with a business plan. Specifically, generative AI understands the business plan and operates the company based on it. For example, based on the business plan, generative AI appropriately performs tasks such as financial management and human resource management.

[0748] "Example 4"

[0749] Furthermore, a means will be provided for the generative AI to call on humans when necessary. Specifically, when a matter arises that the generative AI cannot decide on or that requires human judgment, the generative AI will call on humans. For example, the generative AI will call on humans when a legal decision is required or when human sensibility is required.

[0750] The process flow of each embodiment will be described below.

[0751] "Example 1"

[0752] Step 1: A generative AI issues instructions to other generative AIs. These instructions are set up so that each generative AI is responsible for a specific task.

[0753] Step 2: Each generative AI performs tasks according to instructions. For example, some generative AIs are responsible for managing the company's finances, while others are responsible for managing human resources.

[0754] "Example 2"

[0755] Step 1: Build a generative AI network that mimics a company's organizational structure.

[0756] Step 2: Place the generative AIs based on the company's organizational chart. Set each generative AI to be in charge of a different department in the company.

[0757] "Example 3"

[0758] Step 1: Generative AI understands the business plan.

[0759] Step 2: The generative AI runs the company based on the business plan. For example, based on the business plan, the generative AI appropriately performs tasks such as financial management and human resources management.

[0760] "Example 4"

[0761] Step 1: When an issue arises that the generative AI cannot decide or that requires human judgment, the generative AI calls in a human.

[0762] Step 2: If a human is called in, that human makes the necessary decisions. For example, when a legal decision is required or when human sensibility is required, the generative AI calls in a human.

[0763] Example 1

[0764] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal".

[0765] Traditional business operations required a lot of manpower, making it difficult to carry out business efficiently. Even when generative AI was used, each individual AI operated independently, making it difficult to coordinate and collaborate as a whole, and optimizing the entire company. Furthermore, generative AI could not give instructions to humans, making it impossible to make the most of its capabilities.

[0766] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0767] In this invention, the server includes a means for a generative AI to give commands to and discuss with other generative AIs, a means for reproducing the organizational structure of a company using a large number of generative AIs, a means for aiming for semi-automation of a company, a means for a user to input a prompt sentence for the generative AI, a means for the server to receive the prompt sentence and give instructions to an appropriate generative AI, a means for the generative AI to receive the instruction and perform processing, a means for the generative AI to return the processing result to the server, and a means for the server to display the processing result to the user. This makes it possible to efficiently carry out the business of a company, strengthen cooperation between generative AIs, and optimize the entire company. In addition, since the generative AI can give instructions to humans, the capabilities of the generative AI can be utilized to the maximum extent.

[0768] "Generative AI" is a system that uses artificial intelligence technology to generate data and perform specific tasks.

[0769] An "instruction" is an act of instructing to perform a particular action or process.

[0770] "Discussion" is a process in which multiple generative AIs exchange opinions and information with each other to find the optimal solution.

[0771] A "corporate organizational structure" is a structure that shows the arrangement of departments and positions within a company and their interrelationships.

[0772] "Semi-automated" refers to a state in which human intervention is minimized and tasks are carried out automatically by systems and machines.

[0773] A "prompt" is an instruction given to a generative AI to carry out a specific task.

[0774] A "server" is a computer system that provides services to other computers over a network.

[0775] "Processing results" are data and information generated as a result of a task performed by a generative AI based on instructions.

[0776] The "user" is the human who operates the system and issues instructions to the generative AI.

[0777] This invention is a system in which generative AI issues commands and holds discussions with other generative AIs, and aims to reproduce the organizational structure of a company and make the company semi-automated. A specific embodiment of this system is described below.

[0778] First, a user logs into the system using a terminal. The user enters a user name and password on the login screen and is authenticated. If the authentication is successful, the user is redirected to the system's main screen.

[0779] Next, the user finds a form on the main screen to enter a prompt for the generative AI. The user enters a prompt containing specific tasks and instructions, and clicks the send button. For example, the user can enter the following prompt:

[0780] "Prepare a proposed budget for the coming year based on the most recent financial data. Specifically, include revenue projections and cost-cutting proposals."

[0781] The server receives the prompt sent by the user. The server analyzes the contents of the prompt and issues instructions to the appropriate generative AI. For example, if the prompt is about financial management, it issues instructions to the generative AI in charge of financial management. In this case, the server uses a generative AI model such as OpenAI's GPT-4.

[0782] The generative AI initiates processing based on instructions received from the server. For example, a generative AI in charge of financial management would analyze the latest financial data and create a budget proposal with sales forecasts and cost-cutting suggestions.

[0783] When the generative AI completes the process, it returns the results to the server. For example, this may include the generated budget proposal or analysis results. The server displays the processing results received from the generative AI to the user. The user checks the results on the device screen and takes the next action if necessary.

[0784] This system will enable companies to efficiently carry out their business, strengthen cooperation between generative AIs, and optimize the entire company. In addition, generative AI can give instructions to humans, making the most of its capabilities.

[0785] As a concrete example, consider instructions for a generative AI responsible for managing a company's financial affairs. The user inputs the following prompt sentence into the generative AI model:

[0786] "Prepare a proposed budget for the coming year based on the most recent financial data. Specifically, include revenue projections and cost-cutting proposals."

[0787] The generative AI that receives this prompt will analyze the financial data and create a budget proposal that includes sales forecasts and cost-cutting proposals. Similarly, the generative AI responsible for human resources management will be given the following prompt:

[0788] "Perform performance evaluations of employees and select candidates for promotion. Evaluation criteria will be business results, teamwork, and leadership skills."

[0789] The generative AI receives this prompt and performs an evaluation based on employee performance data to select promotion candidates.

[0790] The above is a specific embodiment for carrying out the present invention.

[0791] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0792] Step 1:

[0793] A user logs into the system.

[0794] A user accesses the login screen of the system using a terminal. The user enters a user name and password to perform authentication. If the authentication is successful, the user transitions to the main screen of the system.

[0795] Input: Username, Password

[0796] Output: Access to the main screen

[0797] Specific operation: The user opens a browser, enters the system URL, and accesses the system. On the login screen, the user enters "User name: user123" and "Password: password123" and clicks the login button.

[0798] Step 2:

[0799] The user inputs a prompt for the generative AI.

[0800] On the main screen, the user finds a form for entering a prompt for the generative AI. The user enters a prompt containing specific tasks and instructions, and clicks the send button.

[0801] Input: Prompt statement

[0802] Output: Prompt sent to server

[0803] Specific actions: The user enters the following into the form on the main screen: "Please prepare a budget proposal for the next fiscal year based on the latest financial data. In particular, please include sales forecasts and cost reduction proposals." and clicks the submit button.

[0804] Step 3:

[0805] The server receives the prompt and issues instructions to the appropriate generative AI.

[0806] The server receives the prompt sent by the user, analyzes the contents of the prompt, and issues instructions to the appropriate AI generator.

[0807] Input: Prompt statement

[0808] Output: Instructions for the generative AI

[0809] Specific operation: The server analyzes the prompt text and instructs the "Financial Management AI" to "prepare the next fiscal year's budget proposal based on the latest financial data."

[0810] Step 4:

[0811] The generative AI receives instructions and carries out the processing.

[0812] The generative AI initiates processing based on instructions received from the server. For example, a generative AI in charge of financial management would analyze the latest financial data and create a budget proposal with sales forecasts and cost-cutting suggestions.

[0813] Input: Instructions from the server, financial data

[0814] Output: Budget proposal

[0815] How it works: The financial management AI retrieves the latest financial data from the database and creates a budget proposal with sales forecasts and cost-cutting suggestions.

[0816] Step 5:

[0817] The generative AI returns the processing results to the server.

[0818] Once the generative AI completes its processing, it returns the results to the server, including the generated budget proposal and analysis results, for example.

[0819] Input: Budget proposal

[0820] Output: Sending the results of processing to the server

[0821] Specific operation: The financial management AI returns the budget proposal it has created to the server. The budget proposal includes a sales forecast graph and specific proposals for cost reduction.

[0822] Step 6:

[0823] The server displays the processing results to the user.

[0824] The server displays the processing results received from the generative AI to the user, who then checks the results on the device screen and takes the next action if necessary.

[0825] Input: Processing results from generative AI

[0826] Output: Displaying the results to the user

[0827] Specific operation: The server displays the received budget proposal on the user's terminal. The user can check the details of the budget proposal on the screen and download or print it as necessary.

[0828] (Application example 1)

[0829] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal".

[0830] In modern factories, improving the efficiency of production lines and automating management are important issues. In particular, there is a demand for collecting real-time data from each process and issuing instructions quickly based on that data. However, in conventional systems, the management of each process is decentralized, making it difficult to optimize the entire system. In addition, there are limited means for factory managers to grasp the status of each process and intervene as necessary. This can lead to reduced production efficiency and variations in quality.

[0831] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0832] In this invention, the server includes a means for a generative AI to give commands to and discuss with other generative AIs, a means for reproducing the organizational structure of a company using a large number of generative AIs, a means for aiming for semi-automation of the company, a means for a generative AI to manage each process on the production line in a factory, collect data in real time, and give commands to other generative AIs, and a means for a factory manager to monitor the activity status of each generative AI through a smartphone application and intervene as necessary. This makes it possible to improve the efficiency of the entire production line and automate management.

[0833] "Generative AI" is a type of artificial intelligence that generates new information and content based on data.

[0834] An "instruction" is an act of instructing to perform a particular action or process.

[0835] "Discussion" is a process in which multiple generative AIs exchange opinions and find the optimal solution.

[0836] A "corporate organizational structure" is a structure that shows the arrangement and relationships of each department and position within a company.

[0837] "Semi-automated" refers to a state in which systems and machines operate automatically with minimal human intervention.

[0838] A "production line" is a line in which a series of steps are arranged in succession to manufacture a product.

[0839] "Collecting data in real time" refers to instantly obtaining data about ongoing events or situations.

[0840] "Giving instructions" means the act of ordering something to perform a particular action or process.

[0841] A "factory manager" is a person responsible for managing production activities and employees within a factory.

[0842] A "smartphone application" is a software program that runs on a smartphone.

[0843] "Monitoring" is the act of observing the status of a particular system or process and responding as necessary.

[0844] "Intervention" is the act of actively engaging in and influencing a particular situation or process.

[0845] A system for implementing this invention includes means for a generative AI to give commands and hold discussions with other generative AI, means for recreating the organizational structure of a company using a large number of generative AI, means for aiming to make a company semi-automated, means for a generative AI to manage each process on a production line in a factory, collect data in real time, and issue instructions to other generative AI, and means for a factory manager to monitor the activity status of each generative AI through a smartphone application and intervene as necessary.

[0846] System Programming

[0847] The server uses Python to implement a generative AI model and its coordinator. The generative AI model is responsible for each process in the factory (e.g., material supply, assembly, quality inspection), collects data in real time, and issues instructions to other generative AIs. The coordinator monitors the status of each generative AI and issues appropriate instructions.

[0848] Hardware and Software

[0849] Hardware: Smartphones, IoT devices in factories

[0850] Software: Python, Flask (as API server), SQLite (database)

[0851] Data processing and calculation

[0852] Generative AI collects real-time data from IoT devices in factories and processes it based on that data. For example, a material supply AI collects material inventory data, and when inventory is low, it instructs the assembly AI to "require material supply." The assembly AI collects progress data on the assembly process, and when progress is delayed, it instructs the quality inspection AI to "delay quality inspection because assembly is delayed."

[0853] Examples

[0854] The material supply AI collects material inventory data, and when inventory is low, it instructs the assembly AI that "material supply is required."

[0855] The assembly AI collects progress data on the assembly process, and if progress is delayed, it instructs the quality inspection AI to "delay quality inspection because assembly is delayed."

[0856] Examples of prompt statements

[0857] The material supply AI collects material inventory data, and when inventory is low, it instructs the assembly AI that "material supply is required."

[0858] In this way, generative AI can issue instructions to other generative AI, allowing efficient management of the production line in the factory. Factory managers can monitor the activity of each generative AI through a smartphone application and intervene if necessary.

[0859] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0860] Step 1:

[0861] The server collects real-time data from IoT devices in the factory. Specifically, it obtains data related to the processes that each generative AI model is responsible for (e.g., material supply, assembly, quality inspection). The input is sensor data from the IoT devices, and the output is raw data passed to the generative AI model.

[0862] Step 2:

[0863] The server passes the collected real-time data to each generative AI model. The generative AI model analyzes the current situation based on the received data. The input is the raw data collected in step 1, and the output is the analysis result. In concrete terms, the material supply AI analyzes the inventory data and generates an alert if inventory is low.

[0864] Step 3:

[0865] A generative AI model issues instructions to other generative AI models based on the analysis results. For example, if a material supply AI detects an inventory shortage, it issues an instruction to an assembly AI that "materials need to be supplied." The input is the analysis result, and the output is the instruction content. In terms of specific operations, the instructions are generated in the form of a prompt sentence.

[0866] Step 4:

[0867] The server sends the generated instructions to other generative AI models. The generative AI models that receive the instructions begin to operate according to the instructions. The input is the instructions, and the output is the execution result. In concrete terms, the assembly AI receives instructions from the material supply AI and requests replenishment of materials.

[0868] Step 5:

[0869] The server monitors the status of each generative AI model and notifies the factory manager as necessary. The input is the status data of each generative AI model, and the output is the notification to the manager. Specifically, the server reports the status to the manager in real time via a smartphone application.

[0870] Step 6:

[0871] The user (factory manager) monitors the activity of each generative AI model through a smartphone application and intervenes as necessary. The input is notifications from the server, and the output is instructions from the manager. In concrete terms, the manager can operate the application to issue new instructions to specific generative AI models.

[0872] In this way, it is possible to efficiently manage the production line within the factory through the specific operations and data flow at each step.

[0873] Example 2

[0874] Next, a description will be given of Example 2 of the embodiment 2. In the following description, the data processing device 12 is referred to as a "server" and the smart glasses 214 are referred to as a "terminal".

[0875] In traditional corporate operations, data processing and decision-making in each department was carried out separately, which led to problems with overall efficiency. In addition, since there was no system that could mimic the organizational structure of a company, it was difficult to achieve semi-automation. Furthermore, there was a lack of means for generative AI to work with other generative AI to operate the company, making it difficult to optimize the entire company.

[0876] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0877] In this invention, the server includes a means for a generative AI to give commands to and discuss with other generative AIs, a means for reproducing the organizational structure of a company using a large number of generative AIs, a means for aiming for semi-automation of the company, a means for receiving and analyzing organizational chart data of the company, a means for arranging generative AIs corresponding to each department, a means for connecting each generative AI via a network and sharing information, a means for each generative AI to process data of the department in charge and generate necessary information, and a means for aggregating the results generated by each generative AI and providing them to the user. This makes it possible to mimic the organizational structure of a company, streamline data processing and decision-making in each department, and optimize and semi-automate the entire company.

[0878] "Generative AI" is artificial intelligence that generates new information and content based on data.

[0879] An "order" is the act of giving instructions to perform a particular action or process.

[0880] A "discussion" is the act of multiple generative AIs exchanging opinions and information and engaging in dialogue to find the optimal solution.

[0881] A "corporate organizational structure" is a structure that shows the arrangement of departments and positions within a company and the relationships between them.

[0882] "Semi-automated" refers to a state in which human intervention is minimized and systems and machines operate and manage automatically.

[0883] "Corporate organization chart data" refers to data showing the arrangement of departments and positions within a company and the relationships between them.

[0884] "Analysis" is the act of examining data in detail and understanding its meaning and structure.

[0885] "Deployment" is the act of assigning a generative AI to a specific location or role.

[0886] A "network" is a connection structure that allows multiple generative AIs to communicate with each other and share information.

[0887] "Information sharing" refers to the act of exchanging data and knowledge between generative AIs and using them jointly.

[0888] "Processing data" is the act of performing calculations and analysis based on input data to generate necessary information.

[0889] "Aggregating results" refers to the act of combining information generated by multiple generative AIs into one.

[0890] A "user" is a human or other entity that uses the system.

[0891] This invention is a system that mimics the organizational structure of a company, improves the efficiency of data processing and decision-making in each department, and realizes optimization and semi-automation of the entire company. A specific embodiment of this system will be described below.

[0892] 1. Program Generation

[0893] The server generates a program that constructs a generative AI network to mimic the organizational structure of a company. The program receives the company's organizational chart as input and includes an algorithm for placing generative AIs corresponding to each department.

[0894] 2. Program Processing Explanation

[0895] The server executes the generated program and performs the following processes.

[0896] Data Entry

[0897] The server receives organizational chart data for the company. This data can come from a CSV file or a database. For example, the CSV file contains the names of each department and the relationships between the departments.

[0898] Generative AI placement

[0899] The server assigns generative AIs to each department based on the organizational chart. For example, the sales department assigns a generative AI specialized in sales, and the marketing department assigns a generative AI specialized in marketing.

[0900] Network Construction

[0901] The server will connect each generative AI to a network, mimicking the organizational structure of a company. This network will be designed so that each generative AI can cooperate with each other and share information.

[0902] Data processing and calculation

[0903] Each generative AI processes data for the department it is responsible for and generates the necessary information. For example, the generative AI for the sales department analyzes sales data and generates future sales forecasts.

[0904] The hardware used will be high-performance cloud servers (e.g., AWS EC2 instances) as servers, and the software will be a generative AI model (e.g., OpenAI's GPT-4).

[0905] 3. Examples and prompts

[0906] As a concrete example, consider the following corporate organizational chart.

[0907] Sales Department

[0908] Marketing Department

[0909] Human resources department

[0910] The server will place the generative AI based on this organizational chart as follows:

[0911] The sales department will have generative AI to analyze sales data.

[0912] The marketing department needs generative AI to generate marketing strategies.

[0913] HR departments need generative AI to generate recruitment plans

[0914] An example of a prompt sentence might be:

[0915] "Analyze the sales data from the sales department and generate sales forecasts for the next quarter."

[0916] By inputting this prompt sentence into the generative AI model, the sales department's generative AI will generate a sales forecast.

[0917] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0918] Step 1: Data entry

[0919] The server receives the organizational chart data of the company. This data can come from a CSV file or a database. For example, the CSV file contains the names of each department and the relationships between the departments. The server reads the CSV file from the specified file path and parses the information of each department. In case of using a database, the server executes an SQL query to get the required data. The input is the organizational chart data of the company and the output is the parsed department information.

[0920] Step 2: Placing the generative AI

[0921] The server assigns a generative AI to each department based on the organizational chart. For example, the sales department assigns a generative AI specialized in sales, and the marketing department assigns a generative AI specialized in marketing. Based on the information of each department, the server selects an appropriate generative AI model (e.g. GPT-4) and assigns it to each department. The input is the analyzed department information, and the output is the generative AI assigned to each department.

[0922] Step 3: Networking

[0923] The server connects each generative AI to a network, mimicking the organizational structure of a company. This network is designed to allow each generative AI to work together and share information. The server sets the communication protocol between each generative AI, enabling data to be sent and received. For example, sales forecast data generated by an AI in the sales department is sent to an AI in the marketing department, and used to generate a marketing strategy. The input is the generative AI placed in each department, and the output is the generative AI connected via the network.

[0924] Step 4: Data processing and calculations

[0925] Each generative AI processes data from its assigned department and generates the necessary information. For example, the sales department's generative AI analyzes sales data and generates future sales forecasts. The sales department's generative AI takes past sales data as input and predicts future sales using statistical methods and machine learning algorithms. Similarly, the marketing department's generative AI analyzes advertising campaign data and proposes optimal marketing strategies. The input is the data from each department, and the output is the generated information.

[0926] Step 5: Output the results

[0927] The server aggregates the results generated by each generative AI and provides them to the user. The user makes decisions based on these results. The server collects output data from each generative AI and displays it to the user in the form of a dashboard or report. For example, it visualizes sales forecasts and marketing strategy proposals in the form of graphs or tables. The input is the generated information, and the output is the aggregated results provided to the user.

[0928] (Application example 2)

[0929] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server", and the smart glasses 214 will be referred to as a "terminal".

[0930] In traditional business and factory operations, human managers were required to oversee each department, analyze data, and make decisions. However, this required a great deal of time and effort, making efficient operation difficult. In addition, it was difficult to analyze data in real time and provide optimal instructions, which led to problems with overall operational efficiency.

[0931] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means. In this invention, the server includes a means for a generative AI to give commands to and discuss with other generative AIs, a means for reproducing the organizational structure of a company using a large number of generative AIs, a means for aiming for semi-automation of a company, a means for optimizing the operation of the entire factory using generative AIs that mimic each department in the factory, and a means for each generative AI to take on the role of its respective department, analyze data in real time, and issue optimal instructions. This makes it possible to streamline the operation of companies and factories, and to perform data analysis and make optimal decisions in real time.

[0932] "Generative AI" refers to artificial intelligence that generates new information and content based on data.

[0933] An "order" is an instruction to perform a particular action or process.

[0934] "Discussion" is a process in which multiple generative AIs exchange opinions and find the optimal solution.

[0935] "Corporate organizational structure" refers to the arrangement and relationships of departments and positions within a company.

[0936] "Semi-automated" refers to a state in which systems and machines operate automatically with minimal human intervention.

[0937] "Each department within the factory" refers to departments that have specific roles within the factory, such as production, quality control, and logistics.

[0938] "Optimizing operations" means maximizing overall performance through efficient use of resources and improved processes.

[0939] "Analyzing data in real time" means analyzing the data as it is generated and obtaining instant results.

[0940] "Giving optimal instructions" means giving instructions for the most effective actions or processes based on the results of analysis.

[0941] In order to put this invention into practice, it is first necessary to build a generative AI that mimics each department in a factory. The server includes a means for a generative AI to give commands and discuss with other generative AIs, a means for reproducing the organizational structure of a company using a large number of generative AIs, a means for aiming for semi-automation of a company, a means for optimizing the operation of the entire factory using generative AIs that mimic each department in a factory, and a means for each generative AI to take on the role of its respective department, analyze data in real time, and give optimal instructions.

[0942] Specifically, the server uses programming languages ​​such as Python to build generative AI for each department. Each generative AI analyzes data collected from sensors and robots in the factory in real time and issues optimal instructions. For example, the generative AI in the production department analyzes production data and decides whether to increase or decrease production volume. The generative AI in the quality control department analyzes quality data and issues instructions for improving quality. The generative AI in the logistics department analyzes logistics data and decides the optimal logistics route.

[0943] The hardware used is a robot or sensor in the factory that collects data and runs the program on a central server. The software used is Python and its libraries (e.g. NumPy, Pandas).

[0944] For example, when the production department in a factory inputs "current production data," the generative AI may make the decision to "increase production." The quality control department may make the decision to "improve quality."

[0945] Example of a prompt to be input to a generative AI model:

[0946] Please enter your current production data.

[0947] This will streamline operations throughout the factory, enabling real-time data analysis and optimal decision-making.

[0948] The flow of the specific process in the application example 2 will be described with reference to FIG.

[0949] Step 1:

[0950] The server builds a generative AI that mimics each department in the factory. Specifically, it uses a programming language such as Python to program the generative AI in charge of each department. The input includes the role and necessary data of each department. The output is a model of the generative AI in charge of each department.

[0951] Step 2:

[0952] The server collects data from sensors and robots in the factory. Specifically, each sensor and robot transmits data to the server in real time. The input includes data from the sensors and robots. The output is the collected data stored in the server.

[0953] Step 3:

[0954] The server inputs the collected data to each generative AI. Specifically, it filters the data corresponding to the department that each generative AI is responsible for, converts it into an appropriate format, and inputs it. The input includes the collected data. The output includes the data input to the generative AI.

[0955] Step 4:

[0956] Each generative AI analyzes the input data in real time. Specifically, it uses data analysis algorithms to detect trends and anomalies in the data. The inputs include the data that was input to the generative AI. The output is the analysis results.

[0957] Step 5:

[0958] Each generative AI issues optimal instructions based on the analysis results. Specifically, it instructs each department on specific actions and processes based on the analysis results. The input includes the analysis results. The output is instructions for each department.

[0959] Step 6:

[0960] Each department acts according to instructions from the generative AI. Specifically, the production department adjusts production volume, the quality control department takes measures to improve quality, and the logistics department optimizes logistics routes. The input includes instructions from the generative AI. The output is the result of each department's actions.

[0961] Step 7:

[0962] The server again collects the action results of each department and uses them as data for the next cycle. Specifically, it collects feedback data from each department and uses it for the next data analysis. The action results of each department are included as input. The data for the next cycle is generated as output.

[0963] Example 3

[0964] Next, a third embodiment of the third embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal".

[0965] Modern business operations require efficient business management and rapid decision-making. However, conventional systems do not adequately handle operations based on business plans or when human judgment is required. Furthermore, the use of generative AI is essential to achieve semi-automation in companies, but current technology makes this difficult to achieve. To solve these issues, a new system using generative AI is needed.

[0966] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means. In this invention, the server includes a means for a generative AI to command and discuss with other generative AIs, a means for reproducing the organizational structure of a company using a large number of generative AIs, a means for aiming at semi-automation of the company, a means for receiving business plan data, a means for analyzing business plan data, a means for generating work instructions, a means for executing work instructions, and a means for calling a human when an issue arises that the generative AI cannot judge or that requires human judgment. This enables a flexible response that combines efficient business management based on a business plan with human judgment as needed.

[0967] "Generative AI" is an artificial intelligence technology that generates business plans and work instructions to support corporate operations.

[0968] "Commands and means of discussion" is a function that enables a generative AI to give instructions to other generative AIs, exchange information, and hold discussions.

[0969] "Means of reproducing a company's organizational structure" refers to a function that uses a large amount of generative AI to imitate the actual organizational structure of a company and perform simulations.

[0970] "Measures aimed at making companies semi-automated" refers to functions that utilize generative AI to automate corporate operations and minimize human intervention.

[0971] The "means for receiving business plan data" is a function that enables the server to receive data relating to business plans provided by companies.

[0972] "Means of analyzing business plan data" refers to a function that enables the generative AI to analyze the received business plan data and extract the necessary information.

[0973] The "means for generating work instructions" is a function for generating specific work instructions based on the analysis results.

[0974] The "means for executing work instructions" is a function for actually executing the generated work instructions.

[0975] The "means of summoning a human" is a function that sends a notification to a human and requests that he or she respond when an issue arises that the generative AI cannot decide or that requires human decision-making.

[0976] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0977] This invention is a system that aims to streamline corporate operations and achieve semi-automation by using generative AI. A specific embodiment of this system will be described below.

[0978] System Configuration

[0979] This system is mainly composed of three elements: a server, a terminal, and a user. The server hosts the generative AI model and receives and analyzes business plan data and generates work instructions. The terminal receives work instructions from the server and executes specific tasks. The user is called upon when there are matters that the generative AI cannot decide or when human judgment is required.

[0980] Hardware and Software Used

[0981] The server is a high-performance computer with the computational power to host a generative AI model (e.g., GPT-4). The terminal is a device for executing business instructions, such as using accounting software. The user is a device for receiving notifications (e.g., a smartphone or PC).

[0982] Data processing and calculation

[0983] The server receives business plan data provided by companies and stores it in JSON format. It then uses a generative AI model to analyze the business plan data and extract the necessary information. Based on the analysis results, it generates specific work instructions. For example, it generates instructions such as "Increase the marketing budget by 20% to achieve this month's sales target."

[0984] The terminal executes the work instructions received from the server, for example, updating a budget using accounting software, by accessing the accounting software through an API and updating the necessary data.

[0985] The user is called upon when there is a matter that the generative AI cannot decide or when human judgment is required. For example, if a legal decision is required, the server will notify the user, who will then make the professional judgment. Notifications are sent via email or chat app.

[0986] Examples

[0987] For example, a company may have a business plan to bring a new product to market. A generative AI can analyze this business plan and manage the progress of product development and develop a marketing strategy. If the product development progress is delayed, the generative AI can notify the project manager and instruct him or her to take necessary measures.

[0988] Examples of prompt statements

[0989] "Analyze the business plan for the new product launch, monitor the product development progress and develop the marketing strategy. If there is a delay in the progress, notify the project manager."

[0990] In this way, the generative AI manages the business based on the business plan and calls humans as necessary, thereby realizing efficient business management. The flow of the specific process in the third embodiment will be described with reference to FIG.

[0991] Step 1:

[0992] The server receives business plan data from the company. The input is the business plan data provided by the company, and the output is the business plan data stored in JSON format. In concrete terms, the server receives the HTTP request and stores the business plan data in a database.

[0993] Step 2:

[0994] The server analyzes the received business plan data. The input is the stored business plan data, and the output is the analysis result. In concrete terms, the server inputs the business plan data into a generative AI model (e.g., GPT-4), which then analyzes the data using natural language processing technology and extracts the necessary information.

[0995] Step 3:

[0996] The server generates specific business instructions based on the analysis results. The input is the analysis results, and the output is business instructions. In concrete terms, the server inputs a prompt sentence into the generative AI model and obtains the generated business instructions. For example, it generates an instruction such as "Increase the marketing budget by 20% to achieve this month's sales target."

[0997] Step 4:

[0998] The terminal executes the work instructions received from the server. The input is the work instructions from the server, and the output is the executed work. In concrete terms, the terminal accesses the accounting software through the API and updates the necessary data, for example updating the budget or recording expenditures.

[0999] Step 5:

[1000] The server calls the user when a matter arises that the generative AI cannot decide or when a matter that requires human judgment arises. The input is the matter that requires judgment, and the output is a notification to the user. Specifically, the server sends a notification to the user via email or a chat app. For example, if a legal decision is required, the server sends a notification to the user, and the user makes a professional judgment.

[1001] In this way, by clarifying the specific actions and data flow at each step, it is possible to achieve more efficient and semi-automated corporate operations using generative AI.

[1002] (Application example 3)

[1003] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server", and the smart glasses 214 will be referred to as a "terminal".

[1004] In conventional business operations, production schedule optimization, inventory management, and quality control had to be led by humans, which had limitations in efficiency and accuracy. In addition, when it came to matters that were difficult to judge or required human sensitivity, it was difficult to call on humans at the right time. This resulted in issues such as reduced business efficiency and increased costs.

[1005] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1006] In this invention, the server includes a means for generative AI to give commands to and discuss with other generative AI, a means for reproducing the organizational structure of a company using a large number of generative AIs, a means for aiming for semi-automation of the company, a means for generative AI to optimize production schedules, manage inventory, and manage quality based on business plans, and a means for calling humans when matters that generative AI cannot decide or when human decision is required. This makes it possible to significantly improve the operational efficiency of a company and reduce costs.

[1007] "Generative AI" refers to artificial intelligence that generates new information and content based on data.

[1008] An "instruction" is an instruction given to perform a particular action or process.

[1009] "Discussion" is a process in which multiple generative AIs exchange opinions and information to find the optimal solution.

[1010] A "corporate organizational structure" refers to the structure that shows the arrangement and relationships of each department and position within a company.

[1011] "Semi-automated" refers to a state in which human intervention is minimized and the facility is primarily operated by automated systems and robots.

[1012] A business plan is a plan that outlines the goals a company needs to achieve and the specific strategies it will use to achieve them.

[1013] "Production schedule optimization" is the process of optimizing the timing and sequence of production activities to achieve efficient production.

[1014] "Inventory management" is the management activity of maintaining appropriate inventory of products and raw materials and supplying the appropriate amounts when needed.

[1015] "Quality control" is a management activity for maintaining the quality of products and services at a certain standard.

[1016] "Undeterminable matters" are problems or situations that are difficult or impossible for a generative AI to handle automatically.

[1017] "When human judgment is required" refers to situations where generative AI cannot make appropriate decisions and human knowledge and sensibility are required.

[1018] "Invoking a human" refers to a generative AI requesting human intervention for a particular problem or situation.

[1019] A system for implementing this invention includes means for a generative AI to give commands and discuss with other generative AI, means for recreating a company's organizational structure using a large number of generative AI, means for aiming to make a company semi-automated, means for a generative AI to optimize production schedules, manage inventory, and control quality based on business plans, and means for calling humans when there are matters that the generative AI cannot decide or when human decision is required.

[1020] The server uses a generative AI model (e.g., GPT-4) to optimize production schedules based on business plans. Specifically, it schedules production tasks according to the goals of the business plan to achieve efficient production. In inventory management, it analyzes inventory data and generates instructions to maintain appropriate inventory levels. In quality control, it analyzes product quality data to detect quality problems early and take measures.

[1021] Furthermore, when there is a matter that the generative AI cannot decide or when human judgment is required, the system will have the function of calling the appropriate person in charge. For example, when legal judgment or human sensibility is required, the generative AI will automatically send a notification to the person in charge.

[1022] The hardware used includes robots used in factories that operate according to the instructions of the generative AI, while the software uses a Python program to implement the generative AI model and analyze data and generate instructions.

[1023] As a concrete example, the prompt text for optimizing the production schedule within a factory is shown below.

[1024] Example prompt:

[1025] "Based on the business plan, please optimize the production schedule for the next week. The goal is to improve production efficiency by 20%."

[1026] In this way, the server can use generative AI models to make business operations more efficient and reduce costs.

[1027] The flow of the specific process in the application example 3 will be described with reference to FIG.

[1028] Step 1:

[1029] The server receives business plan data as input. The business plan data includes company goals and goals for improving production efficiency. The server analyzes this data and converts it into a format suitable for the generative AI model. Specifically, it extracts the goals of the business plan and generates a prompt sentence to input to the generative AI model.

[1030] Step 2:

[1031] The server inputs prompt statements into a generative AI model (e.g., GPT-4) to optimize the production schedule. The generative AI model generates an optimal production schedule based on the business plan. Specifically, it optimizes the timing and order of each production task and outputs a schedule for achieving efficient production.

[1032] Step 3:

[1033] The server receives the generated production schedule and sends instructions to the robots in the factory. Specifically, it instructs each robot which task to perform and when. The robots operate according to these instructions and carry out production activities.

[1034] Step 4:

[1035] The server takes inventory data as input and performs inventory management. Specifically, it analyzes the inventory data and generates instructions to maintain appropriate stock levels. It uses a generative AI model to predict inventory surpluses and shortages and makes the necessary adjustments.

[1036] Step 5:

[1037] The server receives quality data as input and performs quality control. Specifically, it analyzes the quality data to detect quality problems early. It uses a generative AI model to identify the cause of the quality problem and generate instructions for taking countermeasures.

[1038] Step 6:

[1039] The server calls on humans when the generative AI cannot make a decision or when human judgment is required. Specifically, when legal judgment or human sensitivity is required, the server sends a notification to the appropriate person. The notification includes details of the problem and the required action.

[1040] Step 7:

[1041] The server monitors the overall operational status and adjusts the parameters of the generative AI model as necessary. Specifically, it evaluates the performance of the generative AI model against operational efficiency and cost reduction goals and makes necessary adjustments.

[1042] In addition, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1043] "Example 1"

[1044] One embodiment of the present invention is a system including an emotion engine in which a generative AI recognizes the emotions of a user. This emotion engine analyzes the emotions of the user from the tone of voice, facial expressions, and language used, and feeds the results back to the generative AI. For example, if the user feels angry, the generative AI adjusts its response to the user. This enables a more appropriate response according to the user's emotions.

[1045] "Example 2"

[1046] Furthermore, some systems include a means for the emotion engine to adjust the behavior of the generative AI based on the user's emotions. In this system, the generative AI adjusts its behavior based on the user's emotions analyzed by the emotion engine. For example, if the generative AI senses that the user is happy, it will respond to the user more proactively. This allows for a more appropriate response according to the user's emotions.

[1047] "Example 3"

[1048] There are also systems that include a means for the emotion engine to adjust the timing at which the generative AI calls a human based on the user's emotions. In this system, the generative AI adjusts the timing at which it calls a human based on the user's emotions analyzed by the emotion engine. For example, if the user feels confused, the generative AI leaves it up to the human to respond to the user. This allows for a more appropriate response according to the user's emotions.

[1049] The process flow of each embodiment will be described below.

[1050] "Example 1"

[1051] Step 1: An interaction with a user begins. This occurs when the user takes some action on the system, or when the system periodically checks the user's status.

[1052] Step 2: The emotion engine analyzes the user's emotions based on their tone of voice, facial expressions, and language.

[1053] Step 3: The emotion engine feeds back the analysis results to the generative AI.

[1054] Step 4: The generative AI uses the feedback to adjust its response to the user.

[1055] "Example 2"

[1056] Step 1: User interaction begins.

[1057] Step 2: The emotion engine analyzes the user's emotions.

[1058] Step 3: The emotion engine adjusts the generative AI’s behavior based on the analysis results.

[1059] Step 4: The generative AI executes the adjusted actions.

[1060] "Example 3"

[1061] Step 1: User interaction begins.

[1062] Step 2: The emotion engine analyzes the user's emotions.

[1063] Step 3: Based on the analysis results, the emotion engine adjusts the timing at which the generative AI will call the human.

[1064] Step 4: The generative AI summons a human at the adjusted timing.

[1065] Example 1

[1066] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal".

[1067] In conventional business operations, humans had to perform many tasks manually, which was inefficient and prone to human error. It was also difficult to respond to users' emotions, making it difficult to improve customer satisfaction. Furthermore, there was a lack of ways for generative AI to work with other generative AI to perform tasks efficiently.

[1068] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1069] In this invention, the server includes a means for a generative AI to give commands to and discuss with other generative AIs, a means for reproducing the organizational structure of a company using a large number of generative AIs, a means for aiming for semi-automation of the company, a means including an emotion engine for analyzing the user's emotions, and a means for the generative AI to adjust its response based on the analysis results of the emotion engine. This significantly improves the business efficiency of the company and enables appropriate responses according to the user's emotions, which is expected to improve customer satisfaction.

[1070] "Generative AI" is artificial intelligence that generates new information and content based on data.

[1071] An "instruction" is an act of instructing to perform a particular action or process.

[1072] "Discussion" is a process in which multiple generative AIs exchange opinions and find the optimal solution.

[1073] A "corporate organizational structure" is a structure that shows the arrangement and relationships of each department and position within a company.

[1074] "Semi-automated" refers to a state in which human intervention is minimized and systems and machines perform tasks automatically.

[1075] An "emotion engine" is a technology that analyzes a user's tone of voice, facial expressions, choice of words, etc. to recognize emotions.

[1076] "Analysis results" refers to data or information obtained after the emotion engine analyzes the user's emotions.

[1077] "Adjusting the response" means that the generative AI changes its response or behavior toward the user based on the results of the analysis.

[1078] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[1079] This invention is a system that aims to provide a means for generative AI to give commands and hold discussions with other generative AI, reproduce the organizational structure of a company, and become semi-automated. It also includes an emotion engine that analyzes the user's emotions, and includes a means for the generative AI to adjust its response based on the analysis results.

[1080] Generating a Program

[1081] First, the generative AI generates a program to give commands and hold discussions with other generative AIs. This program has the function of issuing instructions to the generative AI in charge of each department of the company to carry out specific tasks efficiently.

[1082] Program Processing

[1083] The server receives instructions from the generative AI and transmits them to other generative AIs. For example, the server instructs a generative AI responsible for a company's financial management to analyze specific financial data. The server also instructs a generative AI responsible for human resources management to evaluate employee performance.

[1084] The device is equipped with an emotion engine that recognizes the user's emotions. This emotion engine analyzes the user's tone of voice, facial expressions, and choice of words, and feeds the results back to the generative AI. For example, if the emotion engine determines that the user is angry, the device sends that information to the generative AI.

[1085] The generative AI receives feedback from the emotion engine and adjusts its response based on the user's emotions. For example, if the user is angry, the generative AI is programmed to respond calmly and politely.

[1086] Examples

[1087] As a concrete example, consider the case where a generative AI in charge of financial management of a company is instructed to create a monthly report. The server collects financial data and instructs the generative AI to analyze it. The generative AI creates a monthly report based on the collected data and sends the results back to the server.

[1088] In addition, when a user makes an inquiry to the generative AI through a device, the emotion engine analyzes the user's tone of voice and facial expressions and determines that the user is dissatisfied. Based on that information, the generative AI responds to the user in a more polite manner.

[1089] Examples of prompt statements

[1090] Examples of prompt sentences to be input to a generative AI model include the following:

[1091] "Tell your generative AI, responsible for managing your company's finances, to create an up-to-date monthly report."

[1092] "If the user is angry, explain how the generative AI would adjust its response."

[1093] This makes it clearer the overall picture of a system in which generative AI issues instructions to other generative AIs and responds according to the user's emotions.

[1094] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1095] Program processing flow

[1096] Step 1:

[1097] A user accesses the system through a terminal.

[1098] Input: User request (e.g. request to create monthly report)

[1099] Output: User request data

[1100] Specific operation: A user uses the terminal interface to request a specific task (e.g., preparing a monthly report). This request is entered into the terminal.

[1101] Step 2:

[1102] The device analyzes the user's emotions using an emotion engine.

[1103] Input: User tone of voice, facial expressions, and language

[1104] Output: Sentiment analysis result (e.g. user is angry)

[1105] How it works: The device uses a built-in emotion engine to analyze the user's tone of voice, facial expressions, and speech to determine the user's emotional state.

[1106] Step 3:

[1107] The server issues instructions to the generation AI.

[1108] Input: User request data, sentiment analysis results

[1109] Output: Instructions for the generative AI (e.g., create a monthly report)

[1110] Specific operation: Based on the user's request and the results of emotion analysis, the server issues specific instructions to the generative AI. For example, it might instruct a generative AI in charge of financial management to "create the latest monthly report."

[1111] Step 4:

[1112] The generative AI carries out processing based on instructions.

[1113] Input: Instructions for the generative AI, required data (e.g. financial data)

[1114] Output: Processing results (e.g. monthly report)

[1115] Specific operation: The generative AI receives instructions from the server, collects and analyzes the necessary data, and performs processing. For example, it creates a monthly report based on financial data.

[1116] Step 5:

[1117] The generative AI sends the processing results back to the server.

[1118] Input: Processing result (e.g. monthly report)

[1119] Output: Sending the results of processing to the server

[1120] Specific operation: Once the generative AI has completed the processing, it sends the results back to the server. For example, it sends the monthly report it created to the server.

[1121] Step 6:

[1122] The server transmits the processing result to the terminal.

[1123] Input: Processing results from generative AI (e.g. monthly report)

[1124] Output: Send the results of the operation to the terminal.

[1125] Specific operation: The server sends the processing results received from the generative AI to the terminal. For example, it sends a monthly report to the terminal.

[1126] Step 7:

[1127] The terminal displays the results to the user.

[1128] Input: Processing results from the server (e.g. monthly report)

[1129] Output: Displaying the results to the user

[1130] Specific operation: The terminal displays the processing results received from the server to the user, for example, displaying a monthly report to the user for the user to check.

[1131] This allows efficient business processing in response to user requests, and enables appropriate responses in response to the user's emotions.

[1132] (Application example 1)

[1133] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal".

[1134] Improving the quality of customer service is an important issue in modern business operations. However, with conventional systems, it was difficult to analyze customer emotions in real time and respond appropriately accordingly. Furthermore, as companies aimed to become semi-automated, there was a lack of means for generative AI to give orders and hold discussions with other generative AI and share tasks efficiently. This could lead to a decline in customer satisfaction and a decline in business efficiency.

[1135] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1136] In this invention, the server includes a means for a generative AI to give commands to and discuss with other generative AIs, a means for reproducing the organizational structure of a company using a large number of generative AIs, a means for aiming for semi-automation of the company, a means for analyzing the emotions of a user using an emotion recognition engine, a means for the generative AI to instruct an appropriate response based on the analysis results, and a means for analyzing the emotions of a user in real time using a smart device and instructing a response. This enables appropriate responses according to the emotions of customers, and improves the business efficiency of the company.

[1137] "Generative AI" refers to artificial intelligence that generates new information and content based on data.

[1138] An "instruction" is an act of instructing to perform a particular action or process.

[1139] "Discussion" refers to the process in which multiple generative AIs exchange opinions and find the optimal solution.

[1140] "Massive generative AI" refers to a system in which a large number of generative AIs work together.

[1141] A "corporate organizational structure" refers to the structure that shows the arrangement and relationships of each department and role within a company.

[1142] "Semi-automated" refers to a state in which human intervention is kept to a minimum and tasks are carried out primarily by machines and artificial intelligence.

[1143] An "emotion recognition engine" is software or hardware for analyzing a user's emotions from facial expressions, tone of voice, etc.

[1144] "User emotion" refers to the emotional state, such as happiness, anger, sadness, etc., felt by the user.

[1145] "Analysis results" refers to the data obtained after the emotion recognition engine analyzes the user's emotions.

[1146] "Appropriate response" means taking the most effective action or reaction depending on the user's emotions.

[1147] A "smart device" is an electronic device with Internet connectivity and advanced computing capabilities.

[1148] "Real-time" means that processing or response is immediate, without delay.

[1149] As an embodiment of the present invention, a customer service support system for brick-and-mortar stores will be described as an example. This system uses smart devices to analyze customer emotions in real time, and a generative AI instructs appropriate responses.

[1150] System Configuration

[1151] 1. Hardware:

[1152] Smart glasses: Equipped with a camera and microphone to capture the customer's facial expressions and tone of voice.

[1153] Server: Manages data processing and generative AI instructions.

[1154] 2. Software:

[1155] Emotion recognition engine: Analyzes customer facial expressions and tone of voice to recognize emotions.

[1156] Generative AI: Prescribes appropriate responses based on the analysis results of the emotion recognition engine.

[1157] Data analysis module: processes camera video and audio data in real time.

[1158] Processing flow

[1159] 1. Data Acquisition:

[1160] The smart glasses' cameras and microphones capture the customer's facial expressions and tone of voice.

[1161] The acquired data is sent to the server.

[1162] 2. Emotion analysis:

[1163] An emotion recognition engine on the server analyzes camera footage and audio data to recognize the customer's emotions.

[1164] The analysis results are fed back to the generative AI.

[1165] 3. Instructions:

[1166] Based on the analysis results, the generative AI will instruct the appropriate response.

[1167] The instructions are displayed on the smart glasses' display and communicated to the store clerk.

[1168] Examples

[1169] For example, if a customer is angry, the emotion recognition engine will recognize that emotion, and the generative AI will instruct the store clerk to "apologize to the customer and provide specific suggestions to resolve the issue." This instruction will be displayed on the smart glasses' display, allowing the store clerk to respond immediately.

[1170] Examples of prompt statements

[1171] It analyzes the customer's facial expressions and tone of voice to recognize emotions: if the customer is angry, the generative AI will instruct the store assistant to apologize and offer specific suggestions to resolve the issue.

[1172] In this way, customer service at physical stores can be carried out more effectively. By having the entire system work together, we can expect to improve customer satisfaction and operational efficiency.

[1173] The flow of the specific process in the application example 1 will be described with reference to FIG.

[1174] Step 1:

[1175] The smart glasses' cameras and microphones capture the customer's facial expressions and tone of voice.

[1176] Input: Customer's facial video and audio.

[1177] Output: Video and audio data.

[1178] How it works: The camera in the smart glasses captures the customer's face and the microphone records the customer's voice, and these data are sent to the server in real time.

[1179] Step 2:

[1180] The server passes the received video and audio data to an emotion recognition engine.

[1181] Input: Video and audio data.

[1182] Output: Emotion analysis results.

[1183] How it works: The server inputs video and audio data into the emotion recognition engine, which then analyzes the data to recognize the customer's emotions. For example, the facial expression analysis algorithm analyzes facial features, and the voice analysis algorithm evaluates the tone of voice.

[1184] Step 3:

[1185] The emotion recognition engine feeds back the analysis results to the generative AI.

[1186] Input: Sentiment analysis results.

[1187] Output: Generative AI instructions.

[1188] Specific behavior: The emotion recognition engine sends the analysis results to the generative AI, which then uses the results to determine the appropriate response. For example, if the customer is angry, the generative AI will instruct the AI ​​to "apologize and make specific suggestions to resolve the issue."

[1189] Step 4:

[1190] The generative AI displays the instructions on the smart glasses' display.

[1191] Input: Generative AI instructions.

[1192] Output: Instructions to be displayed on the smart glasses display.

[1193] Specific operation: The instructions determined by the generative AI are sent to the smart glasses and displayed on the screen. The store clerk checks these instructions and takes appropriate action for the customer.

[1194] Step 5:

[1195] Store clerks respond to customers according to instructions from the smart glasses.

[1196] Input: Instructions displayed on the smart glasses display.

[1197] Output: Implementing customer interaction.

[1198] Specific behavior: The salesperson checks the instructions displayed on the smart glasses display and apologizes to the customer or suggests ways to solve the problem. For example, if the customer is angry, the salesperson will ask, "I'm sorry. What problem did you encounter?"

[1199] In this way, the entire system works in tandem to enable appropriate responses according to the customer's emotions.

[1200] Example 2

[1201] Next, a description will be given of Example 2 of the embodiment 2. In the following description, the data processing device 12 is referred to as a "server" and the smart glasses 214 are referred to as a "terminal".

[1202] Conventional corporate management systems have not fully realized the reproduction of corporate organizational structures using generative AI or semi-automation. In addition, it is difficult to respond appropriately according to the user's emotions, and improving the user experience is a challenge.

[1203] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for a generative AI to give commands to and discuss with other generative AIs, a means for reproducing the organizational structure of a company using a large number of generative AIs, a means for aiming for semi-automation of a company, a means for acquiring user emotion data, a means for analyzing the emotion data, and a means for adjusting the behavior of the generative AI based on the analyzed emotion data. This makes it possible to respond appropriately to the user's emotions while reproducing the organizational structure of a company.

[1204] "Generative AI" is a system that uses artificial intelligence technology to generate new data and information.

[1205] An "instruction" is an instruction given to perform a particular action or process.

[1206] A "discussion" is a process in which multiple generative AIs exchange information and exchange opinions.

[1207] A "corporate organizational structure" is a structure that shows the arrangement and relationships of each department and position within a company.

[1208] "Semi-autonomous" refers to a state in which human intervention is minimized and the system operates autonomously.

[1209] "User emotion data" is information about emotions extracted from the user's facial expressions, voice, and the like.

[1210] "Analyzing emotion data" refers to the process of identifying the user's emotional state based on the acquired emotion data.

[1211] "Adjusting behavior" means changing the generative AI's responses and behavior based on analyzed emotional data.

[1212] This invention is a system that builds a generative AI network that mimics the organizational structure of a company and adjusts the behavior of the generative AI based on the emotions of the user. A specific embodiment of this system is described below.

[1213] The server retrieves organizational chart data for a company. This data includes information about the structure and role of each department in the company. The server retrieves the organizational chart data by querying the company's database. For example, the server uses a SQL query such as "SELECT FROM organization_chart WHERE company_id = '12345'".

[1214] Next, the server assigns generative AI to each department based on the acquired organizational chart data. Specifically, it uses OpenAI's GPT-4 model to set up generative AI corresponding to each department. For example, it assigns a "sales-specialized AI" to the sales department and a "marketing-specialized AI" to the marketing department.

[1215] The device acquires the user's emotion data. The data is extracted from the user's facial expressions and voice. The device uses emotion recognition software to analyze the user's facial expressions and voice in real time and extract the emotion data.

[1216] The device analyzes the acquired emotion data and identifies the user's emotional state. It uses emotion recognition software algorithms to identify the user's emotional state (happiness, sadness, anger, etc.). For example, if it detects the user's smile, it analyzes it as "happiness."

[1217] The server adjusts the behavior of the generative AI based on the analyzed emotion data. The server sends a prompt to the generative AI. For example, if the analysis finds that the user is happy, the server sends a prompt saying, "The user seems happy. Please respond more proactively."

[1218] The generative AI responds to the user based on the adjusted behavior. The generative AI responds to the user according to the prompt. For example, a sales-focused AI might respond by "suggesting special offers or additional services."

[1219] As a concrete example, consider a scenario in which a user makes a query to a generative AI in the sales department. If the emotion engine analyzes the emotion of happiness from the user's facial expression, the server sends the following prompt to the generative AI in the sales department:

[1220] Example prompt:

[1221] Your users seem happy, so be more proactive with them, for example by offering them special offers or additional services.

[1222] The generative AI that receives this prompt can then respond by suggesting special offers or additional services to the user, allowing for more appropriate responses based on the user's emotions.

[1223] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1224] Step 1:

[1225] The server retrieves organizational chart data for a company. As input, it queries the organizational chart data from the company database. Specifically, it uses a SQL query to perform an operation such as "SELECT FROM organization_chart WHERE company_id = '12345'". As output, it obtains organizational chart data showing the structure and role of each department in the company.

[1226] Step 2:

[1227] The server assigns generative AI to each department based on the acquired organizational chart data. The organizational chart data is used as input. Specifically, OpenAI's GPT-4 model is used to set up a generative AI corresponding to each department. For example, a "sales-specialized AI" is assigned to the sales department, and a "marketing-specialized AI" is assigned to the marketing department. The output is a network of generative AI that mimics the corporate organizational structure.

[1228] Step 3:

[1229] The device obtains the user's emotion data. As input, the device captures the user's facial expressions and voice in real time. Specifically, emotion recognition software is used to analyze the user's facial expressions and voice and extract emotion data. As output, data on the user's emotions is obtained.

[1230] Step 4:

[1231] The terminal analyzes the acquired emotion data and identifies the user's emotional state. The emotion data is used as input. Specifically, an emotion recognition software algorithm is used to identify the user's emotional state (happiness, sadness, anger, etc.). For example, if the terminal detects a user's smile, it analyzes it as "happiness." The analyzed emotional state is obtained as output.

[1232] Step 5:

[1233] The server adjusts the behavior of the generative AI based on the analyzed emotion data. As input, it uses the analyzed emotion state. Specifically, it sends a prompt sentence to the generative AI. For example, if the user is analyzed as happy, the server sends a prompt sentence saying "The user seems happy. Please be more proactive." As output, the adjusted prompt sentence is sent to the generative AI.

[1234] Step 6:

[1235] The generative AI responds to the user based on the adjusted behavior. As input, it uses the prompt sent from the server. Specifically, it responds to the user according to the prompt. For example, a sales-specific AI responds by "proposing special offers and additional services." As output, an appropriate response is executed for the user.

[1236] (Application example 2)

[1237] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server", and the smart glasses 214 will be referred to as a "terminal".

[1238] Traditional business operations are heavily dependent on human labor, and there are limitations to efficiency and responsiveness. Customer service also depends on the experience and skills of staff, making it difficult to improve customer satisfaction. Furthermore, it is difficult to grasp customer emotions in real time and respond accordingly. To solve these issues, a system that uses a generative AI network that mimics the organizational structure of a company and an emotion engine is needed.

[1239] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1240] In this invention, the server includes a means for a generative AI to give commands to and discuss with other generative AIs, a means for reproducing the organizational structure of a company using a large number of generative AIs, a means for aiming for semi-automation of the company, a means for an emotion engine to adjust the behavior of the generative AI based on the user's emotions, and a means for a smart device to analyze the emotions of customers and for the generative AI to propose appropriate ways of responding. This significantly improves the operational efficiency of the company and enables appropriate responses according to the emotions of customers, thereby improving customer satisfaction.

[1241] "Generative AI" is artificial intelligence that generates new information and content based on large amounts of data.

[1242] An "instruction" is an instruction given to perform a particular action or process.

[1243] A "discussion" is a dialogue to exchange opinions on a particular topic and reach a conclusion.

[1244] A "corporate organizational structure" is a system that shows the allocation of departments and positions within a company and the relationships between them.

[1245] "Semi-automated" refers to a state in which human intervention is minimized and operations are carried out automatically by machines and artificial intelligence.

[1246] An "emotion engine" is a technology for analyzing a user's emotions and adjusting the system's operation based on the results.

[1247] "User" means an individual or organization that uses the system or services.

[1248] "Adjusting behavior" means changing how you act or respond in response to specific situations or conditions.

[1249] A "smart device" is an electronic device with Internet connectivity and advanced computing capabilities.

[1250] "Customer" means an individual or entity that purchases or uses products or services.

[1251] "Analysis" means examining data or information in detail to understand its meaning and structure.

[1252] A "response" is an action or measure to be taken in response to a particular situation or problem.

[1253] A "suggestion" is something that suggests a specific action or option and encourages implementation.

[1254] The system for implementing this invention is configured by combining a generative AI, an emotion engine, and a smart device. A specific embodiment is shown below.

[1255] System Configuration

[1256] The server includes means for generative AI to give commands and hold discussions with other generative AI, means for recreating a company's organizational structure using a large number of generative AI, means for aiming for semi-automation of a company, means for an emotion engine to adjust the behavior of generative AI based on user emotions, and means for analyzing customer emotions using smart devices and for generative AI to suggest appropriate ways of responding.

[1257] Hardware and Software Used

[1258] Hardware: Smart glasses (e.g. Google Glass), camera (built into smart glasses)

[1259] Software: OpenCV (image processing library), EmotionEngine (emotion analysis engine), AINetwork (generative AI network)

[1260] Data processing and calculation

[1261] The server uses OpenCV to process the customer's facial image captured by the smart glasses' camera and analyzes the customer's emotions using EmotionEngine. The analysis results are sent to a generative AI network, which generates an appropriate response method. The generated response method is displayed on the smart glasses' display and suggested to the staff.

[1262] Examples

[1263] For example, if a customer is smiling, the generative AI will suggest to the staff to "actively introduce new products." On the other hand, if the customer looks dissatisfied, the generative AI will suggest to the staff to "carefully listen to the problem and deal with it promptly."

[1264] Examples of prompt statements

[1265] "If the customer is smiling, what response would the generative AI suggest?"

[1266] "If a customer has a dissatisfied expression, how would the generative AI suggest a response?"

[1267] In this way, the server can improve customer satisfaction by suggesting appropriate responses to staff based on the customer's emotions.

[1268] The flow of the identification process in the application example 2 will be described with reference to FIG.

[1269] Step 1:

[1270] The server captures a facial image of the customer using the camera in the smart glasses.

[1271] Input: Real-time video from the smart glasses camera.

[1272] Output: The captured face image of the customer.

[1273] Specific operation: The camera in the smart glasses detects the customer's face and sends the image data to the server.

[1274] Step 2:

[1275] The server pre-processes the captured face images using OpenCV.

[1276] Input: A captured face image of the customer.

[1277] Output: A preprocessed face image.

[1278] Specific operation: Use OpenCV to convert face images to grayscale and remove noise.

[1279] Step 3:

[1280] The server analyzes the customer's emotions from facial images preprocessed using the EmotionEngine.

[1281] Input: Preprocessed face image.

[1282] Output: Parsed customer sentiment data (e.g. happy, sad, anger, etc.).

[1283] How it works: The EmotionEngine analyzes facial features and classifies emotions.

[1284] Step 4:

[1285] The server sends the analyzed emotion data to a generative AI network to generate an appropriate response.

[1286] Input: Parsed customer sentiment data.

[1287] Output: The generated response.

[1288] Specific operation: The generative AI network generates prompt sentences based on emotion data and determines the appropriate response.

[1289] Step 5:

[1290] The server displays the generated response method on the display of the smart glasses.

[1291] Input: The generated response method.

[1292] Output: The corresponding method shown on the smart glasses display.

[1293] Specific operation: The server sends the response method in text format to the smart glasses, which then display it on the display.

[1294] Step 6:

[1295] The user (staff member) responds to the customer according to the method of response displayed on the display of the smart glasses.

[1296] Input: The corresponding instructions displayed on the smart glasses display.

[1297] Output: Appropriate response to the customer.

[1298] Specific actions: Staff follow the instructions from the smart glasses and respond appropriately to customers.

[1299] Example 3

[1300] Next, a third embodiment of the third embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal".

[1301] Conventional business management systems have had difficulty efficiently performing complex tasks such as analyzing business plans, generating work instructions, and detecting items that are difficult to judge. In addition, they were unable to call up humans at the appropriate time based on the user's emotions, making it difficult to improve the user experience. Furthermore, there was a lack of means for generative AI to work with other generative AI and aim for semi-automated enterprises.

[1302] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1303] In this invention, the server includes a means for a generative AI to give commands to and discuss with other generative AIs, a means for reproducing the organizational structure of a company using a large number of generative AIs, a means for aiming for semi-automation of a company, a means for receiving and analyzing data on a business plan, a means for a generative AI to generate work instructions based on the analysis results, a means for a generative AI to detect matters that are difficult to judge and to call a human, and a means for an emotion engine to analyze user emotion data and adjust the timing for the generative AI to call a human. This enables the efficiency of business operations based on a business plan and appropriate responses according to user emotions.

[1304] "Generative AI" refers to artificial intelligence that generates new information and instructions based on data.

[1305] "Commands and means of discussion" is a function that allows a generative AI to give instructions to other generative AIs and exchange opinions.

[1306] "Means of reproducing a company's organizational structure" refers to a function that uses a large amount of generative AI to imitate the departments and roles of a company and simulate actual corporate operations.

[1307] "Measures aimed at making companies semi-automated" refers to functions that utilize generative AI to automate corporate operations and minimize human intervention.

[1308] "Means for receiving and analyzing business plan data" refers to a function that enables the server to receive business plan data provided by a company and analyze it.

[1309] "Means for generating work instructions" refers to a function that enables the generative AI to create specific work instructions based on the analysis results.

[1310] "Means of detecting difficult issues and calling in humans" refers to a function that detects complex problems that the generative AI cannot automatically determine and calls in humans to solve them.

[1311] An "emotion engine" is software that analyzes a user's emotional data and takes appropriate action based on the results.

[1312] "A means of analyzing user emotional data and adjusting the timing for the generative AI to call on humans" refers to a function in which the emotion engine analyzes the user's emotions and, based on the results, determines the optimal timing for the generative AI to call on humans.

[1313] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[1314] The present invention is a system for supporting business operations using generative AI. A specific embodiment of this system will be described below.

[1315] 1. System Configuration

[1316] The system consists of three main components: a server, a terminal, and a user. The server is responsible for running the generative AI model and analyzing the business plan data. The terminal receives instructions from the generative AI and executes appropriate actions for each department of the company. The user operates the system and provides the necessary data.

[1317] 2. Hardware and Software Used

[1318] Server: A server with a powerful processor and large memory capacity is required, specifically, a server with an Intel Xeon processor or AMD EPYC processor is suitable.

[1319] Generative AI model: Natural language processing models such as GPT-3 and BERT are used as generative AI models.

[1320] OCR Software: Use OCR software to convert the business plan PDF file into text data.

[1321] Financial Management Software: Financial management software is used to execute the work instructions generated by the generative AI.

[1322] Sentiment engine: Uses sentiment analysis software such as IBM Watson Tone Analyzer to analyze user sentiment data.

[1323] Notification system: Use a notification system, such as the Communications Tool API, to page a human.

[1324] 3. System Operation

[1325] The server receives business plan data from companies and converts it into text data using OCR software. The text data is then input into a generative AI model, which analyzes the contents of the business plan. The generative AI generates specific work instructions based on the analysis results and sends them to the terminal.

[1326] The terminal receives work instructions from the generative AI and executes appropriate actions for each department of the company. For example, the generative AI generates a prompt such as "Please propose the budget allocation for the next quarter" and sends it to the financial management software.

[1327] When a problem occurs that is difficult to judge or requires human judgment, the generative AI detects it and notifies the server. The server then sends a notification to a designated human using the communication tool API.

[1328] The emotion engine analyzes the user's emotion data and adjusts the timing of when the generative AI should call a human based on the results. For example, if the generative AI determines that the user is confused, it will contact a user support representative.

[1329] 4. Examples of prompts

[1330] Below are examples of prompts to be input to the generative AI model.

[1331] "Please suggest budget allocation for next quarter based on business plan."

[1332] "Do I need to seek an expert opinion on this legal issue?"

[1333] "User appears to be confused. Please contact your support representative."

[1334] In this way, the system manages the business based on the business plan and calls humans as necessary to realize more appropriate responses. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1335] Program processing flow

[1336] Step 1: Receive business plan data

[1337] The server receives PDF files of business plans from companies and stores the PDF files uploaded by users in the file system.

[1338] Input: Business plan PDF file

[1339] Output: PDF file saved to the file system

[1340] Specific behavior:

[1341] The server receives the PDF file uploaded by the user and saves it in the specified directory.

[1342] Step 2: Converting the business plan into text data

[1343] The server converts the PDF file into text data using OCR software.

[1344] Input: A PDF file stored in the file system.

[1345] Output: Text data

[1346] Specific behavior:

[1347] The server calls the OCR software to convert the PDF file into text data and holds the converted text data in memory.

[1348] Step 3: Analyze the business plan

[1349] The server inputs the text data into a generative AI model and analyzes the contents of the business plan.

[1350] Input: Text data

[1351] Output: Analysis results

[1352] Specific behavior:

[1353] The server sends text data to the generative AI model and calls an API to analyze the contents of the business plan.

[1354] Step 4: Generate work instructions

[1355] The generative AI generates specific work instructions based on the analysis results.

[1356] Input: Analysis results

[1357] Output: Work instructions

[1358] Specific behavior:

[1359] The generative AI generates a prompt such as, "Please propose budget allocation for the next quarter," and sends it to the financial management software.

[1360] Step 5: Executing work orders

[1361] The terminal receives work instructions from the generative AI and takes appropriate action for each department within the company.

[1362] Input: Work Instructions

[1363] Output: The action that was performed.

[1364] Specific behavior:

[1365] The terminal inputs the budget allocation instructions received from the generative AI into the financial management software.

[1366] Step 6: Finding tricky decisions

[1367] Generative AI detects when issues arise that are difficult to judge or require human judgment.

[1368] Input: Business data

[1369] Output: Detection results

[1370] Specific behavior:

[1371] When faced with a complex legal issue, the generative AI can generate a prompt such as, "Do you need to seek an expert opinion on this legal issue?"

[1372] Step 7: Human Invocation

[1373] The server receives instructions from the generative AI and sends notifications to designated humans.

[1374] Input: Detection results

[1375] Output: Notification

[1376] Specific behavior:

[1377] The server sends a notification to the legal professional using a communication tool API.

[1378] Step 8: Analyze the sentiment data

[1379] The emotion engine analyzes the user's emotion data, specifically, by analyzing voice data and text data to determine the user's emotion.

[1380] Input: User emotion data

[1381] Output: Emotion analysis results

[1382] Specific behavior:

[1383] The emotion engine sends the user's voice data to the IBM Watson Tone Analyzer to analyze emotions.

[1384] Step 9: Adjusting human call timing based on emotion

[1385] The generative AI receives instructions from the emotion engine and summons humans at the appropriate time.

[1386] Input: Sentiment analysis results

[1387] Output: Call instructions

[1388] Specific behavior:

[1389] The generative AI receives an instruction from the emotion engine that "the user is confused" and contacts a user support representative.

[1390] In this way, the system runs the company based on business plans and, when necessary, calls on humans to respond more appropriately.

[1391] (Application example 3)

[1392] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server", and the smart glasses 214 will be referred to as a "terminal".

[1393] Modern business operations require efficient production planning, inventory management, and quality control, but these tasks require a large number of manpower, which places a heavy burden in terms of cost and time. It is also important to properly manage the emotions and stress of employees, but there is a lack of effective means to achieve this. Another issue is that it is difficult for generative AI to determine the appropriate time to call on humans.

[1394] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means. In this invention, the server includes a means for a generative AI to give commands to and discuss with other generative AIs, a means for reproducing the organizational structure of a company using a large number of generative AIs, a means for aiming for semi-automation of a company, a means for the generative AI to automatically perform production planning, inventory management, and quality control for a factory, and a means for analyzing the user's emotions using an emotion engine and calling a human at an appropriate time. This makes it possible to improve the efficiency of company operations and manage the emotions of employees.

[1395] "Generative AI" refers to artificial intelligence that generates new information and content based on data.

[1396] An "instruction" is an instruction given to perform a particular action or process.

[1397] "Discussion" is a process in which multiple generative AIs exchange opinions and information to find the optimal solution.

[1398] A "corporate organizational structure" refers to the structure that shows the arrangement and relationships of each department and position within a company.

[1399] "Semi-automated" refers to a state in which human intervention is minimized and operations are carried out automatically by machines and artificial intelligence.

[1400] "Production planning" means planning the schedule and procedures for producing a product.

[1401] "Inventory management" is the process of properly managing inventory of products and raw materials and ensuring the needed amount when needed.

[1402] "Quality control" is a management activity for maintaining and improving the quality of products and services.

[1403] An "emotion engine" is a system that analyzes a user's emotions and takes appropriate action based on the results.

[1404] A "user" is someone who uses a system or service.

[1405] "Analysis" means examining data or information in detail to clarify its meaning and structure.

[1406] "Timing" refers to the appropriate time for a particular action or event to occur.

[1407] "Calling a human" means asking a human to handle matters that the generative AI cannot decide or that require human judgment.

[1408] As an embodiment of the present invention, a smart factory management system using generative AI will be described as an example. This system automatically performs production planning, inventory management, and quality control in the factory, and has the function of seeking human judgment when necessary. In addition, it can use an emotion engine to detect stress or confusion of factory staff and call humans at the appropriate time.

[1409] System Configuration

[1410] 1. Hardware:

[1411] Factory robots: Robots used to automate tasks on production lines.

[1412] Sensors: Sensors for monitoring production line conditions and inventory status in real time.

[1413] Cameras: Cameras to monitor the condition of the product for quality control.

[1414] 2. Software:

[1415] OpenAI API: An API for optimizing production planning, inventory management, and quality control using generative AI models.

[1416] EmotionEngine: An emotion engine that analyzes user emotions and calls humans at the appropriate time.

[1417] Program Processing

[1418] The server first initializes a generative AI model using the OpenAI API. This generative AI model operates based on a prompt sentence to optimize the factory's production plan. For example, by inputting a prompt sentence such as "A machine has broken down on the factory's production line. Please propose the optimal repair plan," the optimal repair plan is generated.

[1419] Next, the EmotionEngine is used to analyze the user's emotions. Based on data obtained from sensors and cameras, it determines whether the user is confused or stressed. For example, if the generative AI senses that the user is confused, it leaves it up to the human to respond to the user.

[1420] Examples

[1421] As a concrete example, consider a machine malfunction on a factory production line: In this case, generative AI would suggest an optimal repair plan and an emotion engine would summon a human if it sensed staff stress.

[1422] Example prompt:

[1423] A machine failure has occurred on the factory production line. Please suggest the optimal repair plan.

[1424] In this way, a smart factory management system that combines generative AI and an emotion engine will be realized, which will improve the efficiency of business operations and manage employee emotions.

[1425] The flow of the specific process in the application example 3 will be described with reference to FIG.

[1426] Step 1:

[1427] The server initializes the generative AI model using the OpenAI API. Specifically, it sets an API key and inputs a prompt sentence to optimize the factory production plan. As input, it uses the prompt sentence "Please tell me how to optimize the factory production plan." As output, it gets a proposal for an optimized production plan.

[1428] Step 2:

[1429] The server optimizes the production plan using a generative AI model. Specifically, the generative AI generates an optimal production plan based on a prompt statement. The input is the prompt statement, "A machine has broken down on the factory's production line. Please suggest the optimal repair plan.", and the output is a proposal for an optimal repair plan.

[1430] Step 3:

[1431] The server initializes the EmotionEngine and analyzes the user's emotions. Specifically, it inputs data obtained from sensors and cameras into the EmotionEngine and analyzes the user's emotional state. As input, it uses real-time data from sensors and cameras, and as output, it obtains the user's emotional state (e.g., confusion, stress).

[1432] Step 4:

[1433] The server calls a human as necessary based on the analysis results of the Emotion Engine. Specifically, if the user is confused or stressed, the server requests a human to respond. The input is the analysis results of the Emotion Engine, and the output is to generate instructions to call a human.

[1434] Step 5:

[1435] The server integrates the results of the generative AI model and the Emotion Engine to optimize factory operations. Specifically, it formulates an optimal operation plan based on the production plan obtained from the generative AI model and the user's emotional state obtained from the Emotion Engine. The output of the generative AI model and the output of the Emotion Engine are used as inputs, and the optimal operation plan is obtained as output.

[1436] Step 6:

[1437] The terminal controls the factory robots, sensors, and cameras based on instructions from the server. Specifically, it controls the operation of the factory robots according to an optimal operation plan and monitors data from the sensors and cameras in real time. It uses instructions from the server as input and generates control signals for the factory robots, sensors, and cameras as output.

[1438] Step 7:

[1439] The user responds to the call from the server and takes the necessary action. Specifically, humans respond to matters that the generative AI model and EmotionEngine cannot determine. The input is the call instruction from the server, and the output is the appropriate response.

[1440] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1441] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1442] Another example of generative AI is Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1443] 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 the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1444] [Third embodiment]

[1445] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1446] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1447] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a wide area network (WAN) and / or a local area network (LAN).

[1448] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1449] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[1450] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[1451] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[1452] Fig. 6 shows an example of main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1453] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1454] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1455] In the headset type terminal 314, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1456] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1457] "Example 1"

[1458] As one embodiment of the present invention, a means is provided for a generative AI to command and discuss with other generative AIs. Specifically, a generative AI issues instructions to other generative AIs, so that each generative AI is responsible for a specific task. For example, some generative AIs are responsible for financial management of a company, while other generative AIs are responsible for human resources management, and so on, with each generative AI taking charge of a company's departments.

[1459] "Example 2"

[1460] Furthermore, a means is provided for reproducing the organizational structure of a company using a large number of generative AIs. Specifically, a network of generative AIs that mimics the organizational structure of a company is constructed. For example, generative AIs are arranged based on a company's organizational chart, with each generative AI in charge of a different department of the company.

[1461] "Example 3"

[1462] Furthermore, a means is provided for generative AI to operate a company in accordance with a business plan. Specifically, generative AI understands the business plan and operates the company based on it. For example, based on the business plan, generative AI appropriately performs tasks such as financial management and human resource management.

[1463] "Example 4"

[1464] Furthermore, a means will be provided for the generative AI to call on humans when necessary. Specifically, when a matter arises that the generative AI cannot decide on or that requires human judgment, the generative AI will call on humans. For example, the generative AI will call on humans when a legal decision is required or when human sensibility is required.

[1465] The process flow of each embodiment will be described below.

[1466] "Example 1"

[1467] Step 1: A generative AI issues instructions to other generative AIs. These instructions are set up so that each generative AI is responsible for a specific task.

[1468] Step 2: Each generative AI performs tasks according to instructions. For example, some generative AIs are responsible for managing the company's finances, while others are responsible for managing human resources.

[1469] "Example 2"

[1470] Step 1: Build a generative AI network that mimics a company's organizational structure.

[1471] Step 2: Place the generative AIs based on the company's organizational chart. Set each generative AI to be in charge of a different department in the company.

[1472] "Example 3"

[1473] Step 1: Generative AI understands the business plan.

[1474] Step 2: The generative AI runs the company based on the business plan. For example, based on the business plan, the generative AI appropriately performs tasks such as financial management and human resources management.

[1475] "Example 4"

[1476] Step 1: When an issue arises that the generative AI cannot decide or that requires human judgment, the generative AI calls in a human.

[1477] Step 2: If a human is called in, that human makes the necessary decisions. For example, when a legal decision is required or when human sensibility is required, the generative AI calls in a human.

[1478] Example 1

[1479] Next, a description will be given of Example 1 of the embodiment 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal".

[1480] Traditional business operations required a lot of manpower, making it difficult to carry out business efficiently. Even when generative AI was used, each individual AI operated independently, making it difficult to coordinate and collaborate as a whole, and optimizing the entire company. Furthermore, generative AI could not give instructions to humans, making it impossible to make the most of its capabilities.

[1481] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1482] In this invention, the server includes a means for a generative AI to give commands to and discuss with other generative AIs, a means for reproducing the organizational structure of a company using a large number of generative AIs, a means for aiming for semi-automation of a company, a means for a user to input a prompt sentence for the generative AI, a means for the server to receive the prompt sentence and give instructions to an appropriate generative AI, a means for the generative AI to receive the instruction and perform processing, a means for the generative AI to return the processing result to the server, and a means for the server to display the processing result to the user. This makes it possible to efficiently carry out the business of a company, strengthen cooperation between generative AIs, and optimize the entire company. In addition, since the generative AI can give instructions to humans, the capabilities of the generative AI can be utilized to the maximum extent.

[1483] "Generative AI" is a system that uses artificial intelligence technology to generate data and perform specific tasks.

[1484] An "instruction" is an act of instructing to perform a particular action or process.

[1485] "Discussion" is a process in which multiple generative AIs exchange opinions and information with each other to find the optimal solution.

[1486] A "corporate organizational structure" is a structure that shows the arrangement of departments and positions within a company and their interrelationships.

[1487] "Semi-automated" refers to a state in which human intervention is minimized and tasks are carried out automatically by systems and machines.

[1488] A "prompt" is an instruction given to a generative AI to carry out a specific task.

[1489] A "server" is a computer system that provides services to other computers over a network.

[1490] "Processing results" are data and information generated as a result of a task performed by a generative AI based on instructions.

[1491] The "user" is the human who operates the system and issues instructions to the generative AI.

[1492] This invention is a system in which generative AI issues commands and holds discussions with other generative AIs, and aims to reproduce the organizational structure of a company and make the company semi-automated. A specific embodiment of this system is described below.

[1493] First, a user logs into the system using a terminal. The user enters a user name and password on the login screen and is authenticated. If the authentication is successful, the user is redirected to the system's main screen.

[1494] Next, the user finds a form on the main screen to enter a prompt for the generative AI. The user enters a prompt containing specific tasks and instructions, and clicks the send button. For example, the user can enter the following prompt:

[1495] "Prepare a proposed budget for the coming year based on the most recent financial data. Specifically, include revenue projections and cost-cutting proposals."

[1496] The server receives the prompt sent by the user. The server analyzes the contents of the prompt and issues instructions to the appropriate generative AI. For example, if the prompt is about financial management, it issues instructions to the generative AI in charge of financial management. In this case, the server uses a generative AI model such as OpenAI's GPT-4.

[1497] The generative AI initiates processing based on instructions received from the server. For example, a generative AI in charge of financial management would analyze the latest financial data and create a budget proposal with sales forecasts and cost-cutting suggestions.

[1498] When the generative AI completes the process, it returns the results to the server. For example, this may include the generated budget proposal or analysis results. The server displays the processing results received from the generative AI to the user. The user checks the results on the device screen and takes the next action if necessary.

[1499] This system will enable companies to efficiently carry out their business, strengthen cooperation between generative AIs, and optimize the entire company. In addition, generative AI can give instructions to humans, making the most of its capabilities.

[1500] As a concrete example, consider instructions for a generative AI responsible for managing a company's financial affairs. The user inputs the following prompt sentence into the generative AI model:

[1501] "Prepare a proposed budget for the coming year based on the most recent financial data. Specifically, include revenue projections and cost-cutting proposals."

[1502] The generative AI that receives this prompt will analyze the financial data and create a budget proposal that includes sales forecasts and cost-cutting proposals. Similarly, the generative AI responsible for human resources management will be given the following prompt:

[1503] "Perform performance evaluations of employees and select candidates for promotion. Evaluation criteria will be business results, teamwork, and leadership skills."

[1504] The generative AI receives this prompt and performs an evaluation based on employee performance data to select promotion candidates.

[1505] The above is a specific embodiment for carrying out the present invention.

[1506] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1507] Step 1:

[1508] A user logs into the system.

[1509] A user accesses the login screen of the system using a terminal. The user enters a user name and password to perform authentication. If the authentication is successful, the user transitions to the main screen of the system.

[1510] Input: Username, Password

[1511] Output: Access to the main screen

[1512] Specific operation: The user opens a browser, enters the system URL, and accesses the system. On the login screen, the user enters "User name: user123" and "Password: password123" and clicks the login button.

[1513] Step 2:

[1514] The user inputs a prompt for the generative AI.

[1515] On the main screen, the user finds a form for entering a prompt for the generative AI. The user enters a prompt containing specific tasks and instructions, and clicks the send button.

[1516] Input: Prompt statement

[1517] Output: Prompt sent to server

[1518] Specific actions: The user enters the following into the form on the main screen: "Please prepare a budget proposal for the next fiscal year based on the latest financial data. In particular, please include sales forecasts and cost reduction proposals." and clicks the submit button.

[1519] Step 3:

[1520] The server receives the prompt and issues instructions to the appropriate generative AI.

[1521] The server receives the prompt sent by the user, analyzes the contents of the prompt, and issues instructions to the appropriate AI generator.

[1522] Input: Prompt statement

[1523] Output: Instructions for the generative AI

[1524] Specific operation: The server analyzes the prompt text and instructs the "Financial Management AI" to "prepare the next fiscal year's budget proposal based on the latest financial data."

[1525] Step 4:

[1526] The generative AI receives instructions and carries out the processing.

[1527] The generative AI initiates processing based on instructions received from the server. For example, a generative AI in charge of financial management would analyze the latest financial data and create a budget proposal with sales forecasts and cost-cutting suggestions.

[1528] Input: Instructions from the server, financial data

[1529] Output: Budget proposal

[1530] How it works: The financial management AI retrieves the latest financial data from the database and creates a budget proposal with sales forecasts and cost-cutting suggestions.

[1531] Step 5:

[1532] The generative AI returns the processing results to the server.

[1533] Once the generative AI completes its processing, it returns the results to the server, including the generated budget proposal and analysis results, for example.

[1534] Input: Budget proposal

[1535] Output: Sending the results of processing to the server

[1536] Specific operation: The financial management AI returns the budget proposal it has created to the server. The budget proposal includes a sales forecast graph and specific proposals for cost reduction.

[1537] Step 6:

[1538] The server displays the processing results to the user.

[1539] The server displays the processing results received from the generative AI to the user, who then checks the results on the device screen and takes the next action if necessary.

[1540] Input: Processing results from generative AI

[1541] Output: Displaying the results to the user

[1542] Specific operation: The server displays the received budget proposal on the user's terminal. The user can check the details of the budget proposal on the screen and download or print it as necessary.

[1543] (Application example 1)

[1544] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server", and the headset type terminal 314 will be referred to as a "terminal".

[1545] In modern factories, improving the efficiency of production lines and automating management are important issues. In particular, there is a demand for collecting real-time data from each process and issuing instructions quickly based on that data. However, in conventional systems, the management of each process is decentralized, making it difficult to optimize the entire system. In addition, there are limited means for factory managers to grasp the status of each process and intervene as necessary. This can lead to reduced production efficiency and variations in quality.

[1546] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1547] In this invention, the server includes a means for a generative AI to give commands to and discuss with other generative AIs, a means for reproducing the organizational structure of a company using a large number of generative AIs, a means for aiming for semi-automation of the company, a means for a generative AI to manage each process on the production line in a factory, collect data in real time, and give commands to other generative AIs, and a means for a factory manager to monitor the activity status of each generative AI through a smartphone application and intervene as necessary. This makes it possible to improve the efficiency of the entire production line and automate management.

[1548] "Generative AI" is a type of artificial intelligence that generates new information and content based on data.

[1549] An "instruction" is an act of instructing to perform a particular action or process.

[1550] "Discussion" is a process in which multiple generative AIs exchange opinions and find the optimal solution.

[1551] A "corporate organizational structure" is a structure that shows the arrangement and relationships of each department and position within a company.

[1552] "Semi-automated" refers to a state in which systems and machines operate automatically with minimal human intervention.

[1553] A "production line" is a line in which a series of steps are arranged in succession to manufacture a product.

[1554] "Collecting data in real time" refers to instantly obtaining data about ongoing events or situations.

[1555] "Giving instructions" means the act of ordering something to perform a particular action or process.

[1556] A "factory manager" is a person responsible for managing production activities and employees within a factory.

[1557] A "smartphone application" is a software program that runs on a smartphone.

[1558] "Monitoring" is the act of observing the status of a particular system or process and responding as necessary.

[1559] "Intervention" is the act of actively engaging in and influencing a particular situation or process.

[1560] A system for implementing this invention includes means for a generative AI to give commands and hold discussions with other generative AI, means for recreating the organizational structure of a company using a large number of generative AI, means for aiming to make a company semi-automated, means for a generative AI to manage each process on a production line in a factory, collect data in real time, and issue instructions to other generative AI, and means for a factory manager to monitor the activity status of each generative AI through a smartphone application and intervene as necessary.

[1561] System Programming

[1562] The server uses Python to implement a generative AI model and its coordinator. The generative AI model is responsible for each process in the factory (e.g., material supply, assembly, quality inspection), collects data in real time, and issues instructions to other generative AIs. The coordinator monitors the status of each generative AI and issues appropriate instructions.

[1563] Hardware and Software

[1564] Hardware: Smartphones, IoT devices in factories

[1565] Software: Python, Flask (as API server), SQLite (database)

[1566] Data processing and calculation

[1567] Generative AI collects real-time data from IoT devices in factories and processes it based on that data. For example, a material supply AI collects material inventory data, and when inventory is low, it instructs the assembly AI to "require material supply." The assembly AI collects progress data on the assembly process, and when progress is delayed, it instructs the quality inspection AI to "delay quality inspection because assembly is delayed."

[1568] Examples

[1569] The material supply AI collects material inventory data, and when inventory is low, it instructs the assembly AI that "material supply is required."

[1570] The assembly AI collects progress data on the assembly process, and if progress is delayed, it instructs the quality inspection AI to "delay quality inspection because assembly is delayed."

[1571] Examples of prompt statements

[1572] The material supply AI collects material inventory data, and when inventory is low, it instructs the assembly AI that "material supply is required."

[1573] In this way, generative AI can issue instructions to other generative AI, allowing efficient management of the production line in the factory. Factory managers can monitor the activity of each generative AI through a smartphone application and intervene if necessary.

[1574] The flow of the specific process in the application example 1 will be described with reference to FIG.

[1575] Step 1:

[1576] The server collects real-time data from IoT devices in the factory. Specifically, it obtains data related to the processes that each generative AI model is responsible for (e.g., material supply, assembly, quality inspection). The input is sensor data from the IoT devices, and the output is raw data passed to the generative AI model.

[1577] Step 2:

[1578] The server passes the collected real-time data to each generative AI model. The generative AI model analyzes the current situation based on the received data. The input is the raw data collected in step 1, and the output is the analysis result. In concrete terms, the material supply AI analyzes the inventory data and generates an alert if inventory is low.

[1579] Step 3:

[1580] A generative AI model issues instructions to other generative AI models based on the analysis results. For example, if a material supply AI detects an inventory shortage, it issues an instruction to an assembly AI that "materials need to be supplied." The input is the analysis result, and the output is the instruction content. In terms of specific operations, the instructions are generated in the form of a prompt sentence.

[1581] Step 4:

[1582] The server sends the generated instructions to other generative AI models. The generative AI models that receive the instructions begin to operate according to the instructions. The input is the instructions, and the output is the execution result. In concrete terms, the assembly AI receives instructions from the material supply AI and requests replenishment of materials.

[1583] Step 5:

[1584] The server monitors the status of each generative AI model and notifies the factory manager as necessary. The input is the status data of each generative AI model, and the output is the notification to the manager. Specifically, the server reports the status to the manager in real time via a smartphone application.

[1585] Step 6:

[1586] The user (factory manager) monitors the activity of each generative AI model through a smartphone application and intervenes as necessary. The input is notifications from the server, and the output is instructions from the manager. In concrete terms, the manager can operate the application to issue new instructions to specific generative AI models.

[1587] In this way, it is possible to efficiently manage the production line within the factory through the specific operations and data flow at each step.

[1588] Example 2

[1589] Next, a second embodiment of the second form will be described. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal".

[1590] In traditional corporate operations, data processing and decision-making in each department was carried out separately, which led to problems with overall efficiency. In addition, since there was no system that could mimic the organizational structure of a company, it was difficult to achieve semi-automation. Furthermore, there was a lack of means for generative AI to work with other generative AI to operate the company, making it difficult to optimize the entire company.

[1591] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1592] In this invention, the server includes a means for a generative AI to give commands to and discuss with other generative AIs, a means for reproducing the organizational structure of a company using a large number of generative AIs, a means for aiming for semi-automation of the company, a means for receiving and analyzing organizational chart data of the company, a means for arranging generative AIs corresponding to each department, a means for connecting each generative AI via a network and sharing information, a means for each generative AI to process data of the department in charge and generate necessary information, and a means for aggregating the results generated by each generative AI and providing them to the user. This makes it possible to mimic the organizational structure of a company, streamline data processing and decision-making in each department, and optimize and semi-automate the entire company.

[1593] "Generative AI" is artificial intelligence that generates new information and content based on data.

[1594] An "order" is the act of giving instructions to perform a particular action or process.

[1595] A "discussion" is the act of multiple generative AIs exchanging opinions and information and engaging in dialogue to find the optimal solution.

[1596] A "corporate organizational structure" is a structure that shows the arrangement of departments and positions within a company and the relationships between them.

[1597] "Semi-automated" refers to a state in which human intervention is minimized and systems and machines operate and manage automatically.

[1598] "Corporate organization chart data" refers to data showing the arrangement of departments and positions within a company and the relationships between them.

[1599] "Analysis" is the act of examining data in detail and understanding its meaning and structure.

[1600] "Deployment" is the act of assigning a generative AI to a specific location or role.

[1601] A "network" is a connection structure that allows multiple generative AIs to communicate with each other and share information.

[1602] "Information sharing" refers to the act of exchanging data and knowledge between generative AIs and using them jointly.

[1603] "Processing data" is the act of performing calculations and analysis based on input data to generate necessary information.

[1604] "Aggregating results" refers to the act of combining information generated by multiple generative AIs into one.

[1605] A "user" is a human or other entity that uses the system.

[1606] This invention is a system that mimics the organizational structure of a company, improves the efficiency of data processing and decision-making in each department, and realizes optimization and semi-automation of the entire company. A specific embodiment of this system will be described below.

[1607] 1. Program Generation

[1608] The server generates a program that constructs a generative AI network to mimic the organizational structure of a company. The program receives the company's organizational chart as input and includes an algorithm for placing generative AIs corresponding to each department.

[1609] 2. Program Processing Explanation

[1610] The server executes the generated program and performs the following processes.

[1611] Data Entry

[1612] The server receives organizational chart data for the company. This data can come from a CSV file or a database. For example, the CSV file contains the names of each department and the relationships between the departments.

[1613] Generative AI placement

[1614] The server assigns generative AIs to each department based on the organizational chart. For example, the sales department assigns a generative AI specialized in sales, and the marketing department assigns a generative AI specialized in marketing.

[1615] Network Construction

[1616] The server will connect each generative AI to a network, mimicking the organizational structure of a company. This network will be designed so that each generative AI can cooperate with each other and share information.

[1617] Data processing and calculation

[1618] Each generative AI processes data for the department it is responsible for and generates the necessary information. For example, the generative AI for the sales department analyzes sales data and generates future sales forecasts.

[1619] The hardware used will be high-performance cloud servers (e.g., AWS EC2 instances) as servers, and the software will be a generative AI model (e.g., OpenAI's GPT-4).

[1620] 3. Examples and prompts

[1621] As a concrete example, consider the following corporate organizational chart.

[1622] Sales Department

[1623] Marketing Department

[1624] Human resources department

[1625] The server will place the generative AI based on this organizational chart as follows:

[1626] The sales department will have generative AI to analyze sales data.

[1627] The marketing department needs generative AI to generate marketing strategies.

[1628] HR departments need generative AI to generate recruitment plans

[1629] An example of a prompt sentence might be:

[1630] "Analyze the sales data from the sales department and generate sales forecasts for the next quarter."

[1631] By inputting this prompt sentence into the generative AI model, the sales department's generative AI will generate a sales forecast.

[1632] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1633] Step 1: Data entry

[1634] The server receives the organizational chart data of the company. This data can come from a CSV file or a database. For example, the CSV file contains the names of each department and the relationships between the departments. The server reads the CSV file from the specified file path and parses the information of each department. In case of using a database, the server executes an SQL query to get the required data. The input is the organizational chart data of the company and the output is the parsed department information.

[1635] Step 2: Placing the generative AI

[1636] The server assigns a generative AI to each department based on the organizational chart. For example, the sales department assigns a generative AI specialized in sales, and the marketing department assigns a generative AI specialized in marketing. Based on the information of each department, the server selects an appropriate generative AI model (e.g. GPT-4) and assigns it to each department. The input is the analyzed department information, and the output is the generative AI assigned to each department.

[1637] Step 3: Networking

[1638] The server connects each generative AI to a network, mimicking the organizational structure of a company. This network is designed to allow each generative AI to work together and share information. The server sets the communication protocol between each generative AI, enabling data to be sent and received. For example, sales forecast data generated by an AI in the sales department is sent to an AI in the marketing department, and used to generate a marketing strategy. The input is the generative AI placed in each department, and the output is the generative AI connected via the network.

[1639] Step 4: Data processing and calculations

[1640] Each generative AI processes data from its assigned department and generates the necessary information. For example, the sales department's generative AI analyzes sales data and generates future sales forecasts. The sales department's generative AI takes past sales data as input and predicts future sales using statistical methods and machine learning algorithms. Similarly, the marketing department's generative AI analyzes advertising campaign data and proposes optimal marketing strategies. The input is the data from each department, and the output is the generated information.

[1641] Step 5: Output the results

[1642] The server aggregates the results generated by each generative AI and provides them to the user. The user makes decisions based on these results. The server collects output data from each generative AI and displays it to the user in the form of a dashboard or report. For example, it visualizes sales forecasts and marketing strategy proposals in the form of graphs or tables. The input is the generated information, and the output is the aggregated results provided to the user.

[1643] (Application example 2)

[1644] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server", and the headset type terminal 314 will be referred to as a "terminal".

[1645] In traditional business and factory operations, human managers were required to oversee each department, analyze data, and make decisions. However, this required a great deal of time and effort, making efficient operation difficult. In addition, it was difficult to analyze data in real time and provide optimal instructions, which led to problems with overall operational efficiency.

[1646] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means. In this invention, the server includes a means for a generative AI to give commands to and discuss with other generative AIs, a means for reproducing the organizational structure of a company using a large number of generative AIs, a means for aiming for semi-automation of a company, a means for optimizing the operation of the entire factory using generative AIs that mimic each department in the factory, and a means for each generative AI to take on the role of its respective department, analyze data in real time, and issue optimal instructions. This makes it possible to streamline the operation of companies and factories, and to perform data analysis and make optimal decisions in real time.

[1647] "Generative AI" refers to artificial intelligence that generates new information and content based on data.

[1648] An "order" is an instruction to perform a particular action or process.

[1649] "Discussion" is a process in which multiple generative AIs exchange opinions and find the optimal solution.

[1650] "Corporate organizational structure" refers to the arrangement and relationships of departments and positions within a company.

[1651] "Semi-automated" refers to a state in which systems and machines operate automatically with minimal human intervention.

[1652] "Each department within the factory" refers to departments that have specific roles within the factory, such as production, quality control, and logistics.

[1653] "Optimizing operations" means maximizing overall performance through efficient use of resources and improved processes.

[1654] "Analyzing data in real time" means analyzing the data as it is generated and obtaining instant results.

[1655] "Giving optimal instructions" means giving instructions for the most effective actions or processes based on the results of analysis.

[1656] In order to put this invention into practice, it is first necessary to build a generative AI that mimics each department in a factory. The server includes a means for a generative AI to give commands and discuss with other generative AIs, a means for reproducing the organizational structure of a company using a large number of generative AIs, a means for aiming for semi-automation of a company, a means for optimizing the operation of the entire factory using generative AIs that mimic each department in a factory, and a means for each generative AI to take on the role of its respective department, analyze data in real time, and give optimal instructions.

[1657] Specifically, the server uses programming languages ​​such as Python to build generative AI for each department. Each generative AI analyzes data collected from sensors and robots in the factory in real time and issues optimal instructions. For example, the generative AI in the production department analyzes production data and decides whether to increase or decrease production volume. The generative AI in the quality control department analyzes quality data and issues instructions for improving quality. The generative AI in the logistics department analyzes logistics data and decides the optimal logistics route.

[1658] The hardware used is a robot or sensor in the factory that collects data and runs the program on a central server. The software used is Python and its libraries (e.g. NumPy, Pandas).

[1659] For example, when the production department in a factory inputs "current production data," the generative AI may make the decision to "increase production." The quality control department may make the decision to "improve quality."

[1660] Example of a prompt to be input to a generative AI model:

[1661] Please enter your current production data.

[1662] This will streamline operations throughout the factory, enabling real-time data analysis and optimal decision-making.

[1663] The flow of the specific process in the application example 2 will be described with reference to FIG.

[1664] Step 1:

[1665] The server builds a generative AI that mimics each department in the factory. Specifically, it uses a programming language such as Python to program the generative AI in charge of each department. The input includes the role and necessary data of each department. The output is a model of the generative AI in charge of each department.

[1666] Step 2:

[1667] The server collects data from sensors and robots in the factory. Specifically, each sensor and robot transmits data to the server in real time. The input includes data from the sensors and robots. The output is the collected data stored in the server.

[1668] Step 3:

[1669] The server inputs the collected data to each generative AI. Specifically, it filters the data corresponding to the department that each generative AI is responsible for, converts it into an appropriate format, and inputs it. The input includes the collected data. The output includes the data input to the generative AI.

[1670] Step 4:

[1671] Each generative AI analyzes the input data in real time. Specifically, it uses data analysis algorithms to detect trends and anomalies in the data. The inputs include the data that was input to the generative AI. The output is the analysis results.

[1672] Step 5:

[1673] Each generative AI issues optimal instructions based on the analysis results. Specifically, it instructs each department on specific actions and processes based on the analysis results. The input includes the analysis results. The output is instructions for each department.

[1674] Step 6:

[1675] Each department acts according to instructions from the generative AI. Specifically, the production department adjusts production volume, the quality control department takes measures to improve quality, and the logistics department optimizes logistics routes. The input includes instructions from the generative AI. The output is the result of each department's actions.

[1676] Step 7:

[1677] The server again collects the action results of each department and uses them as data for the next cycle. Specifically, it collects feedback data from each department and uses it for the next data analysis. The action results of each department are included as input. The data for the next cycle is generated as output.

[1678] Example 3

[1679] Next, a third embodiment of the third embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal".

[1680] Modern business operations require efficient business management and rapid decision-making. However, conventional systems do not adequately handle operations based on business plans or when human judgment is required. Furthermore, the use of generative AI is essential to achieve semi-automation in companies, but current technology makes this difficult to achieve. To solve these issues, a new system using generative AI is needed.

[1681] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means. In this invention, the server includes a means for a generative AI to command and discuss with other generative AIs, a means for reproducing the organizational structure of a company using a large number of generative AIs, a means for aiming at semi-automation of the company, a means for receiving business plan data, a means for analyzing business plan data, a means for generating work instructions, a means for executing work instructions, and a means for calling a human when an issue arises that the generative AI cannot judge or that requires human judgment. This enables a flexible response that combines efficient business management based on a business plan with human judgment as needed.

[1682] "Generative AI" is an artificial intelligence technology that generates business plans and work instructions to support corporate operations.

[1683] "Commands and means of discussion" is a function that enables a generative AI to give instructions to other generative AIs, exchange information, and hold discussions.

[1684] "Means of reproducing a company's organizational structure" refers to a function that uses a large amount of generative AI to imitate the actual organizational structure of a company and perform simulations.

[1685] "Measures aimed at making companies semi-automated" refers to functions that utilize generative AI to automate corporate operations and minimize human intervention.

[1686] The "means for receiving business plan data" is a function that enables the server to receive data relating to business plans provided by companies.

[1687] "Means of analyzing business plan data" refers to a function that enables the generative AI to analyze the received business plan data and extract the necessary information.

[1688] The "means for generating work instructions" is a function for generating specific work instructions based on the analysis results.

[1689] The "means for executing work instructions" is a function for actually executing the generated work instructions.

[1690] The "means of summoning a human" is a function that sends a notification to a human and requests that he or she respond when an issue arises that the generative AI cannot decide or that requires human decision-making.

[1691] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[1692] This invention is a system that aims to streamline corporate operations and achieve semi-automation by using generative AI. A specific embodiment of this system will be described below.

[1693] System Configuration

[1694] This system is mainly composed of three elements: a server, a terminal, and a user. The server hosts the generative AI model and receives and analyzes business plan data and generates work instructions. The terminal receives work instructions from the server and executes specific tasks. The user is called upon when there are matters that the generative AI cannot decide or when human judgment is required.

[1695] Hardware and Software Used

[1696] The server is a high-performance computer with the computational power to host a generative AI model (e.g., GPT-4). The terminal is a device for executing business instructions, such as using accounting software. The user is a device for receiving notifications (e.g., a smartphone or PC).

[1697] Data processing and calculation

[1698] The server receives business plan data provided by companies and stores it in JSON format. It then uses a generative AI model to analyze the business plan data and extract the necessary information. Based on the analysis results, it generates specific work instructions. For example, it generates instructions such as "Increase the marketing budget by 20% to achieve this month's sales target."

[1699] The terminal executes the work instructions received from the server, for example, updating a budget using accounting software, by accessing the accounting software through an API and updating the necessary data.

[1700] The user is called upon when there is a matter that the generative AI cannot decide or when human judgment is required. For example, if a legal decision is required, the server will notify the user, who will then make the professional judgment. Notifications are sent via email or chat app.

[1701] Examples

[1702] For example, a company may have a business plan to bring a new product to market. A generative AI can analyze this business plan and manage the progress of product development and develop a marketing strategy. If the product development progress is delayed, the generative AI can notify the project manager and instruct him or her to take necessary measures.

[1703] Examples of prompt statements

[1704] "Analyze the business plan for the new product launch, monitor the product development progress and develop the marketing strategy. If there is a delay in the progress, notify the project manager."

[1705] In this way, the generative AI manages the business based on the business plan and calls humans as necessary, thereby realizing efficient business management. The flow of the specific process in the third embodiment will be described with reference to FIG.

[1706] Step 1:

[1707] The server receives business plan data from the company. The input is the business plan data provided by the company, and the output is the business plan data stored in JSON format. In concrete terms, the server receives the HTTP request and stores the business plan data in a database.

[1708] Step 2:

[1709] The server analyzes the received business plan data. The input is the stored business plan data, and the output is the analysis result. In concrete terms, the server inputs the business plan data into a generative AI model (e.g., GPT-4), which then analyzes the data using natural language processing technology and extracts the necessary information.

[1710] Step 3:

[1711] The server generates specific business instructions based on the analysis results. The input is the analysis results, and the output is business instructions. In concrete terms, the server inputs a prompt sentence into the generative AI model and obtains the generated business instructions. For example, it generates an instruction such as "Increase the marketing budget by 20% to achieve this month's sales target."

[1712] Step 4:

[1713] The terminal executes the work instructions received from the server. The input is the work instructions from the server, and the output is the executed work. In concrete terms, the terminal accesses the accounting software through the API and updates the necessary data, for example updating the budget or recording expenditures.

[1714] Step 5:

[1715] The server calls the user when a matter arises that the generative AI cannot decide or when a matter that requires human judgment arises. The input is the matter that requires judgment, and the output is a notification to the user. Specifically, the server sends a notification to the user via email or a chat app. For example, if a legal decision is required, the server sends a notification to the user, and the user makes a professional judgment.

[1716] In this way, by clarifying the specific actions and data flow at each step, it is possible to achieve more efficient and semi-automated corporate operations using generative AI.

[1717] (Application example 3)

[1718] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server", and the headset type terminal 314 will be referred to as a "terminal".

[1719] In conventional business operations, production schedule optimization, inventory management, and quality control had to be led by humans, which had limitations in efficiency and accuracy. In addition, when it came to matters that were difficult to judge or required human sensitivity, it was difficult to call on humans at the right time. This resulted in issues such as reduced business efficiency and increased costs.

[1720] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1721] In this invention, the server includes a means for generative AI to give commands to and discuss with other generative AI, a means for reproducing the organizational structure of a company using a large number of generative AIs, a means for aiming for semi-automation of the company, a means for generative AI to optimize production schedules, manage inventory, and manage quality based on business plans, and a means for calling humans when matters that generative AI cannot decide or when human decision is required. This makes it possible to significantly improve the operational efficiency of a company and reduce costs.

[1722] "Generative AI" refers to artificial intelligence that generates new information and content based on data.

[1723] An "instruction" is an instruction given to perform a particular action or process.

[1724] "Discussion" is a process in which multiple generative AIs exchange opinions and information to find the optimal solution.

[1725] A "corporate organizational structure" refers to the structure that shows the arrangement and relationships of each department and position within a company.

[1726] "Semi-automated" refers to a state in which human intervention is minimized and the facility is primarily operated by automated systems and robots.

[1727] A business plan is a plan that outlines the goals a company needs to achieve and the specific strategies it will use to achieve them.

[1728] "Production schedule optimization" is the process of optimizing the timing and sequence of production activities to achieve efficient production.

[1729] "Inventory management" is the management activity of maintaining appropriate inventory of products and raw materials and supplying the appropriate amounts when needed.

[1730] "Quality control" is a management activity for maintaining the quality of products and services at a certain standard.

[1731] "Undeterminable matters" are problems or situations that are difficult or impossible for a generative AI to handle automatically.

[1732] "When human judgment is required" refers to situations where generative AI cannot make appropriate decisions and human knowledge and sensibility are required.

[1733] "Invoking a human" refers to a generative AI requesting human intervention for a particular problem or situation.

[1734] A system for implementing this invention includes means for a generative AI to give commands and discuss with other generative AI, means for recreating a company's organizational structure using a large number of generative AI, means for aiming to make a company semi-automated, means for a generative AI to optimize production schedules, manage inventory, and control quality based on business plans, and means for calling humans when there are matters that the generative AI cannot decide or when human decision is required.

[1735] The server uses a generative AI model (e.g., GPT-4) to optimize production schedules based on business plans. Specifically, it schedules production tasks according to the goals of the business plan to achieve efficient production. In inventory management, it analyzes inventory data and generates instructions to maintain appropriate inventory levels. In quality control, it analyzes product quality data to detect quality problems early and take measures.

[1736] Furthermore, when there is a matter that the generative AI cannot decide or when human judgment is required, the system will have the function of calling the appropriate person in charge. For example, when legal judgment or human sensibility is required, the generative AI will automatically send a notification to the person in charge.

[1737] The hardware used includes robots used in factories that operate according to the instructions of the generative AI, while the software uses a Python program to implement the generative AI model and analyze data and generate instructions.

[1738] As a concrete example, the prompt text for optimizing the production schedule within a factory is shown below.

[1739] Example prompt:

[1740] "Based on the business plan, please optimize the production schedule for the next week. The goal is to improve production efficiency by 20%."

[1741] In this way, the server can use generative AI models to make business operations more efficient and reduce costs.

[1742] The flow of the specific process in the application example 3 will be described with reference to FIG.

[1743] Step 1:

[1744] The server receives business plan data as input. The business plan data includes company goals and goals for improving production efficiency. The server analyzes this data and converts it into a format suitable for the generative AI model. Specifically, it extracts the goals of the business plan and generates a prompt sentence to input to the generative AI model.

[1745] Step 2:

[1746] The server inputs prompt statements into a generative AI model (e.g., GPT-4) to optimize the production schedule. The generative AI model generates an optimal production schedule based on the business plan. Specifically, it optimizes the timing and order of each production task and outputs a schedule for achieving efficient production.

[1747] Step 3:

[1748] The server receives the generated production schedule and sends instructions to the robots in the factory. Specifically, it instructs each robot which task to perform and when. The robots operate according to these instructions and carry out production activities.

[1749] Step 4:

[1750] The server takes inventory data as input and performs inventory management. Specifically, it analyzes the inventory data and generates instructions to maintain appropriate stock levels. It uses a generative AI model to predict inventory surpluses and shortages and makes the necessary adjustments.

[1751] Step 5:

[1752] The server receives quality data as input and performs quality control. Specifically, it analyzes the quality data to detect quality problems early. It uses a generative AI model to identify the cause of the quality problem and generate instructions for taking countermeasures.

[1753] Step 6:

[1754] The server calls on humans when the generative AI cannot make a decision or when human judgment is required. Specifically, when legal judgment or human sensitivity is required, the server sends a notification to the appropriate person. The notification includes details of the problem and the required action.

[1755] Step 7:

[1756] The server monitors the overall operational status and adjusts the parameters of the generative AI model as necessary. Specifically, it evaluates the performance of the generative AI model against operational efficiency and cost reduction goals and makes necessary adjustments.

[1757] In addition, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification mo...

Claims

1. A means for receiving and analyzing company organizational chart data; A means for arranging a generative AI corresponding to each division of the company indicated by the analyzed organizational chart data in each of the divisions; A means for connecting the arranged generative AI via a network and sharing information of each department; A means for the generating AI to process data of each of the departments in charge and generate information related to the management of the company; A means for aggregating and outputting the generated management-related information; A system including:

2. The means for sharing is for one of the generating AIs to command or discuss with the other generating AIs; The system of claim 1 .

3. means for calling the user when a user's decision is required; The system of claim 1 .

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