System

Abstract

A system is provided.SOLUTION: A system comprising: means for generating a plurality of natural language generation apparatuses, each of which is in charge of a different intra-organization function; means for inputting a business plan; means for an arbitrary natural language generation apparatus to initialize a task based on the business plan and to share information with other natural language generation apparatuses for discussion; means for simulating a plurality of business plans and collecting and comparing the results; and means for selecting an optimum business plan and displaying the result.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 an instruction sentence related to a description of 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In traditional human organizations, there are limits to the amount of time and speed that can be worked, making it difficult to improve productivity and make quick decisions in corporate operations. In particular, it is difficult to simultaneously evaluate multiple business plans and quickly select the optimal one. There is a need for a system that can solve these problems, significantly improve production speed, and increase the efficiency of corporate operations. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides a system that includes a means for generating multiple natural language generation devices and assigning each device to a different organizational function, a means for inputting a business plan, a means for any natural language generation device to initialize tasks based on the business plan and share and discuss information with other natural language generation devices, a means for simulating multiple business plans and collecting and comparing the results, and a means for selecting an optimal business plan and displaying the results. This enables a company to operate semi-automatedly and quickly and efficiently, and select an optimal business plan.

[0006] A "natural language generation device" is a device that uses artificial intelligence technology to generate natural-sounding sentences.

[0007] A "business plan" is a plan that includes specific goals, strategies, resource allocation, and timelines for a company or organization.

[0008] A "task" is a specific task or activity that is performed to achieve a specific goal.

[0009] "Message sending and receiving" refers to communication activities that take place between natural language generation devices to exchange information.

[0010] "Simulation" is the process of simulating real-world operations and analyzing their results and impacts.

[0011] A "server" is a computer system that manages and processes information and controls the exchange of information between multiple devices.

[0012] "Intermediate results" refer to temporary results or data obtained during the course of a task.

[0013] The "optimal business plan" is the plan that is evaluated as the most effective among multiple business plans and has the highest probability of achieving its goals. [Brief explanation of the drawings]

[0014] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 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. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 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. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 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. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

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

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

[0017] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single 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), and an APU (Accelerated Processing Unit).

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

[0019] 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.

[0020] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0021] 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. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0022] [First embodiment]

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

[0024] 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.

[0025] 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 WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] 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.

[0027] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0031] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" 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 process 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.

[0032] 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.

[0033] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output 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.

[0034] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. 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."

[0035] System Overview

[0036] This invention is a system that virtually reproduces the organizational structure and business operations of a company using multiple natural language generation devices, thereby improving the efficiency of corporate operations and achieving semi-automated operation. This system includes functions such as input of business plans, automatic generation of tasks, discussion and instruction between natural language generation devices, simulation, and selection of the optimal business plan.

[0037] Program processing

[0038] 1. Initial Setup

[0039] server

[0040] The server creates multiple natural language generation devices, each configured to handle a different organizational function (e.g., management, marketing, human resources, product development, etc.).

[0041] 2. Enter your business plan

[0042] User

[0043] Users input business plans into the server, which include specific goals, strategies, resource allocation, and timelines. For example, business plan A may involve the development of a new product, while business plan B may involve strengthening marketing for an existing product.

[0044] server

[0045] The server analyzes the received business plans and distributes them appropriately to each natural language generation device.

[0046] 3. Task execution and discussion for each natural language generator

[0047] server

[0048] Based on the analysis results, the server assigns initial tasks to each natural language generator. For example, the natural language generator in the management department is assigned to conduct market analysis, and the natural language generator in the product development department is assigned to research product specifications.

[0049] natural language generator

[0050] Each natural language generator starts its assigned initial task. For example, a natural language generator in the product development department conducts research on new technologies, and a natural language generator in the marketing department conducts market research.

[0051] As needed, messages are sent between the natural language generators to exchange information and discuss with each other. For example, a marketing natural language generator might propose an advertising campaign idea and ask the management natural language generator for approval.

[0052] 4. Simulation and Comparison

[0053] server

[0054] The server runs simulations based on multiple business plans. During the simulation, it recreates the behavior of a virtual market environment based on data generated by each natural language generator, and collects business indicators (sales forecasts, market share, resource utilization, etc.).

[0055] natural language generator

[0056] Each natural language generation device reports intermediate results to the server and proceeds with the simulation while modifying the task as necessary.

[0057] 5. Selection and implementation of optimal business plans

[0058] server

[0059] The server integrates all simulation results, analyzes the collected data, and selects the optimal business plan.

[0060] The simulation results and details of the optimal business plan are generated as a report and notified to the user.

[0061] User

[0062] Based on the report, users can select the most appropriate business plan and apply it to their actual business operations, thereby semi-automating business operations and improving productivity.

[0063] Specific examples

[0064] As an example, consider the case of launching a new smartphone product onto the market.

[0065] Initial Setup

[0066] The server generates natural language generation devices for the management, marketing, product development, and human resources departments, and assigns each department a role.

[0067] Enter your business plan

[0068] The user inputs "Development of a new smartphone with an emphasis on camera performance" as business plan A and "Development of a smartphone with a long battery life" as business plan B.

[0069] Task execution for each department

[0070] The server analyzes each business plan, the management department begins market analysis, the product development department begins technical research, and the marketing department begins research into the target market.

[0071] Task discussion and coordination

[0072] Each department's natural language generators exchange messages based on collected data and suggestions, adjusting advertising strategies and product details.

[0073] Running the simulation

[0074] The server simulates each business plan in a virtual market environment and collects data such as sales forecasts and market share.

[0075] Selection of the optimal business plan

[0076] The server integrates and analyzes the simulation results and determines that the new smartphone that emphasizes camera performance (Business Plan A) has a higher probability of success in the market.

[0077] Users receive a report of the optimal business plan and apply it to their actual business, thereby improving the efficiency and speed of their business operations.

[0078] This is a specific embodiment for carrying out the present invention. The present invention automates the complex operational processes of a company, enabling efficient and rapid decision-making.

[0079] The processing flow will be explained below.

[0080] Step 1:

[0081] server

[0082] The server creates multiple natural language generation devices and configures them to handle different organizational functions (management, marketing, human resources, product development, etc.).

[0083] Step 2:

[0084] server

[0085] The server provides a business plan input form, which includes fields for goals, strategy, resource allocation, timeline, etc.

[0086] Step 3:

[0087] User

[0088] Users enter their business plans in a designated form. For example, Business Plan A is "Developing a smartphone equipped with new camera technology," and Business Plan B is "Developing a smartphone with long battery life."

[0089] Step 4:

[0090] server

[0091] The server receives and analyzes the business plan submitted by the user, and based on the results, sets an initial task for each natural language generator.

[0092] Step 5:

[0093] server

[0094] The server assigns initial tasks to each natural language generator based on the analysis results. For example, the natural language generator in the management department is assigned to conduct market analysis, and the natural language generator in the product development department is assigned to conduct product specification research.

[0095] Step 6:

[0096] natural language generator

[0097] Each natural language generator performs an assigned initial task. For example, a natural language generator in the product development department researches new technologies, and a natural language generator in the marketing department conducts market research.

[0098] Step 7:

[0099] natural language generator

[0100] Messages are exchanged between the natural language generators, and discussions take place. For example, a marketing natural language generator might propose an idea for an advertising campaign and ask the management natural language generator for approval.

[0101] Step 8:

[0102] natural language generator

[0103] Each natural language generator reports intermediate results to the server, such as research results and market reaction to a new camera technology.

[0104] Step 9:

[0105] server

[0106] The server aggregates the intermediate results and issues task modifications or new instructions to each natural language generator as needed. For example, if a change in marketing strategy is needed, the server issues new instructions to the natural language generator in the marketing department.

[0107] Step 10:

[0108] server

[0109] The server runs simulations based on multiple business plans, including business metrics such as sales forecasts, market share, and resource utilization.

[0110] Step 11:

[0111] natural language generator

[0112] Each natural language generator collects data generated during the simulation and reports it to the server.

[0113] Step 12:

[0114] server

[0115] The server consolidates all the simulation results and compares the performance of each business plan based on the collected data.

[0116] Step 13:

[0117] server

[0118] The server selects the optimal business plan, generates a report of the results, and notifies the user.

[0119] Step 14:

[0120] User

[0121] Based on the report from the server, the user selects the most suitable business plan and applies that plan to actual business operations.

[0122] Example 1

[0123] Next, a description will be given of 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."

[0124] Modern corporate operations require a great deal of time and effort to deal with complex organizational structures and diverse business plans. Particular challenges include the efficient sharing of information and discussions between different departments, as well as the continuous simulation and evaluation of multiple business plans. Furthermore, the process of selecting the optimal operational plan requires advanced data analysis, which is time-consuming. For this reason, there is a demand for effective systems to streamline corporate operations and enable semi-automated operations.

[0125] 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.

[0126] In this invention, the server includes means for generating a plurality of information processing devices and assigning different functions to each of them, means for inputting an operation plan, means for an arbitrary information processing device to initialize tasks based on the operation plan and to share information and hold discussions with other information processing devices, means for simulating a plurality of operation plans and collecting and comparing the results, and means for selecting an optimal operation plan and displaying the results. This enables efficient information sharing and discussion between different departments, effective simulation and evaluation of a plurality of business plans, and rapid selection of an optimal operation plan.

[0127] An "information processing device" is a device that has the function of receiving data and instructions and processing them to perform a specific task.

[0128] An "operational plan" is a plan that includes specific policies, strategies, resource allocations, and timelines for achieving organizational or business goals.

[0129] A "server" is a central processing unit that provides data and services to other devices on a network.

[0130] A "task" is a process or action that performs a specific task to achieve a goal.

[0131] "Simulation" is a method for virtually recreating real-world systems and situations and predicting their behavior and outcomes.

[0132] "Message sending and receiving function" refers to the function of transferring information or data from one device to another.

[0133] "Intermediate results" refer to results or data obtained along the way in the process of obtaining the final result.

[0134] A "report" is a document that organizes information and data on a specific topic.

[0135] The "optimal management plan" is the plan that, among multiple management plans, achieves the goal most effectively and efficiently.

[0136] A "comparison means" is a method or device for evaluating multiple data or results against each other and determining their relative merits and differences.

[0137] The present invention provides a system for virtually reproducing a company's management plan using a plurality of information processing devices, thereby improving the efficiency of management and achieving semi-automated operation. An embodiment of the present invention will be described in detail below.

[0138] Hardware and software used

[0139] server

[0140] Use a high-performance server. Specifically, a server with high computing power is recommended. For example, a server with multiple CPU cores and a GPU (e.g., NVIDIA) is preferable.

[0141] The server is used to create a plurality of information processing devices.

[0142] The software used includes Python, natural language processing libraries (e.g., Transformers), database management systems (e.g., MySQL), and messaging servers (e.g., RabbitMQ).

[0143] Terminal

[0144] The terminal is used by users to input operational plans, specifically using a web browser or dedicated application.

[0145] User

[0146] The user inputs various management plans into the server via a terminal, and the optimal management plan is selected and implemented.

[0147] Explanation of program processing

[0148] server

[0149] 1. The server generates multiple information processing devices and assigns each device to a different function. For example, it generates information processing devices to handle the functions of the management, marketing, human resources, and product development departments.

[0150] 2. The server analyzes the input operation plan and allocates it appropriately to each information processing device. As a result, each device starts to perform the tasks related to its own area of ​​responsibility.

[0151] Information processing device

[0152] 3. Each information processing device starts the initial task assigned to it. For example, the device in the marketing department conducts market research, and the device in the product development department conducts technical research.

[0153] 4. Each information processing device shares information as needed, exchanges messages with other devices, and holds discussions, thereby realizing effective communication between departments.

[0154] simulation

[0155] 5. The server runs simulations of multiple operational plans based on the data obtained from each information processing device, collecting data such as sales forecasts, market share, and resource utilization rates.

[0156] 6. Each information processing device reports intermediate results to the server and proceeds with the simulation while modifying tasks as necessary.

[0157] Selection of the optimal operation plan

[0158] 7. The server integrates all the simulation results and selects the optimal operation plan. For example, it determines that a new smartphone with a superior camera performance has a high probability of success in the market.

[0159] 8. Generate a report detailing the selected optimal operation plan and notify the user.

[0160] Examples and prompts

[0161] As an example, consider the case of launching a new smartphone product onto the market.

[0162] Initial Setup

[0163] The server generates information processing devices for the management department, marketing department, product development department, and human resources department, and assigns each department its respective roles.

[0164] Input of operation plan

[0165] The user inputs "Development of a new smartphone with an emphasis on camera performance" as operational plan A, and "Development of a smartphone with a long battery life" as operational plan B.

[0166] Task execution for each department

[0167] The server analyzes each operational plan, the management department starts market analysis, the product development department starts technology research, and the marketing department starts target market research.

[0168] Task discussion and coordination

[0169] The information processing devices in each department exchange messages based on collected data and suggestions, and adjust advertising strategies and product details.

[0170] Running the simulation

[0171] The server simulates each operational plan in a virtual market environment and collects data such as sales forecasts and market share.

[0172] Selection of the optimal operation plan

[0173] The server integrates and analyzes the simulation results and determines that the new smartphone that emphasizes camera performance (Operation Plan A) has a higher probability of success in the market.

[0174] Users receive a report of the optimal operational plan and apply it to their actual business.

[0175] Prompt Sentence Examples

[0176] "Simulate which is more likely to be successful in the market: a new smartphone with a better camera or a smartphone with longer battery life."

[0177] The above is a specific embodiment for carrying out the present invention. The present invention automates the complex operational processes of a company, enabling efficient and rapid decision-making.

[0178] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0179] Step 1:

[0180] Initial Setup

[0181] The server generates a plurality of information processing devices, each responsible for a different function, for example, the management department, marketing department, human resources department, and product development department.

[0182] Input: Initial server data

[0183] Output: Generate information processing equipment corresponding to each department

[0184] The server executes a Python script to generate information processing devices and set their respective functions.

[0185] Step 2:

[0186] Input of operation plan

[0187] The user uses a terminal to input an operational plan, which includes specific goals, strategies, resource allocation, and a timeline.

[0188] Input: Operational plan data entered by the user

[0189] Output: Operational plan data sent to the server

[0190] The terminal uses a web browser or dedicated application to send data entered by the user to the server.

[0191] Step 3:

[0192] Analysis of operational plans

[0193] The server analyzes the received operational plan and distributes it appropriately to each information processing device. For example, market analysis is distributed to the information processing device of the management department, and technology research is distributed to the information processing device of the product development department.

[0194] Input: Operational plan data

[0195] Output: Task data allocated to each information processing device

[0196] The server uses Python scripts and natural language processing libraries (e.g., Transformers) to analyze the data and dispatch tasks.

[0197] Step 4:

[0198] Executing a task

[0199] Each information processing device starts the assigned initial task. For example, the information processing device in the marketing department performs market research, and the information processing device in the product development department performs technical research.

[0200] Input: Task data distributed from the server

[0201] Output: Intermediate results of task execution

[0202] The information processing devices collect and analyze the necessary data in their respective areas of responsibility and generate intermediate results.

[0203] Step 5:

[0204] Message sending and discussion

[0205] Each information processing device shares information with other information processing devices as needed, and exchanges messages while holding discussions. For example, a device in the marketing department proposes product specifications suited to the target market to a device in the product development department.

[0206] Input: Message data from other information processing devices

[0207] Output: The result of the discussion

[0208] The information processing devices use a messaging server (for example, RabbitMQ) to send and receive messages in real time and share information.

[0209] Step 6:

[0210] Running the simulation

[0211] The server runs simulations of multiple operational plans based on data obtained from each information processing device, and collects data such as sales forecasts, market share, and resource utilization rates through the simulations.

[0212] Input: Data obtained from each information processing device

[0213] Output: Simulation result data

[0214] The server executes the simulation algorithm and stores the simulation results in a database.

[0215] Step 7:

[0216] Reporting interim results and correcting tasks

[0217] Each information processing device reports intermediate results to the server and proceeds with the simulation while modifying the task as necessary.

[0218] Input: Intermediate results data during simulation

[0219] Output: Instruction data for task correction

[0220] The information processing device transmits intermediate results to the server and modifies the task accordingly based on feedback from the server.

[0221] Step 8:

[0222] Selection of the optimal operation plan

[0223] The server integrates all the simulation results and selects the optimal operation plan. For example, it determines that a new smartphone with a superior camera performance has a high probability of success in the market.

[0224] Input: Simulation result data

[0225] Output: Selected optimal operation plan

[0226] The server uses data analysis algorithms to evaluate the simulation results and select the most effective operational plan.

[0227] Step 9:

[0228] Notification of optimal operation plan

[0229] The server generates a report detailing the selected optimal operation plan and notifies the user.

[0230] Input: Optimal operation plan data

[0231] Output: Report sent to the user

[0232] The server stores the reports in cloud storage (e.g. AWS S3) and sends information to users via email or notification system.

[0233] (Application example 1)

[0234] Next, a description will be given of Application 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."

[0235] Virtual corporate operations require the ability to efficiently and quickly formulate business plans and select and execute optimal operational plans. However, existing systems make it difficult to effectively link multiple internal organizational functions, and analyzing simulation results takes time. Furthermore, there is a lack of means to check and adjust product placement and campaign strategies in real time within virtual stores. These challenges mean that efficient and optimized corporate operations are not being fully realized.

[0236] 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.

[0237] In this invention, the server includes means for generating multiple natural language generation devices and assigning each device to a different organizational function, means for inputting a business plan, means for any natural language generation device to initialize tasks based on the business plan and share information and discuss with other natural language generation devices, means for simulating multiple business plans and collecting and comparing the results, means for selecting an optimal business plan and displaying the results, and means for using a head-mounted display to check and adjust product placement and campaign strategies in a virtual store in real time, thereby enabling more efficient corporate management, faster decision-making, and real-time adjustment of management strategies in a virtual environment.

[0238] A "natural language generation device" is a system that uses natural language to execute set tasks and collect, analyze, discuss, and give instructions to each department within an organization.

[0239] A "head-mounted display" is a display device worn by the user on the head, which displays virtual reality or augmented reality environments and provides the user with a three-dimensional visual experience.

[0240] A "virtual store" is a virtual store that recreates a real store in a virtual space, allowing users to access the store from a remote location and check and adjust product placement, campaign strategies, etc.

[0241] A business plan is an operational plan that includes specific goals and strategies, resource allocation, timelines, etc. set by a company.

[0242] A "simulation" is a virtual experiment that assumes the execution of a business plan in a virtual market environment and analyzes the resulting business indicators, such as sales forecasts, market share, and resource utilization rates.

[0243] "Intermediate results" refer to the data and analysis results obtained during the course of each natural language generation device's execution of a given task.

[0244] The "message sending and receiving function" is a communication means used by each natural language generation device to share information with other devices and to give instructions and hold discussions.

[0245] "Displaying a business plan" refers to a method or system that visually presents an optimal business plan to a user.

[0246] System Overview

[0247] This invention is a system that virtually reproduces a company's organizational structure and business operations using multiple natural language generation devices, thereby improving the efficiency of corporate operations and achieving semi-automated operations. Specifically, it includes functions such as inputting business plans, automatically generating tasks, discussions and instructions between natural language generation devices, simulations, selection of optimal business plans, and real-time confirmation and adjustment of product placement and campaign strategies in virtual stores.

[0248] Hardware and Software Configuration

[0249] Hardware

[0250] 1. Server: Use a cloud server equipped with a high-performance CPU and GPU.

[0251] 2. Head-mounted display (HMD): Used by users to visually navigate the virtual store.

[0252] software

[0253] 1. Natural language generation model: Use a generative AI model such as GPT-3.

[0254] 2. Simulation environment: Use a simulation tool such as SimPy.

[0255] 3. Data analysis tools: Utilize data analysis tools such as Pandas.

[0256] Detailed Description of the Embodiments

[0257] The server generates multiple natural language generation devices, each responsible for a different organizational function. Each natural language generation device has the functions of management, marketing, inventory management, and customer service, and performs initial tasks based on the business plan entered by the user, sharing information and holding discussions with other devices. The business plan, which includes specific goals, strategies, resource allocation, timeline, etc., is entered by the user into the server.

[0258] Each natural language generator initializes tasks based on prompts, sends and receives messages as needed, and exchanges information.Furthermore, the server simulates multiple business plans in a virtual market environment, collects and compares the results, and selects the optimal business plan.

[0259] The final business plan is displayed as a report to the user, who can then use a head-mounted display to view and adjust product placement and campaign strategies in real time within a virtual store, thereby improving business operations efficiency and enabling faster decision-making.

[0260] Specific examples

[0261] For example, if a user wants to set up a special corner for a new product, they put on a head-mounted display and input a prompt such as, "I want to create a special corner for a new product. Please suggest the optimal product placement and marketing strategy." Based on this input, the natural language generator will hold a discussion, and the server will run a simulation to suggest the optimal placement and strategy. The user can check the suggestions in real time through the HMD and make adjustments as necessary.

[0262] This enables faster decision-making and more efficient business operations, and facilitates real-time adjustments to operational strategies in a virtual environment, thereby improving corporate productivity and competitiveness.

[0263] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0264] Step 1:

[0265] The server generates multiple natural language generators, each responsible for a different internal organizational function (management, marketing, inventory management, customer service). The server receives configuration information for each function as input, and generates the configured natural language generator as output. Specifically, the server initializes each generation AI model using a pre-configured API and assigns each an appropriate task.

[0266] Step 2:

[0267] The user inputs a business plan into the server. As input, the user enters a business plan including specific goals, strategies, resource allocation, timeline, etc., and as output, the server stores the plan in a database. Specifically, the user enters the business plan using a dedicated input form, and the server analyzes the data and distributes it to the natural language generation device.

[0268] Step 3:

[0269] Any natural language generation device initiates a task based on a business plan, shares information with other natural language generation devices, and engages in discussions. Task instructions are received from a server as input, and the results of the discussion are generated as output. Specifically, the natural language generation device executes the received task and sends the results as a message to other devices.

[0270] Step 4:

[0271] It simulates multiple business plans, collects and compares the results. It receives simulation data reported by each natural language generator as input, and selects the optimal business plan as output. Specifically, the server uses a simulation tool such as SimPy to recreate a virtual market environment and calculates the business indicators for each business plan.

[0272] Step 5:

[0273] The optimal business plan is selected and the results are displayed. The input is data that integrates and analyzes the simulation results, and the output is a report that is visually displayed to the user. Specifically, the server integrates the results using data analysis tools such as Pandas and presents the optimal business plan to the user.

[0274] Step 6:

[0275] A head-mounted display is used to check and adjust product placement and campaign strategies in a virtual store in real time. The input is optimal business plan data, and the output is real-time updates to the settings in the virtual store. Specifically, the user puts on the HMD and visually checks the virtual store, adjusting placement and campaigns as necessary.

[0276] Furthermore, an emotion engine that estimates the user's emotion may be 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.

[0277] System Overview

[0278] This invention is a system that virtually recreates a company's organizational structure and business operations by using multiple natural language generation devices with an emotion engine, achieving efficient corporate operations and semi-automated operations. This system includes functions such as input of business plans, automatic task generation, discussion and instruction between natural language generation devices, simulation, and selection of the optimal business plan. The emotion engine also analyzes user emotions and can make adjustments based on the results.

[0279] Program processing

[0280] 1. Initial Setup

[0281] server

[0282] The server creates multiple natural language generators, assigning each to a different organizational function (e.g., management, marketing, human resources, product development, etc.), and embeds an emotion engine into each natural language generator.

[0283] 2. Enter your business plan

[0284] User

[0285] Users input business plans into the server, which include goals, strategies, resource allocation, and timelines. For example, Business Plan A might include "developing a smartphone equipped with new camera technology," while Business Plan B might include "developing a smartphone with long battery life."

[0286] server

[0287] The server analyzes the input business plan and assigns an appropriate initial task to each natural language generator.

[0288] 3. Task execution and discussion for each natural language generator

[0289] server

[0290] Based on the analysis results, the server assigns initial tasks to each natural language generator. For example, the natural language generator in the management department is assigned to conduct market analysis, and the natural language generator in the product development department is assigned to research product specifications.

[0291] natural language generator

[0292] Each NLG performs an assigned task: for example, a NLG in the product development department researches new technologies, and a NLG in the marketing department conducts market research.

[0293] Messages are exchanged between natural language generators as needed, and discussions are held. During this process, the emotion engine analyzes the user's emotions and provides feedback to the generators.

[0294] 4. Simulation and Comparison

[0295] server

[0296] The server runs a simulation based on each business plan and records business indicators (sales forecast, market share, resource utilization, etc.) collected during the simulation.

[0297] natural language generator

[0298] Each natural language generator reports data during the simulation to the server and modifies the task as necessary.

[0299] 5. Selection and implementation of optimal business plans

[0300] server

[0301] The server integrates all the simulation results, performs analysis, and selects the optimal business plan.

[0302] The results are generated as a report and notified to the user.

[0303] User

[0304] Users can apply the optimal business plan report to their actual business operations.

[0305] Specific examples

[0306] As an example, consider the case of launching a new smartphone product onto the market.

[0307] Initial Setup

[0308] The server generates natural language generators for the management, marketing, product development, and human resources departments, and assigns each department its own role. Each generator has a built-in emotion engine.

[0309] Enter your business plan

[0310] The user inputs "Development of a new smartphone with a high-performance camera" as business plan A and "Development of a smartphone with a long battery life" as business plan B.

[0311] Task execution and discussion

[0312] The server analyzes the business plan and assigns initial tasks to each department. For example, the natural language generator in the product development department is responsible for researching new technologies, while the natural language generator in the marketing department is responsible for market research.

[0313] The generators in each department send and receive messages as needed to advance the discussion, during which the emotion engine analyzes the user's emotions and provides appropriate feedback.

[0314] Running the simulation

[0315] The server performs a simulation of the business plan and observes changes in sales forecasts and market share based on the data generated by each generation device.

[0316] Selection of the optimal business plan

[0317] By integrating and analyzing the simulation results, it is determined that, for example, a new smartphone equipped with a high-performance camera (Business Plan A) is more likely to be successful in the market.

[0318] Send reports to users and reflect them in actual business operations.

[0319] This will enable natural language generation devices using emotion engines to automate complex business processes and promote efficient and rapid decision-making.

[0320] The processing flow will be explained below.

[0321] Step 1:

[0322] server

[0323] The server creates multiple natural language generation devices and configures them to handle different organizational functions, such as the management department, marketing department, product development department, and human resources department.

[0324] Each natural language generator incorporates an emotion engine to enable analysis of the user's emotions.

[0325] Step 2:

[0326] server

[0327] The server provides the user with a business plan input form, which includes input fields for goals, strategies, resource allocation, timeline, etc.

[0328] Step 3:

[0329] User

[0330] The user enters their business plan into an input form and sends it to the server. For example, Business Plan A might be "Development of a new smartphone equipped with a high-performance camera," and Business Plan B might be "Development of a smartphone with a long battery life."

[0331] Step 4:

[0332] server

[0333] The server receives the business plan submitted by the user, analyzes the plan, and assigns an appropriate initial task to each natural language generator.

[0334] Step 5:

[0335] server

[0336] Based on the analysis results, the server assigns initial tasks to each natural language generator. For example, the natural language generator in the management department is assigned to conduct market analysis, and the natural language generator in the product development department is assigned to research product specifications.

[0337] Step 6:

[0338] natural language generator

[0339] Each NLG performs an assigned initial task: for example, a NLG in the product development department researches new technologies, and a NLG in the marketing department conducts market research.

[0340] The emotion engine analyzes the user's emotions and feeds back the analysis results to each natural language generation device.

[0341] Step 7:

[0342] natural language generator

[0343] Messages are exchanged between natural language generators, and discussions take place. For example, a marketing natural language generator proposes an idea for an advertising campaign and asks the management natural language generator for approval. During this process, an emotion engine provides emotional feedback to the communication between the generators.

[0344] Step 8:

[0345] natural language generator

[0346] Each natural language generator reports intermediate results to the server, such as research results and market reaction to a new camera technology.

[0347] Step 9:

[0348] server

[0349] The server aggregates the intermediate results and issues task modifications or new instructions to each natural language generator as needed. For example, if a change in marketing strategy is needed, new instructions are issued to the natural language generator in the marketing department.

[0350] Step 10:

[0351] server

[0352] The server runs a simulation based on each business plan, and during the simulation, business indicators such as sales forecasts, market share, and resource utilization are collected.

[0353] Step 11:

[0354] natural language generator

[0355] Each natural language generator collects data generated during the simulation and reports it to the server.

[0356] Step 12:

[0357] server

[0358] The server consolidates all simulation results and compares and analyzes the performance of each business plan based on the collected data.

[0359] Step 13:

[0360] server

[0361] The server selects the optimal business plan, generates a report of the results, and notifies the user.

[0362] Step 14:

[0363] User

[0364] Based on the reports sent from the server, the user selects the most suitable business plan and applies it to actual business operations, thereby enabling efficient and rapid business operations.

[0365] In this way, the present invention uses a natural language generation device incorporating an emotion engine to virtually reproduce a company's organizational structure and business operations, thereby realizing efficient company operations and semi-automated operations.

[0366] Example 2

[0367] Next, a description will be given of 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."

[0368] Modern business operations require efficient and rapid decision-making. However, traditional systems lack the ability to share information and exchange opinions between departments, and provide feedback that takes user sentiment into account, making it difficult to optimize management efficiency and plans overall. Furthermore, the process of simultaneously evaluating multiple business plans and selecting the optimal one takes time and effort. These challenges currently hinder optimization and efficiency in corporate operations.

[0369] 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.

[0370] In this invention, the server includes means for generating multiple natural language generation devices and assigning each device to a different organizational function, means for inputting a business plan, means for any natural language generation device to initialize tasks based on the business plan and share information and hold discussions with other natural language generation devices, means for simulating multiple business plans and collecting and comparing the results, means for selecting an optimal business plan and displaying the results, and means for analyzing user emotions using an emotion analysis engine and providing the results as feedback to the natural language generation devices. This enables smooth information sharing and discussions between departments, provides timely feedback that takes user emotions into consideration, and enables optimization of business plans and rapid decision-making.

[0371] A "natural language generation device" is a device that uses natural language processing technology to generate human language and automatically perform text-based tasks.

[0372] "Internal functions" refer to roles that correspond to different departments or tasks within a company or organization, such as management, marketing, product development, and human resources.

[0373] A business plan is a document or data that systematically outlines the goals, strategies, resource allocation, timelines, etc. set by a company or organization.

[0374] "Task initialization" is the process of completing the settings and preparations required to start a specific task, and specifically includes inputting data, setting initial parameters, allocating resources, etc.

[0375] "Information sharing" is the process of exchanging data and information between multiple systems and devices, making them mutually available.

[0376] A "discussion" is a process in which messages and information are exchanged between multiple natural language generation devices, and opinions are exchanged to arrive at the optimal conclusion.

[0377] "Simulation" is the process of using virtual environments and computational models to predict the outcome of a particular business plan and test various scenarios.

[0378] An "emotion analysis engine" is an algorithm or model developed to analyze a user's emotions and has the ability to determine their emotional state from text data and other inputs.

[0379] "Feedback" refers to information or advice provided to other systems or users based on data or analysis results acquired by a system or device.

[0380] "Optimization" is the process of adjusting parameters and settings to obtain the most efficient and effective results under given conditions and constraints.

[0381] The present invention is a system that virtually reproduces the organizational structure and business operations of a company, and realizes efficient company operations and semi-automated operations. Specific embodiments are described below.

[0382] System Overview

[0383] The present invention is a system that includes a server, multiple natural language generation devices, a user interface, and a sentiment analysis engine. The server uses a programming language such as Python to launch multiple natural language generation devices (e.g., generative AI models), each responsible for a different organizational function such as management, marketing, human resources, or product development. A sentiment analysis engine is also integrated into each generation device.

[0384] Hardware and Software

[0385] The server utilizes high performance computer hardware and open source or commercial software to perform the following tasks:

[0386] Natural Language Generator: This refers to an instance of a generative AI model (e.g., GPT-3) that is assigned to each department.

[0387] Sentiment analysis engine: A machine learning model for analyzing user sentiment (e.g., a sentiment analysis library with an LSTM model).

[0388] Database: A database system (e.g., MariaDB, PostgreSQL) for storing and managing business plans and simulation results.

[0389] Simulation software: Simulation tools to evaluate multiple business plans (e.g., AnyLogic, Vensim).

[0390] Implementation Procedure

[0391] 1. Initial Setup

[0392] The server creates natural language generators for each department, including management, marketing, product development, and human resources, and assigns each department its own role. Each generator is integrated with a sentiment analysis engine.

[0393] 2. Enter your business plan

[0394] Users use a web browser to input business plans into the server, which include goals, strategies, resource allocation, timelines, etc., and the input data is saved in a database in real time.

[0395] 3. Task assignment and discussion

[0396] The server analyzes the input business plan and assigns appropriate initial tasks to each generator. The generators perform tasks such as new technology research and market surveys, and use an emotion analysis engine to analyze and provide feedback on user emotions. Messages are sent and received between generators to share information and hold discussions.

[0397] 4. Running the Simulation

[0398] The server uses a simulation tool to evaluate the business plan, and records business indicators collected during the simulation, such as sales forecasts, market share, and resource utilization, in a database.

[0399] 5. Selection of the optimal business plan

[0400] The simulation results are integrated and analyzed to select the optimal business plan. The results are generated as a report and notified to the user. The user can then apply this report to their actual business operations.

[0401] Specific examples

[0402] As an example, let us consider the case of launching a new smartphone product into the market.

[0403] 1. Initial Setup

[0404] The server uses Python to generate open-source generative AI models as natural language generators for the management, marketing, product development, and human resources departments, each of which is integrated with a sentiment analysis engine.

[0405] 2. Enter your business plan

[0406] The user enters into a web form business plan A "Development of a new smartphone equipped with a high-performance camera" and business plan B "Development of a smartphone with a long battery life." This data is stored in a database.

[0407] 3. Task assignment and discussion

[0408] The server analyzes business plans and assigns market analysis and technology research tasks to generators. The generators research social media data and technical papers and hold discussions using the Slack API. The sentiment analysis engine analyzes user sentiment in real time and provides feedback to the generators.

[0409] 4. Running the Simulation

[0410] The server uses Vensim to run simulations and observe and record sales forecasts and market share fluctuations.

[0411] 5. Selection of the optimal business plan

[0412] The simulation results are integrated and analyzed, and the company determines that the "New Smartphone with a High-Performance Camera (Business Plan A)" has a high probability of success, and sends a report to the user, who then uses this report to implement an actual business strategy.

[0413] Prompt Sentence Examples

[0414] "Based on the simulation results, which is more likely to be successful in the market: developing a smartphone with new camera technology or developing a smartphone with longer battery life?"

[0415] As described above, the present invention provides a system that supports efficiency and rapid decision-making in business operations.

[0416] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0417] Step 1:

[0418] The server creates multiple natural language generators. These generators are responsible for different internal organizational functions, such as management, marketing, human resources, and product development. The server uses Python to launch instances of the generative AI model. It also integrates a sentiment analysis engine so that each generator can analyze user sentiment. The input for this process is initial configuration information corresponding to each role, and multiple generators with different functions are prepared as the output.

[0419] Step 2:

[0420] A user inputs a business plan into the server via a web browser. The input business plan includes detailed information such as goals, strategies, resource allocation, and timeline. The server stores this data in a database in real time. The input of this process is the detailed business plan information, and the output is the business plan data stored in the database.

[0421] Step 3:

[0422] The server analyzes the input business plan and assigns appropriate initial tasks to each natural language generator. Natural language processing algorithms (such as SpaCy or NLTK) are used for the analysis. For example, tasks such as market analysis are assigned to the management department and technology research is assigned to the product development department. The input for this process is the business plan data, and the output is task information assigned to each generator.

[0423] Step 4:

[0424] Each natural language generator performs an assigned task. For example, a generator from the product development department researches technical papers, while a generator from the marketing department analyzes social media and research reports. Messages are sent and received between generators to share information and discuss. A sentiment analysis engine analyzes user sentiment in real time and provides feedback to the generator. The input to this process is task information, and the output is the results of the task execution and the content of the discussion.

[0425] Step 5:

[0426] The server runs a simulation based on the business plan. It uses a simulation tool (e.g., Vensim or AnyLogic) to observe and record business indicators such as sales forecasts, market share, and resource utilization. The input to this process is the execution result data of the generator, and the output is the simulation result data.

[0427] Step 6:

[0428] Each natural language generator reports the data during the simulation to the server and modifies the task as needed. For example, it may focus on researching new technologies based on market research results. The input to this process is the simulation result data, and the output is modified task information.

[0429] Step 7:

[0430] The server integrates all simulation results and selects the optimal business plan through analysis. It evaluates and selects the optimal plan using data analysis tools (e.g., Pandas, Scikit-learn). The results are generated as a report and notified to the user. The user can then apply this report to their actual business operations. The input to this process is the integrated simulation result data, and the output is a report of the optimal business plan.

[0431] (Application example 2)

[0432] Next, a description will be given of Application 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."

[0433] Conventional advertising display systems have the problem that they do not attract users' interest or attention because they display advertisements uniformly without considering users' emotions. Also, because the content and timing of advertisements do not adapt to users' emotions, the advertising effectiveness is often not as expected.

[0434] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for generating multiple natural language generation devices and assigning each device to a different internal function, means for inputting a business plan, means for any natural language generation device to initialize tasks based on the business plan and share information and discuss it with other natural language generation devices, means for simulating multiple business plans and collecting and comparing the results, means for selecting the optimal business plan and displaying the results, means for collecting and analyzing user emotional data and optimizing the content and display timing of advertisements based on the data, and means for providing feedback on the user's emotional response after the advertisement is displayed. This enables optimal advertisement display according to the user's emotional state, maximizing the effectiveness of the advertisement.

[0435] Term definition

[0436] A "natural language generation device" is a device that uses artificial intelligence technology to generate natural language that can be understood by humans.

[0437] An "internal function" is a function that corresponds to a specific department or role within a company or organization, such as operations related to management, marketing, human resources, product development, etc.

[0438] A business plan is a detailed document detailing the goals and strategies that a company or organization must achieve, the allocation of resources, and the timeline.

[0439] "Tasks" refer to the specific tasks and issues required to execute a business plan.

[0440] "Sharing information and holding discussions" refers to multiple natural language generation devices exchanging data and knowledge with each other and then examining the issue from various perspectives based on that information.

[0441] "Simulation" is a method of executing a business plan in a virtual environment and analyzing the results, and is a means of evaluating the possibility of success and risks in advance.

[0442] "Emotional data" refers to data that indicates the user's current emotional state, and refers to information obtained from facial expressions, tone of voice, etc.

[0443] "Advertising optimization" is a technology that maximizes advertising effectiveness by adjusting the content and timing of advertisements based on user emotional data.

[0444] "Feedback" is the process of collecting user reactions and using them as information to take more appropriate next actions.

[0445] System Overview

[0446] This invention is a system that virtually reproduces a company's organizational structure and business operations by using multiple natural language generation devices with built-in emotion engines, thereby achieving efficient business operations and optimizing advertising display. This system includes functions such as input of business plans, automatic task generation, discussion and instruction between natural language generation devices, simulation, selection of optimal business plans, and advertising optimization through emotion analysis. The emotion engine can also analyze user emotions and display appropriate advertisements based on the results.

[0447] Hardware and Software Configuration

[0448] Hardware: Smartphone (camera, microphone, display, processor)

[0449] Software: Sentiment analysis engine, natural language generation model (e.g. GPT-4), ad management system, server

[0450] Program processing

[0451] The server performs the following process:

[0452] 1. Collecting user emotion data

[0453] It uses the smartphone's camera and microphone to collect the user's facial expressions and tone of voice in real time, and this data is sent to an emotion analysis engine to analyze the user's current emotional state.

[0454] 2. Emotional Data Analysis

[0455] The emotion analysis engine analyzes the user's emotional data from collected facial expressions and tone of voice to identify emotions such as joy, sadness, surprise, and anger.

[0456] 3. Selecting the best ads

[0457] Based on the analysis results, the natural language generator selects the advertisement that best suits the user's current emotions. Appropriate advertising materials (text, images, videos) are retrieved from the advertising management system and displayed on the smartphone screen.

[0458] 4. Feedback of user emotional responses

[0459] After the ad is displayed, the user's reaction is collected again using a camera and microphone and fed back to the sentiment analysis engine. The server uses this feedback to improve the ad selection algorithm of the natural language generation device and optimize the ad to be displayed next time.

[0460] Specific examples

[0461] For example, if the sentiment analysis engine determines that the user is surprised, the natural language generator will display an advertisement for the latest technology product with a "surprise" theme. If the user shows interest in the advertisement, feedback is collected, increasing the likelihood that similar advertisements will be displayed in a similar emotional state in the future.

[0462] Prompt Sentence Examples

[0463] Below are some examples of prompts for generative AI models (e.g., GPT-4):

[0464] plaintext

[0465] Analyze the following user sentiment data and generate the optimal ad copy.

[0466] Emotion data: Surprise

[0467] Ad Category: Technology Products

[0468] Based on this prompt, the natural language generator generates an advertisement that matches the user's surprise and displays it on the smartphone, thereby realizing optimal advertisement display for each user.

[0469] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0470] Program processing steps

[0471] Explain the process flow in detail

[0472] Step 1:

[0473] The server collects facial expression data and tone of voice data using the camera and microphone on the user's smartphone. Specifically, the camera captures facial images and the microphone records voice. The input is real-time video and audio data, which is then sent to the emotion analysis engine.

[0474] Step 2:

[0475] The server's emotion analysis engine analyzes the video and audio data received from the smartphone. It analyzes facial expressions using a facial recognition algorithm and audio tones using an audio analysis algorithm. As a result of the analysis, emotional data such as joy, sadness, surprise, and anger are obtained. This is the output of the analysis.

[0476] Step 3:

[0477] The server inputs a prompt sentence into the generative AI model based on the emotion data obtained from the emotion analysis engine. Specifically, it generates the following prompt sentence based on the analysis results and sends it to the AI ​​model. The prompt sentence, which includes "emotion data" and "advertising category," is used as input. The prompt sentence has the following format:

[0478] plaintext

[0479] Analyze the following user sentiment data and generate the optimal ad copy.

[0480] Emotion data: Surprise

[0481] Ad Category: Technology Products

[0482] The generated prompt text becomes the input to the model, which then generates optimal advertising text based on it.

[0483] Step 4:

[0484] The server's generative AI model analyzes the prompt and generates ad copy that best suits the user's emotional state. The model's output is a text ad copy, which is then sent to the ad management system.

[0485] Step 5:

[0486] The server's advertising management system combines the generated advertising copy with appropriate advertising materials (images and videos). These materials are retrieved from a database and an advertising package is created accordingly. This advertising package is the output sent to the smartphone.

[0487] Step 6:

[0488] The smartphone displays the received advertising package on the display. At this time, it adjusts the timing of displaying the advertisement taking into account the user's current operating state and usage situation. Specifically, the advertisement is displayed while the user is operating the app or on the notification screen.

[0489] Step 7:

[0490] After the user watches the ad, the smartphone again uses the camera and microphone to collect the user's reaction. The input is real-time video and audio data from after the ad is viewed, which is then sent to the sentiment analysis engine.

[0491] Step 8:

[0492] The emotion analysis engine on the server analyzes the video and audio data again to obtain emotion data after viewing the advertisement. This is the output of the analysis.

[0493] Step 9:

[0494] The server collects emotional data after viewing an ad and feeds it back into the ad selection algorithm. The feedback data is used to optimize the display of the next ad and as learning data for ad display optimization.

[0495] This series of processes realizes optimal advertisement display based on the user's emotional state, maximizing advertising effectiveness.

[0496] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0497] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with 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.

[0498] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0499] [Second embodiment]

[0500] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0501] 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.

[0502] 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 WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0503] 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.

[0504] 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 audio in accordance with instructions from the processor 46.

[0505] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0506] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0507] Fig. 4 shows an example of the 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.

[0508] 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.

[0509] 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.

[0510] In the smart glasses 214, the processor 46 performs the reception output process. 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.

[0511] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0512] System Overview

[0513] This invention is a system that virtually reproduces the organizational structure and business operations of a company using multiple natural language generation devices, thereby improving the efficiency of corporate operations and achieving semi-automated operation. This system includes functions such as input of business plans, automatic generation of tasks, discussion and instruction between natural language generation devices, simulation, and selection of the optimal business plan.

[0514] Program processing

[0515] 1. Initial Setup

[0516] server

[0517] The server creates multiple natural language generation devices, each configured to handle a different organizational function (e.g., management, marketing, human resources, product development, etc.).

[0518] 2. Enter your business plan

[0519] User

[0520] Users input business plans into the server, which include specific goals, strategies, resource allocation, and timelines. For example, business plan A may involve the development of a new product, while business plan B may involve strengthening marketing for an existing product.

[0521] server

[0522] The server analyzes the received business plans and distributes them appropriately to each natural language generation device.

[0523] 3. Task execution and discussion for each natural language generator

[0524] server

[0525] Based on the analysis results, the server assigns initial tasks to each natural language generator. For example, the natural language generator in the management department is assigned to conduct market analysis, and the natural language generator in the product development department is assigned to research product specifications.

[0526] natural language generator

[0527] Each natural language generator starts its assigned initial task. For example, a natural language generator in the product development department conducts research on new technologies, and a natural language generator in the marketing department conducts market research.

[0528] As needed, messages are sent between the natural language generators to exchange information and discuss with each other. For example, a marketing natural language generator might propose an advertising campaign idea and ask the management natural language generator for approval.

[0529] 4. Simulation and Comparison

[0530] server

[0531] The server runs simulations based on multiple business plans. During the simulation, it recreates the behavior of a virtual market environment based on data generated by each natural language generator, and collects business indicators (sales forecasts, market share, resource utilization, etc.).

[0532] natural language generator

[0533] Each natural language generation device reports intermediate results to the server and proceeds with the simulation while modifying the task as necessary.

[0534] 5. Selection and implementation of optimal business plans

[0535] server

[0536] The server integrates all simulation results, analyzes the collected data, and selects the optimal business plan.

[0537] The simulation results and details of the optimal business plan are generated as a report and notified to the user.

[0538] User

[0539] Based on the report, users can select the most appropriate business plan and apply it to their actual business operations, thereby semi-automating business operations and improving productivity.

[0540] Specific examples

[0541] As an example, consider the case of launching a new smartphone product onto the market.

[0542] Initial Setup

[0543] The server generates natural language generation devices for the management, marketing, product development, and human resources departments, and assigns each department a role.

[0544] Enter your business plan

[0545] The user inputs "Development of a new smartphone with an emphasis on camera performance" as business plan A and "Development of a smartphone with a long battery life" as business plan B.

[0546] Task execution for each department

[0547] The server analyzes each business plan, the management department begins market analysis, the product development department begins technical research, and the marketing department begins research into the target market.

[0548] Task discussion and coordination

[0549] Each department's natural language generators exchange messages based on collected data and suggestions, adjusting advertising strategies and product details.

[0550] Running the simulation

[0551] The server simulates each business plan in a virtual market environment and collects data such as sales forecasts and market share.

[0552] Selection of the optimal business plan

[0553] The server integrates and analyzes the simulation results and determines that the new smartphone that emphasizes camera performance (Business Plan A) has a higher probability of success in the market.

[0554] Users receive a report of the optimal business plan and apply it to their actual business, thereby improving the efficiency and speed of their business operations.

[0555] This is a specific embodiment for carrying out the present invention. The present invention automates the complex operational processes of a company, enabling efficient and rapid decision-making.

[0556] The processing flow will be explained below.

[0557] Step 1:

[0558] server

[0559] The server creates multiple natural language generation devices and configures them to handle different organizational functions (management, marketing, human resources, product development, etc.).

[0560] Step 2:

[0561] server

[0562] The server provides a business plan input form, which includes fields for goals, strategy, resource allocation, timeline, etc.

[0563] Step 3:

[0564] User

[0565] Users enter their business plans in a designated form. For example, Business Plan A is "Developing a smartphone equipped with new camera technology," and Business Plan B is "Developing a smartphone with long battery life."

[0566] Step 4:

[0567] server

[0568] The server receives and analyzes the business plan submitted by the user, and based on the results, sets an initial task for each natural language generator.

[0569] Step 5:

[0570] server

[0571] The server assigns initial tasks to each natural language generator based on the analysis results. For example, the natural language generator in the management department is assigned to conduct market analysis, and the natural language generator in the product development department is assigned to conduct product specification research.

[0572] Step 6:

[0573] natural language generator

[0574] Each natural language generator performs an assigned initial task. For example, a natural language generator in the product development department researches new technologies, and a natural language generator in the marketing department conducts market research.

[0575] Step 7:

[0576] natural language generator

[0577] Messages are exchanged between the natural language generators, and discussions take place. For example, a marketing natural language generator might propose an idea for an advertising campaign and ask the management natural language generator for approval.

[0578] Step 8:

[0579] natural language generator

[0580] Each natural language generator reports intermediate results to the server, such as research results and market reaction to a new camera technology.

[0581] Step 9:

[0582] server

[0583] The server aggregates the intermediate results and issues task modifications or new instructions to each natural language generator as needed. For example, if a change in marketing strategy is needed, the server issues new instructions to the natural language generator in the marketing department.

[0584] Step 10:

[0585] server

[0586] The server runs simulations based on multiple business plans, including business metrics such as sales forecasts, market share, and resource utilization.

[0587] Step 11:

[0588] natural language generator

[0589] Each natural language generator collects data generated during the simulation and reports it to the server.

[0590] Step 12:

[0591] server

[0592] The server consolidates all the simulation results and compares the performance of each business plan based on the collected data.

[0593] Step 13:

[0594] server

[0595] The server selects the optimal business plan, generates a report of the results, and notifies the user.

[0596] Step 14:

[0597] User

[0598] Based on the report from the server, the user selects the most suitable business plan and applies that plan to actual business operations.

[0599] Example 1

[0600] Next, a description will be given of 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."

[0601] Modern corporate operations require a great deal of time and effort to deal with complex organizational structures and diverse business plans. Particular challenges include the efficient sharing of information and discussions between different departments, as well as the continuous simulation and evaluation of multiple business plans. Furthermore, the process of selecting the optimal operational plan requires advanced data analysis, which is time-consuming. For this reason, there is a demand for effective systems to streamline corporate operations and enable semi-automated operations.

[0602] 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.

[0603] In this invention, the server includes means for generating a plurality of information processing devices and assigning different functions to each of them, means for inputting an operation plan, means for an arbitrary information processing device to initialize tasks based on the operation plan and to share information and hold discussions with other information processing devices, means for simulating a plurality of operation plans and collecting and comparing the results, and means for selecting an optimal operation plan and displaying the results. This enables efficient information sharing and discussion between different departments, effective simulation and evaluation of a plurality of business plans, and rapid selection of an optimal operation plan.

[0604] An "information processing device" is a device that has the function of receiving data and instructions and processing them to perform a specific task.

[0605] An "operational plan" is a plan that includes specific policies, strategies, resource allocations, and timelines for achieving organizational or business goals.

[0606] A "server" is a central processing unit that provides data and services to other devices on a network.

[0607] A "task" is a process or action that performs a specific task to achieve a goal.

[0608] "Simulation" is a method for virtually recreating real-world systems and situations and predicting their behavior and outcomes.

[0609] "Message sending and receiving function" refers to the function of transferring information or data from one device to another.

[0610] "Intermediate results" refer to results or data obtained along the way in the process of obtaining the final result.

[0611] A "report" is a document that organizes information and data on a specific topic.

[0612] The "optimal management plan" is the plan that, among multiple management plans, achieves the goal most effectively and efficiently.

[0613] A "comparison means" is a method or device for evaluating multiple data or results against each other and determining their relative merits and differences.

[0614] The present invention provides a system for virtually reproducing a company's management plan using a plurality of information processing devices, thereby improving the efficiency of management and achieving semi-automated operation. An embodiment of the present invention will be described in detail below.

[0615] Hardware and software used

[0616] server

[0617] Use a high-performance server. Specifically, a server with high computing power is recommended. For example, a server with multiple CPU cores and a GPU (e.g., NVIDIA) is preferable.

[0618] The server is used to create a plurality of information processing devices.

[0619] The software used includes Python, natural language processing libraries (e.g., Transformers), database management systems (e.g., MySQL), and messaging servers (e.g., RabbitMQ).

[0620] Terminal

[0621] The terminal is used by users to input operational plans, specifically using a web browser or dedicated application.

[0622] User

[0623] The user inputs various management plans into the server via a terminal, and the optimal management plan is selected and implemented.

[0624] Explanation of program processing

[0625] server

[0626] 1. The server generates multiple information processing devices and assigns each device to a different function. For example, it generates information processing devices to handle the functions of the management, marketing, human resources, and product development departments.

[0627] 2. The server analyzes the input operation plan and allocates it appropriately to each information processing device. As a result, each device starts to perform the tasks related to its own area of ​​responsibility.

[0628] Information processing device

[0629] 3. Each information processing device starts the initial task assigned to it. For example, the device in the marketing department conducts market research, and the device in the product development department conducts technical research.

[0630] 4. Each information processing device shares information as needed, exchanges messages with other devices, and holds discussions, thereby realizing effective communication between departments.

[0631] simulation

[0632] 5. The server runs simulations of multiple operational plans based on the data obtained from each information processing device, collecting data such as sales forecasts, market share, and resource utilization rates.

[0633] 6. Each information processing device reports intermediate results to the server and proceeds with the simulation while modifying tasks as necessary.

[0634] Selection of the optimal operation plan

[0635] 7. The server integrates all the simulation results and selects the optimal operation plan. For example, it determines that a new smartphone with a superior camera performance has a high probability of success in the market.

[0636] 8. Generate a report detailing the selected optimal operation plan and notify the user.

[0637] Examples and prompts

[0638] As an example, consider the case of launching a new smartphone product onto the market.

[0639] Initial Setup

[0640] The server generates information processing devices for the management department, marketing department, product development department, and human resources department, and assigns each department its respective roles.

[0641] Input of operation plan

[0642] The user inputs "Development of a new smartphone with an emphasis on camera performance" as operational plan A, and "Development of a smartphone with a long battery life" as operational plan B.

[0643] Task execution for each department

[0644] The server analyzes each operational plan, the management department starts market analysis, the product development department starts technology research, and the marketing department starts target market research.

[0645] Task discussion and coordination

[0646] The information processing devices in each department exchange messages based on collected data and suggestions, and adjust advertising strategies and product details.

[0647] Running the simulation

[0648] The server simulates each operational plan in a virtual market environment and collects data such as sales forecasts and market share.

[0649] Selection of the optimal operation plan

[0650] The server integrates and analyzes the simulation results and determines that the new smartphone that emphasizes camera performance (Operation Plan A) has a higher probability of success in the market.

[0651] Users receive a report of the optimal operational plan and apply it to their actual business.

[0652] Prompt Sentence Examples

[0653] "Simulate which is more likely to be successful in the market: a new smartphone with a better camera or a smartphone with longer battery life."

[0654] The above is a specific embodiment for carrying out the present invention. The present invention automates the complex operational processes of a company, enabling efficient and rapid decision-making.

[0655] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0656] Step 1:

[0657] Initial Setup

[0658] The server generates a plurality of information processing devices, each responsible for a different function, for example, the management department, marketing department, human resources department, and product development department.

[0659] Input: Initial server data

[0660] Output: Generate information processing equipment corresponding to each department

[0661] The server executes a Python script to generate information processing devices and set their respective functions.

[0662] Step 2:

[0663] Input of operation plan

[0664] The user uses a terminal to input an operational plan, which includes specific goals, strategies, resource allocation, and a timeline.

[0665] Input: Operational plan data entered by the user

[0666] Output: Operational plan data sent to the server

[0667] The terminal uses a web browser or dedicated application to send data entered by the user to the server.

[0668] Step 3:

[0669] Analysis of operational plans

[0670] The server analyzes the received operational plan and distributes it appropriately to each information processing device. For example, market analysis is distributed to the information processing device of the management department, and technology research is distributed to the information processing device of the product development department.

[0671] Input: Operational plan data

[0672] Output: Task data allocated to each information processing device

[0673] The server uses Python scripts and natural language processing libraries (e.g., Transformers) to analyze the data and dispatch tasks.

[0674] Step 4:

[0675] Executing a task

[0676] Each information processing device starts the assigned initial task. For example, the information processing device in the marketing department performs market research, and the information processing device in the product development department performs technical research.

[0677] Input: Task data distributed from the server

[0678] Output: Intermediate results of task execution

[0679] The information processing devices collect and analyze the necessary data in their respective areas of responsibility and generate intermediate results.

[0680] Step 5:

[0681] Message sending and discussion

[0682] Each information processing device shares information with other information processing devices as needed, and exchanges messages while holding discussions. For example, a device in the marketing department proposes product specifications suited to the target market to a device in the product development department.

[0683] Input: Message data from other information processing devices

[0684] Output: The result of the discussion

[0685] The information processing devices use a messaging server (for example, RabbitMQ) to send and receive messages in real time and share information.

[0686] Step 6:

[0687] Running the simulation

[0688] The server runs simulations of multiple operational plans based on data obtained from each information processing device, and collects data such as sales forecasts, market share, and resource utilization rates through the simulations.

[0689] Input: Data obtained from each information processing device

[0690] Output: Simulation result data

[0691] The server executes the simulation algorithm and stores the simulation results in a database.

[0692] Step 7:

[0693] Reporting interim results and correcting tasks

[0694] Each information processing device reports intermediate results to the server and proceeds with the simulation while modifying the task as necessary.

[0695] Input: Intermediate results data during simulation

[0696] Output: Instruction data for task correction

[0697] The information processing device transmits intermediate results to the server and modifies the task accordingly based on feedback from the server.

[0698] Step 8:

[0699] Selection of the optimal operation plan

[0700] The server integrates all the simulation results and selects the optimal operation plan. For example, it determines that a new smartphone with a superior camera performance has a high probability of success in the market.

[0701] Input: Simulation result data

[0702] Output: Selected optimal operation plan

[0703] The server uses data analysis algorithms to evaluate the simulation results and select the most effective operational plan.

[0704] Step 9:

[0705] Notification of optimal operation plan

[0706] The server generates a report detailing the selected optimal operation plan and notifies the user.

[0707] Input: Optimal operation plan data

[0708] Output: Report sent to the user

[0709] The server stores the reports in cloud storage (e.g. AWS S3) and sends information to users via email or notification system.

[0710] (Application example 1)

[0711] Next, a description will be given of Application 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."

[0712] Virtual corporate operations require the ability to efficiently and quickly formulate business plans and select and execute optimal operational plans. However, existing systems make it difficult to effectively link multiple internal organizational functions, and analyzing simulation results takes time. Furthermore, there is a lack of means to check and adjust product placement and campaign strategies in real time within virtual stores. These challenges mean that efficient and optimized corporate operations are not being fully realized.

[0713] 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.

[0714] In this invention, the server includes means for generating multiple natural language generation devices and assigning each device to a different organizational function, means for inputting a business plan, means for any natural language generation device to initialize tasks based on the business plan and share information and discuss with other natural language generation devices, means for simulating multiple business plans and collecting and comparing the results, means for selecting an optimal business plan and displaying the results, and means for using a head-mounted display to check and adjust product placement and campaign strategies in a virtual store in real time, thereby enabling more efficient corporate management, faster decision-making, and real-time adjustment of management strategies in a virtual environment.

[0715] A "natural language generation device" is a system that uses natural language to execute set tasks and collect, analyze, discuss, and give instructions to each department within an organization.

[0716] A "head-mounted display" is a display device worn by the user on the head, which displays virtual reality or augmented reality environments and provides the user with a three-dimensional visual experience.

[0717] A "virtual store" is a virtual store that recreates a real store in a virtual space, allowing users to access the store from a remote location and check and adjust product placement, campaign strategies, etc.

[0718] A business plan is an operational plan that includes specific goals and strategies, resource allocation, timelines, etc. set by a company.

[0719] A "simulation" is a virtual experiment that assumes the execution of a business plan in a virtual market environment and analyzes the resulting business indicators, such as sales forecasts, market share, and resource utilization rates.

[0720] "Intermediate results" refer to the data and analysis results obtained during the course of each natural language generation device's execution of a given task.

[0721] The "message sending and receiving function" is a communication means used by each natural language generation device to share information with other devices and to give instructions and hold discussions.

[0722] "Displaying a business plan" refers to a method or system that visually presents an optimal business plan to a user.

[0723] System Overview

[0724] This invention is a system that virtually reproduces a company's organizational structure and business operations using multiple natural language generation devices, thereby improving the efficiency of corporate operations and achieving semi-automated operations. Specifically, it includes functions such as inputting business plans, automatically generating tasks, discussions and instructions between natural language generation devices, simulations, selection of optimal business plans, and real-time confirmation and adjustment of product placement and campaign strategies in virtual stores.

[0725] Hardware and Software Configuration

[0726] Hardware

[0727] 1. Server: Use a cloud server equipped with a high-performance CPU and GPU.

[0728] 2. Head-mounted display (HMD): Used by users to visually navigate the virtual store.

[0729] software

[0730] 1. Natural language generation model: Use a generative AI model such as GPT-3.

[0731] 2. Simulation environment: Use a simulation tool such as SimPy.

[0732] 3. Data analysis tools: Utilize data analysis tools such as Pandas.

[0733] Detailed Description of the Embodiments

[0734] The server generates multiple natural language generation devices, each responsible for a different organizational function. Each natural language generation device has the functions of management, marketing, inventory management, and customer service, and performs initial tasks based on the business plan entered by the user, sharing information and holding discussions with other devices. The business plan, which includes specific goals, strategies, resource allocation, timeline, etc., is entered by the user into the server.

[0735] Each natural language generator initializes tasks based on prompts, sends and receives messages as needed, and exchanges information.Furthermore, the server simulates multiple business plans in a virtual market environment, collects and compares the results, and selects the optimal business plan.

[0736] The final business plan is displayed as a report to the user, who can then use a head-mounted display to view and adjust product placement and campaign strategies in real time within a virtual store, thereby improving business operations efficiency and enabling faster decision-making.

[0737] Specific examples

[0738] For example, if a user wants to set up a special corner for a new product, they put on a head-mounted display and input a prompt such as, "I want to create a special corner for a new product. Please suggest the optimal product placement and marketing strategy." Based on this input, the natural language generator will hold a discussion, and the server will run a simulation to suggest the optimal placement and strategy. The user can check the suggestions in real time through the HMD and make adjustments as necessary.

[0739] This enables faster decision-making and more efficient business operations, and facilitates real-time adjustments to operational strategies in a virtual environment, thereby improving corporate productivity and competitiveness.

[0740] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0741] Step 1:

[0742] The server generates multiple natural language generators, each responsible for a different internal organizational function (management, marketing, inventory management, customer service). The server receives configuration information for each function as input, and generates the configured natural language generator as output. Specifically, the server initializes each generation AI model using a pre-configured API and assigns each an appropriate task.

[0743] Step 2:

[0744] The user inputs a business plan into the server. As input, the user enters a business plan including specific goals, strategies, resource allocation, timeline, etc., and as output, the server stores the plan in a database. Specifically, the user enters the business plan using a dedicated input form, and the server analyzes the data and distributes it to the natural language generation device.

[0745] Step 3:

[0746] Any natural language generation device initiates a task based on a business plan, shares information with other natural language generation devices, and engages in discussions. Task instructions are received from a server as input, and the results of the discussion are generated as output. Specifically, the natural language generation device executes the received task and sends the results as a message to other devices.

[0747] Step 4:

[0748] It simulates multiple business plans, collects and compares the results. It receives simulation data reported by each natural language generator as input, and selects the optimal business plan as output. Specifically, the server uses a simulation tool such as SimPy to recreate a virtual market environment and calculates the business indicators for each business plan.

[0749] Step 5:

[0750] The optimal business plan is selected and the results are displayed. The input is data that integrates and analyzes the simulation results, and the output is a report that is visually displayed to the user. Specifically, the server integrates the results using data analysis tools such as Pandas and presents the optimal business plan to the user.

[0751] Step 6:

[0752] A head-mounted display is used to check and adjust product placement and campaign strategies in a virtual store in real time. The input is optimal business plan data, and the output is real-time updates to the settings in the virtual store. Specifically, the user puts on the HMD and visually checks the virtual store, adjusting placement and campaigns as necessary.

[0753] Furthermore, 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.

[0754] System Overview

[0755] This invention is a system that virtually recreates a company's organizational structure and business operations by using multiple natural language generation devices with an emotion engine, achieving efficient corporate operations and semi-automated operations. This system includes functions such as input of business plans, automatic task generation, discussion and instruction between natural language generation devices, simulation, and selection of the optimal business plan. The emotion engine also analyzes user emotions and can make adjustments based on the results.

[0756] Program processing

[0757] 1. Initial Setup

[0758] server

[0759] The server creates multiple natural language generators, assigning each to a different organizational function (e.g., management, marketing, human resources, product development, etc.), and embeds an emotion engine into each natural language generator.

[0760] 2. Enter your business plan

[0761] User

[0762] Users input business plans into the server, which include goals, strategies, resource allocation, and timelines. For example, Business Plan A might include "developing a smartphone equipped with new camera technology," while Business Plan B might include "developing a smartphone with long battery life."

[0763] server

[0764] The server analyzes the input business plan and assigns an appropriate initial task to each natural language generator.

[0765] 3. Task execution and discussion for each natural language generator

[0766] server

[0767] Based on the analysis results, the server assigns initial tasks to each natural language generator. For example, the natural language generator in the management department is assigned to conduct market analysis, and the natural language generator in the product development department is assigned to research product specifications.

[0768] natural language generator

[0769] Each NLG performs an assigned task: for example, a NLG in the product development department researches new technologies, and a NLG in the marketing department conducts market research.

[0770] Messages are exchanged between natural language generators as needed, and discussions are held. During this process, the emotion engine analyzes the user's emotions and provides feedback to the generators.

[0771] 4. Simulation and Comparison

[0772] server

[0773] The server runs a simulation based on each business plan and records business indicators (sales forecast, market share, resource utilization, etc.) collected during the simulation.

[0774] natural language generator

[0775] Each natural language generator reports data during the simulation to the server and modifies the task as necessary.

[0776] 5. Selection and implementation of optimal business plans

[0777] server

[0778] The server integrates all the simulation results, performs analysis, and selects the optimal business plan.

[0779] The results are generated as a report and notified to the user.

[0780] User

[0781] Users can apply the optimal business plan report to their actual business operations.

[0782] Specific examples

[0783] As an example, consider the case of launching a new smartphone product onto the market.

[0784] Initial Setup

[0785] The server generates natural language generators for the management, marketing, product development, and human resources departments, and assigns each department its own role. Each generator has a built-in emotion engine.

[0786] Enter your business plan

[0787] The user inputs "Development of a new smartphone with a high-performance camera" as business plan A and "Development of a smartphone with a long battery life" as business plan B.

[0788] Task execution and discussion

[0789] The server analyzes the business plan and assigns initial tasks to each department. For example, the natural language generator in the product development department is responsible for researching new technologies, while the natural language generator in the marketing department is responsible for market research.

[0790] The generators in each department send and receive messages as needed to advance the discussion, during which the emotion engine analyzes the user's emotions and provides appropriate feedback.

[0791] Running the simulation

[0792] The server performs a simulation of the business plan and observes changes in sales forecasts and market share based on the data generated by each generation device.

[0793] Selection of the optimal business plan

[0794] By integrating and analyzing the simulation results, it is determined that, for example, a new smartphone equipped with a high-performance camera (Business Plan A) is more likely to be successful in the market.

[0795] Send reports to users and reflect them in actual business operations.

[0796] This will enable natural language generation devices using emotion engines to automate complex business processes and promote efficient and rapid decision-making.

[0797] The processing flow will be explained below.

[0798] Step 1:

[0799] server

[0800] The server creates multiple natural language generation devices and configures them to handle different organizational functions, such as the management department, marketing department, product development department, and human resources department.

[0801] Each natural language generator incorporates an emotion engine to enable analysis of the user's emotions.

[0802] Step 2:

[0803] server

[0804] The server provides the user with a business plan input form, which includes input fields for goals, strategies, resource allocation, timeline, etc.

[0805] Step 3:

[0806] User

[0807] The user enters their business plan into an input form and sends it to the server. For example, Business Plan A might be "Development of a new smartphone equipped with a high-performance camera," and Business Plan B might be "Development of a smartphone with a long battery life."

[0808] Step 4:

[0809] server

[0810] The server receives the business plan submitted by the user, analyzes the plan, and assigns an appropriate initial task to each natural language generator.

[0811] Step 5:

[0812] server

[0813] Based on the analysis results, the server assigns initial tasks to each natural language generator. For example, the natural language generator in the management department is assigned to conduct market analysis, and the natural language generator in the product development department is assigned to research product specifications.

[0814] Step 6:

[0815] natural language generator

[0816] Each NLG performs an assigned initial task: for example, a NLG in the product development department researches new technologies, and a NLG in the marketing department conducts market research.

[0817] The emotion engine analyzes the user's emotions and feeds back the analysis results to each natural language generation device.

[0818] Step 7:

[0819] natural language generator

[0820] Messages are exchanged between natural language generators, and discussions take place. For example, a marketing natural language generator proposes an idea for an advertising campaign and asks the management natural language generator for approval. During this process, an emotion engine provides emotional feedback to the communication between the generators.

[0821] Step 8:

[0822] natural language generator

[0823] Each natural language generator reports intermediate results to the server, such as research results and market reaction to a new camera technology.

[0824] Step 9:

[0825] server

[0826] The server aggregates the intermediate results and issues task modifications or new instructions to each natural language generator as needed. For example, if a change in marketing strategy is needed, new instructions are issued to the natural language generator in the marketing department.

[0827] Step 10:

[0828] server

[0829] The server runs a simulation based on each business plan, and during the simulation, business indicators such as sales forecasts, market share, and resource utilization are collected.

[0830] Step 11:

[0831] natural language generator

[0832] Each natural language generator collects data generated during the simulation and reports it to the server.

[0833] Step 12:

[0834] server

[0835] The server consolidates all simulation results and compares and analyzes the performance of each business plan based on the collected data.

[0836] Step 13:

[0837] server

[0838] The server selects the optimal business plan, generates a report of the results, and notifies the user.

[0839] Step 14:

[0840] User

[0841] Based on the reports sent from the server, the user selects the most suitable business plan and applies it to actual business operations, thereby enabling efficient and rapid business operations.

[0842] In this way, the present invention uses a natural language generation device incorporating an emotion engine to virtually reproduce a company's organizational structure and business operations, thereby realizing efficient company operations and semi-automated operations.

[0843] Example 2

[0844] Next, a description will be given of 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."

[0845] Modern business operations require efficient and rapid decision-making. However, traditional systems lack the ability to share information and exchange opinions between departments, and provide feedback that takes user sentiment into account, making it difficult to optimize management efficiency and plans overall. Furthermore, the process of simultaneously evaluating multiple business plans and selecting the optimal one takes time and effort. These challenges currently hinder optimization and efficiency in corporate operations.

[0846] 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.

[0847] In this invention, the server includes means for generating multiple natural language generation devices and assigning each device to a different organizational function, means for inputting a business plan, means for any natural language generation device to initialize tasks based on the business plan and share information and hold discussions with other natural language generation devices, means for simulating multiple business plans and collecting and comparing the results, means for selecting an optimal business plan and displaying the results, and means for analyzing user emotions using an emotion analysis engine and providing the results as feedback to the natural language generation devices. This enables smooth information sharing and discussions between departments, provides timely feedback that takes user emotions into consideration, and enables optimization of business plans and rapid decision-making.

[0848] A "natural language generation device" is a device that uses natural language processing technology to generate human language and automatically perform text-based tasks.

[0849] "Internal functions" refer to roles that correspond to different departments or tasks within a company or organization, such as management, marketing, product development, and human resources.

[0850] A business plan is a document or data that systematically outlines the goals, strategies, resource allocation, timelines, etc. set by a company or organization.

[0851] "Task initialization" is the process of completing the settings and preparations required to start a specific task, and specifically includes inputting data, setting initial parameters, allocating resources, etc.

[0852] "Information sharing" is the process of exchanging data and information between multiple systems and devices, making them mutually available.

[0853] A "discussion" is a process in which messages and information are exchanged between multiple natural language generation devices, and opinions are exchanged to arrive at the optimal conclusion.

[0854] "Simulation" is the process of using virtual environments and computational models to predict the outcome of a particular business plan and test various scenarios.

[0855] An "emotion analysis engine" is an algorithm or model developed to analyze a user's emotions and has the ability to determine their emotional state from text data and other inputs.

[0856] "Feedback" refers to information or advice provided to other systems or users based on data or analysis results acquired by a system or device.

[0857] "Optimization" is the process of adjusting parameters and settings to obtain the most efficient and effective results under given conditions and constraints.

[0858] The present invention is a system that virtually reproduces the organizational structure and business operations of a company, and realizes efficient company operations and semi-automated operations. Specific embodiments are described below.

[0859] System Overview

[0860] The present invention is a system that includes a server, multiple natural language generation devices, a user interface, and a sentiment analysis engine. The server uses a programming language such as Python to launch multiple natural language generation devices (e.g., generative AI models), each responsible for a different organizational function such as management, marketing, human resources, or product development. A sentiment analysis engine is also integrated into each generation device.

[0861] Hardware and Software

[0862] The server utilizes high performance computer hardware and open source or commercial software to perform the following tasks:

[0863] Natural Language Generator: This refers to an instance of a generative AI model (e.g., GPT-3) that is assigned to each department.

[0864] Sentiment analysis engine: A machine learning model for analyzing user sentiment (e.g., a sentiment analysis library with an LSTM model).

[0865] Database: A database system (e.g., MariaDB, PostgreSQL) for storing and managing business plans and simulation results.

[0866] Simulation software: Simulation tools to evaluate multiple business plans (e.g., AnyLogic, Vensim).

[0867] Implementation Procedure

[0868] 1. Initial Setup

[0869] The server creates natural language generators for each department, including management, marketing, product development, and human resources, and assigns each department its own role. Each generator is integrated with a sentiment analysis engine.

[0870] 2. Enter your business plan

[0871] Users use a web browser to input business plans into the server, which include goals, strategies, resource allocation, timelines, etc., and the input data is saved in a database in real time.

[0872] 3. Task assignment and discussion

[0873] The server analyzes the input business plan and assigns appropriate initial tasks to each generator. The generators perform tasks such as new technology research and market surveys, and use an emotion analysis engine to analyze and provide feedback on user emotions. Messages are sent and received between generators to share information and hold discussions.

[0874] 4. Running the Simulation

[0875] The server uses a simulation tool to evaluate the business plan, and records business indicators collected during the simulation, such as sales forecasts, market share, and resource utilization, in a database.

[0876] 5. Selection of the optimal business plan

[0877] The simulation results are integrated and analyzed to select the optimal business plan. The results are generated as a report and notified to the user. The user can then apply this report to their actual business operations.

[0878] Specific examples

[0879] As an example, let us consider the case of launching a new smartphone product into the market.

[0880] 1. Initial Setup

[0881] The server uses Python to generate open-source generative AI models as natural language generators for the management, marketing, product development, and human resources departments, each of which is integrated with a sentiment analysis engine.

[0882] 2. Enter your business plan

[0883] The user enters into a web form business plan A "Development of a new smartphone equipped with a high-performance camera" and business plan B "Development of a smartphone with a long battery life." This data is stored in a database.

[0884] 3. Task assignment and discussion

[0885] The server analyzes business plans and assigns market analysis and technology research tasks to generators. The generators research social media data and technical papers and hold discussions using the Slack API. The sentiment analysis engine analyzes user sentiment in real time and provides feedback to the generators.

[0886] 4. Running the Simulation

[0887] The server uses Vensim to run simulations and observe and record sales forecasts and market share fluctuations.

[0888] 5. Selection of the optimal business plan

[0889] The simulation results are integrated and analyzed, and the company determines that the "New Smartphone with a High-Performance Camera (Business Plan A)" has a high probability of success, and sends a report to the user, who then uses this report to implement an actual business strategy.

[0890] Prompt Sentence Examples

[0891] "Based on the simulation results, which is more likely to be successful in the market: developing a smartphone with new camera technology or developing a smartphone with longer battery life?"

[0892] As described above, the present invention provides a system that supports efficiency and rapid decision-making in business operations.

[0893] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0894] Step 1:

[0895] The server creates multiple natural language generators. These generators are responsible for different internal organizational functions, such as management, marketing, human resources, and product development. The server uses Python to launch instances of the generative AI model. It also integrates a sentiment analysis engine so that each generator can analyze user sentiment. The input for this process is initial configuration information corresponding to each role, and multiple generators with different functions are prepared as the output.

[0896] Step 2:

[0897] A user inputs a business plan into the server via a web browser. The input business plan includes detailed information such as goals, strategies, resource allocation, and timeline. The server stores this data in a database in real time. The input of this process is the detailed business plan information, and the output is the business plan data stored in the database.

[0898] Step 3:

[0899] The server analyzes the input business plan and assigns appropriate initial tasks to each natural language generator. Natural language processing algorithms (such as SpaCy or NLTK) are used for the analysis. For example, tasks such as market analysis are assigned to the management department and technology research is assigned to the product development department. The input for this process is the business plan data, and the output is task information assigned to each generator.

[0900] Step 4:

[0901] Each natural language generator performs an assigned task. For example, a generator from the product development department researches technical papers, while a generator from the marketing department analyzes social media and research reports. Messages are sent and received between generators to share information and discuss. A sentiment analysis engine analyzes user sentiment in real time and provides feedback to the generator. The input to this process is task information, and the output is the results of the task execution and the content of the discussion.

[0902] Step 5:

[0903] The server runs a simulation based on the business plan. It uses a simulation tool (e.g., Vensim or AnyLogic) to observe and record business indicators such as sales forecasts, market share, and resource utilization. The input to this process is the execution result data of the generator, and the output is the simulation result data.

[0904] Step 6:

[0905] Each natural language generator reports the data during the simulation to the server and modifies the task as needed. For example, it may focus on researching new technologies based on market research results. The input to this process is the simulation result data, and the output is modified task information.

[0906] Step 7:

[0907] The server integrates all simulation results and selects the optimal business plan through analysis. It evaluates and selects the optimal plan using data analysis tools (e.g., Pandas, Scikit-learn). The results are generated as a report and notified to the user. The user can then apply this report to their actual business operations. The input to this process is the integrated simulation result data, and the output is a report of the optimal business plan.

[0908] (Application example 2)

[0909] Next, a description will be given of Application 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."

[0910] Conventional advertising display systems have the problem that they do not attract users' interest or attention because they display advertisements uniformly without considering users' emotions. Also, because the content and timing of advertisements do not adapt to users' emotions, the advertising effectiveness is often not as expected.

[0911] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for generating multiple natural language generation devices and assigning each device to a different internal function, means for inputting a business plan, means for any natural language generation device to initialize tasks based on the business plan and share information and discuss it with other natural language generation devices, means for simulating multiple business plans and collecting and comparing the results, means for selecting the optimal business plan and displaying the results, means for collecting and analyzing user emotional data and optimizing the content and display timing of advertisements based on the data, and means for providing feedback on the user's emotional response after the advertisement is displayed. This enables optimal advertisement display according to the user's emotional state, maximizing the effectiveness of the advertisement.

[0912] Term definition

[0913] A "natural language generation device" is a device that uses artificial intelligence technology to generate natural language that can be understood by humans.

[0914] An "internal function" is a function that corresponds to a specific department or role within a company or organization, such as operations related to management, marketing, human resources, product development, etc.

[0915] A business plan is a detailed document detailing the goals and strategies that a company or organization must achieve, the allocation of resources, and the timeline.

[0916] "Tasks" refer to the specific tasks and issues required to execute a business plan.

[0917] "Sharing information and holding discussions" refers to multiple natural language generation devices exchanging data and knowledge with each other and then examining the issue from various perspectives based on that information.

[0918] "Simulation" is a method of executing a business plan in a virtual environment and analyzing the results, and is a means of evaluating the possibility of success and risks in advance.

[0919] "Emotional data" refers to data that indicates the user's current emotional state, and refers to information obtained from facial expressions, tone of voice, etc.

[0920] "Advertising optimization" is a technology that maximizes advertising effectiveness by adjusting the content and timing of advertisements based on user emotional data.

[0921] "Feedback" is the process of collecting user reactions and using them as information to take more appropriate next actions.

[0922] System Overview

[0923] This invention is a system that virtually reproduces a company's organizational structure and business operations by using multiple natural language generation devices with built-in emotion engines, thereby achieving efficient business operations and optimizing advertising display. This system includes functions such as input of business plans, automatic task generation, discussion and instruction between natural language generation devices, simulation, selection of optimal business plans, and advertising optimization through emotion analysis. The emotion engine can also analyze user emotions and display appropriate advertisements based on the results.

[0924] Hardware and Software Configuration

[0925] Hardware: Smartphone (camera, microphone, display, processor)

[0926] Software: Sentiment analysis engine, natural language generation model (e.g. GPT-4), ad management system, server

[0927] Program processing

[0928] The server performs the following process:

[0929] 1. Collecting user emotion data

[0930] It uses the smartphone's camera and microphone to collect the user's facial expressions and tone of voice in real time, and this data is sent to an emotion analysis engine to analyze the user's current emotional state.

[0931] 2. Emotional Data Analysis

[0932] The emotion analysis engine analyzes the user's emotional data from collected facial expressions and tone of voice to identify emotions such as joy, sadness, surprise, and anger.

[0933] 3. Selecting the best ads

[0934] Based on the analysis results, the natural language generator selects the advertisement that best suits the user's current emotions. Appropriate advertising materials (text, images, videos) are retrieved from the advertising management system and displayed on the smartphone screen.

[0935] 4. Feedback of user emotional responses

[0936] After the ad is displayed, the user's reaction is collected again using a camera and microphone and fed back to the sentiment analysis engine. The server uses this feedback to improve the ad selection algorithm of the natural language generation device and optimize the ad to be displayed next time.

[0937] Specific examples

[0938] For example, if the sentiment analysis engine determines that the user is surprised, the natural language generator will display an advertisement for the latest technology product with a "surprise" theme. If the user shows interest in the advertisement, feedback is collected, increasing the likelihood that similar advertisements will be displayed in a similar emotional state in the future.

[0939] Prompt Sentence Examples

[0940] Below are some examples of prompts for generative AI models (e.g., GPT-4):

[0941] plaintext

[0942] Analyze the following user sentiment data and generate the optimal ad copy.

[0943] Emotion data: Surprise

[0944] Ad Category: Technology Products

[0945] Based on this prompt, the natural language generator generates an advertisement that matches the user's surprise and displays it on the smartphone, thereby realizing optimal advertisement display for each user.

[0946] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0947] Program processing steps

[0948] Explain the process flow in detail

[0949] Step 1:

[0950] The server collects facial expression data and tone of voice data using the camera and microphone on the user's smartphone. Specifically, the camera captures facial images and the microphone records voice. The input is real-time video and audio data, which is then sent to the emotion analysis engine.

[0951] Step 2:

[0952] The server's emotion analysis engine analyzes the video and audio data received from the smartphone. It analyzes facial expressions using a facial recognition algorithm and audio tones using an audio analysis algorithm. As a result of the analysis, emotional data such as joy, sadness, surprise, and anger are obtained. This is the output of the analysis.

[0953] Step 3:

[0954] The server inputs a prompt sentence into the generative AI model based on the emotion data obtained from the emotion analysis engine. Specifically, it generates the following prompt sentence based on the analysis results and sends it to the AI ​​model. The prompt sentence, which includes "emotion data" and "advertising category," is used as input. The prompt sentence has the following format:

[0955] plaintext

[0956] Analyze the following user sentiment data and generate the optimal ad copy.

[0957] Emotion data: Surprise

[0958] Ad Category: Technology Products

[0959] The generated prompt text becomes the input to the model, which then generates optimal advertising text based on it.

[0960] Step 4:

[0961] The server's generative AI model analyzes the prompt and generates ad copy that best suits the user's emotional state. The model's output is a text ad copy, which is then sent to the ad management system.

[0962] Step 5:

[0963] The server's advertising management system combines the generated advertising copy with appropriate advertising materials (images and videos). These materials are retrieved from a database and an advertising package is created accordingly. This advertising package is the output sent to the smartphone.

[0964] Step 6:

[0965] The smartphone displays the received advertising package on the display. At this time, it adjusts the timing of displaying the advertisement taking into account the user's current operating state and usage situation. Specifically, the advertisement is displayed while the user is operating the app or on the notification screen.

[0966] Step 7:

[0967] After the user watches the ad, the smartphone again uses the camera and microphone to collect the user's reaction. The input is real-time video and audio data from after the ad is viewed, which is then sent to the sentiment analysis engine.

[0968] Step 8:

[0969] The emotion analysis engine on the server analyzes the video and audio data again to obtain emotion data after viewing the advertisement. This is the output of the analysis.

[0970] Step 9:

[0971] The server collects emotional data after viewing an ad and feeds it back into the ad selection algorithm. The feedback data is used to optimize the display of the next ad and as learning data for ad display optimization.

[0972] This series of processes realizes optimal advertisement display based on the user's emotional state, maximizing advertising effectiveness.

[0973] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0974] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with 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.

[0975] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0976] [Third embodiment]

[0977] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0978] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0979] 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 WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0980] 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.

[0981] 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 audio in accordance with instructions from the processor 46.

[0982] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0983] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0984] Fig. 6 shows an example of the 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.

[0985] 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.

[0986] 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.

[0987] In the headset type terminal 314, a 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.

[0988] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0989] System Overview

[0990] This invention is a system that virtually reproduces the organizational structure and business operations of a company using multiple natural language generation devices, thereby improving the efficiency of corporate operations and achieving semi-automated operation. This system includes functions such as input of business plans, automatic generation of tasks, discussion and instruction between natural language generation devices, simulation, and selection of the optimal business plan.

[0991] Program processing

[0992] 1. Initial Setup

[0993] server

[0994] The server creates multiple natural language generation devices, each configured to handle a different organizational function (e.g., management, marketing, human resources, product development, etc.).

[0995] 2. Enter your business plan

[0996] User

[0997] Users input business plans into the server, which include specific goals, strategies, resource allocation, and timelines. For example, business plan A may involve the development of a new product, while business plan B may involve strengthening marketing for an existing product.

[0998] server

[0999] The server analyzes the received business plans and distributes them appropriately to each natural language generation device.

[1000] 3. Task execution and discussion for each natural language generator

[1001] server

[1002] Based on the analysis results, the server assigns initial tasks to each natural language generator. For example, the natural language generator in the management department is assigned to conduct market analysis, and the natural language generator in the product development department is assigned to research product specifications.

[1003] natural language generator

[1004] Each natural language generator starts its assigned initial task. For example, a natural language generator in the product development department conducts research on new technologies, and a natural language generator in the marketing department conducts market research.

[1005] As needed, messages are sent between the natural language generators to exchange information and discuss with each other. For example, a marketing natural language generator might propose an advertising campaign idea and ask the management natural language generator for approval.

[1006] 4. Simulation and Comparison

[1007] server

[1008] The server runs simulations based on multiple business plans. During the simulation, it recreates the behavior of a virtual market environment based on data generated by each natural language generator, and collects business indicators (sales forecasts, market share, resource utilization, etc.).

[1009] natural language generator

[1010] Each natural language generation device reports intermediate results to the server and proceeds with the simulation while modifying the task as necessary.

[1011] 5. Selection and implementation of optimal business plans

[1012] server

[1013] The server integrates all simulation results, analyzes the collected data, and selects the optimal business plan.

[1014] The simulation results and details of the optimal business plan are generated as a report and notified to the user.

[1015] User

[1016] Based on the report, users can select the most appropriate business plan and apply it to their actual business operations, thereby semi-automating business operations and improving productivity.

[1017] Specific examples

[1018] As an example, consider the case of launching a new smartphone product onto the market.

[1019] Initial Setup

[1020] The server generates natural language generation devices for the management, marketing, product development, and human resources departments, and assigns each department a role.

[1021] Enter your business plan

[1022] The user inputs "Development of a new smartphone with an emphasis on camera performance" as business plan A and "Development of a smartphone with a long battery life" as business plan B.

[1023] Task execution for each department

[1024] The server analyzes each business plan, the management department begins market analysis, the product development department begins technical research, and the marketing department begins research into the target market.

[1025] Task discussion and coordination

[1026] Each department's natural language generators exchange messages based on collected data and suggestions, adjusting advertising strategies and product details.

[1027] Running the simulation

[1028] The server simulates each business plan in a virtual market environment and collects data such as sales forecasts and market share.

[1029] Selection of the optimal business plan

[1030] The server integrates and analyzes the simulation results and determines that the new smartphone that emphasizes camera performance (Business Plan A) has a higher probability of success in the market.

[1031] Users receive a report of the optimal business plan and apply it to their actual business, thereby improving the efficiency and speed of their business operations.

[1032] This is a specific embodiment for carrying out the present invention. The present invention automates the complex operational processes of a company, enabling efficient and rapid decision-making.

[1033] The processing flow will be explained below.

[1034] Step 1:

[1035] server

[1036] The server creates multiple natural language generation devices and configures them to handle different organizational functions (management, marketing, human resources, product development, etc.).

[1037] Step 2:

[1038] server

[1039] The server provides a business plan input form, which includes fields for goals, strategy, resource allocation, timeline, etc.

[1040] Step 3:

[1041] User

[1042] Users enter their business plans in a designated form. For example, Business Plan A is "Developing a smartphone equipped with new camera technology," and Business Plan B is "Developing a smartphone with long battery life."

[1043] Step 4:

[1044] server

[1045] The server receives and analyzes the business plan submitted by the user, and based on the results, sets an initial task for each natural language generator.

[1046] Step 5:

[1047] server

[1048] The server assigns initial tasks to each natural language generator based on the analysis results. For example, the natural language generator in the management department is assigned to conduct market analysis, and the natural language generator in the product development department is assigned to conduct product specification research.

[1049] Step 6:

[1050] natural language generator

[1051] Each natural language generator performs an assigned initial task. For example, a natural language generator in the product development department researches new technologies, and a natural language generator in the marketing department conducts market research.

[1052] Step 7:

[1053] natural language generator

[1054] Messages are exchanged between the natural language generators, and discussions take place. For example, a marketing natural language generator might propose an idea for an advertising campaign and ask the management natural language generator for approval.

[1055] Step 8:

[1056] natural language generator

[1057] Each natural language generator reports intermediate results to the server, such as research results and market reaction to a new camera technology.

[1058] Step 9:

[1059] server

[1060] The server aggregates the intermediate results and issues task modifications or new instructions to each natural language generator as needed. For example, if a change in marketing strategy is needed, the server issues new instructions to the natural language generator in the marketing department.

[1061] Step 10:

[1062] server

[1063] The server runs simulations based on multiple business plans, including business metrics such as sales forecasts, market share, and resource utilization.

[1064] Step 11:

[1065] natural language generator

[1066] Each natural language generator collects data generated during the simulation and reports it to the server.

[1067] Step 12:

[1068] server

[1069] The server consolidates all the simulation results and compares the performance of each business plan based on the collected data.

[1070] Step 13:

[1071] server

[1072] The server selects the optimal business plan, generates a report of the results, and notifies the user.

[1073] Step 14:

[1074] User

[1075] Based on the report from the server, the user selects the most suitable business plan and applies that plan to actual business operations.

[1076] Example 1

[1077] Next, a description will be given of 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."

[1078] Modern corporate operations require a great deal of time and effort to deal with complex organizational structures and diverse business plans. Particular challenges include the efficient sharing of information and discussions between different departments, as well as the continuous simulation and evaluation of multiple business plans. Furthermore, the process of selecting the optimal operational plan requires advanced data analysis, which is time-consuming. For this reason, there is a demand for effective systems to streamline corporate operations and enable semi-automated operations.

[1079] 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.

[1080] In this invention, the server includes means for generating a plurality of information processing devices and assigning different functions to each of them, means for inputting an operation plan, means for an arbitrary information processing device to initialize tasks based on the operation plan and to share information and hold discussions with other information processing devices, means for simulating a plurality of operation plans and collecting and comparing the results, and means for selecting an optimal operation plan and displaying the results. This enables efficient information sharing and discussion between different departments, effective simulation and evaluation of a plurality of business plans, and rapid selection of an optimal operation plan.

[1081] An "information processing device" is a device that has the function of receiving data and instructions and processing them to perform a specific task.

[1082] An "operational plan" is a plan that includes specific policies, strategies, resource allocations, and timelines for achieving organizational or business goals.

[1083] A "server" is a central processing unit that provides data and services to other devices on a network.

[1084] A "task" is a process or action that performs a specific task to achieve a goal.

[1085] "Simulation" is a method for virtually recreating real-world systems and situations and predicting their behavior and outcomes.

[1086] "Message sending and receiving function" refers to the function of transferring information or data from one device to another.

[1087] "Intermediate results" refer to results or data obtained along the way in the process of obtaining the final result.

[1088] A "report" is a document that organizes information and data on a specific topic.

[1089] The "optimal management plan" is the plan that, among multiple management plans, achieves the goal most effectively and efficiently.

[1090] A "comparison means" is a method or device for evaluating multiple data or results against each other and determining their relative merits and differences.

[1091] The present invention provides a system for virtually reproducing a company's management plan using a plurality of information processing devices, thereby improving the efficiency of management and achieving semi-automated operation. An embodiment of the present invention will be described in detail below.

[1092] Hardware and software used

[1093] server

[1094] Use a high-performance server. Specifically, a server with high computing power is recommended. For example, a server with multiple CPU cores and a GPU (e.g., NVIDIA) is preferable.

[1095] The server is used to create a plurality of information processing devices.

[1096] The software used includes Python, natural language processing libraries (e.g., Transformers), database management systems (e.g., MySQL), and messaging servers (e.g., RabbitMQ).

[1097] Terminal

[1098] The terminal is used by users to input operational plans, specifically using a web browser or dedicated application.

[1099] User

[1100] The user inputs various management plans into the server via a terminal, and the optimal management plan is selected and implemented.

[1101] Explanation of program processing

[1102] server

[1103] 1. The server generates multiple information processing devices and assigns each device to a different function. For example, it generates information processing devices to handle the functions of the management, marketing, human resources, and product development departments.

[1104] 2. The server analyzes the input operation plan and allocates it appropriately to each information processing device. As a result, each device starts to perform the tasks related to its own area of ​​responsibility.

[1105] Information processing device

[1106] 3. Each information processing device starts the initial task assigned to it. For example, the device in the marketing department conducts market research, and the device in the product development department conducts technical research.

[1107] 4. Each information processing device shares information as needed, exchanges messages with other devices, and holds discussions, thereby realizing effective communication between departments.

[1108] simulation

[1109] 5. The server runs simulations of multiple operational plans based on the data obtained from each information processing device, collecting data such as sales forecasts, market share, and resource utilization rates.

[1110] 6. Each information processing device reports intermediate results to the server and proceeds with the simulation while modifying tasks as necessary.

[1111] Selection of the optimal operation plan

[1112] 7. The server integrates all the simulation results and selects the optimal operation plan. For example, it determines that a new smartphone with a superior camera performance has a high probability of success in the market.

[1113] 8. Generate a report detailing the selected optimal operation plan and notify the user.

[1114] Examples and prompts

[1115] As an example, consider the case of launching a new smartphone product onto the market.

[1116] Initial Setup

[1117] The server generates information processing devices for the management department, marketing department, product development department, and human resources department, and assigns each department its respective roles.

[1118] Input of operation plan

[1119] The user inputs "Development of a new smartphone with an emphasis on camera performance" as operational plan A, and "Development of a smartphone with a long battery life" as operational plan B.

[1120] Task execution for each department

[1121] The server analyzes each operational plan, the management department starts market analysis, the product development department starts technology research, and the marketing department starts target market research.

[1122] Task discussion and coordination

[1123] The information processing devices in each department exchange messages based on collected data and suggestions, and adjust advertising strategies and product details.

[1124] Running the simulation

[1125] The server simulates each operational plan in a virtual market environment and collects data such as sales forecasts and market share.

[1126] Selection of the optimal operation plan

[1127] The server integrates and analyzes the simulation results and determines that the new smartphone that emphasizes camera performance (Operation Plan A) has a higher probability of success in the market.

[1128] Users receive a report of the optimal operational plan and apply it to their actual business.

[1129] Prompt Sentence Examples

[1130] "Simulate which is more likely to be successful in the market: a new smartphone with a better camera or a smartphone with longer battery life."

[1131] The above is a specific embodiment for carrying out the present invention. The present invention automates the complex operational processes of a company, enabling efficient and rapid decision-making.

[1132] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1133] Step 1:

[1134] Initial Setup

[1135] The server generates a plurality of information processing devices, each responsible for a different function, for example, the management department, marketing department, human resources department, and product development department.

[1136] Input: Initial server data

[1137] Output: Generate information processing equipment corresponding to each department

[1138] The server executes a Python script to generate information processing devices and set their respective functions.

[1139] Step 2:

[1140] Input of operation plan

[1141] The user uses a terminal to input an operational plan, which includes specific goals, strategies, resource allocation, and a timeline.

[1142] Input: Operational plan data entered by the user

[1143] Output: Operational plan data sent to the server

[1144] The terminal uses a web browser or dedicated application to send data entered by the user to the server.

[1145] Step 3:

[1146] Analysis of operational plans

[1147] The server analyzes the received operational plan and distributes it appropriately to each information processing device. For example, market analysis is distributed to the information processing device of the management department, and technology research is distributed to the information processing device of the product development department.

[1148] Input: Operational plan data

[1149] Output: Task data allocated to each information processing device

[1150] The server uses Python scripts and natural language processing libraries (e.g., Transformers) to analyze the data and dispatch tasks.

[1151] Step 4:

[1152] Executing a task

[1153] Each information processing device starts the assigned initial task. For example, the information processing device in the marketing department performs market research, and the information processing device in the product development department performs technical research.

[1154] Input: Task data distributed from the server

[1155] Output: Intermediate results of task execution

[1156] The information processing devices collect and analyze the necessary data in their respective areas of responsibility and generate intermediate results.

[1157] Step 5:

[1158] Message sending and discussion

[1159] Each information processing device shares information with other information processing devices as needed, and exchanges messages while holding discussions. For example, a device in the marketing department proposes product specifications suited to the target market to a device in the product development department.

[1160] Input: Message data from other information processing devices

[1161] Output: The result of the discussion

[1162] The information processing devices use a messaging server (for example, RabbitMQ) to send and receive messages in real time and share information.

[1163] Step 6:

[1164] Running the simulation

[1165] The server runs simulations of multiple operational plans based on data obtained from each information processing device, and collects data such as sales forecasts, market share, and resource utilization rates through the simulations.

[1166] Input: Data obtained from each information processing device

[1167] Output: Simulation result data

[1168] The server executes the simulation algorithm and stores the simulation results in a database.

[1169] Step 7:

[1170] Reporting interim results and correcting tasks

[1171] Each information processing device reports intermediate results to the server and proceeds with the simulation while modifying the task as necessary.

[1172] Input: Intermediate results data during simulation

[1173] Output: Instruction data for task correction

[1174] The information processing device transmits intermediate results to the server and modifies the task accordingly based on feedback from the server.

[1175] Step 8:

[1176] Selection of the optimal operation plan

[1177] The server integrates all the simulation results and selects the optimal operation plan. For example, it determines that a new smartphone with a superior camera performance has a high probability of success in the market.

[1178] Input: Simulation result data

[1179] Output: Selected optimal operation plan

[1180] The server uses data analysis algorithms to evaluate the simulation results and select the most effective operational plan.

[1181] Step 9:

[1182] Notification of optimal operation plan

[1183] The server generates a report detailing the selected optimal operation plan and notifies the user.

[1184] Input: Optimal operation plan data

[1185] Output: Report sent to the user

[1186] The server stores the reports in cloud storage (e.g. AWS S3) and sends information to users via email or notification system.

[1187] (Application example 1)

[1188] Next, a description will be given of Application 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."

[1189] Virtual corporate operations require the ability to efficiently and quickly formulate business plans and select and execute optimal operational plans. However, existing systems make it difficult to effectively link multiple internal organizational functions, and analyzing simulation results takes time. Furthermore, there is a lack of means to check and adjust product placement and campaign strategies in real time within virtual stores. These challenges mean that efficient and optimized corporate operations are not being fully realized.

[1190] 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.

[1191] In this invention, the server includes means for generating multiple natural language generation devices and assigning each device to a different organizational function, means for inputting a business plan, means for any natural language generation device to initialize tasks based on the business plan and share information and discuss with other natural language generation devices, means for simulating multiple business plans and collecting and comparing the results, means for selecting an optimal business plan and displaying the results, and means for using a head-mounted display to check and adjust product placement and campaign strategies in a virtual store in real time, thereby enabling more efficient corporate management, faster decision-making, and real-time adjustment of management strategies in a virtual environment.

[1192] A "natural language generation device" is a system that uses natural language to execute set tasks and collect, analyze, discuss, and give instructions to each department within an organization.

[1193] A "head-mounted display" is a display device worn by the user on the head, which displays virtual reality or augmented reality environments and provides the user with a three-dimensional visual experience.

[1194] A "virtual store" is a virtual store that recreates a real store in a virtual space, allowing users to access the store from a remote location and check and adjust product placement, campaign strategies, etc.

[1195] A business plan is an operational plan that includes specific goals and strategies, resource allocation, timelines, etc. set by a company.

[1196] A "simulation" is a virtual experiment that assumes the execution of a business plan in a virtual market environment and analyzes the resulting business indicators, such as sales forecasts, market share, and resource utilization rates.

[1197] "Intermediate results" refer to the data and analysis results obtained during the course of each natural language generation device's execution of a given task.

[1198] The "message sending and receiving function" is a communication means used by each natural language generation device to share information with other devices and to give instructions and hold discussions.

[1199] "Displaying a business plan" refers to a method or system that visually presents an optimal business plan to a user.

[1200] System Overview

[1201] This invention is a system that virtually reproduces a company's organizational structure and business operations using multiple natural language generation devices, thereby improving the efficiency of corporate operations and achieving semi-automated operations. Specifically, it includes functions such as inputting business plans, automatically generating tasks, discussions and instructions between natural language generation devices, simulations, selection of optimal business plans, and real-time confirmation and adjustment of product placement and campaign strategies in virtual stores.

[1202] Hardware and Software Configuration

[1203] Hardware

[1204] 1. Server: Use a cloud server equipped with a high-performance CPU and GPU.

[1205] 2. Head-mounted display (HMD): Used by users to visually navigate the virtual store.

[1206] software

[1207] 1. Natural language generation model: Use a generative AI model such as GPT-3.

[1208] 2. Simulation environment: Use a simulation tool such as SimPy.

[1209] 3. Data analysis tools: Utilize data analysis tools such as Pandas.

[1210] Detailed Description of the Embodiments

[1211] The server generates multiple natural language generation devices, each responsible for a different organizational function. Each natural language generation device has the functions of management, marketing, inventory management, and customer service, and performs initial tasks based on the business plan entered by the user, sharing information and holding discussions with other devices. The business plan, which includes specific goals, strategies, resource allocation, timeline, etc., is entered by the user into the server.

[1212] Each natural language generator initializes tasks based on prompts, sends and receives messages as needed, and exchanges information.Furthermore, the server simulates multiple business plans in a virtual market environment, collects and compares the results, and selects the optimal business plan.

[1213] The final business plan is displayed as a report to the user, who can then use a head-mounted display to view and adjust product placement and campaign strategies in real time within a virtual store, thereby improving business operations efficiency and enabling faster decision-making.

[1214] Specific examples

[1215] For example, if a user wants to set up a special corner for a new product, they put on a head-mounted display and input a prompt such as, "I want to create a special corner for a new product. Please suggest the optimal product placement and marketing strategy." Based on this input, the natural language generator will hold a discussion, and the server will run a simulation to suggest the optimal placement and strategy. The user can check the suggestions in real time through the HMD and make adjustments as necessary.

[1216] This enables faster decision-making and more efficient business operations, and facilitates real-time adjustments to operational strategies in a virtual environment, thereby improving corporate productivity and competitiveness.

[1217] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1218] Step 1:

[1219] The server generates multiple natural language generators, each responsible for a different internal organizational function (management, marketing, inventory management, customer service). The server receives configuration information for each function as input, and generates the configured natural language generator as output. Specifically, the server initializes each generation AI model using a pre-configured API and assigns each an appropriate task.

[1220] Step 2:

[1221] The user inputs a business plan into the server. As input, the user enters a business plan including specific goals, strategies, resource allocation, timeline, etc., and as output, the server stores the plan in a database. Specifically, the user enters the business plan using a dedicated input form, and the server analyzes the data and distributes it to the natural language generation device.

[1222] Step 3:

[1223] Any natural language generation device initiates a task based on a business plan, shares information with other natural language generation devices, and engages in discussions. Task instructions are received from a server as input, and the results of the discussion are generated as output. Specifically, the natural language generation device executes the received task and sends the results as a message to other devices.

[1224] Step 4:

[1225] It simulates multiple business plans, collects and compares the results. It receives simulation data reported by each natural language generator as input, and selects the optimal business plan as output. Specifically, the server uses a simulation tool such as SimPy to recreate a virtual market environment and calculates the business indicators for each business plan.

[1226] Step 5:

[1227] The optimal business plan is selected and the results are displayed. The input is data that integrates and analyzes the simulation results, and the output is a report that is visually displayed to the user. Specifically, the server integrates the results using data analysis tools such as Pandas and presents the optimal business plan to the user.

[1228] Step 6:

[1229] A head-mounted display is used to check and adjust product placement and campaign strategies in a virtual store in real time. The input is optimal business plan data, and the output is real-time updates to the settings in the virtual store. Specifically, the user puts on the HMD and visually checks the virtual store, adjusting placement and campaigns as necessary.

[1230] Furthermore, 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.

[1231] System Overview

[1232] This invention is a system that virtually recreates a company's organizational structure and business operations by using multiple natural language generation devices with an emotion engine, achieving efficient corporate operations and semi-automated operations. This system includes functions such as input of business plans, automatic task generation, discussion and instruction between natural language generation devices, simulation, and selection of the optimal business plan. The emotion engine also analyzes user emotions and can make adjustments based on the results.

[1233] Program processing

[1234] 1. Initial Setup

[1235] server

[1236] The server creates multiple natural language generators, assigning each to a different organizational function (e.g., management, marketing, human resources, product development, etc.), and embeds an emotion engine into each natural language generator.

[1237] 2. Enter your business plan

[1238] User

[1239] Users input business plans into the server, which include goals, strategies, resource allocation, and timelines. For example, Business Plan A might include "developing a smartphone equipped with new camera technology," while Business Plan B might include "developing a smartphone with long battery life."

[1240] server

[1241] The server analyzes the input business plan and assigns an appropriate initial task to each natural language generator.

[1242] 3. Task execution and discussion for each natural language generator

[1243] server

[1244] Based on the analysis results, the server assigns initial tasks to each natural language generator. For example, the natural language generator in the management department is assigned to conduct market analysis, and the natural language generator in the product development department is assigned to research product specifications.

[1245] natural language generator

[1246] Each NLG performs an assigned task: for example, a NLG in the product development department researches new technologies, and a NLG in the marketing department conducts market research.

[1247] Messages are exchanged between natural language generators as needed, and discussions are held. During this process, the emotion engine analyzes the user's emotions and provides feedback to the generators.

[1248] 4. Simulation and Comparison

[1249] server

[1250] The server runs a simulation based on each business plan and records business indicators (sales forecast, market share, resource utilization, etc.) collected during the simulation.

[1251] natural language generator

[1252] Each natural language generator reports data during the simulation to the server and modifies the task as necessary.

[1253] 5. Selection and implementation of optimal business plans

[1254] server

[1255] The server integrates all the simulation results, performs analysis, and selects the optimal business plan.

[1256] The results are generated as a report and notified to the user.

[1257] User

[1258] Users can apply the optimal business plan report to their actual business operations.

[1259] Specific examples

[1260] As an example, consider the case of launching a new smartphone product onto the market.

[1261] Initial Setup

[1262] The server generates natural language generators for the management, marketing, product development, and human resources departments, and assigns each department its own role. Each generator has a built-in emotion engine.

[1263] Enter your business plan

[1264] The user inputs "Development of a new smartphone with a high-performance camera" as business plan A and "Development of a smartphone with a long battery life" as business plan B.

[1265] Task execution and discussion

[1266] The server analyzes the business plan and assigns initial tasks to each department. For example, the natural language generator in the product development department is responsible for researching new technologies, while the natural language generator in the marketing department is responsible for market research.

[1267] The generators in each department send and receive messages as needed to advance the discussion, during which the emotion engine analyzes the user's emotions and provides appropriate feedback.

[1268] Running the simulation

[1269] The server performs a simulation of the business plan and observes changes in sales forecasts and market share based on the data generated by each generation device.

[1270] Selection of the optimal business plan

[1271] By integrating and analyzing the simulation results, it is determined that, for example, a new smartphone equipped with a high-performance camera (Business Plan A) is more likely to be successful in the market.

[1272] Send reports to users and reflect them in actual business operations.

[1273] This will enable natural language generation devices using emotion engines to automate complex business processes and promote efficient and rapid decision-making.

[1274] The processing flow will be explained below.

[1275] Step 1:

[1276] server

[1277] The server creates multiple natural language generation devices and configures them to handle different organizational functions, such as the management department, marketing department, product development department, and human resources department.

[1278] Each natural language generator incorporates an emotion engine to enable analysis of the user's emotions.

[1279] Step 2:

[1280] server

[1281] The server provides the user with a business plan input form, which includes input fields for goals, strategies, resource allocation, timeline, etc.

[1282] Step 3:

[1283] User

[1284] The user enters their business plan into an input form and sends it to the server. For example, Business Plan A might be "Development of a new smartphone equipped with a high-performance camera," and Business Plan B might be "Development of a smartphone with a long battery life."

[1285] Step 4:

[1286] server

[1287] The server receives the business plan submitted by the user, analyzes the plan, and assigns an appropriate initial task to each natural language generator.

[1288] Step 5:

[1289] server

[1290] Based on the analysis results, the server assigns initial tasks to each natural language generator. For example, the natural language generator in the management department is assigned to conduct market analysis, and the natural language generator in the product development department is assigned to research product specifications.

[1291] Step 6:

[1292] natural language generator

[1293] Each NLG performs an assigned initial task: for example, a NLG in the product development department researches new technologies, and a NLG in the marketing department conducts market research.

[1294] The emotion engine analyzes the user's emotions and feeds back the analysis results to each natural language generation device.

[1295] Step 7:

[1296] natural language generator

[1297] Messages are exchanged between natural language generators, and discussions take place. For example, a marketing natural language generator proposes an idea for an advertising campaign and asks the management natural language generator for approval. During this process, an emotion engine provides emotional feedback to the communication between the generators.

[1298] Step 8:

[1299] natural language generator

[1300] Each natural language generator reports intermediate results to the server, such as research results and market reaction to a new camera technology.

[1301] Step 9:

[1302] server

[1303] The server aggregates the intermediate results and issues task modifications or new instructions to each natural language generator as needed. For example, if a change in marketing strategy is needed, new instructions are issued to the natural language generator in the marketing department.

[1304] Step 10:

[1305] server

[1306] The server runs a simulation based on each business plan, and during the simulation, business indicators such as sales forecasts, market share, and resource utilization are collected.

[1307] Step 11:

[1308] natural language generator

[1309] Each natural language generator collects data generated during the simulation and reports it to the server.

[1310] Step 12:

[1311] server

[1312] The server consolidates all simulation results and compares and analyzes the performance of each business plan based on the collected data.

[1313] Step 13:

[1314] server

[1315] The server selects the optimal business plan, generates a report of the results, and notifies the user.

[1316] Step 14:

[1317] User

[1318] Based on the reports sent from the server, the user selects the most suitable business plan and applies it to actual business operations, thereby enabling efficient and rapid business operations.

[1319] In this way, the present invention uses a natural language generation device incorporating an emotion engine to virtually reproduce a company's organizational structure and business operations, thereby realizing efficient company operations and semi-automated operations.

[1320] Example 2

[1321] Next, a description will be given of 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."

[1322] Modern business operations require efficient and rapid decision-making. However, traditional systems lack the ability to share information and exchange opinions between departments, and provide feedback that takes user sentiment into account, making it difficult to optimize management efficiency and plans overall. Furthermore, the process of simultaneously evaluating multiple business plans and selecting the optimal one takes time and effort. These challenges currently hinder optimization and efficiency in corporate operations.

[1323] 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.

[1324] In this invention, the server includes means for generating multiple natural language generation devices and assigning each device to a different organizational function, means for inputting a business plan, means for any natural language generation device to initialize tasks based on the business plan and share information and hold discussions with other natural language generation devices, means for simulating multiple business plans and collecting and comparing the results, means for selecting an optimal business plan and displaying the results, and means for analyzing user emotions using an emotion analysis engine and providing the results as feedback to the natural language generation devices. This enables smooth information sharing and discussions between departments, provides timely feedback that takes user emotions into consideration, and enables optimization of business plans and rapid decision-making.

[1325] A "natural language generation device" is a device that uses natural language processing technology to generate human language and automatically perform text-based tasks.

[1326] "Internal functions" refer to roles that correspond to different departments or tasks within a company or organization, such as management, marketing, product development, and human resources.

[1327] A business plan is a document or data that systematically outlines the goals, strategies, resource allocation, timelines, etc. set by a company or organization.

[1328] "Task initialization" is the process of completing the settings and preparations required to start a specific task, and specifically includes inputting data, setting initial parameters, allocating resources, etc.

[1329] "Information sharing" is the process of exchanging data and information between multiple systems and devices, making them mutually available.

[1330] A "discussion" is a process in which messages and information are exchanged between multiple natural language generation devices, and opinions are exchanged to arrive at the optimal conclusion.

[1331] "Simulation" is the process of using virtual environments and computational models to predict the outcome of a particular business plan and test various scenarios.

[1332] An "emotion analysis engine" is an algorithm or model developed to analyze a user's emotions and has the ability to determine their emotional state from text data and other inputs.

[1333] "Feedback" refers to information or advice provided to other systems or users based on data or analysis results acquired by a system or device.

[1334] "Optimization" is the process of adjusting parameters and settings to obtain the most efficient and effective results under given conditions and constraints.

[1335] The present invention is a system that virtually reproduces the organizational structure and business operations of a company, and realizes efficient company operations and semi-automated operations. Specific embodiments are described below.

[1336] System Overview

[1337] The present invention is a system that includes a server, multiple natural language generation devices, a user interface, and a sentiment analysis engine. The server uses a programming language such as Python to launch multiple natural language generation devices (e.g., generative AI models), each responsible for a different organizational function such as management, marketing, human resources, or product development. A sentiment analysis engine is also integrated into each generation device.

[1338] Hardware and Software

[1339] The server utilizes high performance computer hardware and open source or commercial software to perform the following tasks:

[1340] Natural Language Generator: This refers to an instance of a generative AI model (e.g., GPT-3) that is assigned to each department.

[1341] Sentiment analysis engine: A machine learning model for analyzing user sentiment (e.g., a sentiment analysis library with an LSTM model).

[1342] Database: A database system (e.g., MariaDB, PostgreSQL) for storing and managing business plans and simulation results.

[1343] Simulation software: Simulation tools to evaluate multiple business plans (e.g., AnyLogic, Vensim).

[1344] Implementation Procedure

[1345] 1. Initial Setup

[1346] The server creates natural language generators for each department, including management, marketing, product development, and human resources, and assigns each department its own role. Each generator is integrated with a sentiment analysis engine.

[1347] 2. Enter your business plan

[1348] Users use a web browser to input business plans into the server, which include goals, strategies, resource allocation, timelines, etc., and the input data is saved in a database in real time.

[1349] 3. Task assignment and discussion

[1350] The server analyzes the input business plan and assigns appropriate initial tasks to each generator. The generators perform tasks such as new technology research and market surveys, and use an emotion analysis engine to analyze and provide feedback on user emotions. Messages are sent and received between generators to share information and hold discussions.

[1351] 4. Running the Simulation

[1352] The server uses a simulation tool to evaluate the business plan, and records business indicators collected during the simulation, such as sales forecasts, market share, and resource utilization, in a database.

[1353] 5. Selection of the optimal business plan

[1354] The simulation results are integrated and analyzed to select the optimal business plan. The results are generated as a report and notified to the user. The user can then apply this report to their actual business operations.

[1355] Specific examples

[1356] As an example, let us consider the case of launching a new smartphone product into the market.

[1357] 1. Initial Setup

[1358] The server uses Python to generate open-source generative AI models as natural language generators for the management, marketing, product development, and human resources departments, each of which is integrated with a sentiment analysis engine.

[1359] 2. Enter your business plan

[1360] The user enters into a web form business plan A "Development of a new smartphone equipped with a high-performance camera" and business plan B "Development of a smartphone with a long battery life." This data is stored in a database.

[1361] 3. Task assignment and discussion

[1362] The server analyzes business plans and assigns market analysis and technology research tasks to generators. The generators research social media data and technical papers and hold discussions using the Slack API. The sentiment analysis engine analyzes user sentiment in real time and provides feedback to the generators.

[1363] 4. Running the Simulation

[1364] The server uses Vensim to run simulations and observe and record sales forecasts and market share fluctuations.

[1365] 5. Selection of the optimal business plan

[1366] The simulation results are integrated and analyzed, and the company determines that the "New Smartphone with a High-Performance Camera (Business Plan A)" has a high probability of success, and sends a report to the user, who then uses this report to implement an actual business strategy.

[1367] Prompt Sentence Examples

[1368] "Based on the simulation results, which is more likely to be successful in the market: developing a smartphone with new camera technology or developing a smartphone with longer battery life?"

[1369] As described above, the present invention provides a system that supports efficiency and rapid decision-making in business operations.

[1370] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1371] Step 1:

[1372] The server creates multiple natural language generators. These generators are responsible for different internal organizational functions, such as management, marketing, human resources, and product development. The server uses Python to launch instances of the generative AI model. It also integrates a sentiment analysis engine so that each generator can analyze user sentiment. The input for this process is initial configuration information corresponding to each role, and multiple generators with different functions are prepared as the output.

[1373] Step 2:

[1374] A user inputs a business plan into the server via a web browser. The input business plan includes detailed information such as goals, strategies, resource allocation, and timeline. The server stores this data in a database in real time. The input of this process is the detailed business plan information, and the output is the business plan data stored in the database.

[1375] Step 3:

[1376] The server analyzes the input business plan and assigns appropriate initial tasks to each natural language generator. Natural language processing algorithms (such as SpaCy or NLTK) are used for the analysis. For example, tasks such as market analysis are assigned to the management department and technology research is assigned to the product development department. The input for this process is the business plan data, and the output is task information assigned to each generator.

[1377] Step 4:

[1378] Each natural language generator performs an assigned task. For example, a generator from the product development department researches technical papers, while a generator from the marketing department analyzes social media and research reports. Messages are sent and received between generators to share information and discuss. A sentiment analysis engine analyzes user sentiment in real time and provides feedback to the generator. The input to this process is task information, and the output is the results of the task execution and the content of the discussion.

[1379] Step 5:

[1380] The server runs a simulation based on the business plan. It uses a simulation tool (e.g., Vensim or AnyLogic) to observe and record business indicators such as sales forecasts, market share, and resource utilization. The input to this process is the execution result data of the generator, and the output is the simulation result data.

[1381] Step 6:

[1382] Each natural language generator reports the data during the simulation to the server and modifies the task as needed. For example, it may focus on researching new technologies based on market research results. The input to this process is the simulation result data, and the output is modified task information.

[1383] Step 7:

[1384] The server integrates all simulation results and selects the optimal business plan through analysis. It evaluates and selects the optimal plan using data analysis tools (e.g., Pandas, Scikit-learn). The results are generated as a report and notified to the user. The user can then apply this report to their actual business operations. The input to this process is the integrated simulation result data, and the output is a report of the optimal business plan.

[1385] (Application example 2)

[1386] Next, a description will be given of Application 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."

[1387] Conventional advertising display systems have the problem that they do not attract users' interest or attention because they display advertisements uniformly without considering users' emotions. Also, because the content and timing of advertisements do not adapt to users' emotions, the advertising effectiveness is often not as expected.

[1388] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for generating multiple natural language generation devices and assigning each device to a different internal function, means for inputting a business plan, means for any natural language generation device to initialize tasks based on the business plan and share information and discuss it with other natural language generation devices, means for simulating multiple business plans and collecting and comparing the results, means for selecting the optimal business plan and displaying the results, means for collecting and analyzing user emotional data and optimizing the content and display timing of advertisements based on the data, and means for providing feedback on the user's emotional response after the advertisement is displayed. This enables optimal advertisement display according to the user's emotional state, maximizing the effectiveness of the advertisement.

[1389] Term definition

[1390] A "natural language generation device" is a device that uses artificial intelligence technology to generate natural language that can be understood by humans.

[1391] An "internal function" is a function that corresponds to a specific department or role within a company or organization, such as operations related to management, marketing, human resources, product development, etc.

[1392] A business plan is a detailed document detailing the goals and strategies that a company or organization must achieve, the allocation of resources, and the timeline.

[1393] "Tasks" refer to the specific tasks and issues required to execute a business plan.

[1394] "Sharing information and holding discussions" refers to multiple natural language generation devices exchanging data and knowledge with each other and then examining the issue from various perspectives based on that information.

[1395] "Simulation" is a method of executing a business plan in a virtual environment and analyzing the results, and is a means of evaluating the possibility of success and risks in advance.

[1396] "Emotional data" refers to data that indicates the user's current emotional state, and refers to information obtained from facial expressions, tone of voice, etc.

[1397] "Advertising optimization" is a technology that maximizes advertising effectiveness by adjusting the content and timing of advertisements based on user emotional data.

[1398] "Feedback" is the process of collecting user reactions and using them as information to take more appropriate next actions.

[1399] System Overview

[1400] This invention is a system that virtually reproduces a company's organizational structure and business operations by using multiple natural language generation devices with built-in emotion engines, thereby achieving efficient business operations and optimizing advertising display. This system includes functions such as input of business plans, automatic task generation, discussion and instruction between natural language generation devices, simulation, selection of optimal business plans, and advertising optimization through emotion analysis. The emotion engine can also analyze user emotions and display appropriate advertisements based on the results.

[1401] Hardware and Software Configuration

[1402] Hardware: Smartphone (camera, microphone, display, processor)

[1403] Software: Sentiment analysis engine, natural language generation model (e.g. GPT-4), ad management system, server

[1404] Program processing

[1405] The server performs the following process:

[1406] 1. Collecting user emotion data

[1407] It uses the smartphone's camera and microphone to collect the user's facial expressions and tone of voice in real time, and this data is sent to an emotion analysis engine to analyze the user's current emotional state.

[1408] 2. Emotional Data Analysis

[1409] The emotion analysis engine analyzes the user's emotional data from collected facial expressions and tone of voice to identify emotions such as joy, sadness, surprise, and anger.

[1410] 3. Selecting the best ads

[1411] Based on the analysis results, the natural language generator selects the advertisement that best suits the user's current emotions. Appropriate advertising materials (text, images, videos) are retrieved from the advertising management system and displayed on the smartphone screen.

[1412] 4. Feedback of user emotional responses

[1413] After the ad is displayed, the user's reaction is collected again using a camera and microphone and fed back to the sentiment analysis engine. The server uses this feedback to improve the ad selection algorithm of the natural language generation device and optimize the ad to be displayed next time.

[1414] Specific examples

[1415] For example, if the sentiment analysis engine determines that the user is surprised, the natural language generator will display an advertisement for the latest technology product with a "surprise" theme. If the user shows interest in the advertisement, feedback is collected, increasing the likelihood that similar advertisements will be displayed in a similar emotional state in the future.

[1416] Prompt Sentence Examples

[1417] Below are some examples of prompts for generative AI models (e.g., GPT-4):

[1418] plaintext

[1419] Analyze the following user sentiment data and generate the optimal ad copy.

[1420] Emotion data: Surprise

[1421] Ad Category: Technology Products

[1422] Based on this prompt, the natural language generator generates an advertisement that matches the user's surprise and displays it on the smartphone, thereby realizing optimal advertisement display for each user.

[1423] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1424] Program processing steps

[1425] Explain the process flow in detail

[1426] Step 1:

[1427] The server collects facial expression data and tone of voice data using the camera and microphone on the user's smartphone. Specifically, the camera captures facial images and the microphone records voice. The input is real-time video and audio data, which is then sent to the emotion analysis engine.

[1428] Step 2:

[1429] The server's emotion analysis engine analyzes the video and audio data received from the smartphone. It analyzes facial expressions using a facial recognition algorithm and audio tones using an audio analysis algorithm. As a result of the analysis, emotional data such as joy, sadness, surprise, and anger are obtained. This is the output of the analysis.

[1430] Step 3:

[1431] The server inputs a prompt sentence into the generative AI model based on the emotion data obtained from the emotion analysis engine. Specifically, it generates the following prompt sentence based on the analysis results and sends it to the AI ​​model. The prompt sentence, which includes "emotion data" and "advertising category," is used as input. The prompt sentence has the following format:

[1432] plaintext

[1433] Analyze the following user sentiment data and generate the optimal ad copy.

[1434] Emotion data: Surprise

[1435] Ad Category: Technology Products

[1436] The generated prompt text becomes the input to the model, which then generates optimal advertising text based on it.

[1437] Step 4:

[1438] The server's generative AI model analyzes the prompt and generates ad copy that best suits the user's emotional state. The model's output is a text ad copy, which is then sent to the ad management system.

[1439] Step 5:

[1440] The server's advertising management system combines the generated advertising copy with appropriate advertising materials (images and videos). These materials are retrieved from a database and an advertising package is created accordingly. This advertising package is the output sent to the smartphone.

[1441] Step 6:

[1442] The smartphone displays the received advertising package on the display. At this time, it adjusts the timing of displaying the advertisement taking into account the user's current operating state and usage situation. Specifically, the advertisement is displayed while the user is operating the app or on the notification screen.

[1443] Step 7:

[1444] After the user watches the ad, the smartphone again uses the camera and microphone to collect the user's reaction. The input is real-time video and audio data from after the ad is viewed, which is then sent to the sentiment analysis engine.

[1445] Step 8:

[1446] The emotion analysis engine on the server analyzes the video and audio data again to obtain emotion data after viewing the advertisement. This is the output of the analysis.

[1447] Step 9:

[1448] The server collects emotional data after viewing an ad and feeds it back into the ad selection algorithm. The feedback data is used to optimize the display of the next ad and as learning data for ad display optimization.

[1449] This series of processes realizes optimal advertisement display based on the user's emotional state, maximizing advertising effectiveness.

[1450] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[1451] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with 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.

[1452] 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 headset type terminal 314.

[1453] [Fourth embodiment]

[1454] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1455] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1456] 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 WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1457] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. 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 control target 443 are also connected to the bus 52.

[1458] 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 audio in accordance with instructions from the processor 46.

[1459] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1460] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1461] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1462] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, 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.

[1463] 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.

[1464] 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.

[1465] In the robot 414, the processor 46 performs the reception output process. 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.

[1466] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1467] System Overview

[1468] This invention is a system that virtually reproduces the organizational structure and business operations of a company using multiple natural language generation devices, thereby improving the efficiency of corporate operations and achieving semi-automated operation. This system includes functions such as input of business plans, automatic generation of tasks, discussion and instruction between natural language generation devices, simulation, and selection of the optimal business plan.

[1469] Program processing

[1470] 1. Initial Setup

[1471] server

[1472] The server creates multiple natural language generation devices, each configured to handle a different organizational function (e.g., management, marketing, human resources, product development, etc.).

[1473] 2. Enter your business plan

[1474] User

[1475] Users input business plans into the server, which include specific goals, strategies, resource allocation, and timelines. For example, business plan A may involve the development of a new product, while business plan B may involve strengthening marketing for an existing product.

[1476] server

[1477] The server analyzes the received business plans and distributes them appropriately to each natural language generation device.

[1478] 3. Task execution and discussion for each natural language generator

[1479] server

[1480] Based on the analysis results, the server assigns initial tasks to each natural language generator. For example, the natural language generator in the management department is assigned to conduct market analysis, and the natural language generator in the product development department is assigned to research product specifications.

[1481] natural language generator

[1482] Each natural language generator starts its assigned initial task. For example, a natural language generator in the product development department conducts research on new technologies, and a natural language generator in the marketing department conducts market research.

[1483] As needed, messages are sent between the natural language generators to exchange information and discuss with each other. For example, a marketing natural language generator might propose an advertising campaign idea and ask the management natural language generator for approval.

[1484] 4. Simulation and Comparison

[1485] server

[1486] The server runs simulations based on multiple business plans. During the simulation, it recreates the behavior of a virtual market environment based on data generated by each natural language generator, and collects business indicators (sales forecasts, market share, resource utilization, etc.).

[1487] natural language generator

[1488] Each natural language generation device reports intermediate results to the server and proceeds with the simulation while modifying the task as necessary.

[1489] 5. Selection and implementation of optimal business plans

[1490] server

[1491] The server integrates all simulation results, analyzes the collected data, and selects the optimal business plan.

[1492] The simulation results and details of the optimal business plan are generated as a report and notified to the user.

[1493] User

[1494] Based on the report, users can select the most appropriate business plan and apply it to their actual business operations, thereby semi-automating business operations and improving productivity.

[1495] Specific examples

[1496] As an example, consider the case of launching a new smartphone product onto the market.

[1497] Initial Setup

[1498] The server generates natural language generation devices for the management, marketing, product development, and human resources departments, and assigns each department a role.

[1499] Enter your business plan

[1500] The user inputs "Development of a new smartphone with an emphasis on camera performance" as business plan A and "Development of a smartphone with a long battery life" as business plan B.

[1501] Task execution for each department

[1502] The server analyzes each business plan, the management department begins market analysis, the product development department begins technical research, and the marketing department begins research into the target market.

[1503] Task discussion and coordination

[1504] Each department's natural language generators exchange messages based on collected data and suggestions, adjusting advertising strategies and product details.

[1505] Running the simulation

[1506] The server simulates each business plan in a virtual market environment and collects data such as sales forecasts and market share.

[1507] Selection of the optimal business plan

[1508] The server integrates and analyzes the simulation results and determines that the new smartphone that emphasizes camera performance (Business Plan A) has a higher probability of success in the market.

[1509] Users receive a report of the optimal business plan and apply it to their actual business, thereby improving the efficiency and speed of their business operations.

[1510] This is a specific embodiment for carrying out the present invention. The present invention automates the complex operational processes of a company, enabling efficient and rapid decision-making.

[1511] The processing flow will be explained below.

[1512] Step 1:

[1513] server

[1514] The server creates multiple natural language generation devices and configures them to handle different organizational functions (management, marketing, human resources, product development, etc.).

[1515] Step 2:

[1516] server

[1517] The server provides a business plan input form, which includes fields for goals, strategy, resource allocation, timeline, etc.

[1518] Step 3:

[1519] User

[1520] Users enter their business plans in a designated form. For example, Business Plan A is "Developing a smartphone equipped with new camera technology," and Business Plan B is "Developing a smartphone with long battery life."

[1521] Step 4:

[1522] server

[1523] The server receives and analyzes the business plan submitted by the user, and based on the results, sets an initial task for each natural language generator.

[1524] Step 5:

[1525] server

[1526] The server assigns initial tasks to each natural language generator based on the analysis results. For example, the natural language generator in the management department is assigned to conduct market analysis, and the natural language generator in the product development department is assigned to conduct product specification research.

[1527] Step 6:

[1528] natural language generator

[1529] Each natural language generator performs an assigned initial task. For example, a natural language generator in the product development department researches new technologies, and a natural language generator in the marketing department conducts market research.

[1530] Step 7:

[1531] natural language generator

[1532] Messages are exchanged between the natural language generators, and discussions take place. For example, a marketing natural language generator might propose an idea for an advertising campaign and ask the management natural language generator for approval.

[1533] Step 8:

[1534] natural language generator

[1535] Each natural language generator reports intermediate results to the server, such as research results and market reaction to a new camera technology.

[1536] Step 9:

[1537] server

[1538] The server aggregates the intermediate results and issues task modifications or new instructions to each natural language generator as needed. For example, if a change in marketing strategy is needed, the server issues new instructions to the natural language generator in the marketing department.

[1539] Step 10:

[1540] server

[1541] The server runs simulations based on multiple business plans, including business metrics such as sales forecasts, market share, and resource utilization.

[1542] Step 11:

[1543] natural language generator

[1544] Each natural language generator collects data generated during the simulation and reports it to the server.

[1545] Step 12:

[1546] server

[1547] The server consolidates all the simulation results and compares the performance of each business plan based on the collected data.

[1548] Step 13:

[1549] server

[1550] The server selects the optimal business plan, generates a report of the results, and notifies the user.

[1551] Step 14:

[1552] User

[1553] Based on the report from the server, the user selects the most suitable business plan and applies that plan to actual business operations.

[1554] Example 1

[1555] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1556] Modern corporate operations require a great deal of time and effort to deal with complex organizational structures and diverse business plans. Particular challenges include the efficient sharing of information and discussions between different departments, as well as the continuous simulation and evaluation of multiple business plans. Furthermore, the process of selecting the optimal operational plan requires advanced data analysis, which is time-consuming. For this reason, there is a demand for effective systems to streamline corporate operations and enable semi-automated operations.

[1557] 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.

[1558] In this invention, the server includes means for generating a plurality of information processing devices and assigning different functions to each of them, means for inputting an operation plan, means for an arbitrary information processing device to initialize tasks based on the operation plan and to share information and hold discussions with other information processing devices, means for simulating a plurality of operation plans and collecting and comparing the results, and means for selecting an optimal operation plan and displaying the results. This enables efficient information sharing and discussion between different departments, effective simulation and evaluation of a plurality of business plans, and rapid selection of an optimal operation plan.

[1559] An "information processing device" is a device that has the function of receiving data and instructions and processing them to perform a specific task.

[1560] An "operational plan" is a plan that includes specific policies, strategies, resource allocations, and timelines for achieving organizational or business goals.

[1561] A "server" is a central processing unit that provides data and services to other devices on a network.

[1562] A "task" is a process or action that performs a specific task to achieve a goal.

[1563] "Simulation" is a method for virtually recreating real-world systems and situations and predicting their behavior and outcomes.

[1564] "Message sending and receiving function" refers to the function of transferring information or data from one device to another.

[1565] "Intermediate results" refer to results or data obtained along the way in the process of obtaining the final result.

[1566] A "report" is a document that organizes information and data on a specific topic.

[1567] The "optimal management plan" is the plan that, among multiple management plans, achieves the goal most effectively and efficiently.

[1568] A "comparison means" is a method or device for evaluating multiple data or results against each other and determining their relative merits and differences.

[1569] The present invention provides a system for virtually reproducing a company's management plan using a plurality of information processing devices, thereby improving the efficiency of management and achieving semi-automated operation. An embodiment of the present invention will be described in detail below.

[1570] Hardware and software used

[1571] server

[1572] Use a high-performance server. Specifically, a server with high computing power is recommended. For example, a server with multiple CPU cores and a GPU (e.g., NVIDIA) is preferable.

[1573] The server is used to create a plurality of information processing devices.

[1574] The software used includes Python, natural language processing libraries (e.g., Transformers), database management systems (e.g., MySQL), and messaging servers (e.g., RabbitMQ).

[1575] Terminal

[1576] The terminal is used by users to input operational plans, specifically using a web browser or dedicated application.

[1577] User

[1578] The user inputs various management plans into the server via a terminal, and the optimal management plan is selected and implemented.

[1579] Explanation of program processing

[1580] server

[1581] 1. The server generates multiple information processing devices and assigns each device to a different function. For example, it generates information processing devices to handle the functions of the management, marketing, human resources, and product development departments.

[1582] 2. The server analyzes the input operation plan and allocates it appropriately to each information processing device. As a result, each device starts to perform the tasks related to its own area of ​​responsibility.

[1583] Information processing device

[1584] 3. Each information processing device starts the initial task assigned to it. For example, the device in the marketing department conducts market research, and the device in the product development department conducts technical research.

[1585] 4. Each information processing device shares information as needed, exchanges messages with other devices, and holds discussions, thereby realizing effective communication between departments.

[1586] simulation

[1587] 5. The server runs simulations of multiple operational plans based on the data obtained from each information processing device, collecting data such as sales forecasts, market share, and resource utilization rates.

[1588] 6. Each information processing device reports intermediate results to the server and proceeds with the simulation while modifying tasks as necessary.

[1589] Selection of the optimal operation plan

[1590] 7. The server integrates all the simulation results and selects the optimal operation plan. For example, it determines that a new smartphone with a superior camera performance has a high probability of success in the market.

[1591] 8. Generate a report detailing the selected optimal operation plan and notify the user.

[1592] Examples and prompts

[1593] As an example, consider the case of launching a new smartphone product onto the market.

[1594] Initial Setup

[1595] The server generates information processing devices for the management department, marketing department, product development department, and human resources department, and assigns each department its respective roles.

[1596] Input of operation plan

[1597] The user inputs "Development of a new smartphone with an emphasis on camera performance" as operational plan A, and "Development of a smartphone with a long battery life" as operational plan B.

[1598] Task execution for each department

[1599] The server analyzes each operational plan, the management department starts market analysis, the product development department starts technology research, and the marketing department starts target market research.

[1600] Task discussion and coordination

[1601] The information processing devices in each department exchange messages based on collected data and suggestions, and adjust advertising strategies and product details.

[1602] Running the simulation

[1603] The server simulates each operational plan in a virtual market environment and collects data such as sales forecasts and market share.

[1604] Selection of the optimal operation plan

[1605] The server integrates and analyzes the simulation results and determines that the new smartphone that emphasizes camera performance (Operation Plan A) has a higher probability of success in the market.

[1606] Users receive a report of the optimal operational plan and apply it to their actual business.

[1607] Prompt Sentence Examples

[1608] "Simulate which is more likely to be successful in the market: a new smartphone with a better camera or a smartphone with longer battery life."

[1609] The above is a specific embodiment for carrying out the present invention. The present invention automates the complex operational processes of a company, enabling efficient and rapid decision-making.

[1610] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1611] Step 1:

[1612] Initial Setup

[1613] The server generates a plurality of information processing devices, each responsible for a different function, for example, the management department, marketing department, human resources department, and product development department.

[1614] Input: Initial server data

[1615] Output: Generate information processing equipment corresponding to each department

[1616] The server executes a Python script to generate information processing devices and set their respective functions.

[1617] Step 2:

[1618] Input of operation plan

[1619] The user uses a terminal to input an operational plan, which includes specific goals, strategies, resource allocation, and a timeline.

[1620] Input: Operational plan data entered by the user

[1621] Output: Operational plan data sent to the server

[1622] The terminal uses a web browser or dedicated application to send data entered by the user to the server.

[1623] Step 3:

[1624] Analysis of operational plans

[1625] The server analyzes the received operational plan and distributes it appropriately to each information processing device. For example, market analysis is distributed to the information processing device of the management department, and technology research is distributed to the information processing device of the product development department.

[1626] Input: Operational plan data

[1627] Output: Task data allocated to each information processing device

[1628] The server uses Python scripts and natural language processing libraries (e.g., Transformers) to analyze the data and dispatch tasks.

[1629] Step 4:

[1630] Executing a task

[1631] Each information processing device starts the assigned initial task. For example, the information processing device in the marketing department performs market research, and the information processing device in the product development department performs technical research.

[1632] Input: Task data distributed from the server

[1633] Output: Intermediate results of task execution

[1634] The information processing devices collect and analyze the necessary data in their respective areas of responsibility and generate intermediate results.

[1635] Step 5:

[1636] Message sending and discussion

[1637] Each information processing device shares information with other information processing devices as needed, and exchanges messages while holding discussions. For example, a device in the marketing department proposes product specifications suited to the target market to a device in the product development department.

[1638] Input: Message data from other information processing devices

[1639] Output: The result of the discussion

[1640] The information processing devices use a messaging server (for example, RabbitMQ) to send and receive messages in real time and share information.

[1641] Step 6:

[1642] Running the simulation

[1643] The server runs simulations of multiple operational plans based on data obtained from each information processing device, and collects data such as sales forecasts, market share, and resource utilization rates through the simulations.

[1644] Input: Data obtained from each information processing device

[1645] Output: Simulation result data

[1646] The server executes the simulation algorithm and stores the simulation results in a database.

[1647] Step 7:

[1648] Reporting interim results and correcting tasks

[1649] Each information processing device reports intermediate results to the server and proceeds with the simulation while modifying the task as necessary.

[1650] Input: Intermediate results data during simulation

[1651] Output: Instruction data for task correction

[1652] The information processing device transmits intermediate results to the server and modifies the task accordingly based on feedback from the server.

[1653] Step 8:

[1654] Selection of the optimal operation plan

[1655] The server integrates all the simulation results and selects the optimal operation plan. For example, it determines that a new smartphone with a superior camera performance has a high probability of success in the market.

[1656] Input: Simulation result data

[1657] Output: Selected optimal operation plan

[1658] The server uses data analysis algorithms to evaluate the simulation results and select the most effective operational plan.

[1659] Step 9:

[1660] Notification of optimal operation plan

[1661] The server generates a report detailing the selected optimal operation plan and notifies the user.

[1662] Input: Optimal operation plan data

[1663] Output: Report sent to the user

[1664] The server stores the reports in cloud storage (e.g. AWS S3) and sends information to users via email or notification system.

[1665] (Application example 1)

[1666] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1667] Virtual corporate operations require the ability to efficiently and quickly formulate business plans and select and execute optimal operational plans. However, existing systems make it difficult to effectively link multiple internal organizational functions, and analyzing simulation results takes time. Furthermore, there is a lack of means to check and adjust product placement and campaign strategies in real time within virtual stores. These challenges mean that efficient and optimized corporate operations are not being fully realized.

[1668] 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.

[1669] In this invention, the server includes means for generating multiple natural language generation devices and assigning each device to a different organizational function, means for inputting a business plan, means for any natural language generation device to initialize tasks based on the business plan and share information and discuss with other natural language generation devices, means for simulating multiple business plans and collecting and comparing the results, means for selecting an optimal business plan and displaying the results, and means for using a head-mounted display to check and adjust product placement and campaign strategies in a virtual store in real time, thereby enabling more efficient corporate management, faster decision-making, and real-time adjustment of management strategies in a virtual environment.

[1670] A "natural language generation device" is a system that uses natural language to execute set tasks and collect, analyze, discuss, and give instructions to each department within an organization.

[1671] A "head-mounted display" is a display device worn by the user on the head, which displays virtual reality or augmented reality environments and provides the user with a three-dimensional visual experience.

[1672] A "virtual store" is a virtual store that recreates a real store in a virtual space, allowing users to access the store from a remote location and check and adjust product placement, campaign strategies, etc.

[1673] A business plan is an operational plan that includes specific goals and strategies, resource allocation, timelines, etc. set by a company.

[1674] A "simulation" is a virtual experiment that assumes the execution of a business plan in a virtual market environment and analyzes the resulting business indicators, such as sales forecasts, market share, and resource utilization rates.

[1675] "Intermediate results" refer to the data and analysis results obtained during the course of each natural language generation device's execution of a given task.

[1676] The "message sending and receiving function" is a communication means used by each natural language generation device to share information with other devices and to give instructions and hold discussions.

[1677] "Displaying a business plan" refers to a method or system that visually presents an optimal business plan to a user.

[1678] System Overview

[1679] This invention is a system that virtually reproduces a company's organizational structure and business operations using multiple natural language generation devices, thereby improving the efficiency of corporate operations and achieving semi-automated operations. Specifically, it includes functions such as inputting business plans, automatically generating tasks, discussions and instructions between natural language generation devices, simulations, selection of optimal business plans, and real-time confirmation and adjustment of product placement and campaign strategies in virtual stores.

[1680] Hardware and Software Configuration

[1681] Hardware

[1682] 1. Server: Use a cloud server equipped with a high-performance CPU and GPU.

[1683] 2. Head-mounted display (HMD): Used by users to visually navigate the virtual store.

[1684] software

[1685] 1. Natural language generation model: Use a generative AI model such as GPT-3.

[1686] 2. Simulation environment: Use a simulation tool such as SimPy.

[1687] 3. Data analysis tools: Utilize data analysis tools such as Pandas.

[1688] Detailed Description of the Embodiments

[1689] The server generates multiple natural language generation devices, each responsible for a different organizational function. Each natural language generation device has the functions of management, marketing, inventory management, and customer service, and performs initial tasks based on the business plan entered by the user, sharing information and holding discussions with other devices. The business plan, which includes specific goals, strategies, resource allocation, timeline, etc., is entered by the user into the server.

[1690] Each natural language generator initializes tasks based on prompts, sends and receives messages as needed, and exchanges information.Furthermore, the server simulates multiple business plans in a virtual market environment, collects and compares the results, and selects the optimal business plan.

[1691] The final business plan is displayed as a report to the user, who can then use a head-mounted display to view and adjust product placement and campaign strategies in real time within a virtual store, thereby improving business operations efficiency and enabling faster decision-making.

[1692] Specific examples

[1693] For example, if a user wants to set up a special corner for a new product, they put on a head-mounted display and input a prompt such as, "I want to create a special corner for a new product. Please suggest the optimal product placement and marketing strategy." Based on this input, the natural language generator will hold a discussion, and the server will run a simulation to suggest the optimal placement and strategy. The user can check the suggestions in real time through the HMD and make adjustments as necessary.

[1694] This enables faster decision-making and more efficient business operations, and facilitates real-time adjustments to operational strategies in a virtual environment, thereby improving corporate productivity and competitiveness.

[1695] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1696] Step 1:

[1697] The server generates multiple natural language generators, each responsible for a different internal organizational function (management, marketing, inventory management, customer service). The server receives configuration information for each function as input, and generates the configured natural language generator as output. Specifically, the server initializes each generation AI model using a pre-configured API and assigns each an appropriate task.

[1698] Step 2:

[1699] The user inputs a business plan into the server. As input, the user enters a business plan including specific goals, strategies, resource allocation, timeline, etc., and as output, the server stores the plan in a database. Specifically, the user enters the business plan using a dedicated input form, and the server analyzes the data and distributes it to the natural language generation device.

[1700] Step 3:

[1701] Any natural language generation device initiates a task based on a business plan, shares information with other natural language generation devices, and engages in discussions. Task instructions are received from a server as input, and the results of the discussion are generated as output. Specifically, the natural language generation device executes the received task and sends the results as a message to other devices.

[1702] Step 4:

[1703] It simulates multiple business plans, collects and compares the results. It receives simulation data reported by each natural language generator as input, and selects the optimal business plan as output. Specifically, the server uses a simulation tool such as SimPy to recreate a virtual market environment and calculates the business indicators for each business plan.

[1704] Step 5:

[1705] The optimal business plan is selected and the results are displayed. The input is data that integrates and analyzes the simulation results, and the output is a report that is visually displayed to the user. Specifically, the server integrates the results using data analysis tools such as Pandas and presents the optimal business plan to the user.

[1706] Step 6:

[1707] A head-mounted display is used to check and adjust product placement and campaign strategies in a virtual store in real time. The input is optimal business plan data, and the output is real-time updates to the settings in the virtual store. Specifically, the user puts on the HMD and visually checks the virtual store, adjusting placement and campaigns as necessary.

[1708] Furthermore, 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.

[1709] System Overview

[1710] This invention is a system that virtually recreates a company's organizational structure and business operations by using multiple natural language generation devices with an emotion engine, achieving efficient corporate operations and semi-automated operations. This system includes functions such as input of business plans, automatic task generation, discussion and instruction between natural language generation devices, simulation, and selection of the optimal business plan. The emotion engine also analyzes user emotions and can make adjustments based on the results.

[1711] Program processing

[1712] 1. Initial Setup

[1713] server

[1714] The server creates multiple natural language generators, assigning each to a different organizational function (e.g., management, marketing, human resources, product development, etc.), and embeds an emotion engine into each natural language generator.

[1715] 2. Enter your business plan

[1716] User

[1717] Users input business plans into the server, which include goals, strategies, resource allocation, and timelines. For example, Business Plan A might include "developing a smartphone equipped with new camera technology," while Business Plan B might include "developing a smartphone with long battery life."

[1718] server

[1719] The server analyzes the input business plan and assigns an appropriate initial task to each natural language generator.

[1720] 3. Task execution and discussion for each natural language generator

[1721] server

[1722] Based on the analysis results, the server assigns initial tasks to each natural language generator. For example, the natural language generator in the management department is assigned to conduct market analysis, and the natural language generator in the product development department is assigned to research product specifications.

[1723] natural language generator

[1724] Each NLG performs an assigned task: for example, a NLG in the product development department researches new technologies, and a NLG in the marketing department conducts market research.

[1725] Messages are exchanged between natural language generators as needed, and discussions are held. During this process, the emotion engine analyzes the user's emotions and provides feedback to the generators.

[1726] 4. Simulation and Comparison

[1727] server

[1728] The server runs a simulation based on each business plan and records business indicators (sales forecast, market share, resource utilization, etc.) collected during the simulation.

[1729] natural language generator

[1730] Each natural language generator reports data during the simulation to the server and modifies the task as necessary.

[1731] 5. Selection and implementation of optimal business plans

[1732] server

[1733] The server integrates all the simulation results, performs analysis, and selects the optimal business plan.

[1734] The results are generated as a report and notified to the user.

[1735] User

[1736] Users can apply the optimal business plan report to their actual business operations.

[1737] Specific examples

[1738] As an example, consider the case of launching a new smartphone product onto the market.

[1739] Initial Setup

[1740] The server generates natural language generators for the management, marketing, product development, and human resources departments, and assigns each department its own role. Each generator has a built-in emotion engine.

[1741] Enter your business plan

[1742] The user inputs "Development of a new smartphone with a high-performance camera" as business plan A and "Development of a smartphone with a long battery life" as business plan B.

[1743] Task execution and discussion

[1744] The server analyzes the business plan and assigns initial tasks to each department. For example, the natural language generator in the product development department is responsible for researching new technologies, while the natural language generator in the marketing department is responsible for market research.

[1745] The generators in each department send and receive messages as needed to advance the discussion, during which the emotion engine analyzes the user's emotions and provides appropriate feedback.

[1746] Running the simulation

[1747] The server performs a simulation of the business plan and observes changes in sales forecasts and market share based on the data generated by each generation device.

[1748] Selection of the optimal business plan

[1749] By integrating and analyzing the simulation results, it is determined that, for example, a new smartphone equipped with a high-performance camera (Business Plan A) is more likely to be successful in the market.

[1750] Send reports to users and reflect them in actual business operations.

[1751] This will enable natural language generation devices using emotion engines to automate complex business processes and promote efficient and rapid decision-making.

[1752] The processing flow will be explained below.

[1753] Step 1:

[1754] server

[1755] The server creates multiple natural language generation devices and configures them to handle different organizational functions, such as the management department, marketing department, product development department, and human resources department.

[1756] Each natural language generator incorporates an emotion engine to enable analysis of the user's emotions.

[1757] Step 2:

[1758] server

[1759] The server provides the user with a business plan input form, which includes input fields for goals, strategies, resource allocation, timeline, etc.

[1760] Step 3:

[1761] User

[1762] The user enters their business plan into an input form and sends it to the server. For example, Business Plan A might be "Development of a new smartphone equipped with a high-performance camera," and Business Plan B might be "Development of a smartphone with a long battery life."

[1763] Step 4:

[1764] server

[1765] The server receives the business plan submitted by the user, analyzes the plan, and assigns an appropriate initial task to each natural language generator.

[1766] Step 5:

[1767] server

[1768] Based on the analysis results, the server assigns initial tasks to each natural language generator. For example, the natural language generator in the management department is assigned to conduct market analysis, and the natural language generator in the product development department is assigned to research product specifications.

[1769] Step 6:

[1770] natural language generator

[1771] Each NLG performs an assigned initial task: for example, a NLG in the product development department researches new technologies, and a NLG in the marketing department conducts market research.

[1772] The emotion engine analyzes the user's emotions and feeds back the analysis results to each natural language generation device.

[1773] Step 7:

[1774] natural language generator

[1775] Messages are exchanged between natural language generators, and discussions take place. For example, a marketing natural language generator proposes an idea for an advertising campaign and asks the management natural language generator for approval. During this process, an emotion engine provides emotional feedback to the communication between the generators.

[1776] Step 8:

[1777] natural language generator

[1778] Each natural language generator reports intermediate results to the server, such as research results and market reaction to a new camera technology.

[1779] Step 9:

[1780] server

[1781] The server aggregates the intermediate results and issues task modifications or new instructions to each natural language generator as needed. For example, if a change in marketing strategy is needed, new instructions are issued to the natural language generator in the marketing department.

[1782] Step 10:

[1783] server

[1784] The server runs a simulation based on each business plan, and during the simulation, business indicators such as sales forecasts, market share, and resource utilization are collected.

[1785] Step 11:

[1786] natural language generator

[1787] Each natural language generator collects data generated during the simulation and reports it to the server.

[1788] Step 12:

[1789] server

[1790] The server consolidates all simulation results and compares and analyzes the performance of each business plan based on the collected data.

[1791] Step 13:

[1792] server

[1793] The server selects the optimal business plan, generates a report of the results, and notifies the user.

[1794] Step 14:

[1795] User

[1796] Based on the reports sent from the server, the user selects the most suitable business plan and applies it to actual business operations, thereby enabling efficient and rapid business operations.

[1797] In this way, the present invention uses a natural language generation device incorporating an emotion engine to virtually reproduce a company's organizational structure and business operations, thereby realizing efficient company operations and semi-automated operations.

[1798] Example 2

[1799] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1800] Modern business operations require efficient and rapid decision-making. However, traditional systems lack the ability to share information and exchange opinions between departments, and provide feedback that takes user sentiment into account, making it difficult to optimize management efficiency and plans overall. Furthermore, the process of simultaneously evaluating multiple business plans and selecting the optimal one takes time and effort. These challenges currently hinder optimization and efficiency in corporate operations.

[1801] 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.

[1802] In this invention, the server includes means for generating multiple natural language generation devices and assigning each device to a different organizational function, means for inputting a business plan, means for any natural language generation device to initialize tasks based on the business plan and share information and hold discussions with other natural language generation devices, means for simulating multiple business plans and collecting and comparing the results, means for selecting an optimal business plan and displaying the results, and means for analyzing user emotions using an emotion analysis engine and providing the results as feedback to the natural language generation devices. This enables smooth information sharing and discussions between departments, provides timely feedback that takes user emotions into consideration, and enables optimization of business plans and rapid decision-making.

[1803] A "natural language generation device" is a device that uses natural language processing technology to generate human language and automatically perform text-based tasks.

[1804] "Internal functions" refer to roles that correspond to different departments or tasks within a company or organization, such as management, marketing, product development, and human resources.

[1805] A business plan is a document or data that systematically outlines the goals, strategies, resource allocation, timelines, etc. set by a company or organization.

[1806] "Task initialization" is the process of completing the settings and preparations required to start a specific task, and specifically includes inputting data, setting initial parameters, allocating resources, etc.

[1807] "Information sharing" is the process of exchanging data and information between multiple systems and devices, making them mutually available.

[1808] A "discussion" is a process in which messages and information are exchanged between multiple natural language generation devices, and opinions are exchanged to arrive at the optimal conclusion.

[1809] "Simulation" is the process of using virtual environments and computational models to predict the outcome of a particular business plan and test various scenarios.

[1810] An "emotion analysis engine" is an algorithm or model developed to analyze a user's emotions and has the ability to determine their emotional state from text data and other inputs.

[1811] "Feedback" refers to information or advice provided to other systems or users based on data or analysis results acquired by a system or device.

[1812] "Optimization" is the process of adjusting parameters and settings to obtain the most efficient and effective results under given conditions and constraints.

[1813] The present invention is a system that virtually reproduces the organizational structure and business operations of a company, and realizes efficient company operations and semi-automated operations. Specific embodiments are described below.

[1814] System Overview

[1815] The present invention is a system that includes a server, multiple natural language generation devices, a user interface, and a sentiment analysis engine. The server uses a programming language such as Python to launch multiple natural language generation devices (e.g., generative AI models), each responsible for a different organizational function such as management, marketing, human resources, or product development. A sentiment analysis engine is also integrated into each generation device.

[1816] Hardware and Software

[1817] The server utilizes high performance computer hardware and open source or commercial software to perform the following tasks:

[1818] Natural Language Generator: This refers to an instance of a generative AI model (e.g., GPT-3) that is assigned to each department.

[1819] Sentiment analysis engine: A machine learning model for analyzing user sentiment (e.g., a sentiment analysis library with an LSTM model).

[1820] Database: A database system (e.g., MariaDB, PostgreSQL) for storing and managing business plans and simulation results.

[1821] Simulation software: Simulation tools to evaluate multiple business plans (e.g., AnyLogic, Vensim).

[1822] Implementation Procedure

[1823] 1. Initial Setup

[1824] The server creates natural language generators for each department, including management, marketing, product development, and human resources, and assigns each department its own role. Each generator is integrated with a sentiment analysis engine.

[1825] 2. Enter your business plan

[1826] Users use a web browser to input business plans into the server, which include goals, strategies, resource allocation, timelines, etc., and the input data is saved in a database in real time.

[1827] 3. Task assignment and discussion

[1828] The server analyzes the input business plan and assigns appropriate initial tasks to each generator. The generators perform tasks such as new technology research and market surveys, and use an emotion analysis engine to analyze and provide feedback on user emotions. Messages are sent and received between generators to share information and hold discussions.

[1829] 4. Running the Simulation

[1830] The server uses a simulation tool to evaluate the business plan, and records business indicators collected during the simulation, such as sales forecasts, market share, and resource utilization, in a database.

[1831] 5. Selection of the optimal business plan

[1832] The simulation results are integrated and analyzed to select the optimal business plan. The results are generated as a report and notified to the user. The user can then apply this report to their actual business operations.

[1833] Specific examples

[1834] As an example, let us consider the case of launching a new smartphone product into the market.

[1835] 1. Initial Setup

[1836] The server uses Python to generate open-source generative AI models as natural language generators for the management, marketing, product development, and human resources departments, each of which is integrated with a sentiment analysis engine.

[1837] 2. Enter your business plan

[1838] The user enters into a web form business plan A "Development of a new smartphone equipped with a high-performance camera" and business plan B "Development of a smartphone with a long battery life." This data is stored in a database.

[1839] 3. Task assignment and discussion

[1840] The server analyzes business plans and assigns market analysis and technology research tasks to generators. The generators research social media data and technical papers and hold discussions using the Slack API. The sentiment analysis engine analyzes user sentiment in real time and provides feedback to the generators.

[1841] 4. Running the Simulation

[1842] The server uses Vensim to run simulations and observe and record sales forecasts and market share fluctuations.

[1843] 5. Selection of the optimal business plan

[1844] The simulation results are integrated and analyzed, and the company determines that the "New Smartphone with a High-Performance Camera (Business Plan A)" has a high probability of success, and sends a report to the user, who then uses this report to implement an actual business strategy.

[1845] Prompt Sentence Examples

[1846] "Based on the simulation results, which is more likely to be successful in the market: developing a smartphone with new camera technology or developing a smartphone with longer battery life?"

[1847] As described above, the present invention provides a system that supports efficiency and rapid decision-making in business operations.

[1848] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1849] Step 1:

[1850] The server creates multiple natural language generators. These generators are responsible for different internal organizational functions, such as management, marketing, human resources, and product development. The server uses Python to launch instances of the generative AI model. It also integrates a sentiment analysis engine so that each generator can analyze user sentiment. The input for this process is initial configuration information corresponding to each role, and multiple generators with different functions are prepared as the output.

[1851] Step 2:

[1852] A user inputs a business plan into the server via a web browser. The input business plan includes detailed information such as goals, strategies, resource allocation, and timeline. The server stores this data in a database in real time. The input of this process is the detailed business plan information, and the output is the business plan data stored in the database.

[1853] Step 3:

[1854] The server analyzes the input business plan and assigns appropriate initial tasks to each natural language generator. Natural language processing algorithms (such as SpaCy or NLTK) are used for the analysis. For example, tasks such as market analysis are assigned to the management department and technology research is assigned to the product development department. The input for this process is the business plan data, and the output is task information assigned to each generator.

[1855] Step 4:

[1856] Each natural language generator performs an assigned task. For example, a generator from the product development department researches technical papers, while a generator from the marketing department analyzes social media and research reports. Messages are sent and received between generators to share information and discuss. A sentiment analysis engine analyzes user sentiment in real time and provides feedback to the generator. The input to this process is task information, and the output is the results of the task execution and the content of the discussion.

[1857] Step 5:

[1858] The server runs a simulation based on the business plan. It uses a simulation tool (e.g., Vensim or AnyLogic) to observe and record business indicators such as sales forecasts, market share, and resource utilization. The input to this process is the execution result data of the generator, and the output is the simulation result data.

[1859] Step 6:

[1860] Each natural language generator reports the data during the simulation to the server and modifies the task as needed. For example, it may focus on researching new technologies based on market research results. The input to this process is the simulation result data, and the output is modified task information.

[1861] Step 7:

[1862] The server integrates all simulation results and selects the optimal business plan through analysis. It evaluates and selects the optimal plan using data analysis tools (e.g., Pandas, Scikit-learn). The results are generated as a report and notified to the user. The user can then apply this report to their actual business operations. The input to this process is the integrated simulation result data, and the output is a report of the optimal business plan.

[1863] (Application example 2)

[1864] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1865] Conventional advertising display systems have the problem that they do not attract users' interest or attention because they display advertisements uniformly without considering users' emotions. Also, because the content and timing of advertisements do not adapt to users' emotions, the advertising effectiveness is often not as expected.

[1866] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for generating multiple natural language generation devices and assigning each device to a different internal function, means for inputting a business plan, means for any natural language generation device to initialize tasks based on the business plan and share information and discuss it with other natural language generation devices, means for simulating multiple business plans and collecting and comparing the results, means for selecting the optimal business plan and displaying the results, means for collecting and analyzing user emotional data and optimizing the content and display timing of advertisements based on the data, and means for providing feedback on the user's emotional response after the advertisement is displayed. This enables optimal advertisement display according to the user's emotional state, maximizing the effectiveness of the advertisement.

[1867] Term definition

[1868] A "natural language generation device" is a device that uses artificial intelligence technology to generate natural language that can be understood by humans.

[1869] An "internal function" is a function that corresponds to a specific department or role within a company or organization, such as operations related to management, marketing, human resources, product development, etc.

[1870] A business plan is a detailed document detailing the goals and strategies that a company or organization must achieve, the allocation of resources, and the timeline.

[1871] "Tasks" refer to the specific tasks and issues required to execute a business plan.

[1872] "Sharing information and holding discussions" refers to multiple natural language generation devices exchanging data and knowledge with each other and then examining the issue from various perspectives based on that information.

[1873] "Simulation" is a method of executing a business plan in a virtual environment and analyzing the results, and is a means of evaluating the possibility of success and risks in advance.

[1874] "Emotional data" refers to data that indicates the user's current emotional state, and refers to information obtained from facial expressions, tone of voice, etc.

[1875] "Advertising optimization" is a technology that maximizes advertising effectiveness by adjusting the content and timing of advertisements based on user emotional data.

[1876] "Feedback" is the process of collecting user reactions and using them as information to take more appropriate next actions.

[1877] System Overview

[1878] This invention is a system that virtually reproduces a company's organizational structure and business operations by using multiple natural language generation devices with built-in emotion engines, thereby achieving efficient business operations and optimizing advertising display. This system includes functions such as input of business plans, automatic task generation, discussion and instruction between natural language generation devices, simulation, selection of optimal business plans, and advertising optimization through emotion analysis. The emotion engine can also analyze user emotions and display appropriate advertisements based on the results.

[1879] Hardware and Software Configuration

[1880] Hardware: Smartphone (camera, microphone, display, processor)

[1881] Software: Sentiment analysis engine, natural language generation model (e.g. GPT-4), ad management system, server

[1882] Program processing

[1883] The server performs the following process:

[1884] 1. Collecting user emotion data

[1885] It uses the smartphone's camera and microphone to collect the user's facial expressions and tone of voice in real time, and this data is sent to an emotion analysis engine to analyze the user's current emotional state.

[1886] 2. Emotional Data Analysis

[1887] The emotion analysis engine analyzes the user's emotional data from collected facial expressions and tone of voice to identify emotions such as joy, sadness, surprise, and anger.

[1888] 3. Selecting the best ads

[1889] Based on the analysis results, the natural language generator selects the advertisement that best suits the user's current emotions. Appropriate advertising materials (text, images, videos) are retrieved from the advertising management system and displayed on the smartphone screen.

[1890] 4. Feedback of user emotional responses

[1891] After the ad is displayed, the user's reaction is collected again using a camera and microphone and fed back to the sentiment analysis engine. The server uses this feedback to improve the ad selection algorithm of the natural language generation device and optimize the ad to be displayed next time.

[1892] Specific examples

[1893] For example, if the sentiment analysis engine determines that the user is surprised, the natural language generator will display an advertisement for the latest technology product with a "surprise" theme. If the user shows interest in the advertisement, feedback is collected, increasing the likelihood that similar advertisements will be displayed in a similar emotional state in the future.

[1894] Prompt Sentence Examples

[1895] Below are some examples of prompts for generative AI models (e.g., GPT-4):

[1896] plaintext

[1897] Analyze the following user sentiment data and generate the optimal ad copy.

[1898] Emotion data: Surprise

[1899] Ad Category: Technology Products

[1900] Based on this prompt, the natural language generator generates an advertisement that matches the user's surprise and displays it on the smartphone, thereby realizing optimal advertisement display for each user.

[1901] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1902] Program processing steps

[1903] Explain the process flow in detail

[1904] Step 1:

[1905] The server collects facial expression data and tone of voice data using the camera and microphone on the user's smartphone. Specifically, the camera captures facial images and the microphone records voice. The input is real-time video and audio data, which is then sent to the emotion analysis engine.

[1906] Step 2:

[1907] The server's emotion analysis engine analyzes the video and audio data received from the smartphone. It analyzes facial expressions using a facial recognition algorithm and audio tones using an audio analysis algorithm. As a result of the analysis, emotional data such as joy, sadness, surprise, and anger are obtained. This is the output of the analysis.

[1908] Step 3:

[1909] The server inputs a prompt sentence into the generative AI model based on the emotion data obtained from the emotion analysis engine. Specifically, it generates the following prompt sentence based on the analysis results and sends it to the AI ​​model. The prompt sentence, which includes "emotion data" and "advertising category," is used as input. The prompt sentence has the following format:

[1910] plaintext

[1911] Analyze the following user sentiment data and generate the optimal ad copy.

[1912] Emotion data: Surprise

[1913] Ad Category: Technology Products

[1914] The generated prompt text becomes the input to the model, which then generates optimal advertising text based on it.

[1915] Step 4:

[1916] The server's generative AI model analyzes the prompt and generates ad copy that best suits the user's emotional state. The model's output is a text ad copy, which is then sent to the ad management system.

[1917] Step 5:

[1918] The server's advertising management system combines the generated advertising copy with appropriate advertising materials (images and videos). These materials are retrieved from a database and an advertising package is created accordingly. This advertising package is the output sent to the smartphone.

[1919] Step 6:

[1920] The smartphone displays the received advertising package on the display. At this time, it adjusts the timing of displaying the advertisement taking into account the user's current operating state and usage situation. Specifically, the advertisement is displayed while the user is operating the app or on the notification screen.

[1921] Step 7:

[1922] After the user watches the ad, the smartphone again uses the camera and microphone to collect the user's reaction. The input is real-time video and audio data from after the ad is viewed, which is then sent to the sentiment analysis engine.

[1923] Step 8:

[1924] The emotion analysis engine on the server analyzes the video and audio data again to obtain emotion data after viewing the advertisement. This is the output of the analysis.

[1925] Step 9:

[1926] The server collects emotional data after viewing an ad and feeds it back into the ad selection algorithm. The feedback data is used to optimize the display of the next ad and as learning data for ad display optimization.

[1927] This series of processes realizes optimal advertisement display based on the user's emotional state, maximizing advertising effectiveness.

[1928] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits 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.

[1929] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with 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.

[1930] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1931] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1932] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1933] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1934] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1935] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1936] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1937] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1938] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1939] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1940] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1941] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1942] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1943] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1944] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1945] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1946] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1947] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illus...

Claims

1. a means for generating a plurality of natural language generation devices and assigning each of the devices to a different function within the organization; a means for inputting a business plan; A means for any natural language generation device to initiate a task based on the business plan and to share information and hold discussions with other natural language generation devices; A means of simulating multiple business plans and collecting and comparing the results; A means of selecting the most suitable business plan and displaying the results; A system including:

2. 2. The system of claim 1, wherein the means for sharing and discussing information includes a message sending and receiving function.

3. 2. The system of claim 1, wherein each of said natural language generation devices comprises means for reporting intermediate results to a server.

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