System

Abstract

A system is provided.SOLUTION: The system includes a means for importing data related to the target management of the company and training an AI model, a means for authenticating the user, providing a target setting interface to the user, analyzing the user's input and proposing a target setting based on the target management rule of the company, a means for receiving the user's feedback, regenerating and determining a target, and a means for storing the determined target in a database.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] Many companies manage employee goals, but problems exist where goals are not set correctly or where goals are not set consistently by managers, resulting in a lack of fairness. In particular, goal levels often vary depending on the supervisor, which can have a negative impact on employee motivation and performance. This poses a risk of reducing the efficiency and results of the entire organization. The present invention aims to solve these problems and provide a system for efficient and fair goal setting. [Means for solving the problem]

[0005] The present invention provides a chat-type tool service equipped with a generative AI that learns a company's goal management rules and proposes appropriate goal setting in an interactive format. Specifically, the system includes a means for importing data related to the company's goal management and training an AI model, a means for authenticating users and providing them with a goal setting interface, a means for analyzing user input and proposing goal setting based on the company's goal management rules, a means for receiving user feedback and regenerating and confirming goals, and a means for storing the confirmed goals in a database (Claim 1). The system also includes a natural language processing algorithm for categorizing and analyzing data related to the company's goal management (Claim 2). Furthermore, the system has a function for training the AI ​​model by combining past goal data with new data to continuously improve accuracy (Claim 3).

[0006] "Corporate goal management rules" are internal regulations and guidelines that a company uses as standards and procedures when setting goals and managing progress for its employees.

[0007] "Generative AI" refers to artificial intelligence that uses natural language processing and machine learning techniques to generate appropriate responses and suggestions in response to user input.

[0008] A "chat-type tool service" is an online service that supports a specific function by providing a chat interface that allows users to interact with each other in real time.

[0009] "Data import" refers to the operation or process of bringing necessary data from outside into a system.

[0010] An "AI model" is a machine learning algorithm or neural network that is trained to perform a specific task based on training data.

[0011] "User authentication" is the process of verifying the legitimacy of an individual attempting to access a system, and includes verifying IDs and passwords.

[0012] A "goal setting interface" is a GUI or chat interface that provides a screen or interactive input means for a user to input and define goals.

[0013] "Natural language processing algorithms" refers to a series of algorithms and technologies that allow computers to understand, interpret, and generate the natural language that humans use every day.

[0014] "Means for regenerating and confirming goals" refers to the process or process for revising initial goal proposals based on user feedback and finalizing the goals.

[0015] A "database" is a system for efficiently storing, retrieving, and managing structured data. [Brief explanation of the drawings]

[0016] [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

[0017] 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.

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

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

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

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

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

[0031] 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.

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

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] This invention relates to a chat tool service equipped with a generation AI for efficient and fair corporate goal management. Below, we will generate a program for this system and explain its processing overview in natural language.

[0038] System Overview

[0039] This system consists of three main elements: a server, a user device, and the user. The generative AI model running on the server sets goals while interacting with the user via the user device. The server has learned the company's goal management rules and suggests appropriate goal settings based on user input.

[0040] Program processing overview

[0041] 1. Initial setup and learning

[0042] The server imports data on the company's goal management from the administrator, who then uploads the data to the system using a terminal.

[0043] 2. User Authentication

[0044] A user accesses the system from their own terminal and enters their ID and password on the login screen. The server receives this, performs authentication, and verifies the user's access rights.

[0045] 3. The dialogue process of goal setting

[0046] Users use the chat interface to begin goal setting, for example by typing, "I'd like to set goals for this month," which starts the process.

[0047] The server analyzes the user's input and generates appropriate target candidates based on past target data and company rules.

[0048] The proposed target candidates are displayed in a chat interface on the user's device, and the user can provide feedback and request any necessary revisions.

[0049] The server re-analyzes the feedback and regenerates goals tailored to the user's needs.

[0050] 4. Set and save your goals

[0051] If the user is satisfied with the regenerated goal, he or she performs an operation to confirm the goal.

[0052] The server stores the determined goals in a database for future reference.

[0053] Specific examples

[0054] Initial setup and training example

[0055] Administrators upload a file containing goal data from the past three years and the company's goal management rules from their device, which allows the generative AI to set goals according to the company's specific needs and patterns.

[0056] User Authentication Example

[0057] When an employee attempts to log in to the system, they enter their ID and password on the login screen, which the server receives and executes the authentication process. After successful authentication, they can access the dashboard.

[0058] Goal Setting Examples

[0059] An employee may enter a goal setting such as "increase sales by 500,000 yen" into the chat interface. The AI ​​generator may compare this with past data and suggest a goal such as "increase sales by 500,000 yen and acquire 10 new customers."

[0060] If an employee gives feedback such as, "Sales of 500,000 yen are difficult; how about 400,000 yen?", the generating AI will analyze again and respond, "A goal of increasing sales by 400,000 yen would be appropriate."

[0061] Once the employee is satisfied, the goal is finalized and the server stores this goal in a database.

[0062] As described above, the system of the present invention provides a chat-based tool using generative AI technology to achieve efficiency and fairness in corporate goal management. This system is expected to improve employee motivation and the performance of the entire organization.

[0063] The processing flow will be explained below.

[0064] Step 1:

[0065] The administrator logs in to the system using a terminal. The terminal displays the login screen, and the administrator enters their ID and password. The server receives this information and authenticates the administrator.

[0066] Step 2:

[0067] The administrator uses the terminal to upload the company's goal management rules and past goal setting data. The terminal displays the file upload interface, and the administrator selects the required data file and presses the upload button. The server receives these files, analyzes the data, and imports it.

[0068] Step 3:

[0069] The server trains the AI ​​model based on the uploaded data, analyzing the goal management rules stored in the database and past goal setting data to learn appropriate goal setting patterns.

[0070] Step 4:

[0071] A regular employee accesses the system using a terminal and enters their ID and password on the login screen. The terminal then sends this information to the server for user authentication.

[0072] Step 5:

[0073] The server checks the user's ID and password, and if authentication is successful, it confirms the user's authority and retrieves the dashboard information. The terminal displays the retrieved dashboard information to the user.

[0074] Step 6:

[0075] To start goal setting, the user accesses the chat interface and enters, "I want to set my goal for this month." The device then sends the user's request to the server.

[0076] Step 7:

[0077] The server analyzes the user's input using natural language processing (NLP) algorithms and generates appropriate goal candidates based on past data and the company's goal management rules.

[0078] Step 8:

[0079] The device displays the generated target candidates in a chat interface and suggests them to the user, who can then provide feedback and request any necessary corrections or adjustments.

[0080] Step 9:

[0081] The server receives the user's feedback, analyzes it again using a natural language processing algorithm, and generates new target candidates based on the feedback. The device then presents the regenerated target candidates to the user.

[0082] Step 10:

[0083] When the user confirms the final goal and is satisfied with it, he / she inputs "confirm goal" into the chat interface, and the terminal sends a confirmation request to the server.

[0084] Step 11:

[0085] The server stores the determined goals in a database and manages them for later reference and evaluation. The terminal displays the status of the saved goal to the user.

[0086] As described above, the system efficiently supports the user in setting goals through each step.

[0087] Example 1

[0088] 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."

[0089] One of the challenges in corporate goal management is the difficulty of setting goals efficiently and fairly. In conventional systems, goal setting is subjective, which can lead to situations where improvements in overall corporate performance cannot be expected. Furthermore, there is a problem in that systems lack the flexibility to regenerate goals based on user feedback, making it difficult to respond to individual needs.

[0090] 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.

[0091] In this invention, the server includes means for importing data related to corporate goal management and training an AI model, means for authenticating users and providing a goal setting interface to the users, means for analyzing user input and proposing goal setting based on the corporate goal management rules, means for receiving user feedback and regenerating and confirming goals, means for storing the confirmed goals in a database, a procedure for reanalyzing the data and regenerating goals based on the user feedback, and means for verifying the training results of the AI ​​model and fine-tuning them as necessary, thereby improving the efficiency and fairness of corporate goal management and enabling flexible goal setting that meets individual needs.

[0092] "Corporate goal management" is the process of managing and evaluating the specific goals set to achieve a company's objectives, as well as the plans and measures based on those goals.

[0093] "Generative AI" refers to technology that uses artificial intelligence to analyze and generate data, automatically creating new proposals and goals based on user input.

[0094] A "chat-type tool service" refers to a system that provides an interface that allows users to interact in a conversational format using natural language.

[0095] "Importing data" refers to the process of bringing external data into a system and converting it into a usable format.

[0096] "Training an AI model" refers to the process of training an artificial intelligence algorithm using large amounts of data to improve its ability to perform a specified task.

[0097] "User authentication" refers to the process of verifying the identity of users accessing a system and ensuring that only authorized users can use it.

[0098] "Goal setting interface" refers to an interactive input screen for a user to input the goals they wish to achieve.

[0099] "Analysis" is the process of interpreting, understanding, and evaluating input data to extract necessary information and generate appropriate output.

[0100] "Receiving feedback" refers to the system collecting responses and opinions from users and using them as input for the next step.

[0101] "Regeneration" is the process of creating new proposals and goals that incorporate user feedback based on the initial analysis results and proposals.

[0102] "Confirmation" refers to the user agreeing with the presented goal and officially registering that goal in the system.

[0103] "Saving to the database" refers to storing the determined data in the system's storage device for future reference.

[0104] "Reanalysis" refers to the process of generating more appropriate suggestions and goals by reanalyzing based on the feedback received.

[0105] "Verification" refers to the process of evaluating the training results of an AI model and confirming its accuracy and reliability.

[0106] "Fine-tuning" is the process of making small modifications to an AI model to improve its accuracy based on training and validation results.

[0107] This invention relates to a chat tool service equipped with generative AI for efficient and fair corporate goal management. This system consists of three main elements: a server, a user terminal, and a user. Specific details for implementing the invention are described below.

[0108] System configuration

[0109] 1. Server:

[0110] The server hosts the generative AI model and includes a database for storing data related to the company's goal management. The server trains the AI ​​model, authenticates users, suggests goal setting, reanalyzes feedback, and stores established goals.

[0111] Example of hardware to be used: A server machine with a powerful processor and large memory capacity.

[0112] Examples of software used: Database Management Systems (DBMS), generative AI models (e.g., Python TensorFlow, etc.).

[0113] 2. User Device:

[0114] The user terminal is a device through which the user accesses the system, and provides a login interface and a chat interface for goal setting.

[0115] Examples of hardware used: PCs, tablets, and smartphones.

[0116] Examples of software used: web browsers (e.g., Google Chrome, Microsoft Edge, etc.), dedicated applications.

[0117] 3. User:

[0118] Users are company employees or managers who participate in the goal setting process. Users access the server using their terminals and set their own goals.

[0119] Specific processing details

[0120] 1. Initial setup and learning

[0121] The server receives the company's goal management data provided by the administrator and stores it in a database. This data is used for initial training of the AI ​​model.

[0122] Example: An administrator uploads an Excel file containing goal data from the past three years and the company's goal management rules to the system from a terminal. The server imports this data and provides it as training data for the AI ​​model.

[0123] 2. User Authentication

[0124] Users access the system from their own terminals and enter their ID and password into the login interface. The server receives this and verifies it against the information in the database.

[0125] Example: When an employee logs in to the system, they enter their employee ID "12345" and password "password123" on the login screen. The server receives this and performs authentication, and if successful, the employee can access the dashboard.

[0126] 3. The dialogue process of goal setting

[0127] Users initiate goal setting using the chat interface, for example by typing, "I'd like to set goals for this month."

[0128] The server analyzes the user's input and generates candidate goals based on the company's goal management rules. The generative AI model then suggests appropriate goals.

[0129] Example: When an employee types "increase sales by 500,000 yen" into the chat interface, the generative AI model suggests the goal "increase sales by 500,000 yen and acquire 10 new customers" based on past data and company rules.

[0130] 4. Set and save your goals

[0131] The user can provide feedback on the proposed goals and request any necessary modifications. The server re-analyzes the feedback and regenerates the goals tailored to the user's needs.

[0132] If the user is satisfied with the regenerated goal, he / she confirms it, and the server stores the confirmed goal in the database for future reference.

[0133] Example: If a user gives feedback saying, "Sales of 500,000 yen are difficult, how about 400,000 yen?", the server's generative AI model will reanalyze and re-propose a goal of "increasing sales by 400,000 yen." If the user is satisfied, the goal is confirmed and the server saves it in the database.

[0134] This system ensures efficiency and fairness in corporate goal management and enables flexible goal setting to meet individual needs. The use of generative AI models is expected to improve overall corporate performance.

[0135] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0136] Step 1:

[0137] Initial Setup and Data Import

[0138] Administrator: Use your own device to upload the company's goal management data to the system as an Excel file.

[0139] Input: A file containing goal data from the past three years and the company's goal management rules.

[0140] Server: Receives the uploaded data, stores it in a database, and then starts training a generative AI model based on this data.

[0141] Output: A dataset used as training data and a trained AI model.

[0142] Specific operation: The server stores the received data in storage and uses Python TensorFlow to train the AI ​​model.

[0143] Step 2:

[0144] User Authentication

[0145] User: Accesses the system from their own terminal and enters their ID and password on the login screen.

[0146] Input: ID and password.

[0147] Server: Receives the user's input information and authenticates it by comparing it with the registered information in the database.

[0148] Output: The authentication result (success or failure).

[0149] What happens: The user enters their login information, the server checks it against a database to authenticate them, and if successful, gives them access to the dashboard.

[0150] Step 3:

[0151] Start setting goals

[0152] User: Using the chat interface, type "I want to set my goals for this month."

[0153] Input: A goal-setting opening prompt such as "I'd like to set a goal for this month."

[0154] Server: Parses user input and initiates the goal setting process.

[0155] Output: Triggering the goal setting process for the generative AI model.

[0156] What happens: The chat interface receives the user's prompt, the server parses the prompt and triggers the goal setting process.

[0157] Step 4:

[0158] Proposing goals

[0159] Server: Analyzes user input, and a generative AI model generates candidate goals based on past goal data and company rules.

[0160] Input: User-entered goal setting prompts and historical goal data, company rules.

[0161] Generative AI model: Generates suitable target candidates.

[0162] Output: Candidate goals suggested to the user.

[0163] Specific operation: The server analyzes the user's input, and the generative AI model generates and suggests goals such as "increase sales by 500,000 yen and acquire 10 new customers."

[0164] Step 5:

[0165] Receiving and regenerating feedback

[0166] User: Give feedback on the proposed goal, such as "Sales of 500,000 yen are difficult. How about 400,000 yen?"

[0167] Input: User feedback.

[0168] Server: Receives feedback, reanalyzes, and the generative AI model recreates the goal.

[0169] Output: Revised target candidates.

[0170] Specific operation: The server reanalyzes the feedback content, and the generative AI model re-proposes the goal of "increasing sales by 400,000 yen."

[0171] Step 6:

[0172] Confirm and save your goals

[0173] User: If satisfied with the regenerated goal, type "Confirm this goal."

[0174] Input: Enter your final intention.

[0175] Server: Stores the determined goals in a database for future reference.

[0176] Output: Confirmed targets stored in the database.

[0177] Specific behavior: The server receives the user's confirmation input and saves the goal in the database.

[0178] This process flow enables efficient and fair goal management for companies. The use of generative AI models enables appropriate goal setting based on diverse data and can flexibly respond to user feedback.

[0179] (Application example 1)

[0180] 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."

[0181] A chat-based tool service equipped with generative AI for efficient and fair corporate goal management is problematic because conventional methods are labor-intensive, time-consuming, and subjective judgment bias is unavoidable. Furthermore, managing manufacturing processes within factories is difficult to do efficiently because of the complex procedures and real-time progress management required. A new system is needed to solve these issues.

[0182] 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.

[0183] In this invention, the server includes means for importing data related to corporate goal management and training an AI model, means for authenticating users and providing a goal setting interface to the users, means for analyzing user input and proposing goal setting based on the corporate goal management rules, means for receiving user feedback and regenerating and confirming goals, means for saving the confirmed goals in a database, means for importing data related to manufacturing processes in a factory and proposing production goals, and means for receiving input from workers, monitoring the production process in real time, and tracking progress. This enables improved efficiency and fairness in corporate goal management and efficient management of manufacturing processes in a factory.

[0184] "Corporate goal management rules" refer to the standards and procedures for achieving the goals set by a company.

[0185] A "generative AI model" refers to an artificial intelligence system that has been trained to perform a specific task using machine learning algorithms.

[0186] "Chat-type tool service" refers to a software service that communicates with users in an interactive format using text or voice.

[0187] "Means for importing data" refers to a means or method for bringing data into the system from outside.

[0188] "Means for authenticating a user" refers to a means or method for verifying a user's identity and authority.

[0189] "Goal setting interface" refers to the screens and processes through which a user sets goals.

[0190] "Means for parsing user input" refers to the means or methods for understanding and processing information provided by a user.

[0191] The "means for proposing goal setting" refers to a means or method for generating and proposing appropriate goals based on the user's needs and the company's rules.

[0192] "Means for regenerating and finalizing goals" refers to the means or method for regenerating and finalizing goals based on user feedback.

[0193] "Means of storing data in a database" refers to a means or method of systematically accumulating data and storing it in a manner that allows it to be accessed later.

[0194] A "manufacturing process" refers to the series of steps or operations required to create a product.

[0195] "Means for proposing production targets" refers to a means or method for proposing production volume or quality standards to be achieved in the manufacturing process.

[0196] "Means for monitoring the production process in real time" refers to a means or method for observing and checking the progress of the manufacturing process in real time.

[0197] "Means for tracking progress" refers to the means or method for continually tracking and recording the progress of a manufacturing process or goal achievement.

[0198] This invention is a system for efficiently and fairly managing corporate targets and implementing manufacturing processes within factories. This system consists of three main elements: a server, a user terminal, and a user, and provides a chat-type tool service equipped with a generative AI model.

[0199] System configuration and operation overview

[0200] 1. Server Initial Setup and Learning

[0201] As an initial setting, the server imports data on the company's goal management and manufacturing process data from the manager. The server uses this data to train the generative AI model. For example, by importing data on the company's goal management rules, past goal data, and manufacturing process data, the AI ​​model learns specific needs and patterns.

[0202] 2. User Authentication

[0203] The user accesses the system from a smartphone or head-mounted display and enters the necessary information on the login screen. The server receives this information, authenticates it, and verifies whether the user has the appropriate access rights. This authentication process is carried out using a security module (e.g., OAuth 2.0).

[0204] 3. The dialogue process of goal setting

[0205] Users initiate goal setting using a chat interface (text or voice). For example, if a user types, "I want to set a goal for this month," the server analyzes the user's input and generates appropriate goal candidates based on past data and company rules. These suggested goal candidates are displayed on the user's device interface. The user can then provide feedback and request any necessary revisions.

[0206] 4. Interactive process of manufacturing process

[0207] Similarly, for manufacturing processes within factories, users can use the chat interface to ask about production targets. For example, by typing, "What is today's production target?", the server analyzes the production line settings and progress and suggests appropriate production targets. Feedback can also be provided in response, and the production process is monitored and progress is tracked in real time.

[0208] Hardware and software used

[0209] Hardware

[0210] Smartphone or head-mounted display (e.g., general mobile device, display device)

[0211] software

[0212] User authentication: security module (e.g. OAuth 2.0)

[0213] Speech recognition system (e.g., Google Cloud Speech-to-Text)

[0214] Generative AI models (e.g., OpenAI GPT-4)

[0215] Examples of concrete examples and prompts

[0216] Example 1: Corporate goal setting

[0217] When a user inputs a goal setting of "increase sales by 500,000 yen" on their smartphone, the server proposes the goal of "increase sales by 500,000 yen and acquire 10 new customers." If the user provides feedback saying, "500,000 yen in sales is difficult. How about 400,000 yen?", the AI ​​responds, "A goal of increasing sales by 400,000 yen is appropriate." If the user is satisfied, the goal is confirmed and the server saves it in the database.

[0218] Example 2: Factory manufacturing process management

[0219] When a user wears the head-mounted display and asks, "What is today's production target?", the server responds, "Line 1 is scheduled to produce 500 units." The production line is monitored in real time, and progress is displayed on the display. For example, if a defective product occurs, a notification will appear saying, "A defective product has been found on Line 1," enabling a prompt response.

[0220] Prompt Sentence Examples

[0221] "Please tell me the achievement rate for yesterday's production target."

[0222] "Show me the production plan for the next shift."

[0223] "Please list the problems that have occurred on the third line and propose solutions."

[0224] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0225] Step 1:

[0226] Initial setup and training:

[0227] The server imports the company's goal management data and manufacturing process data within the factory. The manager uses a terminal to upload the necessary data files to the system. The server trains the generative AI model based on the imported data, learning the company's rules and patterns. Specifically, it analyzes past goal data and manufacturing process setting data to improve the accuracy of the model.

[0228] input:

[0229] Manager-provided historical goal data, manufacturing process data, and company goal management rules.

[0230] output:

[0231] A trained generative AI model.

[0232] Step 2:

[0233] User authentication:

[0234] A user accesses the system on their own device and enters their ID and password on the login screen. The server receives this information and uses a security module (e.g., OAuth 2.0) to verify the user's identity and permissions. If authentication is successful, a dashboard is displayed on the user's device.

[0235] input:

[0236] User ID and password.

[0237] output:

[0238] Dashboard after successful authentication.

[0239] Step 3:

[0240] The goal-setting dialogue process:

[0241] The user inputs "I want to set a goal for this month" through the chat interface. The server analyzes this input and generates appropriate goal candidates by referencing past data and company rules. The generated goal candidates are displayed on the user's device.

[0242] input:

[0243] User goal setting request.

[0244] output:

[0245] Proposed target candidates.

[0246] Data processing / calculation:

[0247] Generate meaningful candidates for goals based on historical data and company rules.

[0248] Step 4:

[0249] Receiving user feedback and regenerating goals:

[0250] The user provides feedback on the proposed goal. For example, they might say, "Sales of 500,000 yen are difficult. How about 400,000 yen?" The server analyzes this feedback again and regenerates the goal. The regenerated goal is displayed on the user's device.

[0251] input:

[0252] User feedback.

[0253] output:

[0254] Regenerated target candidates.

[0255] Data processing / calculation:

[0256] Generate new goals based on user feedback.

[0257] Step 5:

[0258] Save confirmed goals:

[0259] If the user is satisfied with the regenerated goal, he / she confirms the goal in the system, and the server stores this confirmed goal in the database for future reference.

[0260] input:

[0261] A goal established by the user.

[0262] output:

[0263] Confirmed goals stored in the database.

[0264] Data processing / calculation:

[0265] The determined goals are saved in a database format.

[0266] Step 6:

[0267] Manufacturing process interaction process:

[0268] A factory worker or manager can use the chat interface to ask, "What is today's production target?" The server analyzes the production line settings and progress, generates the production target for that day, and displays it on the user's device. The production process is monitored in real time, and progress is tracked.

[0269] input:

[0270] Questions about production goals for workers and managers.

[0271] output:

[0272] Proposed production target for the day.

[0273] Data processing / calculation:

[0274] Analyze production line settings and progress data to generate daily production targets.

[0275] Step 7:

[0276] Real-time process monitoring and feedback:

[0277] The server monitors the production process in real time, tracking progress and notifying workers of any irregularities, such as defective products, so that workers and managers can quickly respond and enter instructions into the system to resolve the issues.

[0278] input:

[0279] Real-time production process data and defect occurrence information.

[0280] output:

[0281] Progress tracking and anomaly notifications.

[0282] Data processing / calculation:

[0283] Analyze real-time data to detect anomalies and provide appropriate notifications.

[0284] 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.

[0285] This invention relates to a chat tool service that incorporates a generation AI for efficient and fair corporate goal management, and combines it with an emotion engine for recognizing user emotions and reflecting them in goal setting. Below, we will generate a program for this system and explain its processing overview in natural language.

[0286] System Overview

[0287] This system consists of three main elements: a server, a user device, and the user. The generative AI model and emotion engine running on the server set goals while interacting with the user via the user device. The server has learned the company's goal management rules and suggests appropriate goal settings based on input from the user. It also recognizes the user's emotions and reflects them in the goal suggestions.

[0288] Program processing overview

[0289] 1. Initial setup and learning

[0290] The server imports data on the company's goal management from the administrator, who then uploads the data to the system using a terminal.

[0291] 2. User Authentication

[0292] A user accesses the system from their own terminal and enters their ID and password on the login screen. The server receives this, performs authentication, and verifies the user's access rights.

[0293] 3. Emotion recognition

[0294] When a user accesses the chat interface and enters input related to goal setting, the emotion engine analyzes the user's input to determine emotions, for example, by recognizing emotions based on specific keywords or context within the text.

[0295] The server uses the emotion data obtained from the emotion engine to adjust the suggested goal settings, suggesting realistic goals if the user is overly stressed, or setting challenging goals if the user is highly motivated.

[0296] 4. The dialogue process of goal setting

[0297] To start goal setting, the user accesses the chat interface and inputs, "I want to set my goals for this month." The terminal then sends the user's request to the server.

[0298] The server generates appropriate goal candidates based on the user's input and the emotional information output by the emotion engine. For example, if a user inputs "increase sales by 500,000 yen," and the emotion engine determines that the load is too high based on the user's emotions, it will suggest a more realistic "increase sales by 400,000 yen."

[0299] 5. Suggestions and Feedback

[0300] The device displays the generated target candidates in a chat interface and suggests them to the user, who can then provide feedback and request any necessary corrections or adjustments.

[0301] The server receives the user's feedback, analyzes it again using the emotion engine and natural language processing algorithm, and generates new goal candidates based on the feedback. The device then presents the regenerated goal candidates to the user.

[0302] 6. Set and save your goals

[0303] If the user is satisfied with the regenerated goal, he / she inputs "confirm goal" into the chat interface, and the device sends a confirmation request to the server.

[0304] The server stores the determined goals in a database and manages them for later reference and evaluation. The terminal displays the status of the saved goal to the user.

[0305] Specific examples

[0306] Initial setup and training example

[0307] Administrators upload a file containing goal data from the past three years and the company's goal management rules from their device, which allows the generative AI to set goals according to the company's specific needs and patterns.

[0308] User Authentication Example

[0309] When an employee attempts to log in to the system, they enter their ID and password on the login screen, which the server receives and executes the authentication process. After successful authentication, they can access the dashboard.

[0310] Goal Setting Examples

[0311] An employee may enter a goal setting such as "increase sales by 500,000 yen" into the chat interface. The AI ​​generator may compare this with past data and suggest a goal such as "increase sales by 500,000 yen and acquire 10 new customers."

[0312] If the emotion engine detects high levels of stress from the user's input, the generative AI will suggest realistic goals such as "increase sales by 400,000 yen."

[0313] If the employee is satisfied with the proposal, the goal is confirmed and the server stores the goal in the database.

[0314] As described above, the system of the present invention provides a chat-type tool that uses generative AI technology and an emotion engine to achieve efficiency and fairness in corporate goal management. This system enables goal setting that takes into account employees' emotions, which is expected to improve the performance of the entire organization.

[0315] The processing flow will be explained below.

[0316] Step 1:

[0317] The administrator logs in to the system using a terminal. The terminal displays the login screen, and the administrator enters their ID and password. The server receives this information and authenticates the administrator.

[0318] Step 2:

[0319] The administrator uses the terminal to upload the company's goal management rules and past goal setting data. The terminal displays the file upload interface, and the administrator selects the required data file and presses the upload button. The server receives these files, analyzes the data, and imports it.

[0320] Step 3:

[0321] The server trains the AI ​​model based on the uploaded data, analyzing the goal management rules stored in the database and past goal setting data to learn appropriate goal setting patterns.

[0322] Step 4:

[0323] A regular employee accesses the system using a terminal and enters their ID and password on the login screen. The terminal then sends this information to the server for user authentication.

[0324] Step 5:

[0325] The server checks the user's ID and password, and if authentication is successful, it confirms the user's authority and retrieves the dashboard information. The terminal displays the retrieved dashboard information to the user.

[0326] Step 6:

[0327] To start goal setting, the user accesses the chat interface and enters, "I want to set my goal for this month." The device then sends the user's request to the server.

[0328] Step 7:

[0329] The server analyzes the user's input using a natural language processing (NLP) algorithm and generates appropriate goal candidates based on past data and the company's goal management rules. Meanwhile, the emotion engine recognizes emotions from the user's input and sends the analysis results to the server.

[0330] Step 8:

[0331] The server reflects the emotional information output by the emotion engine in the generated goal candidates and proposes appropriate goal settings. For example, if a user is suggested to "increase sales by 500,000 yen" and the emotion engine detects high stress, it will adjust it to "increase sales by 400,000 yen."

[0332] Step 9:

[0333] The device displays the generated target candidates in a chat interface and suggests them to the user, who can then provide feedback and request any necessary corrections or adjustments.

[0334] Step 10:

[0335] The server receives the user's feedback, analyzes it again using the emotion engine and natural language processing algorithm, and generates new goal candidates based on the feedback. The device then presents the regenerated goal candidates to the user.

[0336] Step 11:

[0337] When the user confirms the final goal and is satisfied with it, he / she inputs "confirm goal" into the chat interface, and the terminal sends a confirmation request to the server.

[0338] Step 12:

[0339] The server stores the determined goals in a database and manages them for later reference and evaluation. The terminal displays the status of the saved goal to the user.

[0340] Through the above steps, the system supports users in setting goals efficiently and fairly, and also provides goal setting that takes into account the user's emotions.

[0341] Example 2

[0342] 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."

[0343] Corporate goal management is complex, and it is particularly difficult to consider the emotions and motivation of individual employees. As a result, goal setting tends to be one-sided, which can have a negative impact on employee performance and motivation. It is also difficult to effectively utilize past goal data and appropriately set realistic yet challenging goals.

[0344] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for importing data related to corporate goal management and training an AI model, means for authenticating users and providing a goal setting interface to the users, means for analyzing user input, recognizing the user's emotions using an emotion engine, and proposing goal setting based on the corporate goal management rules, means for receiving user feedback and regenerating and confirming goals using an emotion engine and a natural language processing algorithm, and means for storing the confirmed goals in a database. This enables realistic and challenging goal setting that takes into account employees' emotions and motivation, improving the efficiency and fairness of corporate-wide goal management.

[0345] "Generative AI" is artificial intelligence that learns the data necessary for setting corporate goals and suggests appropriate goals through dialogue.

[0346] An "emotion engine" is an algorithm that analyzes user input and recognizes emotions.

[0347] A "chat-type tool" is a tool that provides an interface for setting goals through dialogue with the user.

[0348] "Corporate goal management rules" refer to guidelines and policies that a company uses to set and manage goals.

[0349] "Goal setting interface" means an interactive interface through which a user sets goals.

[0350] A "natural language processing algorithm" is an algorithm for analyzing text data and understanding its meaning.

[0351] A "database" is a system for storing and managing determined goals, emotional data, etc.

[0352] "User authentication" is the process of verifying a user's authenticity using the user's ID and password.

[0353] "Feedback" refers to the opinions and requests for revisions that users provide to proposed goals.

[0354] "Regeneration" refers to the process of regenerating new goals based on user feedback and emotional data.

[0355] "Training" refers to the process by which an AI model learns a company's goal management rules and data.

[0356] This invention relates to a chat tool service that incorporates a generation AI for efficient and fair corporate goal management, and combines it with an emotion engine for recognizing user emotions and reflecting them in goal setting. This system is composed of three main elements: a server, a user terminal, and the user, and is specifically implemented as follows:

[0357] System configuration

[0358] Hardware and Software

[0359] Server: The central part of the system, it manages the company's goal management data and runs the generative AI model and emotion engine. The specific software on which the generative AI model and emotion engine are installed is

[0360] User terminal: A device such as a PC or smartphone that allows a user to access the system. An application that provides a chat-style interface is installed on this device.

[0361] Database: A database system for storing corporate goal management data, defined goals, and user authentication information.

[0362] Program processing

[0363] Initial setup and training

[0364] Importing Data

[0365] The administrator uploads past goal data and the company's goal management rules from a terminal. The server receives this data and stores it in a database.

[0366] Training an AI model

[0367] The server uses the uploaded data to train the generative AI model, which learns the specific needs and patterns of the company through this training process.

[0368] User Authentication

[0369] Input on the login screen

[0370] Users access the system from their own terminal and enter their ID and password on the login screen.

[0371] Authentication Process

[0372] The device sends the entered ID and password to the server, which receives it and checks it against the authentication information in its database. If authentication is successful, the user can access the system dashboard.

[0373] emotion recognition

[0374] Parsing input

[0375] The user types "I want to set my goals for this month" into the chat interface, and the device sends this input to the server.

[0376] Running the Emotion Engine

[0377] The server passes the received input to the emotion engine, which analyzes the input text and recognizes the user's emotion from specific keywords and context.

[0378] The interactive process of goal setting

[0379] Target candidate generation

[0380] The server inputs goal-setting prompts into the generative AI model based on the user's input and the emotional information obtained from the emotion engine.

[0381] The generative AI model generates multiple target candidates and returns them to the server.

[0382] Suggestions for users

[0383] The server transmits the generated target candidates to the terminal, which displays them on the chat interface.

[0384] Suggestions and Feedback

[0385] Collecting feedback

[0386] The user can input feedback for the proposed goal candidates, for example, "I would like the goal to be adjusted to be more realistic."

[0387] Analyzing feedback and adjusting goals

[0388] The device sends the feedback to the server, which then receives it and analyzes it again using the emotion engine and natural language processing algorithm. New target candidates are generated and sent to the device, which then presents them to the user.

[0389] Confirm and save your goals

[0390] Determine your goals

[0391] If the user is satisfied with the proposed goal, he / she inputs "confirm goal" into the chat interface, and the device sends a confirmation request to the server.

[0392] Saving to a database

[0393] The server saves the confirmed goal in the database. After saving is complete, the device displays "Goal confirmed" to the user.

[0394] Specific examples

[0395] Initial setup and training example

[0396] Managers upload goal data from the past three years and their company's goal management rules via their devices, allowing the generative AI to set goals according to the company's specific needs and patterns.

[0397] User Authentication Example

[0398] When an employee logs in to the system, they enter their ID and password on the login screen. The server receives this and performs an authentication process. After successful authentication, they can access the dashboard.

[0399] Goal Setting Examples

[0400] An employee enters a goal setting, such as "increase sales by 500,000 yen," into the chat interface. The generation AI compares it with past data and may suggest a goal such as "increase sales by 500,000 yen and acquire 10 new customers." If the emotion engine detects high stress from the user's emotions, the generation AI will suggest a more realistic goal, such as "increase sales by 400,000 yen." If the employee agrees with this suggestion, the goal is confirmed and the server saves it in the database.

[0401] Prompt Sentence Examples

[0402] "Sales increased by 500,000 yen"

[0403] "Acquired 10 new customers"

[0404] "Complete Project A"

[0405] As described above, the system of the present invention provides a chat-type tool that uses generative AI technology and an emotion engine to achieve efficiency and fairness in corporate goal management. This enables goal setting that takes into account employees' emotions, and is expected to improve the performance of the entire organization.

[0406] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0407] Step 1: Initial setup and training

[0408] Input: The administrator uploads the company's goal management data and past goal data from the terminal.

[0409] Operation: The device sends the uploaded data to the server.

[0410] Data processing: The server receives the data and stores it in a database.

[0411] AI training: The server uses the stored data to train the generative AI model, learning the company's goal management rules and past patterns.

[0412] Output: A trained generative AI model.

[0413] Step 2: User authentication

[0414] Input: The user accesses the system on their own terminal and enters their ID and password on the login screen.

[0415] Operation: The terminal sends the entered ID and password to the server.

[0416] Data processing: The server receives the transmitted information and checks it against authentication data in its database.

[0417] Output: Authentication result (success or failure).

[0418] What happens: If authentication is successful, the server allows the user to access the dashboard.

[0419] Step 3: Emotion Recognition

[0420] Input: The user types "I want to set my goals for this month" into the chat interface.

[0421] Operation: The terminal sends the user's input to the server.

[0422] Data processing: The server passes the received input to the emotion engine, which analyzes the input text and recognizes the user's emotion.

[0423] Output: Emotion data (e.g., stress, high motivation, etc.).

[0424] How it works: The server stores emotion data in a database.

[0425] Step 4: The goal-setting dialogue process

[0426] Input: Emotion data and user input.

[0427] How it works: The server inputs goal-setting prompts into the generative AI model based on emotion data and user input.

[0428] Data processing: The generative AI model generates multiple target candidates.

[0429] Output: Generated target candidates.

[0430] How it works: The server sends potential targets to the device, which displays them in the chat interface.

[0431] Step 5: Suggestions and feedback

[0432] Input: User feedback (e.g., "Please adjust your goal to be more realistic").

[0433] Operation: The device sends the feedback content to the server.

[0434] Data processing: The server receives the feedback, analyzes it using an emotion engine and natural language processing algorithms, and generates new target candidates.

[0435] Output: New target candidates.

[0436] Operation: The server sends the newly generated target candidates to the terminal, which then presents them to the user.

[0437] Step 6: Confirm and save your goal

[0438] Input: "Confirm goal" typed by the user into the chat interface.

[0439] Operation: The terminal sends a confirmation request to the server.

[0440] Data processing: The server stores the determined goals in a database.

[0441] Output: Save completion status.

[0442] Action: The device displays "Target confirmed" to the user.

[0443] The above is the specific processing flow of the system. At each step, appropriate data processing and analysis are performed based on the input data, allowing users to smoothly proceed through the goal setting process.

[0444] (Application example 2)

[0445] 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."

[0446] Conventional corporate goal management systems have problems with efficiency and objectivity, and in particular, they often set goals without considering the emotional state of individual users. This can result in excessive burdens on users, leading to a decrease in motivation and increased stress. Furthermore, in certain fields, such as logistics centers, detailed goal setting and rapid feedback are required to respond to the complexity of work content and fluctuating demand. An efficient and fair goal management system that can meet these needs is needed.

[0447] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0448] In this invention, the server includes: means for importing data related to corporate goal management and training an AI model; means for authenticating a user and providing the user with a goal setting interface; means for analyzing user input and suggesting goal setting based on the corporate goal management rules; means including an emotion engine that recognizes the user's emotions and reflects them in goal setting; means using a generative AI model that suggests goals according to the user's emotions; means for receiving user feedback and regenerating and confirming goals; and means for storing the confirmed goals in a database. This enables realistic and efficient goal setting that takes the user's emotional state into consideration, making fair and appropriate goal management possible, especially in specific fields such as logistics centers.

[0449] "Corporate goal management rules" refer to the standards and guidelines for planning, evaluating, and achieving performance goals set by a company.

[0450] A "generative AI model" refers to an artificial intelligence algorithm that learns from large amounts of data and automatically generates appropriate responses and suggestions based on user input.

[0451] "Chat-type tool service" refers to an interactive software application that assists users with specific tasks through text and voice interactions.

[0452] "User authentication" refers to the process of verifying the identity of users accessing a system and granting appropriate access privileges.

[0453] "Goal-setting interface" refers to a system component that provides the screens and input forms through which a user sets goals.

[0454] An "emotion engine" refers to technology that analyzes a user's emotional state from text input and other data and adjusts responses based on the results.

[0455] "Feedback" refers to the evaluations and opinions that users provide in response to suggestions and answers from the system.

[0456] "Database" refers to a digital storage system that systematically organizes and stores data, making it quickly accessible when needed.

[0457] The system that embodies this invention is a chat-type tool service equipped with a generative AI model that learns a company's goal management rules and proposes appropriate goal setting in an interactive format. This system is composed of the following main components:

[0458] Program processing overview

[0459] 1. Importing and training corporate goal management data

[0460] The server trains the AI ​​model using data on corporate goal management imported from administrators, allowing it to learn the company's unique goal-setting rules and patterns and suggest appropriate goals.

[0461] 2. User authentication and interface provision

[0462] Users access the system using their own devices (smartphones, tablets, PCs, etc.) and enter their ID and password on the login screen. The server receives this, performs an authentication process, and grants the user appropriate access rights. If authentication is successful, the user can access the goal setting interface.

[0463] 3. Analyzing user input and suggesting goal setting

[0464] When a user enters text related to goal setting into the chat interface, the server uses a generative AI model to analyze the input. The server also analyzes the user's emotions and reflects them in the proposed goals. For example, if a user enters "increase shipment volume by 50 pallets," and the emotion analysis indicates high stress, the generative AI model will suggest a more realistic goal, such as "increase shipment volume by 40 pallets."

[0465] 4. Utilizing the Emotion Engine

[0466] The emotion engine analyzes the user's input text to determine whether the emotion is positive or negative. For example, if the user's input is negative, such as "I'm not sure if I can achieve this," the emotion engine will detect this and reflect it in the suggested goal.

[0467] 5. Feedback and goal regeneration / confirmation

[0468] The user provides feedback on the proposed goal, and the server regenerates the goal based on that feedback. If the user is satisfied with the goal and clicks "confirm," the server stores the goal in a database for future reference and evaluation.

[0469] 6. Save and manage your goals

[0470] The confirmed goals are stored in a database on the server. This database accumulates past goal data and new data, and is used to continuously improve the accuracy of the AI ​​model. It is also possible to generate reports and alerts according to the company's goal management rules.

[0471] Hardware and software configuration

[0472] Hardware: Smartphones, tablets, PCs, servers

[0473] Software: Flask (web server), TextBlob (sentiment analysis library), OpenAI GPT-3 (generative AI model)

[0474] Examples of concrete examples and prompts

[0475] Examples:

[0476] A staff member types "increase today's shipments by 50 pallets" into the chat interface. If the emotion engine detects anxiety, the generative AI model suggests a more realistic goal, such as "increase today's shipments by 40 pallets."

[0477] Example prompt sentence:

[0478] Given the user's negative sentiment, suggest a realistic goal: Increase today's shipping volume by 50 pallets.

[0479] This system enables realistic and efficient goal setting that takes into account the user's emotional state, enabling fair and appropriate goal management even in specific fields such as logistics centers.

[0480] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0481] Step 1:

[0482] The server imports the company's goal management data and trains the generative AI model. The administrator uploads past goal data and the company's goal management rules to the server. This data is analyzed by the AI ​​model, which learns goal setting patterns and standards. The input is the data file from the administrator, and the output is the trained AI model.

[0483] Step 2:

[0484] The terminal performs user authentication. The user accesses the login screen using their terminal and enters their ID and password. The entered authentication information is sent to the server, which verifies it and authenticates that the user is legitimate. The input is the user's ID and password, and the output is the result of authentication success or failure.

[0485] Step 3:

[0486] The user accesses the goal setting interface and inputs text related to the goal setting. For example, the user inputs a goal such as "I want to increase shipping volume by 50 pallets this month." The input is the goal setting text entered by the user, and the output is a request to suggest appropriate goal settings.

[0487] Step 4:

[0488] The server analyzes the received user input and suggests goal setting using a generative AI model and emotion engine. The server analyzes the user's input text and generates appropriate goal candidates based on its content. At the same time, the emotion engine analyzes the user's emotions and reflects them in the generated goals. For example, if the user's input includes "I'm anxious," the emotion engine recognizes this and instructs the generative AI model to suggest realistic goals. The input is the user's text input and emotion data, and the output is appropriate goal setting suggestions.

[0489] Step 5:

[0490] The device displays the proposed goals to the user and asks for feedback. The user provides feedback on the proposed goals and requests corrections or reconfiguration as necessary. The inputs are the proposed goals from the server and the user's feedback, and the output is the feedback data sent to the server.

[0491] Step 6:

[0492] The server reanalyzes the user's feedback and regenerates goals as necessary. It uses a generative AI model and emotion engine to generate new goal candidates based on the feedback. The input is the user's feedback data, and the output is the regenerated goal settings.

[0493] Step 7:

[0494] When the user is satisfied with the regenerated goal, he / she inputs "confirm goal" into the terminal. The terminal sends this confirmation request to the server. The input is the user's confirmation request, and the output is a confirmation notification to the server.

[0495] Step 8:

[0496] The server saves the confirmed goals in a database. The confirmed goals are managed in the database so that they can be referenced and evaluated later. The input is the confirmed goal information, and the output is a notification that the goal has been saved to the database.

[0497] Through the above processing steps, the present invention is a system that realizes realistic and efficient goal setting that takes into account the user's emotional state.

[0498] 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.

[0499] 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.

[0500] 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.

[0501] [Second embodiment]

[0502] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0503] 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.

[0504] 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).

[0505] 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.

[0506] 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.

[0507] 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).

[0508] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for 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.

[0509] 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.

[0510] 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.

[0511] 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.

[0512] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0513] 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."

[0514] This invention relates to a chat tool service equipped with a generation AI for efficient and fair corporate goal management. Below, we will generate a program for this system and explain its processing overview in natural language.

[0515] System Overview

[0516] This system consists of three main elements: a server, a user device, and the user. The generative AI model running on the server sets goals while interacting with the user via the user device. The server has learned the company's goal management rules and suggests appropriate goal settings based on user input.

[0517] Program processing overview

[0518] 1. Initial setup and learning

[0519] The server imports data on the company's goal management from the administrator, who then uploads the data to the system using a terminal.

[0520] 2. User Authentication

[0521] A user accesses the system from their own terminal and enters their ID and password on the login screen. The server receives this, performs authentication, and verifies the user's access rights.

[0522] 3. The dialogue process of goal setting

[0523] Users use the chat interface to begin goal setting, for example by typing, "I'd like to set goals for this month," which starts the process.

[0524] The server analyzes the user's input and generates appropriate target candidates based on past target data and company rules.

[0525] The proposed target candidates are displayed in a chat interface on the user's device, and the user can provide feedback and request any necessary revisions.

[0526] The server re-analyzes the feedback and regenerates goals tailored to the user's needs.

[0527] 4. Set and save your goals

[0528] If the user is satisfied with the regenerated goal, he or she performs an operation to confirm the goal.

[0529] The server stores the determined goals in a database for future reference.

[0530] Specific examples

[0531] Initial setup and training example

[0532] Administrators upload a file containing goal data from the past three years and the company's goal management rules from their device, which allows the generative AI to set goals according to the company's specific needs and patterns.

[0533] User Authentication Example

[0534] When an employee attempts to log in to the system, they enter their ID and password on the login screen, which the server receives and executes the authentication process. After successful authentication, they can access the dashboard.

[0535] Goal Setting Examples

[0536] An employee may enter a goal setting such as "increase sales by 500,000 yen" into the chat interface. The AI ​​generator may compare this with past data and suggest a goal such as "increase sales by 500,000 yen and acquire 10 new customers."

[0537] If an employee gives feedback such as, "Sales of 500,000 yen are difficult; how about 400,000 yen?", the generating AI will analyze again and respond, "A goal of increasing sales by 400,000 yen would be appropriate."

[0538] Once the employee is satisfied, the goal is finalized and the server stores this goal in a database.

[0539] As described above, the system of the present invention provides a chat-based tool using generative AI technology to achieve efficiency and fairness in corporate goal management. This system is expected to improve employee motivation and the performance of the entire organization.

[0540] The processing flow will be explained below.

[0541] Step 1:

[0542] The administrator logs in to the system using a terminal. The terminal displays the login screen, and the administrator enters their ID and password. The server receives this information and authenticates the administrator.

[0543] Step 2:

[0544] The administrator uses the terminal to upload the company's goal management rules and past goal setting data. The terminal displays the file upload interface, and the administrator selects the required data file and presses the upload button. The server receives these files, analyzes the data, and imports it.

[0545] Step 3:

[0546] The server trains the AI ​​model based on the uploaded data, analyzing the goal management rules stored in the database and past goal setting data to learn appropriate goal setting patterns.

[0547] Step 4:

[0548] A regular employee accesses the system using a terminal and enters their ID and password on the login screen. The terminal then sends this information to the server for user authentication.

[0549] Step 5:

[0550] The server checks the user's ID and password, and if authentication is successful, it confirms the user's authority and retrieves the dashboard information. The terminal displays the retrieved dashboard information to the user.

[0551] Step 6:

[0552] To start goal setting, the user accesses the chat interface and enters, "I want to set my goal for this month." The device then sends the user's request to the server.

[0553] Step 7:

[0554] The server analyzes the user's input using natural language processing (NLP) algorithms and generates appropriate goal candidates based on past data and the company's goal management rules.

[0555] Step 8:

[0556] The device displays the generated target candidates in a chat interface and suggests them to the user, who can then provide feedback and request any necessary corrections or adjustments.

[0557] Step 9:

[0558] The server receives the user's feedback, analyzes it again using a natural language processing algorithm, and generates new target candidates based on the feedback. The device then presents the regenerated target candidates to the user.

[0559] Step 10:

[0560] When the user confirms the final goal and is satisfied with it, he / she inputs "confirm goal" into the chat interface, and the terminal sends a confirmation request to the server.

[0561] Step 11:

[0562] The server stores the determined goals in a database and manages them for later reference and evaluation. The terminal displays the status of the saved goal to the user.

[0563] As described above, the system efficiently supports the user in setting goals through each step.

[0564] Example 1

[0565] 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."

[0566] One of the challenges in corporate goal management is the difficulty of setting goals efficiently and fairly. In conventional systems, goal setting is subjective, which can lead to situations where improvements in overall corporate performance cannot be expected. Furthermore, there is a problem in that systems lack the flexibility to regenerate goals based on user feedback, making it difficult to respond to individual needs.

[0567] 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.

[0568] In this invention, the server includes means for importing data related to corporate goal management and training an AI model, means for authenticating users and providing a goal setting interface to the users, means for analyzing user input and proposing goal setting based on the corporate goal management rules, means for receiving user feedback and regenerating and confirming goals, means for storing the confirmed goals in a database, a procedure for reanalyzing the data and regenerating goals based on the user feedback, and means for verifying the training results of the AI ​​model and fine-tuning them as necessary, thereby improving the efficiency and fairness of corporate goal management and enabling flexible goal setting that meets individual needs.

[0569] "Corporate goal management" is the process of managing and evaluating the specific goals set to achieve a company's objectives, as well as the plans and measures based on those goals.

[0570] "Generative AI" refers to technology that uses artificial intelligence to analyze and generate data, automatically creating new proposals and goals based on user input.

[0571] A "chat-type tool service" refers to a system that provides an interface that allows users to interact in a conversational format using natural language.

[0572] "Importing data" refers to the process of bringing external data into a system and converting it into a usable format.

[0573] "Training an AI model" refers to the process of training an artificial intelligence algorithm using large amounts of data to improve its ability to perform a specified task.

[0574] "User authentication" refers to the process of verifying the identity of users accessing a system and ensuring that only authorized users can use it.

[0575] "Goal setting interface" refers to an interactive input screen for a user to input the goals they wish to achieve.

[0576] "Analysis" is the process of interpreting, understanding, and evaluating input data to extract necessary information and generate appropriate output.

[0577] "Receiving feedback" refers to the system collecting responses and opinions from users and using them as input for the next step.

[0578] "Regeneration" is the process of creating new proposals and goals that incorporate user feedback based on the initial analysis results and proposals.

[0579] "Confirmation" refers to the user agreeing with the presented goal and officially registering that goal in the system.

[0580] "Saving to the database" refers to storing the determined data in the system's storage device for future reference.

[0581] "Reanalysis" refers to the process of generating more appropriate suggestions and goals by reanalyzing based on the feedback received.

[0582] "Verification" refers to the process of evaluating the training results of an AI model and confirming its accuracy and reliability.

[0583] "Fine-tuning" is the process of making small modifications to an AI model to improve its accuracy based on training and validation results.

[0584] This invention relates to a chat tool service equipped with generative AI for efficient and fair corporate goal management. This system consists of three main elements: a server, a user terminal, and a user. Specific details for implementing the invention are described below.

[0585] System configuration

[0586] 1. Server:

[0587] The server hosts the generative AI model and includes a database for storing data related to the company's goal management. The server trains the AI ​​model, authenticates users, suggests goal setting, reanalyzes feedback, and stores established goals.

[0588] Example of hardware to be used: A server machine with a powerful processor and large memory capacity.

[0589] Examples of software used: Database Management Systems (DBMS), generative AI models (e.g., Python TensorFlow, etc.).

[0590] 2. User Device:

[0591] The user terminal is a device through which the user accesses the system, and provides a login interface and a chat interface for goal setting.

[0592] Examples of hardware used: PCs, tablets, and smartphones.

[0593] Examples of software used: web browsers (e.g., Google Chrome, Microsoft Edge, etc.), dedicated applications.

[0594] 3. User:

[0595] Users are company employees or managers who participate in the goal setting process. Users access the server using their terminals and set their own goals.

[0596] Specific processing details

[0597] 1. Initial setup and learning

[0598] The server receives the company's goal management data provided by the administrator and stores it in a database. This data is used for initial training of the AI ​​model.

[0599] Example: An administrator uploads an Excel file containing goal data from the past three years and the company's goal management rules to the system from a terminal. The server imports this data and provides it as training data for the AI ​​model.

[0600] 2. User Authentication

[0601] Users access the system from their own terminals and enter their ID and password into the login interface. The server receives this and verifies it against the information in the database.

[0602] Example: When an employee logs in to the system, they enter their employee ID "12345" and password "password123" on the login screen. The server receives this and performs authentication, and if successful, the employee can access the dashboard.

[0603] 3. The dialogue process of goal setting

[0604] Users initiate goal setting using the chat interface, for example by typing, "I'd like to set goals for this month."

[0605] The server analyzes the user's input and generates candidate goals based on the company's goal management rules. The generative AI model then suggests appropriate goals.

[0606] Example: When an employee types "increase sales by 500,000 yen" into the chat interface, the generative AI model suggests the goal "increase sales by 500,000 yen and acquire 10 new customers" based on past data and company rules.

[0607] 4. Set and save your goals

[0608] The user can provide feedback on the proposed goals and request any necessary modifications. The server re-analyzes the feedback and regenerates the goals tailored to the user's needs.

[0609] If the user is satisfied with the regenerated goal, he / she confirms it, and the server stores the confirmed goal in the database for future reference.

[0610] Example: If a user gives feedback saying, "Sales of 500,000 yen are difficult, how about 400,000 yen?", the server's generative AI model will reanalyze and re-propose a goal of "increasing sales by 400,000 yen." If the user is satisfied, the goal is confirmed and the server saves it in the database.

[0611] This system ensures efficiency and fairness in corporate goal management and enables flexible goal setting to meet individual needs. The use of generative AI models is expected to improve overall corporate performance.

[0612] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0613] Step 1:

[0614] Initial Setup and Data Import

[0615] Administrator: Use your own device to upload the company's goal management data to the system as an Excel file.

[0616] Input: A file containing goal data from the past three years and the company's goal management rules.

[0617] Server: Receives the uploaded data, stores it in a database, and then starts training a generative AI model based on this data.

[0618] Output: A dataset used as training data and a trained AI model.

[0619] Specific operation: The server stores the received data in storage and uses Python TensorFlow to train the AI ​​model.

[0620] Step 2:

[0621] User Authentication

[0622] User: Accesses the system from their own terminal and enters their ID and password on the login screen.

[0623] Input: ID and password.

[0624] Server: Receives the user's input information and authenticates it by comparing it with the registered information in the database.

[0625] Output: The authentication result (success or failure).

[0626] What happens: The user enters their login information, the server checks it against a database to authenticate them, and if successful, gives them access to the dashboard.

[0627] Step 3:

[0628] Start setting goals

[0629] User: Using the chat interface, type "I want to set my goals for this month."

[0630] Input: A goal-setting opening prompt such as "I'd like to set a goal for this month."

[0631] Server: Parses user input and initiates the goal setting process.

[0632] Output: Triggering the goal setting process for the generative AI model.

[0633] What happens: The chat interface receives the user's prompt, the server parses the prompt and triggers the goal setting process.

[0634] Step 4:

[0635] Proposing goals

[0636] Server: Analyzes user input, and a generative AI model generates candidate goals based on past goal data and company rules.

[0637] Input: User-entered goal setting prompts and historical goal data, company rules.

[0638] Generative AI model: Generates suitable target candidates.

[0639] Output: Candidate goals suggested to the user.

[0640] Specific operation: The server analyzes the user's input, and the generative AI model generates and suggests goals such as "increase sales by 500,000 yen and acquire 10 new customers."

[0641] Step 5:

[0642] Receiving and regenerating feedback

[0643] User: Give feedback on the proposed goal, such as "Sales of 500,000 yen are difficult. How about 400,000 yen?"

[0644] Input: User feedback.

[0645] Server: Receives feedback, reanalyzes, and the generative AI model recreates the goal.

[0646] Output: Revised target candidates.

[0647] Specific operation: The server reanalyzes the feedback content, and the generative AI model re-proposes the goal of "increasing sales by 400,000 yen."

[0648] Step 6:

[0649] Confirm and save your goals

[0650] User: If satisfied with the regenerated goal, type "Confirm this goal."

[0651] Input: Enter your final intention.

[0652] Server: Stores the determined goals in a database for future reference.

[0653] Output: Confirmed targets stored in the database.

[0654] Specific behavior: The server receives the user's confirmation input and saves the goal in the database.

[0655] This process flow enables efficient and fair goal management for companies. The use of generative AI models enables appropriate goal setting based on diverse data and can flexibly respond to user feedback.

[0656] (Application example 1)

[0657] 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."

[0658] A chat-based tool service equipped with generative AI for efficient and fair corporate goal management is problematic because conventional methods are labor-intensive, time-consuming, and subjective judgment bias is unavoidable. Furthermore, managing manufacturing processes within factories is difficult to do efficiently because of the complex procedures and real-time progress management required. A new system is needed to solve these issues.

[0659] 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.

[0660] In this invention, the server includes means for importing data related to corporate goal management and training an AI model, means for authenticating users and providing a goal setting interface to the users, means for analyzing user input and proposing goal setting based on the corporate goal management rules, means for receiving user feedback and regenerating and confirming goals, means for saving the confirmed goals in a database, means for importing data related to manufacturing processes in a factory and proposing production goals, and means for receiving input from workers, monitoring the production process in real time, and tracking progress. This enables improved efficiency and fairness in corporate goal management and efficient management of manufacturing processes in a factory.

[0661] "Corporate goal management rules" refer to the standards and procedures for achieving the goals set by a company.

[0662] A "generative AI model" refers to an artificial intelligence system that has been trained to perform a specific task using machine learning algorithms.

[0663] "Chat-type tool service" refers to a software service that communicates with users in an interactive format using text or voice.

[0664] "Means for importing data" refers to a means or method for bringing data into the system from outside.

[0665] "Means for authenticating a user" refers to a means or method for verifying a user's identity and authority.

[0666] "Goal setting interface" refers to the screens and processes through which a user sets goals.

[0667] "Means for parsing user input" refers to the means or methods for understanding and processing information provided by a user.

[0668] The "means for proposing goal setting" refers to a means or method for generating and proposing appropriate goals based on the user's needs and the company's rules.

[0669] "Means for regenerating and finalizing goals" refers to the means or method for regenerating and finalizing goals based on user feedback.

[0670] "Means of storing data in a database" refers to a means or method of systematically accumulating data and storing it in a manner that allows it to be accessed later.

[0671] A "manufacturing process" refers to the series of steps or operations required to create a product.

[0672] "Means for proposing production targets" refers to a means or method for proposing production volume or quality standards to be achieved in the manufacturing process.

[0673] "Means for monitoring the production process in real time" refers to a means or method for observing and checking the progress of the manufacturing process in real time.

[0674] "Means for tracking progress" refers to the means or method for continually tracking and recording the progress of a manufacturing process or goal achievement.

[0675] This invention is a system for efficiently and fairly managing corporate targets and implementing manufacturing processes within factories. This system consists of three main elements: a server, a user terminal, and a user, and provides a chat-type tool service equipped with a generative AI model.

[0676] System configuration and operation overview

[0677] 1. Server Initial Setup and Learning

[0678] As an initial setting, the server imports data on the company's goal management and manufacturing process data from the manager. The server uses this data to train the generative AI model. For example, by importing data on the company's goal management rules, past goal data, and manufacturing process data, the AI ​​model learns specific needs and patterns.

[0679] 2. User Authentication

[0680] The user accesses the system from a smartphone or head-mounted display and enters the necessary information on the login screen. The server receives this information, authenticates it, and verifies whether the user has the appropriate access rights. This authentication process is carried out using a security module (e.g., OAuth 2.0).

[0681] 3. The dialogue process of goal setting

[0682] Users initiate goal setting using a chat interface (text or voice). For example, if a user types, "I want to set a goal for this month," the server analyzes the user's input and generates appropriate goal candidates based on past data and company rules. These suggested goal candidates are displayed on the user's device interface. The user can then provide feedback and request any necessary revisions.

[0683] 4. Interactive process of manufacturing process

[0684] Similarly, for manufacturing processes within factories, users can use the chat interface to ask about production targets. For example, by typing, "What is today's production target?", the server analyzes the production line settings and progress and suggests appropriate production targets. Feedback can also be provided in response, and the production process is monitored and progress is tracked in real time.

[0685] Hardware and software used

[0686] Hardware

[0687] Smartphone or head-mounted display (e.g., general mobile device, display device)

[0688] software

[0689] User authentication: security module (e.g. OAuth 2.0)

[0690] Speech recognition system (e.g., Google Cloud Speech-to-Text)

[0691] Generative AI models (e.g., OpenAI GPT-4)

[0692] Examples of concrete examples and prompts

[0693] Example 1: Corporate goal setting

[0694] When a user inputs a goal setting of "increase sales by 500,000 yen" on their smartphone, the server proposes the goal of "increase sales by 500,000 yen and acquire 10 new customers." If the user provides feedback saying, "500,000 yen in sales is difficult. How about 400,000 yen?", the AI ​​responds, "A goal of increasing sales by 400,000 yen is appropriate." If the user is satisfied, the goal is confirmed and the server saves it in the database.

[0695] Example 2: Factory manufacturing process management

[0696] When a user wears the head-mounted display and asks, "What is today's production target?", the server responds, "Line 1 is scheduled to produce 500 units." The production line is monitored in real time, and progress is displayed on the display. For example, if a defective product occurs, a notification will appear saying, "A defective product has been found on Line 1," enabling a prompt response.

[0697] Prompt Sentence Examples

[0698] "Please tell me the achievement rate for yesterday's production target."

[0699] "Show me the production plan for the next shift."

[0700] "Please list the problems that have occurred on the third line and propose solutions."

[0701] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0702] Step 1:

[0703] Initial setup and training:

[0704] The server imports the company's goal management data and manufacturing process data within the factory. The manager uses a terminal to upload the necessary data files to the system. The server trains the generative AI model based on the imported data, learning the company's rules and patterns. Specifically, it analyzes past goal data and manufacturing process setting data to improve the accuracy of the model.

[0705] input:

[0706] Manager-provided historical goal data, manufacturing process data, and company goal management rules.

[0707] output:

[0708] A trained generative AI model.

[0709] Step 2:

[0710] User authentication:

[0711] A user accesses the system on their own device and enters their ID and password on the login screen. The server receives this information and uses a security module (e.g., OAuth 2.0) to verify the user's identity and permissions. If authentication is successful, a dashboard is displayed on the user's device.

[0712] input:

[0713] User ID and password.

[0714] output:

[0715] Dashboard after successful authentication.

[0716] Step 3:

[0717] The goal-setting dialogue process:

[0718] The user inputs "I want to set a goal for this month" through the chat interface. The server analyzes this input and generates appropriate goal candidates by referencing past data and company rules. The generated goal candidates are displayed on the user's device.

[0719] input:

[0720] User goal setting request.

[0721] output:

[0722] Proposed target candidates.

[0723] Data processing / calculation:

[0724] Generate meaningful candidates for goals based on historical data and company rules.

[0725] Step 4:

[0726] Receiving user feedback and regenerating goals:

[0727] The user provides feedback on the proposed goal. For example, they might say, "Sales of 500,000 yen are difficult. How about 400,000 yen?" The server analyzes this feedback again and regenerates the goal. The regenerated goal is displayed on the user's device.

[0728] input:

[0729] User feedback.

[0730] output:

[0731] Regenerated target candidates.

[0732] Data processing / calculation:

[0733] Generate new goals based on user feedback.

[0734] Step 5:

[0735] Save confirmed goals:

[0736] If the user is satisfied with the regenerated goal, he / she confirms the goal in the system, and the server stores this confirmed goal in the database for future reference.

[0737] input:

[0738] A goal established by the user.

[0739] output:

[0740] Confirmed goals stored in the database.

[0741] Data processing / calculation:

[0742] The determined goals are saved in a database format.

[0743] Step 6:

[0744] Manufacturing process interaction process:

[0745] A factory worker or manager can use the chat interface to ask, "What is today's production target?" The server analyzes the production line settings and progress, generates the production target for that day, and displays it on the user's device. The production process is monitored in real time, and progress is tracked.

[0746] input:

[0747] Questions about production goals for workers and managers.

[0748] output:

[0749] Proposed production target for the day.

[0750] Data processing / calculation:

[0751] Analyze production line settings and progress data to generate daily production targets.

[0752] Step 7:

[0753] Real-time process monitoring and feedback:

[0754] The server monitors the production process in real time, tracking progress and notifying workers of any irregularities, such as defective products, so that workers and managers can quickly respond and enter instructions into the system to resolve the issues.

[0755] input:

[0756] Real-time production process data and defect occurrence information.

[0757] output:

[0758] Progress tracking and anomaly notifications.

[0759] Data processing / calculation:

[0760] Analyze real-time data to detect anomalies and provide appropriate notifications.

[0761] 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.

[0762] This invention relates to a chat tool service that incorporates a generation AI for efficient and fair corporate goal management, and combines it with an emotion engine for recognizing user emotions and reflecting them in goal setting. Below, we will generate a program for this system and explain its processing overview in natural language.

[0763] System Overview

[0764] This system consists of three main elements: a server, a user device, and the user. The generative AI model and emotion engine running on the server set goals while interacting with the user via the user device. The server has learned the company's goal management rules and suggests appropriate goal settings based on input from the user. It also recognizes the user's emotions and reflects them in the goal suggestions.

[0765] Program processing overview

[0766] 1. Initial setup and learning

[0767] The server imports data on the company's goal management from the administrator, who then uploads the data to the system using a terminal.

[0768] 2. User Authentication

[0769] A user accesses the system from their own terminal and enters their ID and password on the login screen. The server receives this, performs authentication, and verifies the user's access rights.

[0770] 3. Emotion recognition

[0771] When a user accesses the chat interface and enters input related to goal setting, the emotion engine analyzes the user's input to determine emotions, for example, by recognizing emotions based on specific keywords or context within the text.

[0772] The server uses the emotion data obtained from the emotion engine to adjust the suggested goal settings, suggesting realistic goals if the user is overly stressed, or setting challenging goals if the user is highly motivated.

[0773] 4. The dialogue process of goal setting

[0774] To start goal setting, the user accesses the chat interface and inputs, "I want to set my goals for this month." The terminal then sends the user's request to the server.

[0775] The server generates appropriate goal candidates based on the user's input and the emotional information output by the emotion engine. For example, if a user inputs "increase sales by 500,000 yen," and the emotion engine determines that the load is too high based on the user's emotions, it will suggest a more realistic "increase sales by 400,000 yen."

[0776] 5. Suggestions and Feedback

[0777] The device displays the generated target candidates in a chat interface and suggests them to the user, who can then provide feedback and request any necessary corrections or adjustments.

[0778] The server receives the user's feedback, analyzes it again using the emotion engine and natural language processing algorithm, and generates new goal candidates based on the feedback. The device then presents the regenerated goal candidates to the user.

[0779] 6. Set and save your goals

[0780] If the user is satisfied with the regenerated goal, he / she inputs "confirm goal" into the chat interface, and the device sends a confirmation request to the server.

[0781] The server stores the determined goals in a database and manages them for later reference and evaluation. The terminal displays the status of the saved goal to the user.

[0782] Specific examples

[0783] Initial setup and training example

[0784] Administrators upload a file containing goal data from the past three years and the company's goal management rules from their device, which allows the generative AI to set goals according to the company's specific needs and patterns.

[0785] User Authentication Example

[0786] When an employee attempts to log in to the system, they enter their ID and password on the login screen, which the server receives and executes the authentication process. After successful authentication, they can access the dashboard.

[0787] Goal Setting Examples

[0788] An employee may enter a goal setting such as "increase sales by 500,000 yen" into the chat interface. The AI ​​generator may compare this with past data and suggest a goal such as "increase sales by 500,000 yen and acquire 10 new customers."

[0789] If the emotion engine detects high levels of stress from the user's input, the generative AI will suggest realistic goals such as "increase sales by 400,000 yen."

[0790] If the employee is satisfied with the proposal, the goal is confirmed and the server stores the goal in the database.

[0791] As described above, the system of the present invention provides a chat-type tool that uses generative AI technology and an emotion engine to achieve efficiency and fairness in corporate goal management. This system enables goal setting that takes into account employees' emotions, which is expected to improve the performance of the entire organization.

[0792] The processing flow will be explained below.

[0793] Step 1:

[0794] The administrator logs in to the system using a terminal. The terminal displays the login screen, and the administrator enters their ID and password. The server receives this information and authenticates the administrator.

[0795] Step 2:

[0796] The administrator uses the terminal to upload the company's goal management rules and past goal setting data. The terminal displays the file upload interface, and the administrator selects the required data file and presses the upload button. The server receives these files, analyzes the data, and imports it.

[0797] Step 3:

[0798] The server trains the AI ​​model based on the uploaded data, analyzing the goal management rules stored in the database and past goal setting data to learn appropriate goal setting patterns.

[0799] Step 4:

[0800] A regular employee accesses the system using a terminal and enters their ID and password on the login screen. The terminal then sends this information to the server for user authentication.

[0801] Step 5:

[0802] The server checks the user's ID and password, and if authentication is successful, it confirms the user's authority and retrieves the dashboard information. The terminal displays the retrieved dashboard information to the user.

[0803] Step 6:

[0804] To start goal setting, the user accesses the chat interface and enters, "I want to set my goal for this month." The device then sends the user's request to the server.

[0805] Step 7:

[0806] The server analyzes the user's input using a natural language processing (NLP) algorithm and generates appropriate goal candidates based on past data and the company's goal management rules. Meanwhile, the emotion engine recognizes emotions from the user's input and sends the analysis results to the server.

[0807] Step 8:

[0808] The server reflects the emotional information output by the emotion engine in the generated goal candidates and proposes appropriate goal settings. For example, if a user is suggested to "increase sales by 500,000 yen" and the emotion engine detects high stress, it will adjust it to "increase sales by 400,000 yen."

[0809] Step 9:

[0810] The device displays the generated target candidates in a chat interface and suggests them to the user, who can then provide feedback and request any necessary corrections or adjustments.

[0811] Step 10:

[0812] The server receives the user's feedback, analyzes it again using the emotion engine and natural language processing algorithm, and generates new goal candidates based on the feedback. The device then presents the regenerated goal candidates to the user.

[0813] Step 11:

[0814] When the user confirms the final goal and is satisfied with it, he / she inputs "confirm goal" into the chat interface, and the terminal sends a confirmation request to the server.

[0815] Step 12:

[0816] The server stores the determined goals in a database and manages them for later reference and evaluation. The terminal displays the status of the saved goal to the user.

[0817] Through the above steps, the system supports users in setting goals efficiently and fairly, and also provides goal setting that takes into account the user's emotions.

[0818] Example 2

[0819] 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."

[0820] Corporate goal management is complex, and it is particularly difficult to consider the emotions and motivation of individual employees. As a result, goal setting tends to be one-sided, which can have a negative impact on employee performance and motivation. It is also difficult to effectively utilize past goal data and appropriately set realistic yet challenging goals.

[0821] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for importing data related to corporate goal management and training an AI model, means for authenticating users and providing a goal setting interface to the users, means for analyzing user input, recognizing the user's emotions using an emotion engine, and proposing goal setting based on the corporate goal management rules, means for receiving user feedback and regenerating and confirming goals using an emotion engine and a natural language processing algorithm, and means for storing the confirmed goals in a database. This enables realistic and challenging goal setting that takes into account employees' emotions and motivation, improving the efficiency and fairness of corporate-wide goal management.

[0822] "Generative AI" is artificial intelligence that learns the data necessary for setting corporate goals and suggests appropriate goals through dialogue.

[0823] An "emotion engine" is an algorithm that analyzes user input and recognizes emotions.

[0824] A "chat-type tool" is a tool that provides an interface for setting goals through dialogue with the user.

[0825] "Corporate goal management rules" refer to guidelines and policies that a company uses to set and manage goals.

[0826] "Goal setting interface" means an interactive interface through which a user sets goals.

[0827] A "natural language processing algorithm" is an algorithm for analyzing text data and understanding its meaning.

[0828] A "database" is a system for storing and managing determined goals, emotional data, etc.

[0829] "User authentication" is the process of verifying a user's authenticity using the user's ID and password.

[0830] "Feedback" refers to the opinions and requests for revisions that users provide to proposed goals.

[0831] "Regeneration" refers to the process of regenerating new goals based on user feedback and emotional data.

[0832] "Training" refers to the process by which an AI model learns a company's goal management rules and data.

[0833] This invention relates to a chat tool service that incorporates a generation AI for efficient and fair corporate goal management, and combines it with an emotion engine for recognizing user emotions and reflecting them in goal setting. This system is composed of three main elements: a server, a user terminal, and the user, and is specifically implemented as follows:

[0834] System configuration

[0835] Hardware and Software

[0836] Server: The central part of the system, it manages the company's goal management data and runs the generative AI model and emotion engine. The specific software on which the generative AI model and emotion engine are installed is

[0837] User terminal: A device such as a PC or smartphone that allows a user to access the system. An application that provides a chat-style interface is installed on this device.

[0838] Database: A database system for storing corporate goal management data, defined goals, and user authentication information.

[0839] Program processing

[0840] Initial setup and training

[0841] Importing Data

[0842] The administrator uploads past goal data and the company's goal management rules from a terminal. The server receives this data and stores it in a database.

[0843] Training an AI model

[0844] The server uses the uploaded data to train the generative AI model, which learns the specific needs and patterns of the company through this training process.

[0845] User Authentication

[0846] Input on the login screen

[0847] Users access the system from their own terminal and enter their ID and password on the login screen.

[0848] Authentication Process

[0849] The device sends the entered ID and password to the server, which receives it and checks it against the authentication information in its database. If authentication is successful, the user can access the system dashboard.

[0850] emotion recognition

[0851] Parsing input

[0852] The user types "I want to set my goals for this month" into the chat interface, and the device sends this input to the server.

[0853] Running the Emotion Engine

[0854] The server passes the received input to the emotion engine, which analyzes the input text and recognizes the user's emotion from specific keywords and context.

[0855] The interactive process of goal setting

[0856] Target candidate generation

[0857] The server inputs goal-setting prompts into the generative AI model based on the user's input and the emotional information obtained from the emotion engine.

[0858] The generative AI model generates multiple target candidates and returns them to the server.

[0859] Suggestions for users

[0860] The server transmits the generated target candidates to the terminal, which displays them on the chat interface.

[0861] Suggestions and Feedback

[0862] Gathering feedback

[0863] The user can input feedback for the proposed goal candidates, for example, "I would like the goal to be adjusted to be more realistic."

[0864] Analyzing feedback and adjusting goals

[0865] The device sends the feedback to the server, which receives it and analyzes it again using the emotion engine and natural language processing algorithm, generating new target candidates and sending them to the device, which then presents them to the user.

[0866] Confirm and save your goals

[0867] Determine your goals

[0868] If the user is satisfied with the proposed goal, he / she inputs "confirm goal" into the chat interface, and the device sends a confirmation request to the server.

[0869] Saving to a database

[0870] The server saves the confirmed goal in the database. After saving is complete, the device displays "Goal confirmed" to the user.

[0871] Specific examples

[0872] Initial setup and training example

[0873] Managers upload goal data from the past three years and their company's goal management rules via their devices, allowing the generative AI to set goals according to the company's specific needs and patterns.

[0874] User Authentication Example

[0875] When an employee logs in to the system, they enter their ID and password on the login screen. The server receives this and performs an authentication process. After successful authentication, they can access the dashboard.

[0876] Goal Setting Examples

[0877] An employee enters a goal setting, such as "increase sales by 500,000 yen," into the chat interface. The generation AI compares it with past data and may suggest a goal such as "increase sales by 500,000 yen and acquire 10 new customers." If the emotion engine detects high stress from the user's emotions, the generation AI will suggest a more realistic goal, such as "increase sales by 400,000 yen." If the employee agrees with this suggestion, the goal is confirmed and the server saves it in the database.

[0878] Prompt Sentence Examples

[0879] "Sales increased by 500,000 yen"

[0880] "Acquired 10 new customers"

[0881] "Complete Project A"

[0882] As described above, the system of the present invention provides a chat-type tool that uses generative AI technology and an emotion engine to achieve efficiency and fairness in corporate goal management. This enables goal setting that takes into account employees' emotions, and is expected to improve the performance of the entire organization.

[0883] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0884] Step 1: Initial setup and training

[0885] Input: The administrator uploads the company's goal management data and past goal data from the terminal.

[0886] Operation: The device sends the uploaded data to the server.

[0887] Data processing: The server receives the data and stores it in a database.

[0888] AI training: The server uses the stored data to train the generative AI model, learning the company's goal management rules and past patterns.

[0889] Output: A trained generative AI model.

[0890] Step 2: User authentication

[0891] Input: The user accesses the system on their own terminal and enters their ID and password on the login screen.

[0892] Operation: The terminal sends the entered ID and password to the server.

[0893] Data processing: The server receives the transmitted information and checks it against authentication data in its database.

[0894] Output: Authentication result (success or failure).

[0895] What happens: If authentication is successful, the server allows the user to access the dashboard.

[0896] Step 3: Emotion Recognition

[0897] Input: The user types "I want to set my goals for this month" into the chat interface.

[0898] Operation: The terminal sends the user's input to the server.

[0899] Data processing: The server passes the received input to the emotion engine, which analyzes the input text and recognizes the user's emotion.

[0900] Output: Emotion data (e.g., stress, high motivation, etc.).

[0901] How it works: The server stores emotion data in a database.

[0902] Step 4: The goal-setting dialogue process

[0903] Input: Emotion data and user input.

[0904] How it works: The server inputs goal-setting prompts into the generative AI model based on emotion data and user input.

[0905] Data processing: The generative AI model generates multiple target candidates.

[0906] Output: Generated target candidates.

[0907] How it works: The server sends potential targets to the device, which displays them in the chat interface.

[0908] Step 5: Suggestions and feedback

[0909] Input: User feedback (e.g., "Please adjust your goal to be more realistic").

[0910] Operation: The device sends the feedback content to the server.

[0911] Data processing: The server receives the feedback, analyzes it using an emotion engine and natural language processing algorithms, and generates new target candidates.

[0912] Output: New target candidates.

[0913] Operation: The server sends the newly generated target candidates to the terminal, which then presents them to the user.

[0914] Step 6: Confirm and save your goal

[0915] Input: "Confirm goal" typed by the user into the chat interface.

[0916] Operation: The terminal sends a confirmation request to the server.

[0917] Data processing: The server stores the determined goals in a database.

[0918] Output: Save completion status.

[0919] Action: The device displays "Target confirmed" to the user.

[0920] The above is the specific processing flow of the system. At each step, appropriate data processing and analysis are performed based on the input data, allowing users to smoothly proceed through the goal setting process.

[0921] (Application example 2)

[0922] 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."

[0923] Conventional corporate goal management systems have problems with efficiency and objectivity, and in particular, they often set goals without considering the emotional state of individual users. This can result in excessive burdens on users, leading to a decrease in motivation and increased stress. Furthermore, in certain fields, such as logistics centers, detailed goal setting and rapid feedback are required to respond to the complexity of work content and fluctuating demand. An efficient and fair goal management system that can meet these needs is needed.

[0924] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0925] In this invention, the server includes: means for importing data related to corporate goal management and training an AI model; means for authenticating a user and providing the user with a goal setting interface; means for analyzing user input and suggesting goal setting based on the corporate goal management rules; means including an emotion engine that recognizes the user's emotions and reflects them in goal setting; means using a generative AI model that suggests goals according to the user's emotions; means for receiving user feedback and regenerating and confirming goals; and means for storing the confirmed goals in a database. This enables realistic and efficient goal setting that takes the user's emotional state into consideration, making fair and appropriate goal management possible, especially in specific fields such as logistics centers.

[0926] "Corporate goal management rules" refer to the standards and guidelines for planning, evaluating, and achieving performance goals set by a company.

[0927] A "generative AI model" refers to an artificial intelligence algorithm that learns from large amounts of data and automatically generates appropriate responses and suggestions based on user input.

[0928] "Chat-type tool service" refers to an interactive software application that assists users with specific tasks through text and voice interactions.

[0929] "User authentication" refers to the process of verifying the identity of users accessing a system and granting appropriate access privileges.

[0930] "Goal-setting interface" refers to a system component that provides the screens and input forms through which a user sets goals.

[0931] An "emotion engine" refers to technology that analyzes a user's emotional state from text input and other data and adjusts responses based on the results.

[0932] "Feedback" refers to the evaluations and opinions that users provide in response to suggestions and answers from the system.

[0933] "Database" refers to a digital storage system that systematically organizes and stores data, making it quickly accessible when needed.

[0934] The system that embodies this invention is a chat-type tool service equipped with a generative AI model that learns a company's goal management rules and proposes appropriate goal setting in an interactive format. This system is composed of the following main components:

[0935] Program processing overview

[0936] 1. Importing and training corporate goal management data

[0937] The server trains the AI ​​model using data on corporate goal management imported from administrators, allowing it to learn the company's unique goal-setting rules and patterns and suggest appropriate goals.

[0938] 2. User authentication and interface provision

[0939] Users access the system using their own devices (smartphones, tablets, PCs, etc.) and enter their ID and password on the login screen. The server receives this, performs an authentication process, and grants the user appropriate access rights. If authentication is successful, the user can access the goal setting interface.

[0940] 3. Analyzing user input and suggesting goal setting

[0941] When a user enters text related to goal setting into the chat interface, the server uses a generative AI model to analyze the input. The server also analyzes the user's emotions and reflects them in the proposed goals. For example, if a user enters "increase shipment volume by 50 pallets," and the emotion analysis indicates high stress, the generative AI model will suggest a more realistic goal, such as "increase shipment volume by 40 pallets."

[0942] 4. Utilizing the Emotion Engine

[0943] The emotion engine analyzes the user's input text to determine whether the emotion is positive or negative. For example, if the user's input is negative, such as "I'm not sure if I can achieve this," the emotion engine will detect this and reflect it in the suggested goal.

[0944] 5. Feedback and goal regeneration / confirmation

[0945] The user provides feedback on the proposed goal, and the server regenerates the goal based on that feedback. If the user is satisfied with the goal and clicks "confirm," the server stores the goal in a database for future reference and evaluation.

[0946] 6. Save and manage your goals

[0947] The confirmed goals are stored in a database on the server. This database accumulates past goal data and new data, and is used to continuously improve the accuracy of the AI ​​model. It is also possible to generate reports and alerts according to the company's goal management rules.

[0948] Hardware and software configuration

[0949] Hardware: Smartphones, tablets, PCs, servers

[0950] Software: Flask (web server), TextBlob (sentiment analysis library), OpenAI GPT-3 (generative AI model)

[0951] Examples of concrete examples and prompts

[0952] Examples:

[0953] A staff member types "increase today's shipments by 50 pallets" into the chat interface. If the emotion engine detects anxiety, the generative AI model suggests a more realistic goal, such as "increase today's shipments by 40 pallets."

[0954] Example prompt sentence:

[0955] Given the user's negative sentiment, suggest a realistic goal: Increase today's shipping volume by 50 pallets.

[0956] This system enables realistic and efficient goal setting that takes into account the user's emotional state, enabling fair and appropriate goal management even in specific fields such as logistics centers.

[0957] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0958] Step 1:

[0959] The server imports the company's goal management data and trains the generative AI model. The administrator uploads past goal data and the company's goal management rules to the server. This data is analyzed by the AI ​​model, which learns goal setting patterns and standards. The input is the data file from the administrator, and the output is the trained AI model.

[0960] Step 2:

[0961] The terminal performs user authentication. The user accesses the login screen using their terminal and enters their ID and password. The entered authentication information is sent to the server, which verifies it and authenticates that the user is legitimate. The input is the user's ID and password, and the output is the result of authentication success or failure.

[0962] Step 3:

[0963] The user accesses the goal setting interface and inputs text related to the goal setting. For example, the user inputs a goal such as "I want to increase shipping volume by 50 pallets this month." The input is the goal setting text entered by the user, and the output is a request to suggest appropriate goal settings.

[0964] Step 4:

[0965] The server analyzes the received user input and suggests goal setting using a generative AI model and emotion engine. The server analyzes the user's input text and generates appropriate goal candidates based on its content. At the same time, the emotion engine analyzes the user's emotions and reflects them in the generated goals. For example, if the user's input includes "I'm anxious," the emotion engine recognizes this and instructs the generative AI model to suggest realistic goals. The input is the user's text input and emotion data, and the output is appropriate goal setting suggestions.

[0966] Step 5:

[0967] The device displays the proposed goals to the user and asks for feedback. The user provides feedback on the proposed goals and requests corrections or reconfiguration as necessary. The inputs are the proposed goals from the server and the user's feedback, and the output is the feedback data sent to the server.

[0968] Step 6:

[0969] The server reanalyzes the user's feedback and regenerates goals as necessary. It uses a generative AI model and emotion engine to generate new goal candidates based on the feedback. The input is the user's feedback data, and the output is the regenerated goal settings.

[0970] Step 7:

[0971] When the user is satisfied with the regenerated goal, he / she inputs "confirm goal" into the terminal. The terminal sends this confirmation request to the server. The input is the user's confirmation request, and the output is a confirmation notification to the server.

[0972] Step 8:

[0973] The server saves the confirmed goals in a database. The confirmed goals are managed in the database so that they can be referenced and evaluated later. The input is the confirmed goal information, and the output is a notification that the goal has been saved to the database.

[0974] Through the above processing steps, the present invention is a system that realizes realistic and efficient goal setting that takes into account the user's emotional state.

[0975] 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.

[0976] 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.

[0977] 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.

[0978] [Third embodiment]

[0979] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0980] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0981] 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).

[0982] 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.

[0983] 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.

[0984] 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).

[0985] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for 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.

[0986] 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.

[0987] 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.

[0988] 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.

[0989] 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.

[0990] 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."

[0991] This invention relates to a chat tool service equipped with a generation AI for efficient and fair corporate goal management. Below, we will generate a program for this system and explain its processing overview in natural language.

[0992] System Overview

[0993] This system consists of three main elements: a server, a user device, and the user. The generative AI model running on the server sets goals while interacting with the user via the user device. The server has learned the company's goal management rules and suggests appropriate goal settings based on user input.

[0994] Program processing overview

[0995] 1. Initial setup and learning

[0996] The server imports data on the company's goal management from the administrator, who then uploads the data to the system using a terminal.

[0997] 2. User Authentication

[0998] A user accesses the system from their own terminal and enters their ID and password on the login screen. The server receives this, performs authentication, and verifies the user's access rights.

[0999] 3. The dialogue process of goal setting

[1000] Users use the chat interface to begin goal setting, for example by typing, "I'd like to set goals for this month," which starts the process.

[1001] The server analyzes the user's input and generates appropriate target candidates based on past target data and company rules.

[1002] The proposed target candidates are displayed in a chat interface on the user's device, and the user can provide feedback and request any necessary revisions.

[1003] The server re-analyzes the feedback and regenerates goals tailored to the user's needs.

[1004] 4. Set and save your goals

[1005] If the user is satisfied with the regenerated goal, he or she performs an operation to confirm the goal.

[1006] The server stores the determined goals in a database for future reference.

[1007] Specific examples

[1008] Initial setup and training example

[1009] Administrators upload a file containing goal data from the past three years and the company's goal management rules from their device, which allows the generative AI to set goals according to the company's specific needs and patterns.

[1010] User Authentication Example

[1011] When an employee attempts to log in to the system, they enter their ID and password on the login screen, which the server receives and executes the authentication process. After successful authentication, they can access the dashboard.

[1012] Goal Setting Examples

[1013] An employee may enter a goal setting such as "increase sales by 500,000 yen" into the chat interface. The AI ​​generator may compare this with past data and suggest a goal such as "increase sales by 500,000 yen and acquire 10 new customers."

[1014] If an employee gives feedback such as, "Sales of 500,000 yen are difficult; how about 400,000 yen?", the generating AI will analyze again and respond, "A goal of increasing sales by 400,000 yen would be appropriate."

[1015] Once the employee is satisfied, the goal is finalized and the server stores this goal in a database.

[1016] As described above, the system of the present invention provides a chat-based tool using generative AI technology to achieve efficiency and fairness in corporate goal management. This system is expected to improve employee motivation and the performance of the entire organization.

[1017] The processing flow will be explained below.

[1018] Step 1:

[1019] The administrator logs in to the system using a terminal. The terminal displays the login screen, and the administrator enters their ID and password. The server receives this information and authenticates the administrator.

[1020] Step 2:

[1021] The administrator uses the terminal to upload the company's goal management rules and past goal setting data. The terminal displays the file upload interface, and the administrator selects the required data file and presses the upload button. The server receives these files, analyzes the data, and imports it.

[1022] Step 3:

[1023] The server trains the AI ​​model based on the uploaded data, analyzing the goal management rules stored in the database and past goal setting data to learn appropriate goal setting patterns.

[1024] Step 4:

[1025] A regular employee accesses the system using a terminal and enters their ID and password on the login screen. The terminal then sends this information to the server for user authentication.

[1026] Step 5:

[1027] The server checks the user's ID and password, and if authentication is successful, it confirms the user's authority and retrieves the dashboard information. The terminal displays the retrieved dashboard information to the user.

[1028] Step 6:

[1029] To start goal setting, the user accesses the chat interface and enters, "I want to set my goal for this month." The device then sends the user's request to the server.

[1030] Step 7:

[1031] The server analyzes the user's input using natural language processing (NLP) algorithms and generates appropriate goal candidates based on past data and the company's goal management rules.

[1032] Step 8:

[1033] The device displays the generated target candidates in a chat interface and suggests them to the user, who can then provide feedback and request any necessary corrections or adjustments.

[1034] Step 9:

[1035] The server receives the user's feedback, analyzes it again using a natural language processing algorithm, and generates new target candidates based on the feedback. The device then presents the regenerated target candidates to the user.

[1036] Step 10:

[1037] When the user confirms the final goal and is satisfied with it, he / she inputs "confirm goal" into the chat interface, and the terminal sends a confirmation request to the server.

[1038] Step 11:

[1039] The server stores the determined goals in a database and manages them for later reference and evaluation. The terminal displays the status of the saved goal to the user.

[1040] As described above, the system efficiently supports the user in setting goals through each step.

[1041] Example 1

[1042] 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."

[1043] One of the challenges in corporate goal management is the difficulty of setting goals efficiently and fairly. In conventional systems, goal setting is subjective, which can lead to situations where improvements in overall corporate performance cannot be expected. Furthermore, there is a problem in that systems lack the flexibility to regenerate goals based on user feedback, making it difficult to respond to individual needs.

[1044] 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.

[1045] In this invention, the server includes means for importing data related to corporate goal management and training an AI model, means for authenticating users and providing a goal setting interface to the users, means for analyzing user input and proposing goal setting based on the corporate goal management rules, means for receiving user feedback and regenerating and confirming goals, means for storing the confirmed goals in a database, a procedure for reanalyzing the data and regenerating goals based on the user feedback, and means for verifying the training results of the AI ​​model and fine-tuning them as necessary, thereby improving the efficiency and fairness of corporate goal management and enabling flexible goal setting that meets individual needs.

[1046] "Corporate goal management" is the process of managing and evaluating the specific goals set to achieve a company's objectives, as well as the plans and measures based on those goals.

[1047] "Generative AI" refers to technology that uses artificial intelligence to analyze and generate data, automatically creating new proposals and goals based on user input.

[1048] A "chat-type tool service" refers to a system that provides an interface that allows users to interact in a conversational format using natural language.

[1049] "Importing data" refers to the process of bringing external data into a system and converting it into a usable format.

[1050] "Training an AI model" refers to the process of training an artificial intelligence algorithm using large amounts of data to improve its ability to perform a specified task.

[1051] "User authentication" refers to the process of verifying the identity of users accessing a system and ensuring that only authorized users can use it.

[1052] "Goal setting interface" refers to an interactive input screen for a user to input the goals they wish to achieve.

[1053] "Analysis" is the process of interpreting, understanding, and evaluating input data to extract necessary information and generate appropriate output.

[1054] "Receiving feedback" refers to the system collecting responses and opinions from users and using them as input for the next step.

[1055] "Regeneration" is the process of creating new proposals and goals that incorporate user feedback based on the initial analysis results and proposals.

[1056] "Confirmation" refers to the user agreeing with the presented goal and officially registering that goal in the system.

[1057] "Saving to the database" refers to storing the determined data in the system's storage device for future reference.

[1058] "Reanalysis" refers to the process of generating more appropriate suggestions and goals by reanalyzing based on the feedback received.

[1059] "Verification" refers to the process of evaluating the training results of an AI model and confirming its accuracy and reliability.

[1060] "Fine-tuning" is the process of making small modifications to an AI model to improve its accuracy based on training and validation results.

[1061] This invention relates to a chat tool service equipped with generative AI for efficient and fair corporate goal management. This system consists of three main elements: a server, a user terminal, and a user. Specific details for implementing the invention are described below.

[1062] System configuration

[1063] 1. Server:

[1064] The server hosts the generative AI model and includes a database for storing data related to the company's goal management. The server trains the AI ​​model, authenticates users, suggests goal setting, reanalyzes feedback, and stores established goals.

[1065] Example of hardware to be used: A server machine with a powerful processor and large memory capacity.

[1066] Examples of software used: Database Management Systems (DBMS), generative AI models (e.g., Python TensorFlow, etc.).

[1067] 2. User Device:

[1068] The user terminal is a device through which the user accesses the system, and provides a login interface and a chat interface for goal setting.

[1069] Examples of hardware used: PCs, tablets, and smartphones.

[1070] Examples of software used: web browsers (e.g., Google Chrome, Microsoft Edge, etc.), dedicated applications.

[1071] 3. User:

[1072] Users are company employees or managers who participate in the goal setting process. Users access the server using their terminals and set their own goals.

[1073] Specific processing details

[1074] 1. Initial setup and learning

[1075] The server receives the company's goal management data provided by the administrator and stores it in a database. This data is used for initial training of the AI ​​model.

[1076] Example: An administrator uploads an Excel file containing goal data from the past three years and the company's goal management rules to the system from a terminal. The server imports this data and provides it as training data for the AI ​​model.

[1077] 2. User Authentication

[1078] Users access the system from their own terminals and enter their ID and password into the login interface. The server receives this and verifies it against the information in the database.

[1079] Example: When an employee logs in to the system, they enter their employee ID "12345" and password "password123" on the login screen. The server receives this and performs authentication, and if successful, the employee can access the dashboard.

[1080] 3. The dialogue process of goal setting

[1081] Users initiate goal setting using the chat interface, for example by typing, "I'd like to set goals for this month."

[1082] The server analyzes the user's input and generates candidate goals based on the company's goal management rules. The generative AI model then suggests appropriate goals.

[1083] Example: When an employee types "increase sales by 500,000 yen" into the chat interface, the generative AI model suggests the goal "increase sales by 500,000 yen and acquire 10 new customers" based on past data and company rules.

[1084] 4. Set and save your goals

[1085] The user can provide feedback on the proposed goals and request any necessary modifications. The server re-analyzes the feedback and regenerates the goals tailored to the user's needs.

[1086] If the user is satisfied with the regenerated goal, he / she confirms it, and the server stores the confirmed goal in the database for future reference.

[1087] Example: If a user gives feedback saying, "Sales of 500,000 yen are difficult, how about 400,000 yen?", the server's generative AI model will reanalyze and re-propose a goal of "increasing sales by 400,000 yen." If the user is satisfied, the goal is confirmed and the server saves it in the database.

[1088] This system ensures efficiency and fairness in corporate goal management and enables flexible goal setting to meet individual needs. The use of generative AI models is expected to improve overall corporate performance.

[1089] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1090] Step 1:

[1091] Initial Setup and Data Import

[1092] Administrator: Use your own device to upload the company's goal management data to the system as an Excel file.

[1093] Input: A file containing goal data from the past three years and the company's goal management rules.

[1094] Server: Receives the uploaded data, stores it in a database, and then starts training a generative AI model based on this data.

[1095] Output: A dataset used as training data and a trained AI model.

[1096] Specific operation: The server stores the received data in storage and uses Python TensorFlow to train the AI ​​model.

[1097] Step 2:

[1098] User Authentication

[1099] User: Accesses the system from their own terminal and enters their ID and password on the login screen.

[1100] Input: ID and password.

[1101] Server: Receives the user's input information and authenticates it by comparing it with the registered information in the database.

[1102] Output: The authentication result (success or failure).

[1103] What happens: The user enters their login information, the server checks it against a database to authenticate them, and if successful, gives them access to the dashboard.

[1104] Step 3:

[1105] Start setting goals

[1106] User: Using the chat interface, type "I want to set my goals for this month."

[1107] Input: A goal-setting opening prompt such as "I'd like to set a goal for this month."

[1108] Server: Parses user input and initiates the goal setting process.

[1109] Output: Triggering the goal setting process for the generative AI model.

[1110] What happens: The chat interface receives the user's prompt, the server parses the prompt and triggers the goal setting process.

[1111] Step 4:

[1112] Proposing goals

[1113] Server: Analyzes user input, and a generative AI model generates candidate goals based on past goal data and company rules.

[1114] Input: User-entered goal setting prompts and historical goal data, company rules.

[1115] Generative AI model: Generates suitable target candidates.

[1116] Output: Candidate goals suggested to the user.

[1117] Specific operation: The server analyzes the user's input, and the generative AI model generates and suggests goals such as "increase sales by 500,000 yen and acquire 10 new customers."

[1118] Step 5:

[1119] Receiving and regenerating feedback

[1120] User: Give feedback on the proposed goal, such as "Sales of 500,000 yen are difficult. How about 400,000 yen?"

[1121] Input: User feedback.

[1122] Server: Receives feedback, reanalyzes, and the generative AI model recreates the goal.

[1123] Output: Revised target candidates.

[1124] Specific operation: The server reanalyzes the feedback content, and the generative AI model re-proposes the goal of "increasing sales by 400,000 yen."

[1125] Step 6:

[1126] Confirm and save your goals

[1127] User: If satisfied with the regenerated goal, type "Confirm this goal."

[1128] Input: Enter your final intention.

[1129] Server: Stores the determined goals in a database for future reference.

[1130] Output: Confirmed targets stored in the database.

[1131] Specific behavior: The server receives the user's confirmation input and saves the goal in the database.

[1132] This process flow enables efficient and fair goal management for companies. The use of generative AI models enables appropriate goal setting based on diverse data and can flexibly respond to user feedback.

[1133] (Application example 1)

[1134] 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."

[1135] A chat-based tool service equipped with generative AI for efficient and fair corporate goal management is problematic because conventional methods are labor-intensive, time-consuming, and subjective judgment bias is unavoidable. Furthermore, managing manufacturing processes within factories is difficult to do efficiently because of the complex procedures and real-time progress management required. A new system is needed to solve these issues.

[1136] 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.

[1137] In this invention, the server includes means for importing data related to corporate goal management and training an AI model, means for authenticating users and providing a goal setting interface to the users, means for analyzing user input and proposing goal setting based on the corporate goal management rules, means for receiving user feedback and regenerating and confirming goals, means for saving the confirmed goals in a database, means for importing data related to manufacturing processes in a factory and proposing production goals, and means for receiving input from workers, monitoring the production process in real time, and tracking progress. This enables improved efficiency and fairness in corporate goal management and efficient management of manufacturing processes in a factory.

[1138] "Corporate goal management rules" refer to the standards and procedures for achieving the goals set by a company.

[1139] A "generative AI model" refers to an artificial intelligence system that has been trained to perform a specific task using machine learning algorithms.

[1140] "Chat-type tool service" refers to a software service that communicates with users in an interactive format using text or voice.

[1141] "Means for importing data" refers to a means or method for bringing data into the system from outside.

[1142] "Means for authenticating a user" refers to a means or method for verifying a user's identity and authority.

[1143] "Goal setting interface" refers to the screens and processes through which a user sets goals.

[1144] "Means for parsing user input" refers to the means or methods for understanding and processing information provided by a user.

[1145] The "means for proposing goal setting" refers to a means or method for generating and proposing appropriate goals based on the user's needs and the company's rules.

[1146] "Means for regenerating and finalizing goals" refers to the means or method for regenerating and finalizing goals based on user feedback.

[1147] "Means of storing data in a database" refers to a means or method of systematically accumulating data and storing it in a manner that allows it to be accessed later.

[1148] A "manufacturing process" refers to the series of steps or operations required to create a product.

[1149] "Means for proposing production targets" refers to a means or method for proposing production volume or quality standards to be achieved in the manufacturing process.

[1150] "Means for monitoring the production process in real time" refers to a means or method for observing and checking the progress of the manufacturing process in real time.

[1151] "Means for tracking progress" refers to the means or method for continually tracking and recording the progress of a manufacturing process or goal achievement.

[1152] This invention is a system for efficiently and fairly managing corporate targets and implementing manufacturing processes within factories. This system consists of three main elements: a server, a user terminal, and a user, and provides a chat-type tool service equipped with a generative AI model.

[1153] System configuration and operation overview

[1154] 1. Server Initial Setup and Learning

[1155] As an initial setting, the server imports data on the company's goal management and manufacturing process data from the manager. The server uses this data to train the generative AI model. For example, by importing data on the company's goal management rules, past goal data, and manufacturing process data, the AI ​​model learns specific needs and patterns.

[1156] 2. User Authentication

[1157] The user accesses the system from a smartphone or head-mounted display and enters the necessary information on the login screen. The server receives this information, authenticates it, and verifies whether the user has the appropriate access rights. This authentication process is carried out using a security module (e.g., OAuth 2.0).

[1158] 3. The dialogue process of goal setting

[1159] Users initiate goal setting using a chat interface (text or voice). For example, if a user types, "I want to set a goal for this month," the server analyzes the user's input and generates appropriate goal candidates based on past data and company rules. These suggested goal candidates are displayed on the user's device interface. The user can then provide feedback and request any necessary revisions.

[1160] 4. Interactive process of manufacturing process

[1161] Similarly, for manufacturing processes within factories, users can use the chat interface to ask about production targets. For example, by typing, "What is today's production target?", the server analyzes the production line settings and progress and suggests appropriate production targets. Feedback can also be provided in response, and the production process is monitored and progress is tracked in real time.

[1162] Hardware and software used

[1163] Hardware

[1164] Smartphone or head-mounted display (e.g., general mobile device, display device)

[1165] software

[1166] User authentication: security module (e.g. OAuth 2.0)

[1167] Speech recognition system (e.g., Google Cloud Speech-to-Text)

[1168] Generative AI models (e.g., OpenAI GPT-4)

[1169] Examples of concrete examples and prompts

[1170] Example 1: Corporate goal setting

[1171] When a user inputs a goal setting of "increase sales by 500,000 yen" on their smartphone, the server proposes the goal of "increase sales by 500,000 yen and acquire 10 new customers." If the user provides feedback saying, "500,000 yen in sales is difficult. How about 400,000 yen?", the AI ​​responds, "A goal of increasing sales by 400,000 yen is appropriate." If the user is satisfied, the goal is confirmed and the server saves it in the database.

[1172] Example 2: Factory manufacturing process management

[1173] When a user wears the head-mounted display and asks, "What is today's production target?", the server responds, "Line 1 is scheduled to produce 500 units." The production line is monitored in real time, and progress is displayed on the display. For example, if a defective product occurs, a notification will appear saying, "A defective product has been found on Line 1," enabling a prompt response.

[1174] Prompt Sentence Examples

[1175] "Please tell me the achievement rate for yesterday's production target."

[1176] "Show me the production plan for the next shift."

[1177] "Please list the problems that have occurred on the third line and propose solutions."

[1178] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1179] Step 1:

[1180] Initial setup and training:

[1181] The server imports the company's goal management data and manufacturing process data within the factory. The manager uses a terminal to upload the necessary data files to the system. The server trains the generative AI model based on the imported data, learning the company's rules and patterns. Specifically, it analyzes past goal data and manufacturing process setting data to improve the accuracy of the model.

[1182] input:

[1183] Manager-provided historical goal data, manufacturing process data, and company goal management rules.

[1184] output:

[1185] A trained generative AI model.

[1186] Step 2:

[1187] User authentication:

[1188] A user accesses the system on their own device and enters their ID and password on the login screen. The server receives this information and uses a security module (e.g., OAuth 2.0) to verify the user's identity and permissions. If authentication is successful, a dashboard is displayed on the user's device.

[1189] input:

[1190] User ID and password.

[1191] output:

[1192] Dashboard after successful authentication.

[1193] Step 3:

[1194] The goal-setting dialogue process:

[1195] The user inputs "I want to set a goal for this month" through the chat interface. The server analyzes this input and generates appropriate goal candidates by referencing past data and company rules. The generated goal candidates are displayed on the user's device.

[1196] input:

[1197] User goal setting request.

[1198] output:

[1199] Proposed target candidates.

[1200] Data processing / calculation:

[1201] Generate meaningful candidates for goals based on historical data and company rules.

[1202] Step 4:

[1203] Receiving user feedback and regenerating goals:

[1204] The user provides feedback on the proposed goal. For example, they might say, "Sales of 500,000 yen are difficult. How about 400,000 yen?" The server analyzes this feedback again and regenerates the goal. The regenerated goal is displayed on the user's device.

[1205] input:

[1206] User feedback.

[1207] output:

[1208] Regenerated target candidates.

[1209] Data processing / calculation:

[1210] Generate new goals based on user feedback.

[1211] Step 5:

[1212] Save confirmed goals:

[1213] If the user is satisfied with the regenerated goal, he / she confirms the goal in the system, and the server stores this confirmed goal in the database for future reference.

[1214] input:

[1215] A goal established by the user.

[1216] output:

[1217] Confirmed goals stored in the database.

[1218] Data processing / calculation:

[1219] The determined goals are saved in a database format.

[1220] Step 6:

[1221] Manufacturing process interaction process:

[1222] A factory worker or manager can use the chat interface to ask, "What is today's production target?" The server analyzes the production line settings and progress, generates the production target for that day, and displays it on the user's device. The production process is monitored in real time, and progress is tracked.

[1223] input:

[1224] Questions about production goals for workers and managers.

[1225] output:

[1226] Proposed production target for the day.

[1227] Data processing / calculation:

[1228] Analyze production line settings and progress data to generate daily production targets.

[1229] Step 7:

[1230] Real-time process monitoring and feedback:

[1231] The server monitors the production process in real time, tracking progress and notifying workers of any irregularities, such as defective products, so that workers and managers can quickly respond and enter instructions into the system to resolve the issues.

[1232] input:

[1233] Real-time production process data and defect occurrence information.

[1234] output:

[1235] Progress tracking and anomaly notifications.

[1236] Data processing / calculation:

[1237] Analyze real-time data to detect anomalies and provide appropriate notifications.

[1238] 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.

[1239] This invention relates to a chat tool service that incorporates a generation AI for efficient and fair corporate goal management, and combines it with an emotion engine for recognizing user emotions and reflecting them in goal setting. Below, we will generate a program for this system and explain its processing overview in natural language.

[1240] System Overview

[1241] This system consists of three main elements: a server, a user device, and the user. The generative AI model and emotion engine running on the server set goals while interacting with the user via the user device. The server has learned the company's goal management rules and suggests appropriate goal settings based on input from the user. It also recognizes the user's emotions and reflects them in the goal suggestions.

[1242] Program processing overview

[1243] 1. Initial setup and learning

[1244] The server imports data on the company's goal management from the administrator, who then uploads the data to the system using a terminal.

[1245] 2. User Authentication

[1246] A user accesses the system from their own terminal and enters their ID and password on the login screen. The server receives this, performs authentication, and verifies the user's access rights.

[1247] 3. Emotion recognition

[1248] When a user accesses the chat interface and enters input related to goal setting, the emotion engine analyzes the user's input to determine emotions, for example, by recognizing emotions based on specific keywords or context within the text.

[1249] The server uses the emotion data obtained from the emotion engine to adjust the suggested goal settings, suggesting realistic goals if the user is overly stressed, or setting challenging goals if the user is highly motivated.

[1250] 4. The dialogue process of goal setting

[1251] To start goal setting, the user accesses the chat interface and inputs, "I want to set my goals for this month." The terminal then sends the user's request to the server.

[1252] The server generates appropriate goal candidates based on the user's input and the emotional information output by the emotion engine. For example, if a user inputs "increase sales by 500,000 yen," and the emotion engine determines that the load is too high based on the user's emotions, it will suggest a more realistic "increase sales by 400,000 yen."

[1253] 5. Suggestions and Feedback

[1254] The device displays the generated target candidates in a chat interface and suggests them to the user, who can then provide feedback and request any necessary corrections or adjustments.

[1255] The server receives the user's feedback, analyzes it again using the emotion engine and natural language processing algorithm, and generates new goal candidates based on the feedback. The device then presents the regenerated goal candidates to the user.

[1256] 6. Set and save your goals

[1257] If the user is satisfied with the regenerated goal, he / she inputs "confirm goal" into the chat interface, and the device sends a confirmation request to the server.

[1258] The server stores the determined goals in a database and manages them for later reference and evaluation. The terminal displays the status of the saved goal to the user.

[1259] Specific examples

[1260] Initial setup and training example

[1261] Administrators upload a file containing goal data from the past three years and the company's goal management rules from their device, which allows the generative AI to set goals according to the company's specific needs and patterns.

[1262] User Authentication Example

[1263] When an employee attempts to log in to the system, they enter their ID and password on the login screen, which the server receives and executes the authentication process. After successful authentication, they can access the dashboard.

[1264] Goal Setting Examples

[1265] An employee may enter a goal setting such as "increase sales by 500,000 yen" into the chat interface. The AI ​​generator may compare this with past data and suggest a goal such as "increase sales by 500,000 yen and acquire 10 new customers."

[1266] If the emotion engine detects high levels of stress from the user's input, the generative AI will suggest realistic goals such as "increase sales by 400,000 yen."

[1267] If the employee is satisfied with the proposal, the goal is confirmed and the server stores the goal in the database.

[1268] As described above, the system of the present invention provides a chat-type tool that uses generative AI technology and an emotion engine to achieve efficiency and fairness in corporate goal management. This system enables goal setting that takes into account employees' emotions, which is expected to improve the performance of the entire organization.

[1269] The processing flow will be explained below.

[1270] Step 1:

[1271] The administrator logs in to the system using a terminal. The terminal displays the login screen, and the administrator enters their ID and password. The server receives this information and authenticates the administrator.

[1272] Step 2:

[1273] The administrator uses the terminal to upload the company's goal management rules and past goal setting data. The terminal displays the file upload interface, and the administrator selects the required data file and presses the upload button. The server receives these files, analyzes the data, and imports it.

[1274] Step 3:

[1275] The server trains the AI ​​model based on the uploaded data, analyzing the goal management rules stored in the database and past goal setting data to learn appropriate goal setting patterns.

[1276] Step 4:

[1277] A regular employee accesses the system using a terminal and enters their ID and password on the login screen. The terminal then sends this information to the server for user authentication.

[1278] Step 5:

[1279] The server checks the user's ID and password, and if authentication is successful, it confirms the user's authority and retrieves the dashboard information. The terminal displays the retrieved dashboard information to the user.

[1280] Step 6:

[1281] To start goal setting, the user accesses the chat interface and enters, "I want to set my goal for this month." The device then sends the user's request to the server.

[1282] Step 7:

[1283] The server analyzes the user's input using a natural language processing (NLP) algorithm and generates appropriate goal candidates based on past data and the company's goal management rules. Meanwhile, the emotion engine recognizes emotions from the user's input and sends the analysis results to the server.

[1284] Step 8:

[1285] The server reflects the emotional information output by the emotion engine in the generated goal candidates and proposes appropriate goal settings. For example, if a user is suggested to "increase sales by 500,000 yen" and the emotion engine detects high stress, it will adjust it to "increase sales by 400,000 yen."

[1286] Step 9:

[1287] The device displays the generated target candidates in a chat interface and suggests them to the user, who can then provide feedback and request any necessary corrections or adjustments.

[1288] Step 10:

[1289] The server receives the user's feedback, analyzes it again using the emotion engine and natural language processing algorithm, and generates new goal candidates based on the feedback. The device then presents the regenerated goal candidates to the user.

[1290] Step 11:

[1291] When the user confirms the final goal and is satisfied with it, he / she inputs "confirm goal" into the chat interface, and the terminal sends a confirmation request to the server.

[1292] Step 12:

[1293] The server stores the determined goals in a database and manages them for later reference and evaluation. The terminal displays the status of the saved goal to the user.

[1294] Through the above steps, the system supports users in setting goals efficiently and fairly, and also provides goal setting that takes into account the user's emotions.

[1295] Example 2

[1296] 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."

[1297] Corporate goal management is complex, and it is particularly difficult to consider the emotions and motivation of individual employees. As a result, goal setting tends to be one-sided, which can have a negative impact on employee performance and motivation. It is also difficult to effectively utilize past goal data and appropriately set realistic yet challenging goals.

[1298] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for importing data related to corporate goal management and training an AI model, means for authenticating users and providing a goal setting interface to the users, means for analyzing user input, recognizing the user's emotions using an emotion engine, and proposing goal setting based on the corporate goal management rules, means for receiving user feedback and regenerating and confirming goals using an emotion engine and a natural language processing algorithm, and means for storing the confirmed goals in a database. This enables realistic and challenging goal setting that takes into account employees' emotions and motivation, improving the efficiency and fairness of corporate-wide goal management.

[1299] "Generative AI" is artificial intelligence that learns the data necessary for setting corporate goals and suggests appropriate goals through dialogue.

[1300] An "emotion engine" is an algorithm that analyzes user input and recognizes emotions.

[1301] A "chat-type tool" is a tool that provides an interface for setting goals through dialogue with the user.

[1302] "Corporate goal management rules" refer to guidelines and policies that a company uses to set and manage goals.

[1303] "Goal setting interface" means an interactive interface through which a user sets goals.

[1304] A "natural language processing algorithm" is an algorithm for analyzing text data and understanding its meaning.

[1305] A "database" is a system for storing and managing determined goals, emotional data, etc.

[1306] "User authentication" is the process of verifying a user's authenticity using the user's ID and password.

[1307] "Feedback" refers to the opinions and requests for revisions that users provide to proposed goals.

[1308] "Regeneration" refers to the process of regenerating new goals based on user feedback and emotional data.

[1309] "Training" refers to the process by which an AI model learns a company's goal management rules and data.

[1310] This invention relates to a chat tool service that incorporates a generation AI for efficient and fair corporate goal management, and combines it with an emotion engine for recognizing user emotions and reflecting them in goal setting. This system is composed of three main elements: a server, a user terminal, and the user, and is specifically implemented as follows:

[1311] System configuration

[1312] Hardware and Software

[1313] Server: The central part of the system, it manages the company's goal management data and runs the generative AI model and emotion engine. The specific software on which the generative AI model and emotion engine are installed is

[1314] User terminal: A device such as a PC or smartphone that allows a user to access the system. An application that provides a chat-style interface is installed on this device.

[1315] Database: A database system for storing corporate goal management data, defined goals, and user authentication information.

[1316] Program processing

[1317] Initial setup and training

[1318] Importing Data

[1319] The administrator uploads past goal data and the company's goal management rules from a terminal. The server receives this data and stores it in a database.

[1320] Training an AI model

[1321] The server uses the uploaded data to train the generative AI model, which learns the specific needs and patterns of the company through this training process.

[1322] User Authentication

[1323] Input on the login screen

[1324] Users access the system from their own terminal and enter their ID and password on the login screen.

[1325] Authentication Process

[1326] The device sends the entered ID and password to the server, which receives it and checks it against the authentication information in its database. If authentication is successful, the user can access the system dashboard.

[1327] emotion recognition

[1328] Parsing input

[1329] The user types "I want to set my goals for this month" into the chat interface, and the device sends this input to the server.

[1330] Running the Emotion Engine

[1331] The server passes the received input to the emotion engine, which analyzes the input text and recognizes the user's emotion from specific keywords and context.

[1332] The interactive process of goal setting

[1333] Target candidate generation

[1334] The server inputs goal-setting prompts into the generative AI model based on the user's input and the emotional information obtained from the emotion engine.

[1335] The generative AI model generates multiple target candidates and returns them to the server.

[1336] Suggestions for users

[1337] The server transmits the generated target candidates to the terminal, which displays them on the chat interface.

[1338] Suggestions and Feedback

[1339] Gathering feedback

[1340] The user can input feedback for the proposed goal candidates, for example, "I would like the goal to be adjusted to be more realistic."

[1341] Analyzing feedback and adjusting goals

[1342] The device sends the feedback to the server, which receives it and analyzes it again using the emotion engine and natural language processing algorithm, generating new target candidates and sending them to the device, which then presents them to the user.

[1343] Confirm and save your goals

[1344] Determine your goals

[1345] If the user is satisfied with the proposed goal, he / she inputs "confirm goal" into the chat interface, and the device sends a confirmation request to the server.

[1346] Saving to a database

[1347] The server saves the confirmed goal in the database. After saving is complete, the device displays "Goal confirmed" to the user.

[1348] Specific examples

[1349] Initial setup and training example

[1350] Managers upload goal data from the past three years and their company's goal management rules via their devices, allowing the generative AI to set goals according to the company's specific needs and patterns.

[1351] User Authentication Example

[1352] When an employee logs in to the system, they enter their ID and password on the login screen. The server receives this and performs an authentication process. After successful authentication, they can access the dashboard.

[1353] Goal Setting Examples

[1354] An employee enters a goal setting, such as "increase sales by 500,000 yen," into the chat interface. The generation AI compares it with past data and may suggest a goal such as "increase sales by 500,000 yen and acquire 10 new customers." If the emotion engine detects high stress from the user's emotions, the generation AI will suggest a more realistic goal, such as "increase sales by 400,000 yen." If the employee agrees with this suggestion, the goal is confirmed and the server saves it in the database.

[1355] Prompt Sentence Examples

[1356] "Sales increased by 500,000 yen"

[1357] "Acquired 10 new customers"

[1358] "Complete Project A"

[1359] As described above, the system of the present invention provides a chat-type tool that uses generative AI technology and an emotion engine to achieve efficiency and fairness in corporate goal management. This enables goal setting that takes into account employees' emotions, and is expected to improve the performance of the entire organization.

[1360] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1361] Step 1: Initial setup and training

[1362] Input: The administrator uploads the company's goal management data and past goal data from the terminal.

[1363] Operation: The device sends the uploaded data to the server.

[1364] Data processing: The server receives the data and stores it in a database.

[1365] AI training: The server uses the stored data to train the generative AI model, learning the company's goal management rules and past patterns.

[1366] Output: A trained generative AI model.

[1367] Step 2: User authentication

[1368] Input: The user accesses the system on their own terminal and enters their ID and password on the login screen.

[1369] Operation: The terminal sends the entered ID and password to the server.

[1370] Data processing: The server receives the transmitted information and checks it against authentication data in its database.

[1371] Output: Authentication result (success or failure).

[1372] What happens: If authentication is successful, the server allows the user to access the dashboard.

[1373] Step 3: Emotion Recognition

[1374] Input: The user types "I want to set my goals for this month" into the chat interface.

[1375] Operation: The terminal sends the user's input to the server.

[1376] Data processing: The server passes the received input to the emotion engine, which analyzes the input text and recognizes the user's emotion.

[1377] Output: Emotion data (e.g., stress, high motivation, etc.).

[1378] How it works: The server stores emotion data in a database.

[1379] Step 4: The goal-setting dialogue process

[1380] Input: Emotion data and user input.

[1381] How it works: The server inputs goal-setting prompts into the generative AI model based on emotion data and user input.

[1382] Data processing: The generative AI model generates multiple target candidates.

[1383] Output: Generated target candidates.

[1384] How it works: The server sends potential targets to the device, which displays them in the chat interface.

[1385] Step 5: Suggestions and feedback

[1386] Input: User feedback (e.g., "Please adjust your goal to be more realistic").

[1387] Operation: The device sends the feedback content to the server.

[1388] Data processing: The server receives the feedback, analyzes it using an emotion engine and natural language processing algorithms, and generates new target candidates.

[1389] Output: New target candidates.

[1390] Operation: The server sends the newly generated target candidates to the terminal, which then presents them to the user.

[1391] Step 6: Confirm and save your goal

[1392] Input: "Confirm goal" typed by the user into the chat interface.

[1393] Operation: The terminal sends a confirmation request to the server.

[1394] Data processing: The server stores the determined goals in a database.

[1395] Output: Save completion status.

[1396] Action: The device displays "Target confirmed" to the user.

[1397] The above is the specific processing flow of the system. At each step, appropriate data processing and analysis are performed based on the input data, allowing users to smoothly proceed through the goal setting process.

[1398] (Application example 2)

[1399] 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."

[1400] Conventional corporate goal management systems have problems with efficiency and objectivity, and in particular, they often set goals without considering the emotional state of individual users. This can result in excessive burdens on users, leading to a decrease in motivation and increased stress. Furthermore, in certain fields, such as logistics centers, detailed goal setting and rapid feedback are required to respond to the complexity of work content and fluctuating demand. An efficient and fair goal management system that can meet these needs is needed.

[1401] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1402] In this invention, the server includes: means for importing data related to corporate goal management and training an AI model; means for authenticating a user and providing the user with a goal setting interface; means for analyzing user input and suggesting goal setting based on the corporate goal management rules; means including an emotion engine that recognizes the user's emotions and reflects them in goal setting; means using a generative AI model that suggests goals according to the user's emotions; means for receiving user feedback and regenerating and confirming goals; and means for storing the confirmed goals in a database. This enables realistic and efficient goal setting that takes the user's emotional state into consideration, making fair and appropriate goal management possible, especially in specific fields such as logistics centers.

[1403] "Corporate goal management rules" refer to the standards and guidelines for planning, evaluating, and achieving performance goals set by a company.

[1404] A "generative AI model" refers to an artificial intelligence algorithm that learns from large amounts of data and automatically generates appropriate responses and suggestions based on user input.

[1405] "Chat-type tool service" refers to an interactive software application that assists users with specific tasks through text and voice interactions.

[1406] "User authentication" refers to the process of verifying the identity of users accessing a system and granting appropriate access privileges.

[1407] "Goal-setting interface" refers to a system component that provides the screens and input forms through which a user sets goals.

[1408] An "emotion engine" refers to technology that analyzes a user's emotional state from text input and other data and adjusts responses based on the results.

[1409] "Feedback" refers to the evaluations and opinions that users provide in response to suggestions and answers from the system.

[1410] "Database" refers to a digital storage system that systematically organizes and stores data, making it quickly accessible when needed.

[1411] The system that embodies this invention is a chat-type tool service equipped with a generative AI model that learns a company's goal management rules and proposes appropriate goal setting in an interactive format. This system is composed of the following main components:

[1412] Program processing overview

[1413] 1. Importing and training corporate goal management data

[1414] The server trains the AI ​​model using data on corporate goal management imported from administrators, allowing it to learn the company's unique goal-setting rules and patterns and suggest appropriate goals.

[1415] 2. User authentication and interface provision

[1416] Users access the system using their own devices (smartphones, tablets, PCs, etc.) and enter their ID and password on the login screen. The server receives this, performs an authentication process, and grants the user appropriate access rights. If authentication is successful, the user can access the goal setting interface.

[1417] 3. Analyzing user input and suggesting goal setting

[1418] When a user enters text related to goal setting into the chat interface, the server uses a generative AI model to analyze the input. The server also analyzes the user's emotions and reflects them in the proposed goals. For example, if a user enters "increase shipment volume by 50 pallets," and the emotion analysis indicates high stress, the generative AI model will suggest a more realistic goal, such as "increase shipment volume by 40 pallets."

[1419] 4. Utilizing the Emotion Engine

[1420] The emotion engine analyzes the user's input text to determine whether the emotion is positive or negative. For example, if the user's input is negative, such as "I'm not sure if I can achieve this," the emotion engine will detect this and reflect it in the suggested goal.

[1421] 5. Feedback and goal regeneration / confirmation

[1422] The user provides feedback on the proposed goal, and the server regenerates the goal based on that feedback. If the user is satisfied with the goal and clicks "confirm," the server stores the goal in a database for future reference and evaluation.

[1423] 6. Save and manage your goals

[1424] The confirmed goals are stored in a database on the server. This database accumulates past goal data and new data, and is used to continuously improve the accuracy of the AI ​​model. It is also possible to generate reports and alerts according to the company's goal management rules.

[1425] Hardware and software configuration

[1426] Hardware: Smartphones, tablets, PCs, servers

[1427] Software: Flask (web server), TextBlob (sentiment analysis library), OpenAI GPT-3 (generative AI model)

[1428] Examples of concrete examples and prompts

[1429] Examples:

[1430] A staff member types "increase today's shipments by 50 pallets" into the chat interface. If the emotion engine detects anxiety, the generative AI model suggests a more realistic goal, such as "increase today's shipments by 40 pallets."

[1431] Example prompt sentence:

[1432] Given the user's negative sentiment, suggest a realistic goal: Increase today's shipping volume by 50 pallets.

[1433] This system enables realistic and efficient goal setting that takes into account the user's emotional state, enabling fair and appropriate goal management even in specific fields such as logistics centers.

[1434] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1435] Step 1:

[1436] The server imports the company's goal management data and trains the generative AI model. The administrator uploads past goal data and the company's goal management rules to the server. This data is analyzed by the AI ​​model, which learns goal setting patterns and standards. The input is the data file from the administrator, and the output is the trained AI model.

[1437] Step 2:

[1438] The terminal performs user authentication. The user accesses the login screen using their terminal and enters their ID and password. The entered authentication information is sent to the server, which verifies it and authenticates that the user is legitimate. The input is the user's ID and password, and the output is the result of authentication success or failure.

[1439] Step 3:

[1440] The user accesses the goal setting interface and inputs text related to the goal setting. For example, the user inputs a goal such as "I want to increase shipping volume by 50 pallets this month." The input is the goal setting text entered by the user, and the output is a request to suggest appropriate goal settings.

[1441] Step 4:

[1442] The server analyzes the received user input and suggests goal setting using a generative AI model and emotion engine. The server analyzes the user's input text and generates appropriate goal candidates based on its content. At the same time, the emotion engine analyzes the user's emotions and reflects them in the generated goals. For example, if the user's input includes "I'm anxious," the emotion engine recognizes this and instructs the generative AI model to suggest realistic goals. The input is the user's text input and emotion data, and the output is appropriate goal setting suggestions.

[1443] Step 5:

[1444] The device displays the proposed goals to the user and asks for feedback. The user provides feedback on the proposed goals and requests corrections or reconfiguration as necessary. The inputs are the proposed goals from the server and the user's feedback, and the output is the feedback data sent to the server.

[1445] Step 6:

[1446] The server reanalyzes the user's feedback and regenerates goals as necessary. It uses a generative AI model and emotion engine to generate new goal candidates based on the feedback. The input is the user's feedback data, and the output is the regenerated goal settings.

[1447] Step 7:

[1448] When the user is satisfied with the regenerated goal, he / she inputs "confirm goal" into the terminal. The terminal sends this confirmation request to the server. The input is the user's confirmation request, and the output is a confirmation notification to the server.

[1449] Step 8:

[1450] The server saves the confirmed goals in a database. The confirmed goals are managed in the database so that they can be referenced and evaluated later. The input is the confirmed goal information, and the output is a notification that the goal has been saved to the database.

[1451] Through the above processing steps, the present invention is a system that realizes realistic and efficient goal setting that takes into account the user's emotional state.

[1452] 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.

[1453] 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.

[1454] 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.

[1455] [Fourth embodiment]

[1456] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1457] 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.

[1458] 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).

[1459] 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.

[1460] 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.

[1461] 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).

[1462] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for 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.

[1463] 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.

[1464] 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.

[1465] 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.

[1466] 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.

[1467] 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.

[1468] 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."

[1469] This invention relates to a chat tool service equipped with a generation AI for efficient and fair corporate goal management. Below, we will generate a program for this system and explain its processing overview in natural language.

[1470] System Overview

[1471] This system consists of three main elements: a server, a user device, and the user. The generative AI model running on the server sets goals while interacting with the user via the user device. The server has learned the company's goal management rules and suggests appropriate goal settings based on user input.

[1472] Program processing overview

[1473] 1. Initial setup and learning

[1474] The server imports data on the company's goal management from the administrator, who then uploads the data to the system using a terminal.

[1475] 2. User Authentication

[1476] A user accesses the system from their own terminal and enters their ID and password on the login screen. The server receives this, performs authentication, and verifies the user's access rights.

[1477] 3. The dialogue process of goal setting

[1478] Users use the chat interface to begin goal setting, for example by typing, "I'd like to set goals for this month," which starts the process.

[1479] The server analyzes the user's input and generates appropriate target candidates based on past target data and company rules.

[1480] The proposed target candidates are displayed in a chat interface on the user's device, and the user can provide feedback and request any necessary revisions.

[1481] The server re-analyzes the feedback and regenerates goals tailored to the user's needs.

[1482] 4. Set and save your goals

[1483] If the user is satisfied with the regenerated goal, he or she performs an operation to confirm the goal.

[1484] The server stores the determined goals in a database for future reference.

[1485] Specific examples

[1486] Initial setup and training example

[1487] Administrators upload a file containing goal data from the past three years and the company's goal management rules from their device, which allows the generative AI to set goals according to the company's specific needs and patterns.

[1488] User Authentication Example

[1489] When an employee attempts to log in to the system, they enter their ID and password on the login screen, which the server receives and executes the authentication process. After successful authentication, they can access the dashboard.

[1490] Goal Setting Examples

[1491] An employee may enter a goal setting such as "increase sales by 500,000 yen" into the chat interface. The AI ​​generator may compare this with past data and suggest a goal such as "increase sales by 500,000 yen and acquire 10 new customers."

[1492] If an employee gives feedback such as, "Sales of 500,000 yen are difficult; how about 400,000 yen?", the generating AI will analyze again and respond, "A goal of increasing sales by 400,000 yen would be appropriate."

[1493] Once the employee is satisfied, the goal is finalized and the server stores this goal in a database.

[1494] As described above, the system of the present invention provides a chat-based tool using generative AI technology to achieve efficiency and fairness in corporate goal management. This system is expected to improve employee motivation and the performance of the entire organization.

[1495] The processing flow will be explained below.

[1496] Step 1:

[1497] The administrator logs in to the system using a terminal. The terminal displays the login screen, and the administrator enters their ID and password. The server receives this information and authenticates the administrator.

[1498] Step 2:

[1499] The administrator uses the terminal to upload the company's goal management rules and past goal setting data. The terminal displays the file upload interface, and the administrator selects the required data file and presses the upload button. The server receives these files, analyzes the data, and imports it.

[1500] Step 3:

[1501] The server trains the AI ​​model based on the uploaded data, analyzing the goal management rules stored in the database and past goal setting data to learn appropriate goal setting patterns.

[1502] Step 4:

[1503] A regular employee accesses the system using a terminal and enters their ID and password on the login screen. The terminal then sends this information to the server for user authentication.

[1504] Step 5:

[1505] The server checks the user's ID and password, and if authentication is successful, it confirms the user's authority and retrieves the dashboard information. The terminal displays the retrieved dashboard information to the user.

[1506] Step 6:

[1507] To start goal setting, the user accesses the chat interface and enters, "I want to set my goal for this month." The device then sends the user's request to the server.

[1508] Step 7:

[1509] The server analyzes the user's input using natural language processing (NLP) algorithms and generates appropriate goal candidates based on past data and the company's goal management rules.

[1510] Step 8:

[1511] The device displays the generated target candidates in a chat interface and suggests them to the user, who can then provide feedback and request any necessary corrections or adjustments.

[1512] Step 9:

[1513] The server receives the user's feedback, analyzes it again using a natural language processing algorithm, and generates new target candidates based on the feedback. The device then presents the regenerated target candidates to the user.

[1514] Step 10:

[1515] When the user confirms the final goal and is satisfied with it, he / she inputs "confirm goal" into the chat interface, and the terminal sends a confirmation request to the server.

[1516] Step 11:

[1517] The server stores the determined goals in a database and manages them for later reference and evaluation. The terminal displays the status of the saved goal to the user.

[1518] As described above, the system efficiently supports the user in setting goals through each step.

[1519] Example 1

[1520] 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."

[1521] One of the challenges in corporate goal management is the difficulty of setting goals efficiently and fairly. In conventional systems, goal setting is subjective, which can lead to situations where improvements in overall corporate performance cannot be expected. Furthermore, there is a problem in that systems lack the flexibility to regenerate goals based on user feedback, making it difficult to respond to individual needs.

[1522] 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.

[1523] In this invention, the server includes means for importing data related to corporate goal management and training an AI model, means for authenticating users and providing a goal setting interface to the users, means for analyzing user input and proposing goal setting based on the corporate goal management rules, means for receiving user feedback and regenerating and confirming goals, means for storing the confirmed goals in a database, a procedure for reanalyzing the data and regenerating goals based on the user feedback, and means for verifying the training results of the AI ​​model and fine-tuning them as necessary, thereby improving the efficiency and fairness of corporate goal management and enabling flexible goal setting that meets individual needs.

[1524] "Corporate goal management" is the process of managing and evaluating the specific goals set to achieve a company's objectives, as well as the plans and measures based on those goals.

[1525] "Generative AI" refers to technology that uses artificial intelligence to analyze and generate data, automatically creating new proposals and goals based on user input.

[1526] A "chat-type tool service" refers to a system that provides an interface that allows users to interact in a conversational format using natural language.

[1527] "Importing data" refers to the process of bringing external data into a system and converting it into a usable format.

[1528] "Training an AI model" refers to the process of training an artificial intelligence algorithm using large amounts of data to improve its ability to perform a specified task.

[1529] "User authentication" refers to the process of verifying the identity of users accessing a system and ensuring that only authorized users can use it.

[1530] "Goal setting interface" refers to an interactive input screen for a user to input the goals they wish to achieve.

[1531] "Analysis" is the process of interpreting, understanding, and evaluating input data to extract necessary information and generate appropriate output.

[1532] "Receiving feedback" refers to the system collecting responses and opinions from users and using them as input for the next step.

[1533] "Regeneration" is the process of creating new proposals and goals that incorporate user feedback based on the initial analysis results and proposals.

[1534] "Confirmation" refers to the user agreeing with the presented goal and officially registering that goal in the system.

[1535] "Saving to the database" refers to storing the determined data in the system's storage device for future reference.

[1536] "Reanalysis" refers to the process of generating more appropriate suggestions and goals by reanalyzing based on the feedback received.

[1537] "Verification" refers to the process of evaluating the training results of an AI model and confirming its accuracy and reliability.

[1538] "Fine-tuning" is the process of making small modifications to an AI model to improve its accuracy based on training and validation results.

[1539] This invention relates to a chat tool service equipped with generative AI for efficient and fair corporate goal management. This system consists of three main elements: a server, a user terminal, and a user. Specific details for implementing the invention are described below.

[1540] System configuration

[1541] 1. Server:

[1542] The server hosts the generative AI model and includes a database for storing data related to the company's goal management. The server trains the AI ​​model, authenticates users, suggests goal setting, reanalyzes feedback, and stores established goals.

[1543] Example of hardware to be used: A server machine with a powerful processor and large memory capacity.

[1544] Examples of software used: Database Management Systems (DBMS), generative AI models (e.g., Python TensorFlow, etc.).

[1545] 2. User Device:

[1546] The user terminal is a device through which the user accesses the system, and provides a login interface and a chat interface for goal setting.

[1547] Examples of hardware used: PCs, tablets, and smartphones.

[1548] Examples of software used: web browsers (e.g., Google Chrome, Microsoft Edge, etc.), dedicated applications.

[1549] 3. User:

[1550] Users are company employees or managers who participate in the goal setting process. Users access the server using their terminals and set their own goals.

[1551] Specific processing details

[1552] 1. Initial setup and learning

[1553] The server receives the company's goal management data provided by the administrator and stores it in a database. This data is used for initial training of the AI ​​model.

[1554] Example: An administrator uploads an Excel file containing goal data from the past three years and the company's goal management rules to the system from a terminal. The server imports this data and provides it as training data for the AI ​​model.

[1555] 2. User Authentication

[1556] Users access the system from their own terminals and enter their ID and password into the login interface. The server receives this and verifies it against the information in the database.

[1557] Example: When an employee logs in to the system, they enter their employee ID "12345" and password "password123" on the login screen. The server receives this and performs authentication, and if successful, the employee can access the dashboard.

[1558] 3. The dialogue process of goal setting

[1559] Users initiate goal setting using the chat interface, for example by typing, "I'd like to set goals for this month."

[1560] The server analyzes the user's input and generates candidate goals based on the company's goal management rules. The generative AI model then suggests appropriate goals.

[1561] Example: When an employee types "increase sales by 500,000 yen" into the chat interface, the generative AI model suggests the goal "increase sales by 500,000 yen and acquire 10 new customers" based on past data and company rules.

[1562] 4. Set and save your goals

[1563] The user can provide feedback on the proposed goals and request any necessary modifications. The server re-analyzes the feedback and regenerates the goals tailored to the user's needs.

[1564] If the user is satisfied with the regenerated goal, he / she confirms it, and the server stores the confirmed goal in the database for future reference.

[1565] Example: If a user gives feedback saying, "Sales of 500,000 yen are difficult, how about 400,000 yen?", the server's generative AI model will reanalyze and re-propose a goal of "increasing sales by 400,000 yen." If the user is satisfied, the goal is confirmed and the server saves it in the database.

[1566] This system ensures efficiency and fairness in corporate goal management and enables flexible goal setting to meet individual needs. The use of generative AI models is expected to improve overall corporate performance.

[1567] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1568] Step 1:

[1569] Initial Setup and Data Import

[1570] Administrator: Use your own device to upload the company's goal management data to the system as an Excel file.

[1571] Input: A file containing goal data from the past three years and the company's goal management rules.

[1572] Server: Receives the uploaded data, stores it in a database, and then starts training a generative AI model based on this data.

[1573] Output: A dataset used as training data and a trained AI model.

[1574] Specific operation: The server stores the received data in storage and uses Python TensorFlow to train the AI ​​model.

[1575] Step 2:

[1576] User Authentication

[1577] User: Accesses the system from their own terminal and enters their ID and password on the login screen.

[1578] Input: ID and password.

[1579] Server: Receives the user's input information and authenticates it by comparing it with the registered information in the database.

[1580] Output: The authentication result (success or failure).

[1581] What happens: The user enters their login information, the server checks it against a database to authenticate them, and if successful, gives them access to the dashboard.

[1582] Step 3:

[1583] Start setting goals

[1584] User: Using the chat interface, type "I want to set my goals for this month."

[1585] Input: A goal-setting opening prompt such as "I'd like to set a goal for this month."

[1586] Server: Parses user input and initiates the goal setting process.

[1587] Output: Triggering the goal setting process for the generative AI model.

[1588] What happens: The chat interface receives the user's prompt, the server parses the prompt and triggers the goal setting process.

[1589] Step 4:

[1590] Proposing goals

[1591] Server: Analyzes user input, and a generative AI model generates candidate goals based on past goal data and company rules.

[1592] Input: User-entered goal setting prompts and historical goal data, company rules.

[1593] Generative AI model: Generates suitable target candidates.

[1594] Output: Candidate goals suggested to the user.

[1595] Specific operation: The server analyzes the user's input, and the generative AI model generates and suggests goals such as "increase sales by 500,000 yen and acquire 10 new customers."

[1596] Step 5:

[1597] Receiving and regenerating feedback

[1598] User: Give feedback on the proposed goal, such as "Sales of 500,000 yen are difficult. How about 400,000 yen?"

[1599] Input: User feedback.

[1600] Server: Receives feedback, reanalyzes, and the generative AI model recreates the goal.

[1601] Output: Revised target candidates.

[1602] Specific operation: The server reanalyzes the feedback content, and the generative AI model re-proposes the goal of "increasing sales by 400,000 yen."

[1603] Step 6:

[1604] Confirm and save your goals

[1605] User: If satisfied with the regenerated goal, type "Confirm this goal."

[1606] Input: Enter your final intention.

[1607] Server: Stores the determined goals in a database for future reference.

[1608] Output: Confirmed targets stored in the database.

[1609] Specific behavior: The server receives the user's confirmation input and saves the goal in the database.

[1610] This process flow enables efficient and fair goal management for companies. The use of generative AI models enables appropriate goal setting based on diverse data and can flexibly respond to user feedback.

[1611] (Application example 1)

[1612] 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."

[1613] A chat-based tool service equipped with generative AI for efficient and fair corporate goal management is problematic because conventional methods are labor-intensive, time-consuming, and subjective judgment bias is unavoidable. Furthermore, managing manufacturing processes within factories is difficult to do efficiently because of the complex procedures and real-time progress management required. A new system is needed to solve these issues.

[1614] 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.

[1615] In this invention, the server includes means for importing data related to corporate goal management and training an AI model, means for authenticating users and providing a goal setting interface to the users, means for analyzing user input and proposing goal setting based on the corporate goal management rules, means for receiving user feedback and regenerating and confirming goals, means for saving the confirmed goals in a database, means for importing data related to manufacturing processes in a factory and proposing production goals, and means for receiving input from workers, monitoring the production process in real time, and tracking progress. This enables improved efficiency and fairness in corporate goal management and efficient management of manufacturing processes in a factory.

[1616] "Corporate goal management rules" refer to the standards and procedures for achieving the goals set by a company.

[1617] A "generative AI model" refers to an artificial intelligence system that has been trained to perform a specific task using machine learning algorithms.

[1618] "Chat-type tool service" refers to a software service that communicates with users in an interactive format using text or voice.

[1619] "Means for importing data" refers to a means or method for bringing data into the system from outside.

[1620] "Means for authenticating a user" refers to a means or method for verifying a user's identity and authority.

[1621] "Goal setting interface" refers to the screens and processes through which a user sets goals.

[1622] "Means for parsing user input" refers to the means or methods for understanding and processing information provided by a user.

[1623] The "means for proposing goal setting" refers to a means or method for generating and proposing appropriate goals based on the user's needs and the company's rules.

[1624] "Means for regenerating and finalizing goals" refers to the means or method for regenerating and finalizing goals based on user feedback.

[1625] "Means of storing data in a database" refers to a means or method of systematically accumulating data and storing it in a manner that allows it to be accessed later.

[1626] A "manufacturing process" refers to the series of steps or operations required to create a product.

[1627] "Means for proposing production targets" refers to a means or method for proposing production volume or quality standards to be achieved in the manufacturing process.

[1628] "Means for monitoring the production process in real time" refers to a means or method for observing and checking the progress of the manufacturing process in real time.

[1629] "Means for tracking progress" refers to the means or method for continually tracking and recording the progress of a manufacturing process or goal achievement.

[1630] This invention is a system for efficiently and fairly managing corporate targets and implementing manufacturing processes within factories. This system consists of three main elements: a server, a user terminal, and a user, and provides a chat-type tool service equipped with a generative AI model.

[1631] System configuration and operation overview

[1632] 1. Server Initial Setup and Learning

[1633] As an initial setting, the server imports data on the company's goal management and manufacturing process data from the manager. The server uses this data to train the generative AI model. For example, by importing data on the company's goal management rules, past goal data, and manufacturing process data, the AI ​​model learns specific needs and patterns.

[1634] 2. User Authentication

[1635] The user accesses the system from a smartphone or head-mounted display and enters the necessary information on the login screen. The server receives this information, authenticates it, and verifies whether the user has the appropriate access rights. This authentication process is carried out using a security module (e.g., OAuth 2.0).

[1636] 3. The dialogue process of goal setting

[1637] Users initiate goal setting using a chat interface (text or voice). For example, if a user types, "I want to set a goal for this month," the server analyzes the user's input and generates appropriate goal candidates based on past data and company rules. These suggested goal candidates are displayed on the user's device interface. The user can then provide feedback and request any necessary revisions.

[1638] 4. Interactive process of manufacturing process

[1639] Similarly, for manufacturing processes within factories, users can use the chat interface to ask about production targets. For example, by typing, "What is today's production target?", the server analyzes the production line settings and progress and suggests appropriate production targets. Feedback can also be provided in response, and the production process is monitored and progress is tracked in real time.

[1640] Hardware and software used

[1641] Hardware

[1642] Smartphone or head-mounted display (e.g., general mobile device, display device)

[1643] software

[1644] User authentication: security module (e.g. OAuth 2.0)

[1645] Speech recognition system (e.g., Google Cloud Speech-to-Text)

[1646] Generative AI models (e.g., OpenAI GPT-4)

[1647] Examples of concrete examples and prompts

[1648] Example 1: Corporate goal setting

[1649] When a user inputs a goal setting of "increase sales by 500,000 yen" on their smartphone, the server proposes the goal of "increase sales by 500,000 yen and acquire 10 new customers." If the user provides feedback saying, "500,000 yen in sales is difficult. How about 400,000 yen?", the AI ​​responds, "A goal of increasing sales by 400,000 yen is appropriate." If the user is satisfied, the goal is confirmed and the server saves it in the database.

[1650] Example 2: Factory manufacturing process management

[1651] When a user wears the head-mounted display and asks, "What is today's production target?", the server responds, "Line 1 is scheduled to produce 500 units." The production line is monitored in real time, and progress is displayed on the display. For example, if a defective product occurs, a notification will appear saying, "A defective product has been found on Line 1," enabling a prompt response.

[1652] Prompt Sentence Examples

[1653] "Please tell me the achievement rate for yesterday's production target."

[1654] "Show me the production plan for the next shift."

[1655] "Please list the problems that have occurred on the third line and propose solutions."

[1656] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1657] Step 1:

[1658] Initial setup and training:

[1659] The server imports the company's goal management data and manufacturing process data within the factory. The manager uses a terminal to upload the necessary data files to the system. The server trains the generative AI model based on the imported data, learning the company's rules and patterns. Specifically, it analyzes past goal data and manufacturing process setting data to improve the accuracy of the model.

[1660] input:

[1661] Manager-provided historical goal data, manufacturing process data, and company goal management rules.

[1662] output:

[1663] A trained generative AI model.

[1664] Step 2:

[1665] User authentication:

[1666] A user accesses the system on their own device and enters their ID and password on the login screen. The server receives this information and uses a security module (e.g., OAuth 2.0) to verify the user's identity and permissions. If authentication is successful, a dashboard is displayed on the user's device.

[1667] input:

[1668] User ID and password.

[1669] output:

[1670] Dashboard after successful authentication.

[1671] Step 3:

[1672] The goal-setting dialogue process:

[1673] The user inputs "I want to set a goal for this month" through the chat interface. The server analyzes this input and generates appropriate goal candidates by referencing past data and company rules. The generated goal candidates are displayed on the user's device.

[1674] input:

[1675] User goal setting request.

[1676] output:

[1677] Proposed target candidates.

[1678] Data processing / calculation:

[1679] Generate meaningful candidates for goals based on historical data and company rules.

[1680] Step 4:

[1681] Receiving user feedback and regenerating goals:

[1682] The user provides feedback on the proposed goal. For example, they might say, "Sales of 500,000 yen are difficult. How about 400,000 yen?" The server analyzes this feedback again and regenerates the goal. The regenerated goal is displayed on the user's device.

[1683] input:

[1684] User feedback.

[1685] output:

[1686] Regenerated target candidates.

[1687] Data processing / calculation:

[1688] Generate new goals based on user feedback.

[1689] Step 5:

[1690] Save confirmed goals:

[1691] If the user is satisfied with the regenerated goal, he / she confirms the goal in the system, and the server stores this confirmed goal in the database for future reference.

[1692] input:

[1693] A goal established by the user.

[1694] output:

[1695] Confirmed goals stored in the database.

[1696] Data processing / calculation:

[1697] The determined goals are saved in a database format.

[1698] Step 6:

[1699] Manufacturing process interaction process:

[1700] A factory worker or manager can use the chat interface to ask, "What is today's production target?" The server analyzes the production line settings and progress, generates the production target for that day, and displays it on the user's device. The production process is monitored in real time, and progress is tracked.

[1701] input:

[1702] Questions about production goals for workers and managers.

[1703] output:

[1704] Proposed production target for the day.

[1705] Data processing / calculation:

[1706] Analyze production line settings and progress data to generate daily production targets.

[1707] Step 7:

[1708] Real-time process monitoring and feedback:

[1709] The server monitors the production process in real time, tracking progress and notifying workers of any irregularities, such as defective products, so that workers and managers can quickly respond and enter instructions into the system to resolve the issues.

[1710] input:

[1711] Real-time production process data and defect occurrence information.

[1712] output:

[1713] Progress tracking and anomaly notifications.

[1714] Data processing / calculation:

[1715] Analyze real-time data to detect anomalies and provide appropriate notifications.

[1716] 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.

[1717] This invention relates to a chat tool service that incorporates a generation AI for efficient and fair corporate goal management, and combines it with an emotion engine for recognizing user emotions and reflecting them in goal setting. Below, we will generate a program for this system and explain its processing overview in natural language.

[1718] System Overview

[1719] This system consists of three main elements: a server, a user device, and the user. The generative AI model and emotion engine running on the server set goals while interacting with the user via the user device. The server has learned the company's goal management rules and suggests appropriate goal settings based on input from the user. It also recognizes the user's emotions and reflects them in the goal suggestions.

[1720] Program processing overview

[1721] 1. Initial setup and learning

[1722] The server imports data on the company's goal management from the administrator, who then uploads the data to the system using a terminal.

[1723] 2. User Authentication

[1724] A user accesses the system from their own terminal and enters their ID and password on the login screen. The server receives this, performs authentication, and verifies the user's access rights.

[1725] 3. Emotion recognition

[1726] When a user accesses the chat interface and enters input related to goal setting, the emotion engine analyzes the user's input to determine emotions, for example, by recognizing emotions based on specific keywords or context within the text.

[1727] The server uses the emotion data obtained from the emotion engine to adjust the suggested goal settings, suggesting realistic goals if the user is overly stressed, or setting challenging goals if the user is highly motivated.

[1728] 4. The dialogue process of goal setting

[1729] To start goal setting, the user accesses the chat interface and inputs, "I want to set my goals for this month." The terminal then sends the user's request to the server.

[1730] The server generates appropriate goal candidates based on the user's input and the emotional information output by the emotion engine. For example, if a user inputs "increase sales by 500,000 yen," and the emotion engine determines that the load is too high based on the user's emotions, it will suggest a more realistic "increase sales by 400,000 yen."

[1731] 5. Suggestions and Feedback

[1732] The device displays the generated target candidates in a chat interface and suggests them to the user, who can then provide feedback and request any necessary corrections or adjustments.

[1733] The server receives the user's feedback, analyzes it again using the emotion engine and natural language processing algorithm, and generates new goal candidates based on the feedback. The device then presents the regenerated goal candidates to the user.

[1734] 6. Set and save your goals

[1735] If the user is satisfied with the regenerated goal, he / she inputs "confirm goal" into the chat interface, and the device sends a confirmation request to the server.

[1736] The server stores the determined goals in a database and manages them for later reference and evaluation. The terminal displays the status of the saved goal to the user.

[1737] Specific examples

[1738] Initial setup and training example

[1739] Administrators upload a file containing goal data from the past three years and the company's goal management rules from their device, which allows the generative AI to set goals according to the company's specific needs and patterns.

[1740] User Authentication Example

[1741] When an employee attempts to log in to the system, they enter their ID and password on the login screen, which the server receives and executes the authentication process. After successful authentication, they can access the dashboard.

[1742] Goal Setting Examples

[1743] An employee may enter a goal setting such as "increase sales by 500,000 yen" into the chat interface. The AI ​​generator may compare this with past data and suggest a goal such as "increase sales by 500,000 yen and acquire 10 new customers."

[1744] If the emotion engine detects high levels of stress from the user's input, the generative AI will suggest realistic goals such as "increase sales by 400,000 yen."

[1745] If the employee is satisfied with the proposal, the goal is confirmed and the server stores the goal in the database.

[1746] As described above, the system of the present invention provides a chat-type tool that uses generative AI technology and an emotion engine to achieve efficiency and fairness in corporate goal management. This system enables goal setting that takes into account employees' emotions, which is expected to improve the performance of the entire organization.

[1747] The processing flow will be explained below.

[1748] Step 1:

[1749] The administrator logs in to the system using a terminal. The terminal displays the login screen, and the administrator enters their ID and password. The server receives this information and authenticates the administrator.

[1750] Step 2:

[1751] The administrator uses the terminal to upload the company's goal management rules and past goal setting data. The terminal displays the file upload interface, and the administrator selects the required data file and presses the upload button. The server receives these files, analyzes the data, and imports it.

[1752] Step 3:

[1753] The server trains the AI ​​model based on the uploaded data, analyzing the goal management rules stored in the database and past goal setting data to learn appropriate goal setting patterns.

[1754] Step 4:

[1755] A regular employee accesses the system using a terminal and enters their ID and password on the login screen. The terminal then sends this information to the server for user authentication.

[1756] Step 5:

[1757] The server checks the user's ID and password, and if authentication is successful, it confirms the user's authority and retrieves the dashboard information. The terminal displays the retrieved dashboard information to the user.

[1758] Step 6:

[1759] To start goal setting, the user accesses the chat interface and enters, "I want to set my goal for this month." The device then sends the user's request to the server.

[1760] Step 7:

[1761] The server analyzes the user's input using a natural language processing (NLP) algorithm and generates appropriate goal candidates based on past data and the company's goal management rules. Meanwhile, the emotion engine recognizes emotions from the user's input and sends the analysis results to the server.

[1762] Step 8:

[1763] The server reflects the emotional information output by the emotion engine in the generated goal candidates and proposes appropriate goal settings. For example, if a user is suggested to "increase sales by 500,000 yen" and the emotion engine detects high stress, it will adjust it to "increase sales by 400,000 yen."

[1764] Step 9:

[1765] The device displays the generated target candidates in a chat interface and suggests them to the user, who can then provide feedback and request any necessary corrections or adjustments.

[1766] Step 10:

[1767] The server receives the user's feedback, analyzes it again using the emotion engine and natural language processing algorithm, and generates new goal candidates based on the feedback. The device then presents the regenerated goal candidates to the user.

[1768] Step 11:

[1769] When the user confirms the final goal and is satisfied with it, he / she inputs "confirm goal" into the chat interface, and the terminal sends a confirmation request to the server.

[1770] Step 12:

[1771] The server stores the determined goals in a database and manages them for later reference and evaluation. The terminal displays the status of the saved goal to the user.

[1772] Through the above steps, the system supports users in setting goals efficiently and fairly, and also provides goal setting that takes into account the user's emotions.

[1773] Example 2

[1774] 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."

[1775] Corporate goal management is complex, and it is particularly difficult to consider the emotions and motivation of individual employees. As a result, goal setting tends to be one-sided, which can have a negative impact on employee performance and motivation. It is also difficult to effectively utilize past goal data and appropriately set realistic yet challenging goals.

[1776] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for importing data related to corporate goal management and training an AI model, means for authenticating users and providing a goal setting interface to the users, means for analyzing user input, recognizing the user's emotions using an emotion engine, and proposing goal setting based on the corporate goal management rules, means for receiving user feedback and regenerating and confirming goals using an emotion engine and a natural language processing algorithm, and means for storing the confirmed goals in a database. This enables realistic and challenging goal setting that takes into account employees' emotions and motivation, improving the efficiency and fairness of corporate-wide goal management.

[1777] "Generative AI" is artificial intelligence that learns the data necessary for setting corporate goals and suggests appropriate goals through dialogue.

[1778] An "emotion engine" is an algorithm that analyzes user input and recognizes emotions.

[1779] A "chat-type tool" is a tool that provides an interface for setting goals through dialogue with the user.

[1780] "Corporate goal management rules" refer to guidelines and policies that a company uses to set and manage goals.

[1781] "Goal setting interface" means an interactive interface through which a user sets goals.

[1782] A "natural language processing algorithm" is an algorithm for analyzing text data and understanding its meaning.

[1783] A "database" is a system for storing and managing determined goals, emotional data, etc.

[1784] "User authentication" is the process of verifying a user's authenticity using the user's ID and password.

[1785] "Feedback" refers to the opinions and requests for revisions that users provide to proposed goals.

[1786] "Regeneration" refers to the process of regenerating new goals based on user feedback and emotional data.

[1787] "Training" refers to the process by which an AI model learns a company's goal management rules and data.

[1788] This invention relates to a chat tool service that incorporates a generation AI for efficient and fair corporate goal management, and combines it with an emotion engine for recognizing user emotions and reflecting them in goal setting. This system is composed of three main elements: a server, a user terminal, and the user, and is specifically implemented as follows:

[1789] System configuration

[1790] Hardware and Software

[1791] Server: The central part of the system, it manages the company's goal management data and runs the generative AI model and emotion engine. The specific software on which the generative AI model and emotion engine are installed is

[1792] User terminal: A device such as a PC or smartphone that allows a user to access the system. An application that provides a chat-style interface is installed on this device.

[1793] Database: A database system for storing corporate goal management data, defined goals, and user authentication information.

[1794] Program processing

[1795] Initial setup and training

[1796] Importing Data

[1797] The administrator uploads past goal data and the company's goal management rules from a terminal. The server receives this data and stores it in a database.

[1798] Training an AI model

[1799] The server uses the uploaded data to train the generative AI model, which learns the specific needs and patterns of the company through this training process.

[1800] User Authentication

[1801] Input on the login screen

[1802] Users access the system from their own terminal and enter their ID and password on the login screen.

[1803] Authentication Process

[1804] The device sends the entered ID and password to the server, which receives it and checks it against the authentication information in its database. If authentication is successful, the user can access the system dashboard.

[1805] emotion recognition

[1806] Parsing input

[1807] The user types "I want to set my goals for this month" into the chat interface, and the device sends this input to the server.

[1808] Running the Emotion Engine

[1809] The server passes the received input to the emotion engine, which analyzes the input text and recognizes the user's emotion from specific keywords and context.

[1810] The interactive process of goal setting

[1811] Target candidate generation

[1812] The server inputs goal-setting prompts into the generative AI model based on the user's input and the emotional information obtained from the emotion engine.

[1813] The generative AI model generates multiple target candidates and returns them to the server.

[1814] Suggestions for users

[1815] The server transmits the generated target candidates to the terminal, which displays them on the chat interface.

[1816] Suggestions and Feedback

[1817] Gathering feedback

[1818] The user can input feedback for the proposed goal candidates, for example, "I would like the goal to be adjusted to be more realistic."

[1819] Analyzing feedback and adjusting goals

[1820] The device sends the feedback to the server, which receives it and analyzes it again using the emotion engine and natural language processing algorithm, generating new target candidates and sending them to the device, which then presents them to the user.

[1821] Confirm and save your goals

[1822] Determine your goals

[1823] If the user is satisfied with the proposed goal, he / she inputs "confirm goal" into the chat interface, and the device sends a confirmation request to the server.

[1824] Saving to a database

[1825] The server saves the confirmed goal in the database. After saving is complete, the device displays "Goal confirmed" to the user.

[1826] Specific examples

[1827] Initial setup and training example

[1828] Managers upload goal data from the past three years and their company's goal management rules via their devices, allowing the generative AI to set goals according to the company's specific needs and patterns.

[1829] User Authentication Example

[1830] When an employee logs in to the system, they enter their ID and password on the login screen. The server receives this and performs an authentication process. After successful authentication, they can access the dashboard.

[1831] Goal Setting Examples

[1832] An employee enters a goal setting, such as "increase sales by 500,000 yen," into the chat interface. The generation AI compares it with past data and may suggest a goal such as "increase sales by 500,000 yen and acquire 10 new customers." If the emotion engine detects high stress from the user's emotions, the generation AI will suggest a more realistic goal, such as "increase sales by 400,000 yen." If the employee agrees with this suggestion, the goal is confirmed and the server saves it in the database.

[1833] Prompt Sentence Examples

[1834] "Sales increased by 500,000 yen"

[1835] "Acquired 10 new customers"

[1836] "Complete Project A"

[1837] As described above, the system of the present invention provides a chat-type tool that uses generative AI technology and an emotion engine to achieve efficiency and fairness in corporate goal management. This enables goal setting that takes into account employees' emotions, and is expected to improve the performance of the entire organization.

[1838] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1839] Step 1: Initial setup and training

[1840] Input: The administrator uploads the company's goal management data and past goal data from the terminal.

[1841] Operation: The device sends the uploaded data to the server.

[1842] Data processing: The server receives the data and stores it in a database.

[1843] AI training: The server uses the stored data to train the generative AI model, learning the company's goal management rules and past patterns.

[1844] Output: A trained generative AI model.

[1845] Step 2: User authentication

[1846] Input: The user accesses the system on their own terminal and enters their ID and password on the login screen.

[1847] Operation: The terminal sends the entered ID and password to the server.

[1848] Data processing: The server receives the transmitted information and checks it against authentication data in its database.

[1849] Output: Authentication result (success or failure).

[1850] What happens: If authentication is successful, the server allows the user to access the dashboard.

[1851] Step 3: Emotion Recognition

[1852] Input: The user types "I want to set my goals for this month" into the chat interface.

[1853] Operation: The terminal sends the user's input to the server.

[1854] Data processing: The server passes the received input to the emotion engine, which analyzes the input text and recognizes the user's emotion.

[1855] Output: Emotion data (e.g., stress, high motivation, etc.).

[1856] How it works: The server stores emotion data in a database.

[1857] Step 4: The goal-setting dialogue process

[1858] Input: Emotion data and user input.

[1859] How it works: The server inputs goal-setting prompts into the generative AI model based on emotion data and user input.

[1860] Data processing: The generative AI model generates multiple target candidates.

[1861] Output: Generated target candidates.

[1862] How it works: The server sends potential targets to the device, which displays them in the chat interface.

[1863] Step 5: Suggestions and feedback

[1864] Input: User feedback (e.g., "Please adjust your goal to be more realistic").

[1865] Operation: The device sends the feedback content to the server.

[1866] Data processing: The server receives the feedback, analyzes it using an emotion engine and natural language processing algorithms, and generates new target candidates.

[1867] Output: New target candidates.

[1868] Operation: The server sends the newly generated target candidates to the terminal, which then presents them to the user.

[1869] Step 6: Confirm and save your goal

[1870] Input: "Confirm goal" typed by the user into the chat interface.

[1871] Operation: The terminal sends a confirmation request to the server.

[1872] Data processing: The server stores the determined goals in a database.

[1873] Output: Save completion status.

[1874] Action: The device displays "Target confirmed" to the user.

[1875] The above is the specific processing flow of the system. At each step, appropriate data processing and analysis are performed based on the input data, allowing users to smoothly proceed through the goal setting process.

[1876] (Application example 2)

[1877] 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."

[1878] Conventional corporate goal management systems have problems with efficiency and objectivity, and in particular, they often set goals without considering the emotional state of individual users. This can result in excessive burdens on users, leading to a decrease in motivation and increased stress. Furthermore, in certain fields, such as logistics centers, detailed goal setting and rapid feedback are required to respond to the complexity of work content and fluctuating demand. An efficient and fair goal management system that can meet these needs is needed.

[1879] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1880] In this invention, the server includes: means for importing data related to corporate goal management and training an AI model; means for authenticating a user and providing the user with a goal setting interface; means for analyzing user input and suggesting goal setting based on the corporate goal management rules; means including an emotion engine that recognizes the user's emotions and reflects them in goal setting; means using a generative AI model that suggests goals according to the user's emotions; means for receiving user feedback and regenerating and confirming goals; and means for storing the confirmed goals in a database. This enables realistic and efficient goal setting that takes the user's emotional state into consideration, making fair and appropriate goal management possible, especially in specific fields such as logistics centers.

[1881] "Corporate goal management rules" refer to the standards and guidelines for planning, evaluating, and achieving performance goals set by a company.

[1882] A "generative AI model" refers to an artificial intelligence algorithm that learns from large amounts of data and automatically generates appropriate responses and suggestions based on user input.

[1883] "Chat-type tool service" refers to an interactive software application that assists users with specific tasks through text and voice interactions.

[1884] "User authentication" refers to the process of verifying the identity of users accessing a system and granting appropriate access privileges.

[1885] "Goal-setting interface" refers to a system component that provides the screens and input forms through which a user sets goals.

[1886] An "emotion engine" refers to technology that analyzes a user's emotional state from text input and other data and adjusts responses based on the results.

[1887] "Feedback" refers to the evaluations and opinions that users provide in response to suggestions and answers from the system.

[1888] "Database" refers to a digital storage system that systematically organizes and stores data, making it quickly accessible when needed.

[1889] The system that embodies this invention is a chat-type tool service equipped with a generative AI model that learns a company's goal management rules and proposes appropriate goal setting in an interactive format. This system is composed of the following main components:

[1890] Program processing overview

[1891] 1. Importing and training corporate goal management data

[1892] The server trains the AI ​​model using data on corporate goal management imported from administrators, allowing it to learn the company's unique goal-setting rules and patterns and suggest appropriate goals.

[1893] 2. User authentication and interface provision

[1894] Users access the system using their own devices (smartphones, tablets, PCs, etc.) and enter their ID and password on the login screen. The server receives this, performs an authentication process, and grants the user appropriate access rights. If authentication is successful, the user can access the goal setting interface.

[1895] 3. Analyzing user input and suggesting goal setting

[1896] When a user enters text related to goal setting into the chat interface, the server uses a generative AI model to analyze the input. The server also analyzes the user's emotions and reflects them in the proposed goals. For example, if a user enters "increase shipment volume by 50 pallets," and the emotion analysis indicates high stress, the generative AI model will suggest a more realistic goal, such as "increase shipment volume by 40 pallets."

[1897] 4. Utilizing the Emotion Engine

[1898] The emotion engine analyzes the user's input text to determine whether the emotion is positive or negative. For example, if the user's input is negative, such as "I'm not sure if I can achieve this," the emotion engine will detect this and reflect it in the suggested goal.

[1899] 5. Feedback and goal regeneration / confirmation

[1900] The user provides feedback on the proposed goal, and the server regenerates the goal based on that feedback. If the user is satisfied with the goal and clicks "confirm," the server stores the goal in a database for future reference and evaluation.

[1901] 6. Save and manage your goals

[1902] The confirmed goals are stored in a database on the server. This database accumulates past goal data and new data, and is used to continuously improve the accuracy of the AI ​​model. It is also possible to generate reports and alerts according to the company's goal management rules.

[1903] Hardware and software configuration

[1904] Hardware: Smartphones, tablets, PCs, servers

[1905] Software: Flask (web server), TextBlob (sentiment analysis library), OpenAI GPT-3 (generative AI model)

[1906] Examples of concrete examples and prompts

[1907] Examples:

[1908] A staff member types "increase today's shipments by 50 pallets" into the chat interface. If the emotion engine detects anxiety, the generative AI model suggests a more realistic goal, such as "increase today's shipments by 40 pallets."

[1909] Example prompt sentence:

[1910] Given the user's negative sentiment, suggest a realistic goal: Increase today's shipping volume by 50 pallets.

[1911] This system enables realistic and efficient goal setting that takes into account the user's emotional state, enabling fair and appropriate goal management even in specific fields such as logistics centers.

[1912] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1913] Step 1:

[1914] The server imports the company's goal management data and trains the generative AI model. The administrator uploads past goal data and the company's goal management rules to the server. This data is analyzed by the AI ​​model, which learns goal setting patterns and standards. The input is the data file from the administrator, and the output is the trained AI model.

[1915] Step 2:

[1916] The terminal performs user authentication. The user accesses the login screen using their terminal and enters their ID and password. The entered authentication information is sent to the server, which verifies it and authenticates that the user is legitimate. The input is the user's ID and password, and the output is the result of authentication success or failure.

[1917] Step 3:

[1918] The user accesses the goal setting interface and inputs text related to the goal setting. For example, the user inputs a goal such as "I want to increase shipping volume by 50 pallets this month." The input is the goal setting text entered by the user, and the output is a request to suggest appropriate goal settings.

[1919] Step 4:

[1920] The server analyzes the received user input and suggests goal setting using a generative AI model and emotion engine. The server analyzes the user's input text and generates appropriate goal candidates based on its content. At the same time, the emotion engine analyzes the user's emotions and reflects them in the generated goals. For example, if the user's input includes "I'm anxious," the emotion engine recognizes this and instructs the generative AI model to suggest realistic goals. The input is the user's text input and emotion data, and the output is appropriate goal setting suggestions.

[1921] Step 5:

[1922] The device displays the proposed goals to the user and asks for feedback. The user provides feedback on the proposed goals and requests corrections or reconfiguration as necessary. The inputs are the proposed goals from the server and the user's feedback, and the output is the feedback data sent to the server.

[1923] Step 6:

[1924] The server reanalyzes the user's feedback and regenerates goals as necessary. It uses a generative AI model and emotion engine to generate new goal candidates based on the feedback. The input is the user's feedback data, and the output is the regenerated goal settings.

[1925] Step 7:

[1926] When the user is satisfied with the regenerated goal, he / she inputs "confirm goal" into the terminal. The terminal sends this confirmation request to the server. The input is the user's confirmation request, and the output is a confirmation notification to the server.

[1927] Step 8:

[1928] The server saves the confirmed goals in a database. The confirmed goals are managed in the database so that they can be referenced and evaluated later. The input is the confirmed goal information, and the output is a notification that the goal has been saved to the database.

[1929] Through the above processing steps, the present invention is a system that realizes realistic and efficient goal setting that takes into account the user's emotional state.

[1930] 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.

[1931] 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.

[1932] 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 robot 414.

[1933] 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.

[1934] 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.

[1935] 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.

[1936] 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).

[1937] 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.

[1938] 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."

[1939] 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.

[1940] 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).

[1941] 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.

[1942] 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.

[1943] 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.

[1944] 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.

[1945] 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.

[1946] 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.

[1947] 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.

[1948] 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.

[1949] 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 illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1950] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1951] The following is further disclosed regarding the above embodiment.

[1952] (Claim 1)

[1953] It is a chat-style tool service equipped with generative AI that learns a company's goal management rules and proposes appropriate goal setting in a dialogue format.

[1954] A means to import data about corporate goal management and train the AI ​​model;

[1955] means for authenticating a user and providing the user with a goal setting interface;

[1956] A means for analyzing user input and proposing goal setting based on the company's goal management rules;

[1957] A means of receiving user feedback and regenerating and confirming goals;

[1958] a means for storing the determined goals in a database;

[1959] A system including:

[1960] (Claim 2)

[1961] 10. The system of claim 1, comprising a natural language processing algorithm for categorizing and analyzing data related to corporate goal management.

[1962] (Claim 3)

[1963] 10. The system of claim 1, wherein the AI ​​model is trained by combining historical target data with new data to continuously improve accuracy.

[1964] "Example 1"

[1965] (Claim 1)

[1966] It is a chat-style tool service equipped with generative AI that learns a company's goal management rules and proposes appropriate goal setting in a dialogue format.

[1967] A means to import data about corporate goal management and train the AI ​​model;

[1968] means for authenticating a user and providing the user with a goal setting interface;

[1969] A means for analyzing user input and proposing goal setting based on the company's goal management rules;

[1970] A means of receiving user feedback and regenerating and confirming goals;

[1971] a means for storing the determined goals in a database;

[1972] re-analyzing the results based on the user's feedback and re-generating the goals;

[1973] A means to validate the training results of the AI ​​model and fine-tune it as needed; and

[1974] A system including:

[1975] (Claim 2)

[1976] 10. The system of claim 1, comprising a natural language processing algorithm for categorizing and analyzing data related to corporate goal management.

[1977] (Claim 3)

[1978] 10. The system of claim 1, wherein the AI ​​model is trained by combining historical target data with new data to continuously improve accuracy.

[1979] "Application Example 1"

[1980] (Claim 1)

[1981] It is a chat-style tool service equipped with generative AI that learns a company's goal management rules and proposes appropriate goal setting in a dialogue format.

[1982] A means to import data about corporate goal management and train the AI ​​model;

[1983] means for authenticating a user and providing the user with a goal setting interface;

[1984] A means for analyzing user input and proposing goal setting based on the company's goal management rules;

[1985] A means of receiving user feedback and regenerating and confirming goals;

[1986] a means for storing the determined goals in a database;

[1987] A means to import data about manufacturing processes in the factory and propose production targets;

[1988] a means of receiving worker input and monitoring the production process in real time and tracking progress;

[1989] A system including:

[1990] (Claim 2)

[1991] 10. The system of claim 1, comprising a natural language processing algorithm for categorizing and analyzing data related to corporate goal management.

[1992] (Claim 3)

[1993] 10. The system of claim 1, wherein the AI ​​model is trained by combining historical target data with new data to continuously improve accuracy.

[1994] "Example 2: Combining Emotion Engines"

[1995] (Claim 1)

[1996] It is a chat-style tool service equipped with generative AI that learns a company's goal management rules and proposes appropriate goal setting in a dialogue format.

[1997] A means to import data about corporate goal management and train the AI ​​model;

[1998] means for authenticating a user and providing the user with a goal setting interface;

[1999] A means for analyzing a user's input, recognizing the user's emotions using an emotion engine, and suggesting goal setting based on the company's goal management rules;

[2000] A means for receiving user feedback and regenerating and confirming goals using an emotion engine and natural language processing algorithms;

[2001] a means for storing the determined goals in a database;

[2002] A system including:

[2003] (Claim 2)

[2004] 10. The system of claim 1, comprising a natural language processing algorithm for categorizing and analyzing data related to corporate goal management.

[2005] (Claim 3)

[2006] 10. The system of claim 1, wherein the AI ​​model is trained by combining historical target data with new data to continuously improve accuracy.

[2007] "Application example 2 when combining emotion engines"

[2008] (Claim 1)

[2009] It is a chat-type tool service equipped with a generative AI model that learns a company's goal management rules and proposes appropriate goal setting in a dialogue format.

[2010] A means to import data about corporate goal management and train the AI ​​model;

[2011] means for authenticating a user and providing the user with a goal setting interface;

[2012] A means for analyzing user input and proposing goal setting based on the company's goal management rules;

[2013] means including an emotion engine for recognizing the emotion of the user and reflecting it in goal setting;

[2014] A method using a generative AI model that suggests goals based on the user's emotions;

[2015] A means of receiving user feedback and regenerating and confirming goals;

[2016] a means for storing the determined goals in a database;

[2017] A system including:

[2018] (Claim 2)

[2019] 10. The system of claim 1, comprising a natural language processing algorithm for categorizing and analyzing data related to corporate goal management.

[2020] (Claim 3)

[2021] 10. The system of claim 1, wherein the AI ​​model is trained by combining historical target data with new data to continuously improve accuracy. [Explanation of symbols]

[2022] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. It is a chat-style tool service equipped with generative AI that learns a company's goal management rules and proposes appropriate goal setting in a dialogue format. A means to import data about corporate goal management and train the AI ​​model; means for authenticating a user and providing the user with a goal setting interface; A means for analyzing user input and proposing goal setting based on the company's goal management rules; A means of receiving user feedback and regenerating and confirming goals; a means for storing the determined goals in a database; A system including:

2. The system of claim 1, further comprising a natural language processing algorithm for categorizing and analyzing data related to corporate goal management.

3. 10. The system of claim 1, wherein the AI ​​model is trained by combining past target data with new data to continuously improve accuracy.

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